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Artículos de revistas sobre el tema "Predictive HCI models"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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1

Paton, Chris, Andre W. Kushniruk, Elizabeth M. Borycki, Mike English, and Jim Warren. "Improving the Usability and Safety of Digital Health Systems: The Role of Predictive Human-Computer Interaction Modeling." Journal of Medical Internet Research 23, no. 5 (2021): e25281. http://dx.doi.org/10.2196/25281.

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In this paper, we describe techniques for predictive modeling of human-computer interaction (HCI) and discuss how they could be used in the development and evaluation of user interfaces for digital health systems such as electronic health record systems. Predictive HCI modeling has the potential to improve the generalizability of usability evaluations of digital health interventions beyond specific contexts, especially when integrated with models of distributed cognition and higher-level sociotechnical frameworks. Evidence generated from building and testing HCI models of the user interface (U

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2

Segkouli, Sofia, Ioannis Paliokas, Dimitrios Tzovaras, Thanos Tsakiris, Magda Tsolaki, and Charalampos Karagiannidis. "Novel Virtual User Models of Mild Cognitive Impairment for Simulating Dementia." Computational and Mathematical Methods in Medicine 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/358638.

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Virtual user modeling research has attempted to address critical issues of human-computer interaction (HCI) such as usability and utility through a large number of analytic, usability-oriented approaches as cognitive models in order to provide users with experiences fitting to their specific needs. However, there is demand for more specific modules embodied in cognitive architecture that will detect abnormal cognitive decline across new synthetic task environments. Also, accessibility evaluation of graphical user interfaces (GUIs) requires considerable effort for enhancing ICT products accessi

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3

Isiaka, Fatima, Kassim S. Mwitondi, and Adamu M. Ibrahim. "Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm." International Journal of Intelligent Computing and Cybernetics 9, no. 1 (2016): 23–41. http://dx.doi.org/10.1108/ijicc-08-2015-0029.

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Purpose – The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. Design/methodology/approach – The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models

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4

Krabbe, Laura-Maria, Aditya Bagrodia, Ahmed Q. Haddad, et al. "Multi-institutional validation of the predictive value of Ki-67 in patients with high-grade urothelial carcinoma of the upper urinary tract." Journal of Clinical Oncology 33, no. 7_suppl (2015): 371. http://dx.doi.org/10.1200/jco.2015.33.7_suppl.371.

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371 Background: To validate the independent predictive value of Ki-67 in patients with high-grade upper tract urothelial carcinoma (UTUC). Methods: 475 patients from the international UTUC collaboration who underwent extirpative surgery for high-grade UTUC were included in this study. Immunohistochemical staining for Ki-67 was performed on tissue microarray (TMA) formed from this patient cohort. Ki-67 expression was assessed in a semi-quantitative fashion and considered overexpressed at a cut-off of 20%. Multivariate analyses (MVA) were performed to assess independent predictors of oncological

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Bakaev, Maxim, Sebastian Heil, and Martin Gaedke. "Reasonable Effectiveness of Features in Modeling Visual Perception of User Interfaces." Big Data and Cognitive Computing 7, no. 1 (2023): 30. http://dx.doi.org/10.3390/bdcc7010030.

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Training data for user behavior models that predict subjective dimensions of visual perception are often too scarce for deep learning methods to be applicable. With the typical datasets in HCI limited to thousands or even hundreds of records, feature-based approaches are still widely used in visual analysis of graphical user interfaces (UIs). In our paper, we benchmarked the predictive accuracy of the two types of neural network (NN) models, and explored the effects of the number of features, and the dataset volume. To this end, we used two datasets that comprised over 4000 webpage screenshots

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6

Lee, Jae Seung, Tae Seop Lim, Hye Won Lee, et al. "Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis." Diagnostics 13, no. 1 (2022): 3. http://dx.doi.org/10.3390/diagnostics13010003.

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This study aimed to evaluate the predictive performance of pre-existing well-validated hepatocellular carcinoma (HCC) prediction models, established in patients with HBV-related cirrhosis who started potent antiviral therapy (AVT). We retrospectively reviewed the cases of 1339 treatment-naïve patients with HBV-related cirrhosis who started AVT (median period, 56.8 months). The scores of the pre-existing HCC risk prediction models were calculated at the time of AVT initiation. HCC developed in 211 patients (15.1%), and the cumulative probability of HCC development at 5 years was 14.6%. Multivar

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7

Nishida, Nao, Jun Ohashi, Goki Suda, et al. "Prediction Model with HLA-A*33:03 Reveals Number of Days to Develop Liver Cancer from Blood Test." International Journal of Molecular Sciences 24, no. 5 (2023): 4761. http://dx.doi.org/10.3390/ijms24054761.

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The development of liver cancer in patients with hepatitis B is a major problem, and several models have been reported to predict the development of liver cancer. However, no predictive model involving human genetic factors has been reported to date. For the items incorporated in the prediction model reported so far, we selected items that were significant in predicting liver carcinogenesis in Japanese patients with hepatitis B and constructed a prediction model of liver carcinogenesis by the Cox proportional hazard model with the addition of Human Leukocyte Antigen (HLA) genotypes. The model,

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Lim, Jihye, Young Eun Chon, Mi Na Kim, et al. "Cirrhosis, Age, and Liver Stiffness-Based Models Predict Hepatocellular Carcinoma in Asian Patients with Chronic Hepatitis B." Cancers 13, no. 22 (2021): 5609. http://dx.doi.org/10.3390/cancers13225609.

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Objectives: Predicting hepatocellular carcinoma (HCC) in patients with chronic hepatitis B who received long-term therapy with potent nucleos(t)ide analogs is of utmost importance to refine the strategy for HCC surveillance. Methods: We conducted a multicenter retrospective cohort study to validate the CAGE-B and SAGE-B scores, HCC prediction models developed for Caucasian patients receiving entecavir (ETV) or tenofovir (TFV) for >5 years. Consecutive patients who started ETV or TFV at two hospitals in Korea from January 2009 to December 2015 were identified. The prediction scores were calc

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Li, Huikai, Han Mu, Yajie Xiao, Zhikun Zhao, Xiaoli Cui, and Dongfang Wu. "Comprehensive Analysis of Histone Modifications in Hepatocellular Carcinoma Reveals Different Subtypes and Key Prognostic Models." Journal of Oncology 2022 (August 1, 2022): 1–20. http://dx.doi.org/10.1155/2022/5961603.

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Histone modification, an important epigenetic mechanism, is related to the carcinogenesis of hepatocellular carcinoma (HCC). In three datasets, we screened 88 epigenetic-dysregulated PCGs (epi-PCGs) , which were significantly associated with HCC survival and could cluster HCC into three molecular subtypes. These subtypes were associated with prognosis, immunomodulatory alterations, and response to different treatment strategies. Based on 88 epi-PCGs in the TCGA training set, a risk prediction model composed of 4 epi-PCGs was established. The model was closely related to the clinicopathological

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Li, Yao, Wei Wang, Weisheng Zeng, Jianjun Wang, and Jinghui Meng. "Development of Crown Ratio and Height to Crown Base Models for Masson Pine in Southern China." Forests 11, no. 11 (2020): 1216. http://dx.doi.org/10.3390/f11111216.

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Crown ratio (CR) and height to crown base (HCB) are important crown characteristics influencing the behavior of forest canopy fires. However, the labor-intensive and costly measurement of CR and HCB have hindered their wide application to forest fire management. Here, we use 301 sample trees collected in 11 provinces in China to produce predictive models of CR and HCB for Masson pine forests (Pinus massoniana Lamb.), which are vulnerable to forest canopy fires. We first identified the best basic model that used only diameter at breast height (DBH) and height (H) as independent variables to pre

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Hui, Yongfeng, Junzhi Leng, Dong Jin, et al. "A Cell Cycle Progression-Derived Gene Signature to Predict Prognosis and Therapeutic Response in Hepatocellular Carcinoma." Disease Markers 2021 (October 21, 2021): 1–36. http://dx.doi.org/10.1155/2021/1986159.

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Objective. Dysregulation of cell cycle progression (CCP) is one of the hallmarks of cancer. Here, our study is aimed at developing a CCP-derived gene signature for predicting high-risk population of hepatocellular carcinoma (HCC). Methods. Our study retrospectively analyzed the transcriptome profiling and clinical information of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects. Uni- and multivariate cox regression models were conducted for identifying which hallmarks of cancer were risk factors of HCC. CCP-derived gene signature was de

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Lin, Tao, Tian-Tian Xie, Yi Mou, and Ning-Jiu Tang. "Markov Chain Models for Menu Item Prediction." International Journal of Technology and Human Interaction 9, no. 4 (2013): 75–94. http://dx.doi.org/10.4018/ijthi.2013100105.

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With the increase in the number of menu items and the menu structure complexity, users have to spend more time in locating menu items when using menu-based interfaces, which tends to result in the decrease of task performance and the increase of mental load. How to reduce the navigation time has been a great challenge in the HCI (human-computer interaction) field. Recently, adaptive menu techniques have been explored in response to the challenge, and menu item prediction plays a crucial role in the techniques. Unfortunately, there still lacks effective prediction models for menu items. This pa

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Wang, W. C. "Personalized Prediction Model for Hepatocellular Carcinoma With a Bayesian Clinical Reasoning Approach." Journal of Global Oncology 4, Supplement 2 (2018): 210s. http://dx.doi.org/10.1200/jgo.18.84600.

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Background: Predictive models for the risk of hepatocellular carcinoma (HCC) are often appropriate for average-risk population but not tailored for a personalized prediction model for individual risk of hepatocellular carcinoma (HCC), namely personalized prediction model. Aim: The objective of this study is to build up an individually tailored predictive model for HCC by using a Bayesian clinical reasoning algorithm to stratify risk groups of the underlying population. Methods: Data were derived from a community-based screening cohort consisting of 98,552 subjects between 1999 and 2007. Inform

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Ma, Jun, Zhiyuan Bo, Zhengxiao Zhao, et al. "Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma." Cancers 15, no. 3 (2023): 625. http://dx.doi.org/10.3390/cancers15030625.

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Background: Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data. Methods: Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination th

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Hu, Xiaojun, Changfeng Li, Qiang Wang, et al. "Development and External Validation of a Radiomics Model Derived from Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Histopathologic Grade of Hepatocellular Carcinoma." Diagnostics 13, no. 3 (2023): 413. http://dx.doi.org/10.3390/diagnostics13030413.

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Histopathologic grade of hepatocellular carcinoma (HCC) is an important predictor of early recurrence and poor prognosis after curative treatments. This study aims to develop a radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting HCC histopathologic grade and to validate its predictive performance in an independent external cohort. Clinical and imaging data of 403 consecutive HCC patients were retrospectively collected from two hospitals (265 and 138, respectively). Patients were categorized into poorly differentiated HCC and non-poorly differentiated HCC groups. A

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Cao, Linping, Qing Wang, Jiawei Hong, et al. "MVI-TR: A Transformer-Based Deep Learning Model with Contrast-Enhanced CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma." Cancers 15, no. 5 (2023): 1538. http://dx.doi.org/10.3390/cancers15051538.

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In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venous phase (VP) of contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital of Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in this study. All preoperative CECT were collected, and the patients were randomly divided i

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Demuyser, Thomas, Lucie Seyler, Rhea Buttiens, et al. "Healthcare-Associated COVID-19 across Five Pandemic Waves: Prediction Models and Genomic Analyses." Viruses 14, no. 10 (2022): 2292. http://dx.doi.org/10.3390/v14102292.

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Background: Healthcare-associated SARS-CoV-2 infections need to be explored further. Our study is an analysis of hospital-acquired infections (HAIs) and ambulatory healthcare workers (aHCWs) with SARS-CoV-2 across the pandemic in a Belgian university hospital. Methods: We compared HAIs with community-associated infections (CAIs) to identify the factors associated with having an HAI. We then performed a genomic cluster analysis of HAIs and aHCWs. We used this alongside the European Centre for Disease Control (ECDC) case source classifications of an HAI. Results: Between March 2020 and March 202

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Niu, Xiang-Ke, and Xiao-Feng He. "A Nomogram Based on Preoperative Lipiodol Deposition after Sequential Retreatment with Transarterial Chemoembolization to Predict Prognoses for Intermediate-Stage Hepatocellular Carcinoma." Journal of Personalized Medicine 12, no. 9 (2022): 1375. http://dx.doi.org/10.3390/jpm12091375.

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(1) Background: Conventional transarterial chemoembolization (cTACE) is the mainstay treatment for patients with Barcelona Clinic Liver Cancer (BCLC) B-stage hepatocellular carcinoma (HCC). However, BCLC B-stage patients treated with cTACE represent a prognostically heterogeneous population. We aim to develop and validate a lipiodol-deposition-based nomogram for predicting the long-term survival of BCLC B-stage HCC patients after sequential cTACE. (2) Methods: In this retrospective study, 229 intermediate-stage HCC patients from two hospitals were separately allocated to a training cohort (n =

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Yang, Haoyu, Roshan Tourani, Ying Zhu, et al. "Strategies for building robust prediction models using data unavailable at prediction time." Journal of the American Medical Informatics Association 29, no. 1 (2021): 72–79. http://dx.doi.org/10.1093/jamia/ocab229.

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Abstract Objective Hospital-acquired infections (HAIs) are associated with significant morbidity, mortality, and prolonged hospital length of stay. Risk prediction models based on pre- and intraoperative data have been proposed to assess the risk of HAIs at the end of the surgery, but the performance of these models lag behind HAI detection models based on postoperative data. Postoperative data are more predictive than pre- or interoperative data since it is closer to the outcomes in time, but it is unavailable when the risk models are applied (end of surgery). The objective is to study whethe

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Dieste-Pérez, Peña, Ricardo Savirón-Cornudella, Mauricio Tajada-Duaso, et al. "Personalized Model to Predict Small for Gestational Age at Delivery Using Fetal Biometrics, Maternal Characteristics, and Pregnancy Biomarkers: A Retrospective Cohort Study of Births Assisted at a Spanish Hospital." Journal of Personalized Medicine 12, no. 5 (2022): 762. http://dx.doi.org/10.3390/jpm12050762.

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Small for gestational age (SGA) is defined as a newborn with a birth weight for gestational age < 10th percentile. Routine third-trimester ultrasound screening for fetal growth assessment has detection rates (DR) from 50 to 80%. For this reason, the addition of other markers is being studied, such as maternal characteristics, biochemical values, and biophysical models, in order to create personalized combinations that can increase the predictive capacity of the ultrasound. With this purpose, this retrospective cohort study of 12,912 cases aims to compare the potential value of third-trimest

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Chen, Yiming, Bin Wu, Yijie Chen, Wenwen Ning, and Huimin Zhang. "A Risk Model for Predicting Fetuses with Trisomy 21 Using Alpha-Fetoprotein Variants L2 Combined with Maternal Serum Biomarkers in Early Pregnancy." Reproductive Sciences 29, no. 4 (2021): 1287–95. http://dx.doi.org/10.1007/s43032-021-00762-5.

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AbstractTo establish a risk prediction model and the clinical value of trisomy 21 using alpha-fetoprotein variants L2 (AFP-L2) combined with maternal serum biomarkers and nuchal translucency (NT) thickness in early pregnancy. A retrospective case–control study was conducted. The subjects were divided into the case group (n = 40) or the control group (n = 40). An enzyme-linked immunosorbent assay was used to measure the maternal serum AFP-L2 level in both groups. The AFP-L2 single-index or multi-index combined risk model was used to predict the efficiency of trisomy 21. The best cut-off value a

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Chu, Hee Ho, Jin Hyoung Kim, Ju Hyun Shim, et al. "Neutrophil-to-Lymphocyte Ratio as a Biomarker Predicting Overall Survival after Chemoembolization for Intermediate-Stage Hepatocellular Carcinoma." Cancers 13, no. 11 (2021): 2830. http://dx.doi.org/10.3390/cancers13112830.

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The clinical impact of neutrophil-to-lymphocyte ratio (NLR) in predicting outcomes in hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE) remain unclear, and additional large-scale studies are required. This retrospective study evaluated outcomes in treatment-naïve patients who received TACE as first-line treatment for intermediate-stage HCC between 2008 and 2017. Patients who underwent TACE before and after 2013 were assigned to the development (n = 495) and validation (n = 436) cohorts, respectively. Multivariable Cox analysis identified six factors pr

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Shen, Po-Chien, Wen-Yen Huang, Yang-Hong Dai, et al. "Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy." Biomedicines 10, no. 3 (2022): 597. http://dx.doi.org/10.3390/biomedicines10030597.

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(1) Background: The application of stereotactic body radiation therapy (SBRT) in hepatocellular carcinoma (HCC) limited the risk of the radiation-induced liver disease (RILD) and we aimed to predict the occurrence of RILD more accurately. (2) Methods: 86 HCC patients were enrolled. We identified key predictive factors from clinical, radiomic, and dose-volumetric parameters using a multivariate analysis, sequential forward selection (SFS), and a K-nearest neighbor (KNN) algorithm. We developed a predictive model for RILD based on these factors, using the random forest or logistic regression alg

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He, Cheng, Jing Yang, Zheng Jin, et al. "An ALBI- and Ascites-Based Model to Predict Survival for BCLC Stage B Hepatocellular Carcinoma." Evidence-Based Complementary and Alternative Medicine 2022 (July 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/1801230.

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Background. We aimed to develop a predictive model constituted with the ALBI grade, the ascites, and tumor burden related parameters in patients with BCLC stage B HCC. Methods. Patients diagnosed as the BCLC stage B HCC were collected from a retrospective database. Construction and validation of the predictive model were performed based on multivariate Cox regression analysis. Predictive accuracy, discrimination (c-index), and fitness performance (calibration curve) of the model were compared with the other eight models. The decision curve analysis (DCA) was used to evaluate the clinical utili

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Argirion, Ilona, Jalen Brown, Sarah Jackson, et al. "Association between Immunologic Markers and Cirrhosis in Individuals from a Prospective Chronic Hepatitis C Cohort." Cancers 14, no. 21 (2022): 5280. http://dx.doi.org/10.3390/cancers14215280.

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Background: Chronic hepatitis C virus (HCV) infection can affect immune response and inflammatory pathways, leading to severe liver diseases such as cirrhosis and hepatocellular carcinoma (HCC). Methods: In a prospective cohort of chronically HCV-infected individuals, we sampled 68 individuals who developed cirrhosis, 91 controls who did not develop cirrhosis, and 94 individuals who developed HCC. Unconditional odds ratios (ORs) from polytomous logistic regression models and canonical discriminant analyses (CDAs) were used to compare categorical (C) baseline plasma levels for 102 markers in in

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Zheng, Xiaomin, Feng Cao, Liting Qian, and Jiangning Dong. "Body Composition Changes in Hepatocellular Carcinoma: Prediction of Survival to Transcatheter Arterial Chemoembolization in Combination With Clinical Prognostic Factors." Cancer Control 28 (January 2021): 107327482110384. http://dx.doi.org/10.1177/10732748211038445.

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Treatment-related toxicities and decreased levels of patient performance during cancer therapy might contribute to body composition changes (BCC) and thereby impact outcomes. This study investigated the association between BCC during transcatheter arterial chemoembolization (TACE) and outcome in patients with hepatocellular carcinoma (HCC), and developed a nomogram for predicting survival in combination with clinical prognostic factors (CPF). Pretreatment and posttreatment computed tomography (CT) images of 75 patients with HCC who were treated between 2015 and 2018 were analyzed. The bone min

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Zheng, Jinghui, Youming Tang, Encun Hou, et al. "Identification of Susceptibility Genes in Hepatic Cancer Using Whole Exome Sequencing and Risk Prediction Model Construction." Revista Romana de Medicina de Laborator 28, no. 1 (2020): 67–74. http://dx.doi.org/10.2478/rrlm-2020-0008.

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AbstractObjective: To identify the susceptible single nucleotide polymorphisms (SNPs) loci in HCC patients in Guangxi Region, screen biomarkers from differential SNPs loci by using predictors, and establish risk prediction models for HCC, to provide a basis of screening high-risk individuals of HCC.Methods: Blood sample and clinical data of 50 normal participants and 50 hepatic cancer (HCC) patients in Rui Kang Hospital affiliated to Guangxi University of Traditional Chinese Medicine were collected. Normal participants and HCC patients were assigned to training set and testing set, respectivel

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Wei, Yi, Meiyi Yang, Lifeng Xu, et al. "Novel Computed-Tomography-Based Transformer Models for the Noninvasive Prediction of PD-1 in Pre-Operative Settings." Cancers 15, no. 3 (2023): 658. http://dx.doi.org/10.3390/cancers15030658.

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The expression status of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC) is associated with the checkpoint blockade treatment responses of PD-1/PD-L1. Thus, accurately and preoperatively identifying the status of PD-1 has great clinical implications for constructing personalized treatment strategies. To investigate the preoperative predictive value of the transformer-based model for identifying the status of PD-1 expression, 93 HCC patients with 75 training cohorts (2859 images) and 18 testing cohorts (670 images) were included. We propose a transformer-b

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Fistouris, Johan, Christina Bergh, and Annika Strandell. "Pregnancy of unknown location: external validation of the hCG-based M6NP and M4 prediction models in an emergency gynaecology unit." BMJ Open 12, no. 11 (2022): e058454. http://dx.doi.org/10.1136/bmjopen-2021-058454.

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ObjectiveTo investigate if M6NP predicting ectopic pregnancy (EP) among women with pregnancy of unknown location (PUL) is valid in an emergency gynaecology setting and comparing it with its predecessor M4.DesignRetrospective cohort study.SettingUniversity Hospital.ParticipantsWomen with PUL.MethodsAll consecutive women with a PUL during a study period of 3 years were screened for inclusion. Risk prediction of an EP was based on two serum human chorionic gonadotropin (hCG) levels taken at least 24 hours and no longer than 72 hours apart.Main outcome measuresThe area under the ROC curve (AUC) ex

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Lee, Ji Hun, Seung Kak Shin, Seong Hee Kang, et al. "Long-Term Prediction Model for Hepatocellular Carcinoma in Patients with Chronic Hepatitis B Receiving Antiviral Therapy: Based on Data from Korean Patients." Journal of Clinical Medicine 11, no. 22 (2022): 6613. http://dx.doi.org/10.3390/jcm11226613.

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Predicting the development of hepatocellular carcinoma (HCC) is a key clinical issue in patients with chronic hepatitis B (CHB). The aim of this study was to develop a precise and simple HCC risk score for up to 10 years. A total of 1895 CHB patients treated with entecavir or tenofovir disoproxil fumarate were retrospectively recruited and randomized into derivation (n = 1239) and validation cohorts (n = 656). Variables proven to be independent risk factors for HCC in the derivation cohort were used to develop the prediction model. The ACCESS-HCC model included five variables (age, cirrhosis,

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Brancato, Valentina, Nunzia Garbino, Marco Salvatore, and Carlo Cavaliere. "MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma." Diagnostics 12, no. 5 (2022): 1085. http://dx.doi.org/10.3390/diagnostics12051085.

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Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contras

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Brancato, Valentina, Nunzia Garbino, Marco Salvatore, and Carlo Cavaliere. "MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma." Diagnostics 12, no. 5 (2022): 1085. http://dx.doi.org/10.3390/diagnostics12051085.

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Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contras

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Lee, Jae Seung, Hyun Woong Lee, Tae Seop Lim, et al. "External Validation of the FSAC Model Using On-Therapy Changes in Noninvasive Fibrosis Markers in Patients with Chronic Hepatitis B: A Multicenter Study." Cancers 14, no. 3 (2022): 711. http://dx.doi.org/10.3390/cancers14030711.

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Antiviral therapy (AVT) induces the regression of non-invasive fibrosis markers (NFMs) and reduces hepatocellular carcinoma (HCC) risk among chronic hepatitis B (CHB) patients. We externally validated the predictive performance of the FSAC prediction model for HCC using on-therapy NFM responses. Our multicenter study consecutively recruited treatment-naïve CHB patients (n = 3026; median age, 50.0 years; male predominant (61.3%); cirrhosis in 1391 (46.0%) patients) receiving potent AVTs for >18 months between 2007 and 2018. During follow-up (median 64.0 months), HCC developed in 303 (10.0%)

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Teixeira, Cláudia, Eduardo Tejera, Helena Martins, António Tomé Pereira, Altamiro Costa-Pereira, and Irene Rebelo. "First Trimester Aneuploidy Screening Program for Preeclampsia Prediction in a Portuguese Obstetric Population." Obstetrics and Gynecology International 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/435037.

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Objective. To evaluate the performance of a first trimester aneuploidy screening program for preeclampsia (PE) prediction in a Portuguese obstetric population, when performed under routine clinical conditions.Materials and Methods. Retrospective cohort study of 5672 pregnant women who underwent routine first trimester aneuploidy screening in a Portuguese university hospital from January 2009 to June 2013. Logistic regression-based predictive models were developed for prediction of PE based on maternal characteristics, crown-rump length (CRL), nuchal translucency thickness (NT), and maternal se

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Tao, Yun-Yun, Yue Shi, Xue-Qin Gong, et al. "Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma." Cancers 15, no. 2 (2023): 365. http://dx.doi.org/10.3390/cancers15020365.

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Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this s

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Wang, Yikai, Le Ma, Pengjun Xue, et al. "Construction and Analysis of Hepatocellular Carcinoma Prognostic Model Based on Random Forest." Canadian Journal of Gastroenterology and Hepatology 2023 (January 12, 2023): 1–20. http://dx.doi.org/10.1155/2023/6707698.

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Introduction and Aims. Hepatocellular carcinoma (HCC) is one of the most lethal tumors of the digestive system, but its mechanisms remain unclear. The purpose of this study was to study HCC-related genes, build a survival prognosis prediction model, and provide references for treatment and mechanism research. Methods. Transcriptome data and clinical data of HCC were downloaded from the TCGA database. Screen important genes based on the random forest method, combined with differential expression genes (DEGs) to screen out important DEGs. The Kaplan‒Meier curve was used to evaluate its prognosti

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Lee, Jae Seung, Hyun Woong Lee, Tae Seop Lim, et al. "Novel Liver Stiffness-Based Nomogram for Predicting Hepatocellular Carcinoma Risk in Patients with Chronic Hepatitis B Virus Infection Initiating Antiviral Therapy." Cancers 13, no. 23 (2021): 5892. http://dx.doi.org/10.3390/cancers13235892.

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Hepatocellular carcinoma (HCC) risk prediction is important to developing individualized surveillance approaches. We designed a novel HCC prediction model using liver stiffness on transient elastography for patients receiving antiviral therapy against hepatitis B virus (HBV) infection. We recruited 2037 patients receiving entecavir or tenofovir as first-line antivirals and used the Cox regression analysis to determine key variables for model construction. Within 58.1 months (median), HCC developed in 182 (8.9%) patients. Patients with HCC showed a higher prevalence of cirrhosis (90.7% vs. 45.9

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Chiu, Herng-Chia, Te-Wei Ho, King-Teh Lee, Hong-Yaw Chen, and Wen-Hsien Ho. "Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network." Scientific World Journal 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/201976.

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The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers

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Rosen, M. A., A. Ruutiainen, E. Siegelman, et al. "Response assessment of HCC undergoing chemoembolization by necrosis-adjusted models." Journal of Clinical Oncology 27, no. 15_suppl (2009): e15528-e15528. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e15528.

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e15528 Background: Chemoembolization (CE) is an accepted therapy for unresectable HCC. As treated tumors may not shrink in size, response assessment via measurement of the enhancing tumor (EASL) has been proposed as an alternate to whole tumor (RECIST) measures. However, the reliability of this model has not been tested in clinical practice. Methods: We identified 29 HCC patients treated with CE with known overall survival (OS) in whom MRI before and after initial CE therapy was available for review. Three radiologists evaluated the imaging, measuring the largest liver lesion in three dimensio

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Guan, Shenheng, Michael F. Moran, and Bin Ma. "Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning." Molecular & Cellular Proteomics 18, no. 10 (2019): 2099–107. http://dx.doi.org/10.1074/mcp.tir119.001412.

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Deep learning models for prediction of three key LC-MS/MS properties from peptide sequences were developed. The LC-MS/MS properties or behaviors are indexed retention times (iRT), MS1 or survey scan charge state distributions, and sequence ion intensities of HCD spectra. A common core deep supervised learning architecture, bidirectional long-short term memory (LSTM) recurrent neural networks was used to construct the three prediction models. Two featurization schemes were proposed and demonstrated to allow for efficient encoding of modifications. The iRT and charge state distribution models we

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Lai, Yung-Chi, Kuo-Chen Wu, Chao-Jen Chang, et al. "Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation." Diagnostics 13, no. 5 (2023): 981. http://dx.doi.org/10.3390/diagnostics13050981.

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Positron emission tomography and computed tomography with 18F-fluorodeoxyglucose (18F-FDG PET-CT) were used to predict outcomes after liver transplantation in patients with hepatocellular carcinoma (HCC). However, few approaches for prediction based on 18F-FDG PET-CT images that leverage automatic liver segmentation and deep learning were proposed. This study evaluated the performance of deep learning from 18F-FDG PET-CT images to predict overall survival in HCC patients before liver transplantation (LT). We retrospectively included 304 patients with HCC who underwent 18F-FDG PET/CT before LT

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Naucler, Pontus, Suzanne D. van der Werff, John Valik, et al. "HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden." Infection Control & Hospital Epidemiology 41, S1 (2020): s39. http://dx.doi.org/10.1017/ice.2020.519.

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Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to de

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Castaldo, Anna, Davide Raffaele De Lucia, Giuseppe Pontillo, et al. "State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma." Diagnostics 11, no. 7 (2021): 1194. http://dx.doi.org/10.3390/diagnostics11071194.

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The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific

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Lok, James, and Kosh Agarwal. "Screening for Hepatocellular Carcinoma in Chronic Hepatitis B: An Update." Viruses 13, no. 7 (2021): 1333. http://dx.doi.org/10.3390/v13071333.

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(1) Background: Hepatocellular carcinoma (HCC) is an important cause of mortality in individuals with chronic hepatitis B infection, with screening of high-risk groups recommended in all major international guidelines. Our understanding of the risk factors involved has improved over time, encouraging researchers to develop models that predict future risk of HCC development. (2) Methods: A literature search of the PubMed database was carried out to identify studies that derive or validate models predicting HCC development in patients with chronic hepatitis B. Subsequently, a second literature s

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Mirza-Aghazadeh-Attari, Mohammad, Bharath Ambale Venkatesh, Mounes Aliyari Ghasabeh, et al. "The Additive Value of Radiomics Features Extracted from Baseline MR Images to the Barcelona Clinic Liver Cancer (BCLC) Staging System in Predicting Transplant-Free Survival in Patients with Hepatocellular Carcinoma: A Single-Center Retrospective Analysis." Diagnostics 13, no. 3 (2023): 552. http://dx.doi.org/10.3390/diagnostics13030552.

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Background: To study the additive value of radiomics features to the BCLC staging system in clustering HCC patients. Methods: A total of 266 patients with HCC were included in this retrospective study. All patients had undergone baseline MR imaging, and 95 radiomics features were extracted from 3D segmentations representative of lesions on the venous phase and apparent diffusion coefficient maps. A random forest algorithm was utilized to extract the most relevant features to transplant-free survival. The selected features were used alongside BCLC staging to construct Kaplan–Meier curves. Resul

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Morsica, Giulia, Laura Galli, Emanuela Messina, et al. "Levels of Alpha-Fetoprotein and Association with Mortality in Hepatocellular Carcinoma of HIV-1-Infected Patients." Journal of Oncology 2022 (February 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/3586064.

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Background and Aim. The clinical course of hepatocellular carcinoma (HCC) is determined by cancer-related and nonrelated factors. We evaluated the effect of a cancer-related factor, alpha-fetoprotein (AFP) levels, on mortality in HIV-1-infected patients with HCC. Methods. This is a retrospective cohort study on patients living with HIV-1 infection (PLWH) followed at the Division of Infectious Diseases of the San Raffaele Hospital, with cirrhosis and HCC diagnosed between 1999 and 2018 and with an available AFP value at HCC diagnosis. The area under the receiver operating characteristic curve (

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Klar, Markus, Florian Fischer, Arthur Fleig, Miroslav Bachinski, and Jörg Müller. "Simulating Interaction Movements via Model Predictive Control." ACM Transactions on Computer-Human Interaction, December 20, 2022. http://dx.doi.org/10.1145/3577016.

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We present a Model Predictive Control (MPC) framework to simulate movement in interaction with computers, focusing on mid-air pointing as an example. Starting from understanding interaction from an Optimal Feedback Control (OFC) perspective, we assume that users aim to minimize an internalized cost function, subject to the constraints imposed by the human body and the interactive system. Unlike previous approaches used in HCI, MPC can compute optimal controls for nonlinear systems. This allows to use state-of-the-art biomechanical models and handle nonlinearities that occur in almost any inter

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Palano, Giorgia, Ariana Foinquinos, and Erik Müllers. "In vitro Assays and Imaging Methods for Drug Discovery for Cardiac Fibrosis." Frontiers in Physiology 12 (July 8, 2021). http://dx.doi.org/10.3389/fphys.2021.697270.

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As a result of stress, injury, or aging, cardiac fibrosis is characterized by excessive deposition of extracellular matrix (ECM) components resulting in pathological remodeling, tissue stiffening, ventricular dilatation, and cardiac dysfunction that contribute to heart failure (HF) and eventually death. Currently, there are no effective therapies specifically targeting cardiac fibrosis, partially due to limited understanding of the pathological mechanisms and the lack of predictive in vitro models for high-throughput screening of antifibrotic compounds. The use of more relevant cell models, th

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Finka, Lauren R., Lucia Ripari, Lindsey Quinlan, et al. "Investigation of humans individual differences as predictors of their animal interaction styles, focused on the domestic cat." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-15194-7.

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AbstractHumans’ individual differences including their demographics, personality, attitudes and experiences are often associated with important outcomes for the animals they interact with. This is pertinent to companion animals such as cats and dogs, given their social and emotional importance to humans and degree of integration into human society. However, the mechanistic underpinnings and causal relationships that characterise links between human individual differences and companion animal behaviour and wellbeing are not well understood. In this exploratory investigation, we firstly quantifi

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Gao, Shan, Elena Albu, Ben van Calster, et al. "A systematic review of risk prediction models for central line-associated bloodstream infection (CLA-BSI) in hospitalized patients (MEDLINE)." searchRxiv 2023 (January 6, 2023). http://dx.doi.org/10.1079/searchrxiv.2023.00113.

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Abstract Objectives: To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients. Introduction: CLA-BSIs are the most common source of hospital-acquired infections (HAIs), and are always associated with higher morbidity, longer length of stay and increased financial burdens. As a priority target for prevention, tools that were developed to predict the risk of CLA-BSI for individuals may help improve the infection control in hospitals. In this systematic review, we evaluat

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