The healthcare sector has experienced a rapid accumulation of digital data recently, especially in the form of electronic health records (EHRs). EHRs constitute a precious resource that IS researchers could utilize for clinical applications (e.g., morbidity prediction). Deep learning seems like the obvious choice to exploit this surfeit of data. However, numerous studies have shown that deep learning does not enjoy the same kind of success on EHR data as it has in other domains; simple models like logistic regression are frequently as good as sophisticated deep learning ones. Inspired by this observation, we develop a novel model called rational logistic regression (RLR) that has standard logistic regression (LR) as its special case (and thus inherits LR's inductive bias that aligns with EHR data). RLR has rational series as its theoretical underpinnings, works on longitudinal time-series data, and learns interpretable patterns. Empirical comparisons on real-world clinical tasks demonstrate RLR's efficacy.
Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI
NEC Laboratories Europe GmbH（NEC欧洲实验室）；NEC Solution Innovators, Ltd.（NEC解决方案创新者有限公司）；Kurashiki Central Hospital Preventive Healthcare Plaza（Kurashiki中央医院预防保健广场）；Kurashiki Central Hospital（Kurashiki中央医院）
Selecting Interpretability Techniques for Healthcare Machine Learning models
University of Antioquia UdeA（安蒂奥基亚大学）；Universidad de Antioquia UdeA（安蒂奥基亚大学）；University of Antioquia（安蒂奥基亚大学）；Hospital Alma Máter de Antioquia（安蒂奥基亚母校医院）；Arcada University of Applied Sciences（阿卡达应用科学大学）
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
Grado Department of Industrial & Systems Engineering, Virginia Tech（弗吉尼亚理工大学格拉多工业与系统工程系）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；School of Mathematics, Physics, and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Center for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Southampton（南安普顿大学）；Athena Research Centre（雅典娜研究中心）
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Technical University of Munich（慕尼黑工业大学）；Ca’ Foscari University of Venice（威尼斯大学）；USI University of Lugano（瑞士意大利语区大学（卢加诺大学））；NVIDIA（英伟达）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Oxford（牛津大学）
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Georgia Institute of Technology（佐治亚理工学院）；Sutter Health（萨特健康）
准确性和可解释性是成功的预测模型的两个主要特征。通常情况下，必须选择支持复杂的黑盒模型，如递归神经网络（RNN）的准确性，而不是准确性较低但更可解释的传统模型，如逻辑回归。这种权衡在准确性和可解释性都很重要的医学中提出了挑战。我们通过开发用于电子健康记录（EHR）数据的REverse Time AttentIoN模型（RETAIN）来应对这一挑战。RETAIN在保持临床可解释性的同时实现了高准确性，并且基于两级神经注意力模型，该模型检测有影响力的既往访视和这些访视中的重要临床变量（例如关键诊断）。RETAIN通过以相反的时间顺序关注EHR数据来模仿医生的实践，以便最近的临床访问可能会受到更高的关注。RETAIN在一个大型健康系统EHR数据集上进行了测试，在8年的时间里，263K名患者完成了1400万次访问，并证明了与RNN等最先进方法相媲美的预测准确性和计算可扩展性，以及与传统模型相媲美的可解释性。
Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
Carnegie Mellon University（卡内基梅隆大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；GenBio AI（基因生物人工智能公司）；Intel（英特尔公司）
Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains
Computer Science Department（计算机科学系）；New York University（纽约大学）；Department of Population Health（人口健康部）；NYU Grossman School of Medicine（NYU Grossman医学院）；School of Global Public Health（全球公共卫生学院）
本文提出Tree of Concepts框架，通过浅层决策树定义固定规则概念接口，并训练概念瓶颈模型预测概念，实现持续学习中的可解释性和稳定性平衡。
Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data
Hood College（胡德学院）；Georgetown University（乔治敦大学）；University of Benin（贝宁大学）；NIST University（NIST大学）；AI CoLab: MedStar–Georgetown Collaborative Center for Artificial Intelligence in Healthcare Research and Education（人工智能合作实验室：梅斯达星 - 乔治敦医疗保健研究与教育人工智能协作中心）；MedStar Health Research Institute（梅斯达星健康研究所）；Albany State University（奥尔巴尼州立大学）
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆应用技术大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Regensburg（雷根斯堡大学）；Leipzig University（莱比锡大学）
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
Georgia Institute of Technology（佐治亚理工学院）；McGill University（麦吉尔大学）；Carnegie Mellon University（卡内基梅隆大学）；Oregon State University（俄勒冈州立大学）；University of Pennsylvania（宾夕法尼亚大学）；Northeastern University（东北大学）；University of California, San Diego（加州大学圣地亚哥分校）
HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System
United Arab Emirates University（阿联酋大学）；The University of Melbourne（墨尔本大学）
An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets
Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；University of Michigan Medical School（密歇根大学医学院）；Beth Israel Deaconess Medical Center / Harvard Medical School（贝斯以色列女执事医疗中心/哈佛医学院）；IBM Research（IBM研究院）
An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records
Department of Electronics, Information and Bioengineering（电子、信息与生物工程系）；Politecnico di Milano（米兰理工大学）；Lero – the Science Foundation Ireland Research Centre for Software（爱尔兰科学基金会软件研究中心）；University of Limerick（利默ick大学）
Recent Methodological Advances in Federated Learning for Healthcare
University of Cambridge（剑桥大学）；University of Bremen（不来梅大学）；University of Helsinki（赫尔辛基大学）；Addenbrooke’s Hospital, Cambridge University Hospitals NHS Trust（剑桥大学医院NHS信托基金会阿登布鲁克医院）
