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Journal articles on the topic 'Personalized prediction'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Thoma, Clemens. "Personalized response prediction." Nature Reviews Gastroenterology & Hepatology 15, no. 11 (2018): 657. http://dx.doi.org/10.1038/s41575-018-0072-z.

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Liu, Jie, Bin Liu, Yanchi Liu, et al. "Personalized Air Travel Prediction." ACM Transactions on Intelligent Systems and Technology 9, no. 3 (2018): 1–26. http://dx.doi.org/10.1145/3078845.

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TAYEBI, MOHAMMAD A., UWE GLÄSSER, MARTIN ESTER, and PATRICIA L. BRANTINGHAM. "Personalized crime location prediction." European Journal of Applied Mathematics 27, no. 3 (2016): 422–50. http://dx.doi.org/10.1017/s0956792516000140.

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Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime

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Yang, Hao, and Maoyu Ran. "Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest." Buildings 15, no. 14 (2025): 2539. https://doi.org/10.3390/buildings15142539.

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Establishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not well suited for practical applications. In this study, real thermal sensation survey data were collected and used to first analyze the discrepancy between PMV model predictions and actual human thermal sensation. Subsequently, a simple thermal sensation prediction model was developed using multiple li

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Galetzka, Wolfgang, Bernd Kowall, Cynthia Jusi, Eva-Maria Huessler, and Andreas Stang. "Distance-Metric Learning for Personalized Survival Analysis." Entropy 25, no. 10 (2023): 1404. http://dx.doi.org/10.3390/e25101404.

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Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to handle such outcomes effectively, they do not provide explanations for their predictions, lacking interpretability. In this paper, an alternative method for survival prediction by weighted nearest neighbors is proposed. Fitting this model to data entails optimizing the weights by learning a metric. An individual prediction of this method can b

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M Durugkar, Sneha, and Saudagar S Barde. "Privacy Protection in Personalized Web Search Using Metric Prediction." International Journal of Scientific Engineering and Research 3, no. 9 (2015): 65–68. https://doi.org/10.70729/ijser15467.

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Localio, A. Russell, Cynthia D. Mulrow, and Michael E. Griswold. "Advancing Personalized Medicine Through Prediction." Annals of Internal Medicine 172, no. 1 (2019): 63. http://dx.doi.org/10.7326/m19-3010.

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Xu, Yanyu, Shenghua Gao, Junru Wu, Nianyi Li, and Jingyi Yu. "Personalized Saliency and Its Prediction." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (2019): 2975–89. http://dx.doi.org/10.1109/tpami.2018.2866563.

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Vassileva, Vessela. "Prostate cancer—personalized response prediction." Nature Reviews Clinical Oncology 6, no. 11 (2009): 618. http://dx.doi.org/10.1038/nrclinonc.2009.156.

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Lee, Chuan-Chun, Chia-Jui Yen, and Tsunglin Liu. "Prediction of personalized microRNA activity." Gene 518, no. 1 (2013): 101–6. http://dx.doi.org/10.1016/j.gene.2012.11.068.

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Kyrochristos, Ioannis D., Demosthenes E. Ziogas, and Dimitrios H. Roukos. "Precision in personalized prediction-based medicine." Personalized Medicine 15, no. 6 (2018): 467–70. http://dx.doi.org/10.2217/pme-2018-0079.

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Zheng, Zibin, and Michael R. Lyu. "Personalized Reliability Prediction of Web Services." ACM Transactions on Software Engineering and Methodology 22, no. 2 (2013): 1–25. http://dx.doi.org/10.1145/2430545.2430548.

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Li, Juan, and Chandima Fernando. "Smartphone-based personalized blood glucose prediction." ICT Express 2, no. 4 (2016): 150–54. http://dx.doi.org/10.1016/j.icte.2016.10.001.

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Malanin, Vladyslav, and Illya Chaykovsky. "Development of a Mathematical Model for Personalized Estimation of Life Expectancy in Ukraine." Cybernetics and Computer Technologies, no. 2 (June 6, 2025): 47–60. https://doi.org/10.34229/2707-451x.25.2.4.

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Introduction. The issue of personalized life expectancy prediction is a relevant task in contemporary medical cybernetics, significantly impacting public health and social planning. It gains special importance during crisis situations such as war, the COVID-19 pandemic, economic hardships, and demographic changes that have intensified in Ukraine in recent years. Existing models for life expectancy prediction, such as the Mortality Population Risk Tool (MPoRT) and the Lee-Carter model, have significant limitations regarding the inclusion of local characteristics specific to the Ukrainian popula

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Wang, Yan, Xinyu Bao, Song Zhang, et al. "Fetal growth prediction: Establishing fetal growth prediction curves in the second trimester." Technology and Health Care 29 (March 25, 2021): 345–50. http://dx.doi.org/10.3233/thc-218032.

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BACKGROUND: Monitoring fetal weight during pregnancy has a guiding role in prenatal care. OBJECTIVE: To establish a personalized fetal growth curve for effectively monitoring fetal growth during pregnancy. METHODS: (1) This study retrospectively analyzed the birth weight database of 2,474 singleton newborns delivered normally at term. The personalized fetal growth curve model was formed by combining the estimating birth weight of newborns with the proportional weight formula. (2) Multiple linear stepwise regression method was used to estimate the birth weight of newborns. RESULTS: (1) Delivery

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Cai, Weihong, Xin Du, and Jianlong Xu. "A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization." Sensors 19, no. 12 (2019): 2749. http://dx.doi.org/10.3390/s19122749.

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Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchai

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Li, Ziyun. "Mathematical statistical methods for stroke prognosis prediction and their clinical application research." Theoretical and Natural Science 49, no. 1 (2024): 22–29. http://dx.doi.org/10.54254/2753-8818/49/20241264.

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Abstract. Stroke is a serious illness, with a global disability rate of over 50% and a mortality rate of up to 30%, making research on stroke prognosis prediction of significant societal importance. This paper comprehensively analyzes the application of mathematical statistical methods in stroke prognosis prediction, aiming to explore how these methods can enhance the accuracy of prognosis predictions, thereby providing patients with personalized treatment plans and improving their long-term rehabilitation process. Initially, the article introduces the severity of stroke and the importance of

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Dixit, Abhishek, and Manish Jain. "A secure and intelligent framework for autonomous driving : Enhancing vehicle trajectory prediction with LSTM-XGboost model." Journal of Information and Optimization Sciences 46, no. 4-A (2025): 893–902. https://doi.org/10.47974/jios-1814.

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Autonomous driving, particularly when it involves smart decision-making and path planning in dynamic settings such as highways, presents far greater challenges compared to navigating static environments. This research paper investigates the effectiveness of various machine learning models in predicting the trajectories of surrounding vehicles, focusing on the personalized framework using LSTM-XGBoost model. In this research, we have categorized the vehicles NGSIM dataset into Traditional, Moderate, and Aggressive driving styles to assess model performance using RMSE and MAE metrics across diff

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Zhang, Zhijun, Gongwen Xu, and Pengfei Zhang. "Research on E-Commerce Platform-Based Personalized Recommendation Algorithm." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5160460.

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Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time.

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Rafiei, Mahdie, Supratim Das, Mohammad Bakhtiari, et al. "Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study." JMIR Rehabilitation and Assistive Technologies 12 (March 21, 2025): e60162-e60162. https://doi.org/10.2196/60162.

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Abstract Background Knee osteoarthritis (OA) is a common chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing OA pain and functional limitations, these strategies are often underused. To motivate and enhance patient engagement, personalized outcome prediction models can be used. However, the accuracy of existing models in predicting changes in knee pain outcomes remains insufficiently examined. Objective This study aims to validate existing models and introduce a concise personalized model pred

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Estrela, Daniel, Rita F. Santos, Alice Masserdotti, et al. "Molecular Biomarkers for Timely and Personalized Prediction of Maternal-Fetal Health Risk." Biomolecules 15, no. 3 (2025): 312. https://doi.org/10.3390/biom15030312.

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Molecular biomarker profiling is an emerging field in maternal-fetal health with the potential to transform early detection and prediction of placental dysfunction. By analysing a range of biomarkers in maternal blood, researchers and clinicians can gain crucial insights into placental health, enabling timely interventions to enhance fetal and maternal outcomes. Placental structural function is vital for fetal growth and development, and disruptions can lead to serious pregnancy complications like preeclampsia. While conventional methods such as ultrasound and Doppler velocimetry offer valuabl

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Wang, Bingkun, Bing Chen, Li Ma, and Gaiyun Zhou. "User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix." Information 10, no. 1 (2018): 1. http://dx.doi.org/10.3390/info10010001.

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With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained n

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Pei, Zejun, Manhong Shi, Junping Guo, and Bairong Shen. "Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives." Current Topics in Medicinal Chemistry 20, no. 18 (2020): 1640–50. http://dx.doi.org/10.2174/1568026620666200603105002.

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Heart rate variability (HRV) signals are reported to be associated with the personalized drug response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc. But the relationships between HRV signals and the personalized drug response in different diseases and patients are complex and remain unclear. With the fast development of modern smart sensor technologies and the popularization of big data paradigm, more and more data on the HRV and drug response will be available, it then provides great opportunities to build models for predicting the association

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An, Ziyan, Taylor T. Johnson, and Meiyi Ma. "Formal Logic Enabled Personalized Federated Learning through Property Inference." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10882–90. http://dx.doi.org/10.1609/aaai.v38i10.28962.

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Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the current research landscape is the ability to enable data-driven client models with symbolic reasoning capabilities. Specifically, the inherent heterogeneity of participating client devices poses a significant challenge, as each client exhibits unique logic reasoning properties. Failing to consider these device-specific specifications can result in c

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Lococo, Filippo, Galal Ghaly, Marco Chiappetta, et al. "Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review." Cancers 16, no. 10 (2024): 1832. http://dx.doi.org/10.3390/cancers16101832.

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Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based m

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Zhang, Yan. "Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 16 (2024): 68–82. http://dx.doi.org/10.3991/ijim.v18i16.51009.

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With the rapid development of information technology, the application of intelligent education platforms has become increasingly widespread. Traditional teaching methods struggle to meet the demands for personalized learning. Personalized learning path decision-making methods, which analyze learners’ behavioral data and mastery of knowledge points, tailor learning paths for each individual. Current research indicates that these methods can significantly improve learning efficiency and effectiveness. However, existing personalized learning path decision-making methods still have shortcomings in

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Vedenin, Yu I., M. I. Turovets, V. V. Mandrikov, and G. V. Mikhailichenko. "Personalized prediction of acute pancreatitis after endoscopic transpapillary interventions." Pirogov Russian Journal of Surgery, no. 1 (February 4, 2025): 29. https://doi.org/10.17116/hirurgia202501129.

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Objective. To develop the personalized model for predicting the risk of acute pancreatitis after endoscopic transpapillary interventions. Material and methods. A retrospective analysis of treatment outcomes included 366 patients with benign and malignant pancreaticobiliary diseases who underwent endoscopic transpapillary interventions. Risk factors associated with patients, underlying diseases and interventions were analyzed. Logistic regression analysis was used to present the personalized model for predicting the risk of acute pancreatitis. Results. Female gender (p=0.028), age <40 years

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Alharbi, Eman, Asma Cherif, and Farrukh Nadeem. "Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation." Smart Cities 6, no. 5 (2023): 2910–31. http://dx.doi.org/10.3390/smartcities6050130.

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Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to del

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Toivonen, Jarkko, Yrjö Koski, Esa Turkulainen, et al. "Prediction and impact of personalized donation intervals." Vox Sanguinis 117, no. 4 (2021): 504–12. http://dx.doi.org/10.1111/vox.13223.

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Bao, Naren, Alexander Carballo, Chiyomi Miyajima, Eijiro Takeuchi, and Kazuya Takeda. "Personalized Subjective Driving Risk: Analysis and Prediction." Journal of Robotics and Mechatronics 32, no. 3 (2020): 503–19. http://dx.doi.org/10.20965/jrm.2020.p0503.

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Subjective risk assessment is an important technology for enhancing driving safety, because an individual adjusts his/her driving behavior according to his/her own subjective perception of risk. This study presents a novel framework for modeling personalized subjective driving risk during expressway lane changes. The objectives of this study are twofold: (i) to use ego vehicle driving signals and surrounding vehicle locations in a data-driven and explainable approach to identify the possible influential factors of subjective risk while driving and (ii) to predict the specific individual’s subj

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Jiang, Miao, Yi Fang, Huangming Xie, Jike Chong, and Meng Meng. "User click prediction for personalized job recommendation." World Wide Web 22, no. 1 (2018): 325–45. http://dx.doi.org/10.1007/s11280-018-0568-z.

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Liu, Jin-Hu, Yu-Xiao Zhu, and Tao Zhou. "Improving personalized link prediction by hybrid diffusion." Physica A: Statistical Mechanics and its Applications 447 (April 2016): 199–207. http://dx.doi.org/10.1016/j.physa.2015.12.036.

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Fan, Yali, Zhen Tu, Yong Li, et al. "Personalized Context-aware Collaborative Online Activity Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 4 (2019): 1–28. http://dx.doi.org/10.1145/3369829.

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Fan, Zipei, Xuan Song, Renhe Jiang, Quanjun Chen, and Ryosuke Shibasaki. "Decentralized Attention-based Personalized Human Mobility Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 4 (2019): 1–26. http://dx.doi.org/10.1145/3369830.

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Zeevi, David, Tal Korem, Niv Zmora, et al. "Personalized Nutrition by Prediction of Glycemic Responses." Cell 163, no. 5 (2015): 1079–94. http://dx.doi.org/10.1016/j.cell.2015.11.001.

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Vinarti, Retno A., and Lucy M. Hederman. "A personalized infectious disease risk prediction system." Expert Systems with Applications 131 (October 2019): 266–74. http://dx.doi.org/10.1016/j.eswa.2019.04.042.

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., Sangeeta. "Pharmacogenomics: Personalized medicine and drug response prediction." Pharma Innovation 8, no. 1 (2019): 845–48. http://dx.doi.org/10.22271/tpi.2019.v8.i1n.25487.

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kartheek, J. Pavan. "Predicting Fetal Features from DNA Through Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41160.

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Predicting fetal features from DNA using machine learning represents a pioneering advancement in prenatal diagnostics and personalized medicine. This research leverages genomic data and advanced machine learning techniques to predict fetal traits, such as physical attributes and potential health conditions, with high precision. By integrating feature selection methods and predictive modeling, the study highlights the potential of machine learning in enabling early diagnosis and personalized healthcare planning. Ethical considerations, including data privacy and responsible use of genetic infor

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Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.

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The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total

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Chen, Rirong, Jieqi Zheng, Li Li, et al. "Metabolomics facilitate the personalized management in inflammatory bowel disease." Therapeutic Advances in Gastroenterology 14 (January 2021): 175628482110644. http://dx.doi.org/10.1177/17562848211064489.

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Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing resea

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Kim, Jinhee, Miseol Son, Da Som Kim, et al. "Abstract 7457: Personalized treatment for hepatocellular carcinoma (HCC) patients." Cancer Research 85, no. 8_Supplement_1 (2025): 7457. https://doi.org/10.1158/1538-7445.am2025-7457.

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Abstract Predicting drug response is crucial for personalized medicine, especially in cancer research and drug resistance studies. Accurate prediction of drug response, such as the half-maximal inhibitory concentration (IC50) in cell culture models, is vital for establishing personalized treatment models. In particular, patient-derived cell culture models have garnered increasing attention due to their precise prediction of therapeutic responses in targeted drug therapies. However, the prediction of drug response in hepatocellular carcinoma (HCC) patient-derived cells (PDCs) has not been fully

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Yang, Jinhao, Junwen Cao, and Mingyu Fang. "Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning." PLOS One 20, no. 7 (2025): e0326937. https://doi.org/10.1371/journal.pone.0326937.

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This study proposes a Driving style-Tri Channel Trajectory Model (DS-TCTM) to enhance vehicle trajectory prediction accuracy and driving safety. The framework operates through three rigorously designed stages: (1)Data preprocessing involving kinematics feature extraction, (2)Driving style recognition utilizing acceleration variation rate and average time headway combined with K-Means++ traffic density clustering and K-neighbor Gaussian mixture model (K-GMM) analysis to classify driving behaviors into conservative, moderate, and radical categories, and (3)Personalized trajectory prediction empl

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Nakhipova, Venera, Yerzhan Kerimbekov, Zhanat Umarova, Halil ibrahim Bulbul, Laura Suleimenova, and Elvira Adylbekova. "Integration of Collaborative Filtering Into Naive Bayes Method to Enhance Student Performance Prediction." International Journal of Information and Communication Technology Education 20, no. 1 (2024): 1–18. http://dx.doi.org/10.4018/ijicte.352512.

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This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores user behavior patterns, while Naive Bayes employs Bayes' theorem for probabilistic data classification. Focused on predicting academic success, the integration incorporates collaborative patterns from student data for increased accuracy. The method con

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Ma, Liantao, Chaohe Zhang, Yasha Wang, et al. "ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 833–40. http://dx.doi.org/10.1609/aaai.v34i01.5428.

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Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those works have shown superior performances in healthcare prediction, they fail to explore the personal characteristics during the clinical visits thoroughly. Moreover, existing works usually assume that the more recent record weights more in the prediction, but this assumption is not su

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Madhuri, Badole, Rane Siddhesh, Bharne Atharv, and Karpe Mayur. "Personalized Alzheimer's Disease Progression Prediction with Machine Learning." Personalized Alzheimer's Disease Progression Prediction with Machine Learning 9, no. 1 (2024): 6. https://doi.org/10.5281/zenodo.10567352.

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One of the most prevalent diseases in the world is Alzheimer’s (AD). It is a neurological condition that can lead to cognitive decline and memory loss. Both the senior population and the prevalence of diseases affecting them have dramatically increased in recent years. It is critical to categorize the progression of Alzheimer’s disease. Alzheimer's disease (AD) is a complicated neurological ailment that progresses in different ways for each individual. In this study, we present a novel approach to personalised Alzheimer's disease progression prediction using machine learning techni

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Anis, Hiba K., Gregory J. Strnad, Alison K. Klika, et al. "Developing a personalized outcome prediction tool for knee arthroplasty." Bone & Joint Journal 102-B, no. 9 (2020): 1183–93. http://dx.doi.org/10.1302/0301-620x.102b9.bjj-2019-1642.r1.

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Aims The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015

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Karimah, Fitrah, and Ahmad. "Short Communication: Drug Discovery Advancements in The Artificial Intelligence Era." Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) 2, no. 1 (2024): 18–23. https://doi.org/10.70356/jafotik.v2i1.29.

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Artificial Intelligence (AI) is significantly transforming drug discovery by enhancing efficiency and reducing costs. Traditional drug development has been slow and expensive, but AI's integration accelerates the process by predicting molecular interactions, identifying drug candidates, and optimizing formulations. Recent advancements highlight AI's role in molecular interaction prediction, target identification, lead optimization, and toxicity prediction. AI models, particularly deep learning algorithms, improve drug efficacy predictions and streamline virtual screening. They also address cha

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Ughade, Ashwini Arun. "Personalized Location Recommendation System Personalized Location Recommendation System." International Journal of Applied Evolutionary Computation 10, no. 1 (2019): 49–58. http://dx.doi.org/10.4018/ijaec.2019010104.

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Location acquisition and wireless communication technologies are growing in location-based social networks. With the rapid development of location-based social networks (LBSNs), location recommendation has become an important for helping users to discover interesting locations. Most current studies on spatial item recommendations do not consider the sequential influence of locations. The authors proposed a personalized location recommendation system as a probabilistic generative model that aims to mimic the process of human decision-making when visiting locations. In this system, three tasks a

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Shreve, Jacob Tyler, Sarah Lee, Christina Felix, et al. "A personalized prediction model for hospital readmission risk for cancer patients." Journal of Clinical Oncology 38, no. 15_suppl (2020): 7057. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.7057.

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7057 Background: Cancer patients (pts) are at high risk of unplanned hospital readmissions. Predicting which cancer patients are at higher risk of readmission would improve post-discharge follow-up/navigation, decrease cost, and improve pt outcomes. Methods: We conducted a retrospective cohort study of non-surgical cancer pts hospitalized at our center between 12/2014 to 7/2018. A machine learning algorithm was trained on 348 medical, sociodemographic and cancer-specific variables with a total of 1,801,944 data points. The cohort was randomly divided into training (80%) and validation (20%) su

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Researcher. "LEVERAGING AI TO TACKLE FINANCIAL DISTRESS: A COMPREHENSIVE APPROACH." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 358–69. https://doi.org/10.5281/zenodo.13255200.

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Artificial Intelligence (AI) has emerged as a powerful tool in predicting and mitigating financial distress for individuals and businesses. This article explores various AI techniques employed in financial management, including early warning systems, liquidity management, debt restructuring, personalized financial planning, and continuous monitoring strategies. AI-powered models have demonstrated remarkable accuracy in predicting financial distress, with some achieving up to 86.4% accuracy in corporate financial distress prediction. These systems utilize advanced algorithms, such as Long Short

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