Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
University of Pittsburgh（匹兹堡大学）；Beijing Information Science & Technology University（北京信息科技大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Astrata Solutions（阿斯特拉塔解决方案公司）
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjust therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms, signs, and lab results from the electronic health records (EHR) of a patient, without directly measuring heart function. We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR, and we further applied the SHapley Additive exPlanations (SHAP) framework to identify informative features and their interpretations. Our results indicate that based on structured data from EHR, our models could predict patients' ejection fraction (EF) scores with moderate accuracy. SHAP analyses identified informative features and revealed potential clinical subtypes of HF. Our findings provide insights on how to design computing systems to accurately monitor disease progression of HF patients through continuously mining patients' EHR data.
Interpretable Survival Analysis for Heart Failure Risk Prediction
Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）；Kaiser Permanente Northern California Division of Research（凯撒医疗北加州研究部）；Stanford Medicine（斯坦福医学院）
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Nimblemind（宁敏德（音译））；Singapore University of Technology and Design（新加坡科技设计大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Florida International University（佛罗里达国际大学）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Rutgers University Newark（罗格斯大学纽瓦克分校）；Rutgers Institute for Health Health Care Policy and Aging Research（罗格斯大学健康、医疗保健政策与老龄化研究所）；Robert Wood Johnson Medical School（罗伯特·伍德·约翰逊医学院）；Rutgers Health（罗格斯医疗）
Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph
Institute of Informatics, Universidade Federal do Rio Grande do Sul (UFRGS)（信息学院，巴西联邦大学里约格兰德杜斯圣路易斯分校）；School of Information Technology, Halmstad University（信息科技学院，哈姆斯塔德大学）；Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA)（生物信息学核心，波尔图阿莱格雷医院）
Mining Social Determinants of Health for Heart Failure Patient 30-Day Readmission via Large Language Model
Department of Computer Science, Emory University（计算机科学系，埃默里大学）；Nell Hodgson Woodruff School of Nursing, Emory University（Nell Hodgson Woodruff护理学院，埃默里大学）
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data
School of Biomedical Informatics, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心生物医学信息学院）；School of Public Health, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心公共卫生学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Computer Science Department, School of Engineering, Rice University（莱斯大学工程学院计算机科学系）
Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas（应用数学与系统研究所）；Instituto Mexicano del Seguro Social（墨西哥社会保险研究所）；Hospital Clínico Quirúrgico Hermanos Ameijeiras（阿梅赫拉斯兄弟临床外科医院）
Trajectory-guided discharge stratification for heart failure using short-context electronic health record sequence modeling
Sahlgrenska University Hospital（斯德哥尔摩大学医院）；Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg（计算机科学与工程系，查尔姆斯理工大学和乌普萨拉大学）；Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg（分子与临床医学系，斯德哥尔摩学院，乌普萨拉大学）；Department of Medicine, Geriatrics and Emergency Medicine, Sahlgrenska University Hospital（医学、老年医学和急诊医学系，斯德哥尔摩大学医院）；Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg（营养学与体育科学系，教育学院，乌普萨拉大学）；School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg（公共卫生与社区医学系，医学院，乌普萨拉大学）
ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis
Taub Faculty of Computer Science, Technion-Israel Institute of Technology（以色列理工学院陶布计算机科学学院）；Faculty of Biology, Technion-Israel Institute of Technology（以色列理工学院生物学院）
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结局与生物医学信息学系，佛罗里达大学医学院）；Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida（心血管医学部，医学院，佛罗里达大学）；Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida（血液学与肿瘤学部，医学院，佛罗里达大学）
Evaluation of the impact of expert knowledge: How decision support scores impact the effectiveness of automatic knowledge-driven feature engineering (aKDFE)
A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运营研究中心）；Wisconsin School of Business, University of Wisconsin–Madison（威斯康星大学麦迪逊分校商学院）；Heart and Vascular Institute, Hartford Hospital（哈特福德医院心脏与血管研究院）
Explainable Artificial Intelligence For The Detection and Characterisation of Stage B Heart Failure
Division of Cardiovascular Sciences, University of Leicester（莱斯特大学心血管科学部）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
Deep Learning: a Heuristic Three-stage Mechanism for Grid Searches to Optimize the Future Risk Prediction of Breast Cancer Metastasis Using EHR-based Clinical Data
University of Pittsburgh（匹兹堡大学）；Pittsburgh VA Health System（匹兹堡退伍军人健康系统）；University of Pittsburgh School of Medicine（匹兹堡大学医学院）；UPMC Hillman Cancer Center（UPMC希尔曼癌症中心）
