Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of the users' activities in different subforums can be strong indicators of their health status changes. This additional information could allow health-care organizations to respond promptly and provide additional help for the patient. However, modeling complex transitions of an individual user's activities among different subforums over time and learning how these correspond to his/her health stage are extremely challenging. In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges. Our proposed DynGraph2Seq model consists of a novel dynamic graph encoder and an interpretable sequence decoder that learn the mapping between a sequence of time-evolving user activity graphs and a sequence of target health stages. We go on to propose dynamic graph hierarchical attention mechanisms to facilitate the necessary multi-level interpretability. A comprehensive experimental analysis of its use for a health stage prediction task demonstrates both the effectiveness and the interpretability of the proposed models.
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。
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）
EVA-Net: Interpretable Anomaly Detection for Brain Health via Learning Continuous Aging Prototypes from One-Class EEG Cohorts
Qilu Hospital, ASU（青岛医院，亚利桑那州立大学）；Zhengzhou University（郑州大学）；University of Science and Technology Beijing（北京科技大学）；Southern University of Science and Technology（南方科技大学）；Qilu Hospital（青岛医院）
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）
Explainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring
Signal and System Theory Group, Paderborn University（信号与系统理论组，帕德博恩大学）；Department of Medicine I, Ludwig Maximilian University（医学部I，路德维希-马克西米利安大学）；Konrad Zuse School of Excellence in Reliable AI, Munich（可靠性人工智能卓越学校，慕尼黑）；Institute of Sports Medicine, Paderborn University（运动医学研究所，帕德博恩大学）；Division of Sports Neurology and Neurosciences, Mass General Brigham（运动神经学与神经科学部，马萨诸塞总医院布里奇顿分院）；Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School（癫痫与临床神经生理学部，波士顿儿童医院，哈佛医学院）
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）
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）
Sera: Semantic Representation Aggregation with Knowledge and LLM for Reliable and Interpretable Battery Health Forecasting
Singapore Institute of Technology（新加坡理工学院）；Singapore University of Social Sciences（新加坡社科大学）；Shanghai Jiao Tong University（上海交通大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高级智能与计算研究所）；Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局材料研究与工程研究所）
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）
Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Technical University of Darmstadt（达姆施塔特工业大学）
DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data
College of Computer Science and Technology（计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）；Beijing Life Science Academy（北京生命科学研究院）；Department of Chemical and Biological Engineering（化学与生物工程系）；School of Science（科学学院）
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records
Department of Electrical and Computer Engineering（电气与计算机工程系）；Duke University（杜克大学）；Department of Surgery（外科系）；Duke University School of Medicine（杜克大学医学院）；Department of Industrial and Systems Engineering（工业与系统工程系）；Georgia Institute of Technology（佐治亚理工学院）；Emory University School of Medicine（埃默里大学医学院）；Sloan School of Management（斯隆管理学院）；Massachusetts Institute of Technology（麻省理工学院）
Interpretable Machine Learning for Cognitive Aging: Handling Missing Data and Uncovering Social Determinant
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；Shandong Mental Health Center（山东省精神卫生中心）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas Austin（德克萨斯大学奥斯汀分校）；Kennesaw State University（肯尼索州立大学）
A Structural FHMM for Interpretable Disease Trajectories in T2DM
Swiss Data Science Center (SDSC)（瑞士数据科学中心（SDSC））；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；ETH Zürich（苏黎世联邦理工学院）；Institute of Pharmaceutical Sciences（药学院）
