Academic literature on the topic 'Predictive qualitative analysis'
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Journal articles on the topic "Predictive qualitative analysis"
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Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (October 30, 2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.
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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) have also been used. These two methods closely resemble the human auditory system. These feature vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to investigate the nature of those acoustic features.
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Verduin, Maikel, Sergey Primakov, Inge Compter, Henry C. Woodruff, Sander M. J. van Kuijk, Bram L. T. Ramaekers, Maarten te Dorsthorst, et al. "Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma." Cancers 13, no. 4 (February 10, 2021): 722. http://dx.doi.org/10.3390/cancers13040722.
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Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.
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Zimmermann, Bettina, Bernice Elger, and David Shaw. "Media Coverage of Ethical Issues in Predictive Genetic Testing: A Qualitative Analysis." AJOB Empirical Bioethics 10, no. 4 (October 2, 2019): 250–64. http://dx.doi.org/10.1080/23294515.2019.1670275.
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Ragin, Charles C. "The Logic of Qualitative Comparative Analysis." International Review of Social History 43, S6 (December 1998): 105–24. http://dx.doi.org/10.1017/s0020859000115111.
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Social scientists often face a fundamental dilemma when they conduct social research. On the one hand, they may emphasize the complexity of social phenomena – a common strategy in ethnographic, historical and macro social research – and offer in–depth case studies sensitive to the specificity of the things they study. On the other hand, they may make broad, homo genizing assumptions about cases, and document generalities – patterns hold across many instances. Research strategies that focus on complexity are often labeled “qualitative”, “case–oriented”, “small–N”, or “intensive”. Those that focus on generality are often labeled “quantitative”, “variable–oriented”, “large–N”, or “extensive”. While the contrasts between these two types social research are substantial, it is easy to exaggerate their differences and t o caricature the two approaches, for example, portraying quantitative work on general patterns as scientific but sterile and oppressive, and qualitative research on small Ns as rich and emancipatory but journalistic. It is important to avoid these caricatures because the contrasts between these two general approaches provide important leads both for finding a middle path between them and for resolving basic methodological issues in social science Social scientists who study cases in an in–depth manner often see empiri cal generalizations simply as a means to another end – the interpretive understanding of cases. In this view, a fundamental goal of social science is t o interpret significant features of the social world and thereby advance our collective understanding of how existing social arrangements came about and why we live the way we do. The rough general patterns that social scientists may be able to identify simply aid the understanding of specific cases; they are not viewed as predictive. Besides, the task of interpreting and then representing socially significant phenomena (or the task of making selected social phenomena significant by representing them) is a much more immediate and tangible goal. In this view, empirical generalizations and social science theory are important – to the extent that they aid the goal interpretive understanding.
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Cao, Mengqiu, Shiteng Suo, Xiao Zhang, Xiaoqing Wang, Jianrong Xu, Wei Yang, and Yan Zhou. "Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach." BioMed Research International 2021 (January 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/1235314.
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Purpose. Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs. Materials and Methods. A total of 102 LGG patients were allocated to training ( n = 67 ) and validation ( n = 35 ) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. Results. After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879. Conclusion. The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.
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Chen, Yi-sheng, Yan-xian Cai, Xue-ran Kang, Zi-hui Zhou, Xin Qi, Chen-ting Ying, Yun-peng Zhang, and Jie Tao. "Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram." PeerJ 8 (April 15, 2020): e8793. http://dx.doi.org/10.7717/peerj.8793.
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Purpose To develop a risk prediction model for postoperative sarcopenia in elderly patients with patellar fractures in China. Patients and methods We conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (lasso analysis) as well as the Support Vector Machine (SVM) algorithm. The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We also conducted internal sampling methods for qualitative assessment. Result We recruited 137 participants (53 male; mean age, 65.7 years). Various risk factors were assessed, and low body mass index and advanced age were identified as the most important risk factor (P < 0.05). The prediction rate of the model was good (C-index: 0.88; 95% CI [0.80552–0.95448]), with a satisfactory correction effect. The C index is 0.97 in the validation queue and 0.894 in the entire cohort. Decision curve analysis suggested good clinical practicability. Conclusion Our prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, less outdoor exercise, no postoperative rehabilitation, different surgical methods, diabetes, open fracture, and removal of internal fixation.
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USMAN, Abdullahi Garba, Selin IŞIK, Sani Isah ABBA, and Filiz MERİÇLİ. "Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method." TURKISH JOURNAL OF CHEMISTRY 44, no. 5 (October 26, 2020): 1339–51. http://dx.doi.org/10.3906/kim-2003-6.
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Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia sinensis (tea), and coriander (Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.
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Schmalenbach, M., M. Jungehülsing, P. Theissen, M. Dietlein, U. Schröder, W. Eschner, E. Stennert, H. Schicha, and M. Schmidt. "18F-FDG PET for detecting recurrent head and neck cancer, local lymph node involvement and distant metastases." Nuklearmedizin 43, no. 03 (2004): 91–101. http://dx.doi.org/10.1055/s-0038-1625597.
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Summary Aim: Assessment of the clinical value of 18F-FDG-PET for detection of recurrent head and neck cancer, local lymph node involvement and distant metastases comparing a qualitative visual with a semiquantitative analysis (SUV values). Patients, methods: Retrospective evaluation of 73 18F-FDG PET studies in 55 patients by use of a four-step qualitative visual grading system and calculation of standard uptake values in pathological lesions. Calculation of SUV values in normal regions for generating a map of physiological 18F-FDG distribution. Correlation to histopathological findings and clinical follow-up. Results: 1. Qualitative visual analysis of 18F-FDG PET studies: a) local recurrence sensitivity 79%, specificity 97%, positive predictive value 95%, negative predictive value 85%, and diagnostic accuracy 89%; b) local metastatic lymph nodes 100%, 95%, 85%, 100%, 96%; c) distant metastases 100%, 98%, 86%, 100%, 98%, respectively. 2. Semiquantitative analysis had only little incremental, non-significant value in comparison to qualitative visual analysis for the detection of a local recurrence in two patients: a) local recurrence: sensitivity 83%, specificity 100%, positive predictive value 100%, negative predictive value 88%, and diagnostic accuracy 93%; b) local metastatic lymph nodes or c) distant metastases did not change in comparison to qualitative visual analysis. Conclusion: 18F-FDG PET is an effective tool for re-staging of patients with suspected recurrence after therapy for head and neck cancer.
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Granados Sánchez, Ana María, and Juan Felipe Orejuela Zapata. "Diagnosis of mesial temporal sclerosis: sensitivity, specificity and predictive values of the quantitative analysis of magnetic resonance imaging." Neuroradiology Journal 31, no. 1 (September 13, 2017): 50–59. http://dx.doi.org/10.1177/1971400917731301.
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In the diagnosis of mesial temporal sclerosis (MTS), sensitivity, specificity and predictive values of qualitative assessment using conventional magnetic resonance imaging are low, mainly in mild or bilateral atrophy. Quantitative analysis may improve this performance. We evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of quantitative analysis using the hippocampal volumetric index (HVI) and hippocampal asymmetry index (HAI) compared with qualitative assessment in the MTS diagnosis. Twenty-five patients diagnosed with MTS, and 25 healthy subjects underwent conventional magnetic resonance imaging. Hippocampal volumes were obtained using an automated software (FreeSurfer); HVI and HAI were calculated. Receiver operating characteristic curve analysis was performed to obtain the optimal threshold values. Sensitivity, specificity and predictive values were calculated. Sensitivity, specificity, PPV and NPV for qualitative analysis were 44.00%, 96.00%, 91.67% and 63.16%, respectively. In the quantitative analysis, a threshold value of K = 0.22 for HVI provided a sensitivity value of 76.00%, specificity value of 96.00%, PPV of 95.00% and NPV of 80.00%. A threshold value of K = 0.06 for HAI provided the minimum C1 and C2 errors, with a sensitivity value of 88.00%, specificity value of 100%, PPV of 100% and NPV of 89.30%. A statistically significant difference was observed for HAI ( P < 0.0001), and ipsilateral HVI (left MTS, P = 0.0152; right MTS, P < 0.0001), between MTS and healthy groups. The HVI and HAI, both individually and in conjunction, improved the sensitivity, specificity and predictive values of magnetic resonance imaging in the diagnosis of MTS compared to the qualitative analysis and other quantitative techniques. The HAI is highly accurate in the diagnosis of unilateral MTS, whereas the HVI may be better for bilateral MTS cases.
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Bozzato, Alessandro, Johannes Zenk, Holger Greess, Joachim Hornung, Frank Gottwald, Christina Rabe, and Heinrich Iro. "Potential of ultrasound diagnosis for parotid tumors: Analysis of qualitative and quantitative parameters." Otolaryngology–Head and Neck Surgery 137, no. 4 (October 2007): 642–46. http://dx.doi.org/10.1016/j.otohns.2007.05.062.
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Objective Histology of parotid tumors determines the extent of surgery. The aim was to test ultrasound (US) contrast enhancer-kinetics to identify histologic entities, possibly being superior to qualitative morphological parameters. Study Design In a cross-sectional assessment of ultrasound diagnosis, the subjective US-classification was compared with contrast analysis with histology as gold standard. Subjects and Methods A total of 64 male and 61 female patients with a mean age of 54 years were included, with 13 malignant tumors. These were classified with US morphology, then time-dependent contrast medium analysis. Results A total of 92.8% of tumors were classified correctly as malignant or benign. The sensitivity, specificity, positive- and negative-predictive values were 66.7%, 86.3%, 60.6%, and 89.1% for differentiating Warthin tumors, but only 46.2%, 98.2%, 75%, and 94% for malignant lesions. Contrast parameters yielded significant parameters for benign tumors, not for malignant entities. Conclusion Although contrast medium analysis provided statistical criteria, these, however, do not possess the ability to improve the diagnostic prediction of tumor histology. Neither the morphologic classification nor contrast medium analysis was able to identify a malignant lesion sufficiently.
Dissertations / Theses on the topic "Predictive qualitative analysis"
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Pervola, Josie. "Adolescents Share their Views: A Qualitative Analysis of Adolescents' Preferences for Learning Genomic Sequencing Results." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522418491824581.
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Rodriguez, Pellière Lineth Arelys. "A qualitative analysis to investigate the enablers of big data analytics that impacts sustainable supply chain." Thesis, Ecole centrale de Nantes, 2019. http://www.theses.fr/2019ECDN0019/document.
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Les académiques et les professionnels ont déjà montré que le Big Data et l'analyse prédictive, également connus dans la littérature sous le nom de BDPA, peuvent jouer un rôle fondamental dans la transformation et l'amélioration des fonctions de l'analyse de la chaîne d'approvisionnement durable (SSCA). Cependant, les connaissances sur la meilleure manière d'utiliser la BDPA pour augmenter simultanément les performances sociales, environnementale et financière. Par conséquent, avec les connaissances tirées de la littérature sur la SSCA, il semble que les entreprises peinent encore à mettre en oeuvre les pratiques de la SSCA. Les chercheursconviennent qu'il est encore nécessaire de comprendre les techniques, outils et facteurs des concepts de base de la SSCA pour adoption. C’est encore plus important d’intégrer BDPA en tant qu’atout stratégique dans les activités commerciales. Par conséquent, cette étude examine, par exemple, quels sont les facteurs de SSCA et quels sont les outils et techniques de BDPA qui permettent de mettre en évidence le 3BL (pour ses abréviations en anglais : "triple bottom line") des rendements de durabilité (environnementale, sociale et financière) via SCA.La thèse a adopté un constructionniste modéré, car elle comprend l’impact des facteurs Big Data sur les applications et les indicateurs de performance de la chaîne logistique analytique et durable. La thèse a également adopté un questionnaire et une étude de cas en tant que stratégie de recherche permettant de saisir les différentes perceptions des personnes et des entreprises dans l'application des mégadonnées sur la chaîne d'approvisionnement analytique et durable. La thèse a révélé une meilleure vision des facteurs pouvant influencer l'adoption du Big Data dans la chaîne d'approvisionnement analytique et durable. Cette recherche a permis de déterminer les facteurs en fonction des variables ayant une incidence sur l'adoption de BDPA pour SSCA, des outils et techniques permettant la prise de décision via SSCA et du coefficient de chaque facteur pour faciliter ou retarder l'adoption de la durabilité. Il n'a pas été étudié avant. Les résultats de la thèse suggèrent que les outils actuels utilisés par les entreprises ne peuvent pas analyser de grandes quantités de données par eux-mêmes. Les entreprises ont besoin d'outils plus appropriés pour effectuer ce travailScholars and practitioners already shown that Big Data and Predictive Analytics also known in the literature as BDPA can play a pivotal role in transforming and improving the functions of sustainable supply chain analytics (SSCA). However, there is limited knowledge about how BDPA can be best leveraged to grow social, environmental and financial performance simultaneously. Therefore, with the knowledge coming from literature around SSCA, it seems that companies still struggled to implement SSCA practices. Researchers agree that is still a need to understand the techniques, tools, and enablers of the basics SSCA for its adoption; this is even more important to integrate BDPA as a strategic asset across business activities. Hence, this study investigates, for instance, what are the enablers of SSCA, and what are the tools and techniques of BDPA that enable the triple bottom line (3BL) of sustainability performances through SCA. The thesis adopted moderate constructionism since understanding of how the enablers of big data impacts sustainable supply chain analytics applications and performances. The thesis also adopted a questionnaire and a case study as a research strategy in order to capture the different perceptions of the people and the company on big data application on sustainable supply chain analytics. The thesis revealed a better insight of the factors that can affect in the adoption of big data on sustainable supply chain analytics. This research was capable to find the factors depending on the variable loadings that impact in the adoption of BDPA for SSCA, tools and techniques that enable decision making through SSCA, and the coefficient of each factor for facilitating or delaying sustainability adoption that wasn’t investigated before. The findings of the thesis suggest that the current tools that companies are using by itself can’t analyses data. The companies need more appropriate tools for the data analysis
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Wang, Kai. "Novel computational methods for accurate quantitative and qualitative protein function prediction /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/11488.
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Harasim, Tomáš. "Návrh diagnostické soustavy pro malý dopravní letoun." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-231664.
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Due to the airliners and aircraft operators pressure on reducing the operating costs, the diagnostic systems are included in aircraft, so that the wearness of single functional elements of other functional systems can be monitored. This monitoring can save money, that are usually spent on maintenance and especially, it leads to an increase of the overall safety of aircraft operations. The task of this work is to design a diagnostic system for small transport aircraft. For the right and proper design, is the correct understanding of the functions of each part on aircraft systems needed. The part of this work is attended to the selection procedure of appropriate diagnostic parameters, next section deals with the suitable options of their scanning. I do mention the summary of the chosen diagnostic parameters for the scanning and the recomended ways of their measuring.
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Kocharyan, Davit. "The relevance of preoperative ultrasound cervical mapping in thyroid cancer." Thèse, 2016. http://hdl.handle.net/1866/18866.
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Pendant les trente dernières années, le taux d'incidence du cancer de la thyroïde chez l'homme et la femme a considérablement augmenté partout dans le monde. Cependant, on estime que d'ici à 2019 le cancer de la thyroïde deviendra le troisième cancer le plus répandu chez les femmes dans tous les groupes d'âge en raison de la tendance d’augmentation plus dramatique chez elles. En général, il n'y a aucune raison claire qui explique l'augmentation mondiale de l'incidence du cancer de la thyroïde et il est émis l'hypothèse que cette recrudescence de l'incidence a une étiologie multifactorielle. Bien qu'il soit clair que le progrès technique des modalités de l’imagerie diagnostique telle que l'échographie peut amener à une augmentation du taux de détection du cancer de la thyroïde secondaire au sur-diagnostic des maladies sous-cliniques, il existe des preuves fortes indiquant une vraie augmentation du cancer de la thyroïde.
La cartographie cervicale échographique préopératoire est un outil important dans l'algorithme diagnostic du cancer de la thyroïde. Elle aide à identifier l’étendue des métastases ganglionnaires cervicales afin de guider la dissection chirurgicale anticipée. La dissection chirurgicale du cou orientée selon les compartiments anatomiques et guidée par la cartographie cervicale échographique peut amener à une réduction des risques des complications postopératoires et des récidives tumorales locorégionales.
Nous avons effectué une analyse qualitative et quantitative de la cartographie cervicale échographique afin d'évaluer la fiabilité diagnostique de ce test. Nos résultats ont démontré une valeur prédictive positive assez élevée de cette modalité diagnostique ainsi q’une association quantitative forte entre les données de la cartographie échographique et les résultats de l’histopathologie. Nous suggérons que l’utilisation de la cartographie cervicale échographique cible les patients présentant un risque plus important d’une maladie persistante / récidivante.Over the last 30 years, the incidence rate of thyroid cancer has drastically increased in both genders all over the world. However, due to a more dramatic pattern in females, it is estimated that by 2019 it will become the third most prevalent cancer in women of all ages. Overall, there are no clear reasons behind the worldwide increase in thyroid cancer incidence and it is hypothesized that this upsurge has a multifactorial etiology. Despite the fact that recent advances in imaging modalities such as ultrasound can lead to thyroid cancer overdiagnosis by improving the detection rate for subclinical disease, there is strong evidence indicating a true increase in the occurrence of thyroid cancer as well. Preoperative ultrasound cervical mapping, an important tool in the diagnostic algorithm of thyroid cancer, helps to identify metastatic spread in cervical lymph nodes and guides the surgeon for subsequent surgical dissection. Compartment oriented neck dissection directed by ultrasound mapping decreases locoregional tumor recurrence and lowers the risk of postsurgical complications. We conducted a qualitative and quantitative analysis of ultrasound mapping to evaluate this test’s diagnostic reliability. Our results demonstrated that the positive predictive value of this diagnostic modality was sufficiently high and that there was a strong quantitative association between ultrasound mapping and histopathology results. We therefore recommend that ultrasound mapping be used to target patients with a higher risk of persistent or recurrent thyroid cancer.
Books on the topic "Predictive qualitative analysis"
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Tammen, Ronald L., Jacek Kugler, and Douglas Lemke. Foundations of Power Transition Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.296.
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Power Transition theory is a dynamic and structural model for analyzing fundamental shifts in global power. The theory itself, while maintaining its core concepts, has metamorphosed over time by adding new dimensions and addressing new topics. It is both data based and qualitatively intuitive.As a probabilistic theory, it has proven useful in predicting the conditions that forecast both conflict and cooperation at the global, national, and subnational levels of analysis. As a foreign policy tool, it creates historical signposts pointing toward tectonic shifts in nation state and alliance power profiles.
Book chapters on the topic "Predictive qualitative analysis"
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Papagelis, Manos, and Dimitris Plexousakis. "Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents." In Cooperative Information Agents VIII, 152–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30104-2_12.
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Alhazmi, Fahad Nasser. "Cloud Computing Big Data Adoption Impacts on Teaching and Learning in Higher Education." In Fostering Communication and Learning With Underutilized Technologies in Higher Education, 53–66. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4846-2.ch004.
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There is a rapid evolution in the purpose and value of higher education brought about by technological advancement and data ubiquity. Data mining and advanced predictive analytics are increasingly being used in higher education institutions around the world to perform tasks, ranging from student recruitment, enrolment, predicting student behaviour, and developing personalised learning schemes. This chapter evaluates and assesses the impact of big data and cloud computing in higher education. The authors adopt systematic literature research approach that employs qualitative content analysis to establish their position with regards to the impact, benefits, challenges, and opportunities of integrating big data and cloud computing to facilitate teaching and learning.
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Serna, Ainhoa, and Jon Kepa Gerrikagoitia. "Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis." In Advances in Business Information Systems and Analytics, 180–99. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4240-8.ch008.
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In recent years, digital technology and research methods have developed natural language processing for better understanding consumers and what they share in social media. There are hardly any studies in transportation analysis with TripAdvisor, and moreover, there is not a complete analysis from the point of view of sentiment analysis. The aim of study is to investigate and discover the presence of sustainable transport modes underlying in non-categorized TripAdvisor texts, such as walking mobility in order to impact positively in public services and businesses. The methodology follows a quantitative and qualitative approach based on knowledge discovery techniques. Thus, data gathering, normalization, classification, polarity analysis, and labelling tasks have been carried out to obtain sentiment labelled training data set in the transport domain as a valuable contribution for predictive analytics. This research has allowed the authors to discover sustainable transport modes underlying the texts, focused on walking mobility but extensible to other means of transport and social media sources.
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Kouassi, Amevi, Jorge Tiago Martins, and Andreea Molnar. "Customer Experience Management System at a University's Student Support Services." In Handbook of Research on Innovations in Information Retrieval, Analysis, and Management, 431–50. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8833-9.ch016.
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The study reported in this chapter evaluates how the Customer Experience Management System (CEMS) used by a University's Student Support Services (StuSS) responds to the objectives of capturing, storing, extracting, interpreting, distributing, using and reporting customer experience information for creating organisational value. Theoretically, the study draws on the concept of organizational ambidexterity. Concerning the research design, the study was undertaken using qualitative methods of data collection and interpretivist methods of data analysis. It has been inductively discovered that the availability of customer experience information obtained through the CEMS allows StuSS to respond effectively to different student needs. Organizationally, there is clarity concerning the ownership and management of customer relationships. Individual student data is collected, coordinated and distributed across lines of business. Because of this, StuSS is able to consistently identify customers across touch points and channels. Further suggestions are advanced to improve StuSS's analytical investigation capability to derive descriptive and predictive customer information, through applying data mining models to the information that is currently collected.
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Hollnagel, Erik. "Qualitative Performance Prediction." In Cognitive Reliability and Error Analysis Method (CREAM), 216–33. Elsevier, 1998. http://dx.doi.org/10.1016/b978-008042848-2/50008-7.
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Pappas, Nikolaos, and Andreas Papatheodorou. "Complexity in the tourism and hospitality online purchasing process." In Tourism Dynamics. Goodfellow Publishers, 2021. http://dx.doi.org/10.23912/9781911635932-4949.
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This chapter examines the complexity of attributes affecting the online purchasing process in tourism and hospitality. Eight elements (i.e. marketing activities, perceived benefits, perceived risks, over-choice confusion, price issues, quality issues, consumer trust and intention to purchase) are identified as of principal importance. Based on a sample of 735 holidaymakers, the empirical findings stress the role of perceived benefits in e-marketing activities; the significance of perceived risks for price and quality issues; and the importance of over-choice confusion to e-shopping. The research employs Qualitative Comparative Analysis (QCA), an innovative technique in tourism. QCA results are compared to regression highlighting the possible superiority of this non-linear mixed method against correlational analysis. The chapter progresses from fit to predictive validity, something that only a handful of studies have employed in tourism and hospitality, and generally in the service sector. It also discusses several theoretical, methodological and managerial implications, whilst it contributes in both, theoretical and methodological domains.
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"The Use of Grounded Theory Methodology in Theory Building." In Advances in Business Strategy and Competitive Advantage, 28–52. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-4252-1.ch002.
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The chapter discusses the qualitative analysis method of the grounded theory (GT) and its application in the generation or building theory. The emphasis in the GT approach is on building theory rather than validating the existing theories. The GT methodology has been a topic of interest to management researchers and is intellectually challenging. In order to enhance understanding and skills for continuous professional development and to improve conditions, theory building should be considered a vital activity. In new circumstances where social systems cannot provide a certain predictive power, the need for new theories is felt. The topics discussed in this chapter revolve around theory and theory building; inductive, deductive, and abductive approaches to constructing theory; levels, functions, components, and evaluation of theories; research methods used in theory development; and in particular, the GT method and its application in theoretical coding and the reliability of this approach in serving these purposes.
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Klepac, Goran, Robert Kopal, and Leo Mrsic. "Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries." In Fuzzy Systems, 202–34. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch009.
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Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.
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Klepac, Goran, Robert Kopal, and Leo Mrsic. "Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries." In Handbook of Research on Artificial Intelligence Techniques and Algorithms, 763–96. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7258-1.ch025.
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Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.
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Arpaci, Ibrahim. "A Theoretical Framework for IT Consumerization." In Advances in Human and Social Aspects of Technology, 114–29. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6367-9.ch006.
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The chapter provided a comprehensive review of previous studies on the adoption of information and communication technology (ICT). The study further conducted a qualitative study on the adoption of “bring your own device” (BYOD). The study systematically reviewed technology acceptance theories and models such as TAM, TPB, and UTAUT at the individual level and technology adoption theories such as “innovation diffusion theory,” “technology-organization-environment framework,” and “institutional theory” at the organizational level. Thereby, key factors predicting the ICT adoption at the individual, organizational, institutional, and environmental level were identified. A theoretical framework that explains the ICT adoption and the consumerization process was proposed based on the theories. The qualitative data collected by semi-structured interviews with senior-level managers was analyzed using the content analysis. The findings suggested that perceived financial cost, compatibility, privacy, and security concerns were significant factors in predicting the enterprise's adoption of BYOD.
Conference papers on the topic "Predictive qualitative analysis"
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Kim, Hyeji, Jing Chen, Euiyoung Kim, and Alice M. Agogino. "Scenario-Based Conjoint Analysis: Measuring Preferences for User Experiences in Early Stage Design." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67690.
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Conjoint analysis has proven to be a useful method for decomposing and estimating consumer preference for each attribute of a product or service through evaluations of sets of different versions of the product with varying attribute levels. The predictive value of conjoint analysis is confounded, however, by increasing market uncertainties and changes in user expectations. We explore the use of scenario-based conjoint analysis in order to complement qualitative design research methods in the early stages of concept development. The proposed methodology focuses on quantitatively assessing user experiences rather than product features to create experience-driven products, especially in cases in which the technology is advancing beyond consumer familiarity. Rather than replace conventional conjoint analysis for feature selection near the end of the product development cycle, our method broadens the scope of conjoint analysis so that this powerful measurement technique can be applied in the early stage of design to complement qualitative research and drive strategic directions for developing product experiences. We illustrate on a new product development case study of a flexible wearable for parent-child communication and tracking as an example of scenario-based conjoint analysis implementation. The results, limitations, and findings are discussed in more depth followed by future research directions.
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Ganapathy, Harish, Amir Shooshtari, Kyosung Choo, Serguei Dessiatoun, Mohamed Alshehhi, and Michael M. Ohadi. "Numerical Analysis of Condensation of R134a in a Single Microchannel." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-89707.
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The present work proposes a numerical, transient modeling approach for the simulation of condensation heat transfer in a single microchannel. The model was based on the volume of fluid approach, which governed the hydrodynamics of the two-phase flow. User-defined routines were implemented in order to simulate the effects of condensation, which included mass transfer at the liquid-vapor interface and the associated release of latent heat. A channel having hydraulic diameter of 100 micrometer was modeled using a two-dimensional computational domain. The working fluid was R134a and the vapor mass fluxes at the channel inlet ranged from 245 to 615 kg/m2s. The channel wall was maintained at a constant heat flux, ranging between 200 to 800 kW/m2. The predictive accuracy of the numerical model was assessed by comparing the two-phase frictional pressure drop and Nusselt number with the available empirical correlations in the literature. A reasonably good agreement was obtained for both parameters with mean absolute errors as low as ±7.5% for pressure drop and ±15.6% for Nusselt number. Further, a qualitative comparison of various flow patterns against experimental visualization data was performed. The predicted flow patterns were classified based on the relative dominance of surface tension and inertia forces, and the results were in close agreement with visualization data. On the whole, the newly developed approach was found to have a high predictive accuracy with respect to the simulation of condensation phenomena in microscale domains and was concluded to be a useful tool in support of the design and optimization of advanced microchannel-based heat exchangers.
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Pandya, Deval, Brian Dennis, and Ronnie Russell. "An Improved Computational Fluid Dynamics (CFD) Model for Erosion Prediction in Oil and Gas Industry Applications." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23136.
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In recent years, the study of flow-induced erosion phenomena has gained interest as erosion has a direct influence on the life, reliability and safety of equipment. Particularly significant erosion can occur inside the drilling tool components caused by the low particle loading (<10%) in the drilling fluid. Due to the difficulty and cost of conducting experiments, significant efforts have been invested in numerical predictive tools to understand and mitigate erosion within drilling tools. Computational fluid dynamics (CFD) is becoming a powerful tool to predict complex flow-erosion and a cost-effective method to re-design drilling equipment for mitigating erosion. Existing CFD-based erosion models predict erosion regions fairly accurately, but these models have poor reliability when it comes to quantitative predictions. In many cases, the error can be greater than an order of magnitude. The present study focuses on development of an improved CFD-erosion model for predicting the qualitative as well as the quantitative aspects of erosion. A finite-volume based CFD-erosion model was developed using a commercially available CFD code. The CFD model involves fluid flow and turbulence modeling, particle tracking, and application of existing empirical erosion models. All parameters like surface velocity, particle concentration, particle volume fraction, etc., used in empirical erosion equations are obtained through CFD analysis. CFD modeling parameters like numerical schemes, turbulence models, near-wall treatments, grid strategy and discrete particle model parameters were investigated in detail to develop guidelines for erosion prediction. As part of this effort, the effect of computed results showed good qualitative and quantitative agreement for the benchmark case of flow through an elbow at different flow rates and particle sizes. This paper proposes a new/modified erosion model. The combination of an improved CFD methodology and a new erosion model provides a novel computational approach that accurately predicts the location and magnitude of erosion. Reliable predictive methodology can help improve designs of downhole equipment to mitigate erosion risk as well as provide guidance on repair and maintenance intervals. This will eventually lead to improvement in the reliability and safety of downhole tool operation.
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DeMore, Daniel, Elham Maghsoudi, Jorge Pacheco, James Sorokes, Brad Hutchinson, William Holmes, Bryan Lobo, and Vaibhav Vashistha. "Investigation of Efficient CFD Methods for Rotating Stall Prediction in a Centrifugal Compressor Stage." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27097.
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The Time Transformation Method in ANSYS CFX is investigated as an efficient substitution to Transient Rotor Stator (TRS) analysis for rotating stall prediction in a centrifugal compressor stage. The computational study was performed by varying the number of blade sectors to determine how the circumferential extent of the computational domain affects the accuracy of the stall prediction. The results obtained using a minimum number of blades, approximately one-quarter the full blade count, and approximately one-half the full blade count were compared to both TRS and steady simulations on the same mesh to characterize the predictive capability of each approach. It is shown that both steady and unsteady methods are able to predict the formation of stall cells, but significant qualitative and quantitative differences exist in the flowfield results. The largest mass flow rate at which rotating stall was captured and the number of stall cells were in good agreement with the experimental data.
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Ferrans, Orlando, Walter Aparicio, Andrea Martínez, Cinthia Audivet, Horacio Pinzón, and Marco Sanjuán. "A Hybrid Methodology Combining Functional and Probabilistic Analysis to Assess the Maintenance Plan of a Natural Gas Transmission System: A Turbo-Compressor Power Pack Case Study." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71521.
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A probabilistic analysis is an approach that allows to identify whether if preventive, predictive and corrective maintenance practices are efficient for keeping the overall system safety. The implementation of methodologies that evaluate the maintenance plans are a way to improve existing programs. Promigas S.A. E.S.P. is a company that must guarantee the reliability and availability of its natural gas transmission systems to its consumers. Therefore, since 2009, the company has implemented reliability centered maintenance practices to achieve it. However, considering the current scenario of the natural gas in Colombia and according to the growth plans of the company, it is necessary to guarantee that the reliability and availability indexes remain close to 100%. To achieve the aforementioned objective, it is necessary to diagnose the current maintenance plan. We propose a hybrid methodology combining functional and probabilistic analysis to assess the maintenance plan of a natural gas transmission system, specifically a turbo-compressor power pack. The proposed methodology includes a new priorization method to identify and select critical components and its critical failure modes, through a qualitative functional and quantitative characterization of the subsystems that conform the turbo-compressor power pack. The probabilistic analyses were simulated for five time periods: one, three, six, nine and thirteen years. The results allow to conclude in terms of availability that while the maintenance plan is optimal for the first-time period, from the second time period the preventive and predictive maintenance practices must be optimized increasing resources or modifying the equipment intervention frequencies.
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Akl, Sayed Y., and Ahmed A. Abdel-Rehim. "Condition Monitoring of a Single Cylinder Engine Running on Gasoline and Gasoline-Ethanol Blend Using Wear Particle Analysis Technique." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-65910.
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Analytical techniques performed on oil samples for lubricated machines can be classified in two categories; used oil analysis and wear particle analysis. Used oil analysis determines the condition of the lubricant itself, determines the quality of the lubricant, and checks its suitability for continued use. Wear particle analysis determines the mechanical condition of machine components that are lubricated. Through wear particle analysis, you can identify the composition of the solid material present and evaluate particle type, size, concentration, distribution, and morphology, thus indicating the machine condition and its predictive maintenance. The above mentioned techniques are suitable methods for the detection of abnormal wear occurring in internal combustion engines, especially for engines running on different fuels. These techniques provide cheap, fast and easy to use predictive maintenance methods which can replace other conventional methods. The objective of the present study is to apply wear particle analysis technique as an engine monitoring technique to compare two new and identical engines running on gasoline (Engine 1) and gasoline-ethanol blend (Engine 2). The two engines were tested for a total running period of 850 hours. Spectrometric and ferrographic analysis were used for the comparison where quantitative and qualitative changes in the concentration and size distribution of different particles were analyzed and compared to baseline values. Results showed an increase of wear rate for the engine running on gasoline-ethanol blend compared to the engine running on gasoline only. Two contents of ethanol were used where 10% content showed a moderate increase of wear rate; however 20% content showed a dramatic increase of wear rate. The predominant wear particles were the ferrous particles and aluminum particles indicating the wear of piston elements and piston rings. Corrosive wear was also highly remarked which indicates a chemical reaction in the presence of ethanol.
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Goodfellow, Graham, and Jane Haswell. "A Comparison of Inherent Risk Levels in ASME B31.8 and UK Gas Pipeline Design Codes." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10507.
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The approach to gas pipeline risk and integrity management in the US, involving the development of integrity management plans for High Consequence Areas (HCA), is usually qualitative, as outlined in ASME B31.8S. Depending on the engineering judgement of the assessment team this can lead to a wide variety of results making risk comparison between pipelines difficult. Qualitative risk ranking methods are popular in Europe, but quantitative risk assessment (QRA) is also used for setting acceptable risk levels and as an input to risk and integrity management planning. It is possible to use quantitative risk assessment methods to compare the levels of risk inherent in different pipeline design codes. This paper discusses the use of pipeline quantitative risk assessment methods to analyse pipelines designed to ASME B31.8 and UK IGE/TD/1 (equivalent to PD 8010, published by BSI, for the design of gas pipelines) codes. The QRA utilises predictive models for consequence assessment, e.g. pipeline blowdown and thermal radiation effects, and failure frequency, in determining the risk levels due to an operational pipeline. The results of the analysis illustrate how the risk levels inherent in the two codes compare for different class locations & minimum housing separation distances. The impact of code requirements on design factor, depth of burial, population density and the impact of third party activity on overall risk levels are also discussed.
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Chen, L., J. Wang, Y. M. Liu, F. Collin, and J. L. Xie. "Thermo-Hydro-Mechanical Simulation of a Heating and Hydration Experimental Study (the China-Mock-Up) in GMZ Bentonite." In ASME 2011 14th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2011. http://dx.doi.org/10.1115/icem2011-59212.
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This paper presents a numerical study of the China-Mock-up test, with the purpose of evaluating the performance of Gaomiaozi (GMZ) bentonite under coupled thermo-hydro-mechanical (THM) conditions. In the paper, the basic THM characteristics of GMZ bentonite are presented first. The formulation of coupling heat, moisture (liquid water and water vapour) and air transfer is given. The model of Alonso-Gens (1) is incorporated to reproduce the mechanical behavior of the GMZ bentonite under unsaturated conditions. With the parameters determined from experimental investigations, numerical simulations of the China-Mock-up test are carried out using the code of LAGAMINE. Owing to the lack of experimental data at present stage, a qualitative analysis of the predictive results is realized. The results suggest that the proposed model is able to reproduce the mechanical behavior of GMZ bentonite, and to predict moisture and air motions under thermal solicitations.
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Yoshida, Hiroyuki, Taku Nagatake, Kazuyuki Takase, Akiko Kaneko, Hideaki Monji, and Yutaka Abe. "Development of Prediction Technology of Two-Phase Flow Dynamics Under Earthquake Acceleration: 10 — Numerical Prediction of Velocity Profile Around Bubble Under Accelerating Condition." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15259.
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An earthquake is one of the most serious phenomena for the safety of a nuclear reactor in Japan. Therefore, structural safety of nuclear reactors has been studied and nuclear reactors ware contracted with structural safety for a big earthquake. However, it is not enough for safety operation of nuclear reactors because thermal-fluid safety is not confirmed under the earthquake. For instance, behavior of gas-liquid two-phase flow is unknown under the earthquake conditions. Especially, fluctuation of void fraction is an important factor for the safety operation of the nuclear reactor. In the previous work, fluctuation of void faction in bubbly flow was studied experimentally and theoretically investigate the stability of the bubbly flow. In such studies, flow rate or void fraction fluctuations were given to the steady bubbly flow. In case of the earthquake, the fluctuation is not only the flow rate, but also a body force on the two-phase flow and shear force through the pipe wall. Interactions of gas and liquid through their interface also act on the behavior of the two-phase flow. The fluctuation of the void fraction is not clear for such complicated situation under the earthquake. Therefore, the behavior of gas-liquid two-phase flow is investigated experimentally and numerically in the series of study. In this study, to develop the predictive technology of two-phase flow dynamics under earthquake acceleration, a detailed two-phase flow simulation code with an advanced interface tracking method TPFIT was expanded to two-phase flow simulation under earthquake conditions. In this paper, the bubbly flow in a horizontal pipe excited by oscillation acceleration and under the fluctuation of the liquid flow was simulated by using the expanded TPFIT. Predicted time series of velocity profiles around the bubbles and shapes of bubbles were compared with measured results under flow rate fluctuation and structural vibration. Predicted results were almost same as measured results qualitatively. And it was concluded that the expanded TPFIT can be applied to qualitative analysis of bubbly flow under accelerating conditions.
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Martin, A., V. Aswathy, S. Balaji, T. Miranda Lakshmi, and V. Prasanna Venkatesan. "An analysis on Qualitative Bankruptcy Prediction using Fuzzy ID3 and Ant Colony Optimization Algorithm." In 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME). IEEE, 2012. http://dx.doi.org/10.1109/icprime.2012.6208382.
Full textReports on the topic "Predictive qualitative analysis"
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Dy, Sydney M., Julie M. Waldfogel, Danetta H. Sloan, Valerie Cotter, Susan Hannum, JaAlah-Ai Heughan, Linda Chyr, et al. Integrating Palliative Care in Ambulatory Care of Noncancer Serious Chronic Illness: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), February 2020. http://dx.doi.org/10.23970/ahrqepccer237.
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Objectives. To evaluate availability, effectiveness, and implementation of interventions for integrating palliative care into ambulatory care for U.S.-based adults with serious life-threatening chronic illness or conditions other than cancer and their caregivers We evaluated interventions addressing identification of patients, patient and caregiver education, shared decision-making tools, clinician education, and models of care. Data sources. We searched key U.S. national websites (March 2020) and PubMed®, CINAHL®, and the Cochrane Central Register of Controlled Trials (through May 2020). We also engaged Key Informants. Review methods. We completed a mixed-methods review; we sought, synthesized, and integrated Web resources; quantitative, qualitative and mixed-methods studies; and input from patient/caregiver and clinician/stakeholder Key Informants. Two reviewers screened websites and search results, abstracted data, assessed risk of bias or study quality, and graded strength of evidence (SOE) for key outcomes: health-related quality of life, patient overall symptom burden, patient depressive symptom scores, patient and caregiver satisfaction, and advance directive documentation. We performed meta-analyses when appropriate. Results. We included 46 Web resources, 20 quantitative effectiveness studies, and 16 qualitative implementation studies across primary care and specialty populations. Various prediction models, tools, and triggers to identify patients are available, but none were evaluated for effectiveness or implementation. Numerous patient and caregiver education tools are available, but none were evaluated for effectiveness or implementation. All of the shared decision-making tools addressed advance care planning; these tools may increase patient satisfaction and advance directive documentation compared with usual care (SOE: low). Patients and caregivers prefer advance care planning discussions grounded in patient and caregiver experiences with individualized timing. Although numerous education and training resources for nonpalliative care clinicians are available, we were unable to draw conclusions about implementation, and none have been evaluated for effectiveness. The models evaluated for integrating palliative care were not more effective than usual care for improving health-related quality of life or patient depressive symptom scores (SOE: moderate) and may have little to no effect on increasing patient satisfaction or decreasing overall symptom burden (SOE: low), but models for integrating palliative care were effective for increasing advance directive documentation (SOE: moderate). Multimodal interventions may have little to no effect on increasing advance directive documentation (SOE: low) and other graded outcomes were not assessed. For utilization, models for integrating palliative care were not found to be more effective than usual care for decreasing hospitalizations; we were unable to draw conclusions about most other aspects of utilization or cost and resource use. We were unable to draw conclusions about caregiver satisfaction or specific characteristics of models for integrating palliative care. Patient preferences for appropriate timing of palliative care varied; costs, additional visits, and travel were seen as barriers to implementation. Conclusions. For integrating palliative care into ambulatory care for serious illness and conditions other than cancer, advance care planning shared decision-making tools and palliative care models were the most widely evaluated interventions and may be effective for improving only a few outcomes. More research is needed, particularly on identification of patients for these interventions; education for patients, caregivers, and clinicians; shared decision-making tools beyond advance care planning and advance directive completion; and specific components, characteristics, and implementation factors in models for integrating palliative care into ambulatory care.