The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming available, which promises to support service delivery and advance clinical and informatics research. Deep learning techniques have demonstrated performance in predictive analytic tasks using EHRs yet they typically lack model result transparency or explainability functionalities and require cumbersome pre-processing tasks. Moreover, EHRs contain heterogeneous and multi-modal data points such as text, numbers and time series which further hinder visualisation and interpretability. This paper proposes a deep learning framework to: 1) encode patient pathways from EHRs into images, 2) highlight important events within pathway images, and 3) enable more complex predictions with additional intelligibility. The proposed method relies on a deep attention mechanism for visualisation of the predictions and allows predicting multiple sequential outcomes.
Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked EHR and Pathology Lab Dataset
University of Bristol（布里斯托大学）；Kettering General Hospital NHS Foundation Trust（凯特林医院国家健康服务基金会信托）；NHS England（英格兰国家健康服务）；University Hospitals Bristol and Weston NHS Foundation Trust（布里斯托和韦斯特医院国家健康服务基金会信托）
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）
xMICD: Explainable Representation of Multiple ICD Codes
Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所——BIPS）；Institute of Science Tokyo（东京科学大学）；Faculty of Medicine Siriraj Hospital, Mahidol University（玛希隆大学诗里拉吉医学院）
Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset
Carol Davila University of Medicine and Pharmacy, Faculty of Medicine（卡罗尔·达维拉医药大学医学院）；POLITEHNICA Bucharest National University of Science and Technology（布加勒斯特理工大学）；General Surgery Department, University Emergency Hospital of Bucharest（布加勒斯特大学急救医院普外科）；Innovation and eHealth Center, Carol Davila University of Medicine and Pharmacy（卡罗尔·达维拉医药大学创新与电子健康中心）
Semantic match: Debugging feature attribution methods in XAI for healthcare
Amsterdam University Medical Center, University of Amsterdam（阿姆斯特丹大学医学中心，阿姆斯特丹大学）；Institute for Logic, Language, and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Pacmed；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Hong Kong University of Science and Technology（香港科技大学）；Massachusetts Institute of Technology（麻省理工学院）；Adobe Research（Adobe研究院）；Children’s Hospital of Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）
Early Warning Index for Patient Deteriorations in Hospitals
Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）；Operations and Information Management, University of Wisconsin Madison（威斯康星大学麦迪逊分校运营与信息管理系）；Technology Management and Innovation, New York University（纽约大学技术管理与创新系）；Hartford HealthCare（哈特福德医疗集团）；Holistic Hospital Optimization（综合医院优化）
