Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover information from patients whose data were used during training. Moreover, this risk is exacerbated by models such as logistic regression (LR), which are typically preferred in clinical settings for their transparency. To assess this empirically, we attack LORIS, a publicly available LR model for immunotherapy response prediction hosted on a U.S. government website. From evaluations through its public interface, we recover the model parameters and identify the training cohort with high confidence. More broadly, we design cohort-level membership inference attacks under three levels of adversarial access---binary black-box, continuous black-box, and white-box---and apply them to both LR models and shallow neural networks (NNs) trained on the same task. Our results reveal that even a cohort of 35 patients can be reliably identified within training sets of hundreds to thousands, and that common practices such as cross-validation amplify rather than mitigate this risk. To address these vulnerabilities, we propose a quantum-inspired defense based on tensorizing discretized models into tensor trains (TTs). This representation obfuscates model parameters and preserves accuracy, while offering black-box protection comparable to practical Differential Privacy baselines. Additionally, the TT representations retain LR interpretability and extend it through efficient computation of marginal and conditional distributions, enabling this richer analysis also for black-box models such as NNs. Our results establish tensorization as a practical, post-hoc tool for private, interpretable, and effective clinical prediction.
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）
Google Research（谷歌研究院）；University of Pittsburgh（匹兹堡大学）
Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future interventions that the patient is likely to receive, based on the intervention policy present in the training data. Without this important context, predictions from such systems are less interpretable for clinicians. We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions. We develop such an intervention policy model on…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（图宾根大学）；University of Cambridge（剑桥大学）
Interpretable predictions, where it is clear why a machine learning model has made a particular decision, can compromise privacy by revealing the characteristics of individual data points. This raises the central question addressed in this paper: Can models be interpretable without compromising privacy? For complex big data fit by correspondingly rich models, balancing privacy and explainability is particularly challenging, such that this question has remained largely unexplored. In this paper, we propose a family of simple models in the aim of approximating complex models using several locall…
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Data-driven Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with personalised probabilistic guidance. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning their feature sets are not perfectly overlapping. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tr…
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation
New York University（纽约大学）；NYU Langone Health（纽约大学兰格医学中心）；Genentech（基因泰克）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Wenzhou Medical University（温州医科大学）；Macau University of Science and Technology（澳门科学大学）
Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases and disorders in many areas of medicine. These models can serve not only as sources of predictions and estimates, but also as discovery tools for clinicians and researchers to reveal new knowledge from the data. High dimensionality of patient information (e.g., phenotype, genotype, and medical history), lack of objective measurements, and the heterogeneity in patient populations often create significant challenges in…
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University（江苏大学附属昆山医院骨科部）；Tianjin University Chest Hospital（天津大学胸科医院）；Haihe Hospital, Tianjin University（天津大学海河医院）；Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（广西师范大学教育区块链与智能技术重点实验室）
H-LDM: Hierarchical Latent Diffusion Models for Controllable and Interpretable PCG Synthesis from Clinical Metadata
School of Cyber Engineering Xidian University Xi'an, China（电子科技大学信息工程学院西安中国）；School of Telecommunications Engineering Xidian University Xi'an, China（电子科技大学电信工程学院西安中国）
Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization
Worcester Polytechnic Institute（伍斯特理工学院）；Centro de Vacunación e Investigación (CEVAXIN)（疫苗接种与研究中心（CEVAXIN））；Universidad Tecnológica de Panamá（巴拿马技术大学）；Massachusetts Institute of Technology（麻省理工学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）；McGill University（麦吉尔大学）；The Montreal Neurological Hospital-Institute（蒙特利尔神经学医院研究所）
Towards symbolic regression for interpretable clinical decision scores
Federal University of ABC（ABC联邦大学）；University of Pennsylvania（宾夕法尼亚大学）；Institute for Biomedical Informatics, University of Pennsylvania（宾夕法尼亚大学生物医学信息学研究所）；Boston Children's Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）
Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
Department of Health Sciences and Technology (DHEST), ETH Zurich（健康科学与技术系，苏黎世联邦理工学院）；Department of Signal Theory and Communications, Universidad Carlos III de Madrid（信号理论与通信系，卡洛斯三世大学）；UCL Hawkes Institute, Department of Computer Science, University College London（UCL Hawkes研究所，伦敦大学学院计算机科学系）；Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid（Gregorio Marañón卫生研究所（IiSGM），马德里）
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Lyda Hill Department of Bioinformatics（Lyda Hill 生物信息学系）；Department of Biomedical Engineering（生物医学工程系）；University of Texas Southwestern Medical Center, Dallas, Texas, USA（德克萨斯西南医学中心，德克萨斯州达拉斯）
Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. The current clinical EEG review process relies heavily on expert visual review, which is unscalable and error-prone. In an effort to augment the expert review process, there is a significant interest in mining population-level EEG patterns using unsupervised approaches. Current approaches rely either on two-dimensional decompositions (e.g., principal and independent component analyses) or deep representation learning (e.g., auto-encoders, self-supervision). However…
Centre for Addiction and Mental Health（成瘾与心理健康中心）；University of Edinburgh（爱丁堡大学）；Royal Infirmary of Edinburgh（爱丁堡皇家医院）
Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and responses to treatment. Better estimations of prognosis would support treatment planning and patient support. Advances in machine learning have informed development of clinical predictive models, but their integration into clinical practice is almost non-existent. One reasons for this is the lack of interpretability of models. In this paper, we use a novel brain tumour dataset to compare two interpretable rule list models against popular machine learning approaches for…
The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-pri…
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）
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。
Recent advances in deep learning have led to the development of models approaching the human level of accuracy. However, healthcare remains an area lacking in widespread adoption. The safety-critical nature of healthcare results in a natural reticence to put these black-box deep learning models into practice. This paper explores interpretable methods for a clinical decision support system called sleep staging, an essential step in diagnosing sleep disorders. Clinical sleep staging is an arduous process requiring manual annotation for each 30s of sleep using physiological signals such as electr…
Time series models with recurrent neural networks (RNNs) can have high accuracy but are unfortunately difficult to interpret as a result of feature-interactions, temporal-interactions, and non-linear transformations. Interpretability is important in domains like healthcare where constructing models that provide insight into the relationships they have learned are required to validate and trust model predictions. We want accurate time series models where users can understand the contribution of individual input features. We present the Interpretable-RNN (I-RNN) that balances model complexity an…
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
organization= Daniel J. Epstein Department of Industrial \& Systems Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= United States；organization= Colorectal Research Center, Iran University of Medical Sciences , city= Tehran , postcode= 14535 , country= Iran；organization= Department of Health Science, California State University , city= Long Beach , state= CA , postcode= 90840 , country= United States；organization= Department of Industrial；Manufacturing Engineering, California State Polytechnic University , city= Pomona , state= CA , postcode= 91768 , country= United States
Unsupervised discovery of the shared and private geometry in multi-view data
Department of Biomedical Engineering（生物医学工程系）；Johns Hopkins University（约翰霍普金斯大学）；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Princeton University（普林斯顿大学）；Department of Psychology and Brain Sciences（心理学与脑科学系）；University of Delaware（德雷塞尔大学）；Center for Imaging Science（成像科学中心）；Kavli Neurodiscovery Institute（Kavli神经发现研究所）
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）
Technical University of Munich（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in computer vision in general, yet, in the medical domain, it requires further examination. Moreover, most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the model in a post hoc fashion. In this paper, we propose an interpretable graph learning-based model which 1) interprets the clinical relevance of the input features towards the task, 2) uses the explanation to improve the model performance and, 3) learns a population level latent graph that may be u…
Harvard University（哈佛大学）；School of Engineering and Applied Sciences（工程与应用科学学院）
Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the intensive care unit. However, many of these models and their learned representations are complex and therefore difficult for clinicians to interpret, creating challenges for validation. Our work proposes a new procedure to learn summaries of clinical time-series that are both predictive and easily understood by humans. Specifically, our summaries consist of simple and intuitive functions of clinical data (e.g. falling…
TA-RNN-Medical-Hybrid: A Time-Aware and Interpretable Framework for Mortality Risk Prediction
Department of Computer Engineering, SRC.,Islamic Azad university（计算机工程系，伊斯兰教Azad大学）；School of Computer Engineering, Iran university of Science and technology（计算机工程学院，伊朗科学技术大学）
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）
Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
1Pritzker School of Medicine, University of Chicago, IL, USA；2Center for Computational Medicine \& Clinical AI, Section of Biomedical Data Science, Department of Medicine, University of Chicago, IL, USA；3Division of Cardiovascular Medicine, Department of Internal Medicine, University of California Davis, CA, USA；4Section of Hospital Medicine, Department of Medicine, University of Chicago, IL, USA
Antwerp University Hospital（安特卫普大学医院）；University of Antwerp（安特卫普大学）
We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. To analyze if these representations are transferable across tasks, we evaluate them in multiple supervised setups to predict patient mortality, primary diagnostic and procedural category, and gender. We compare their performance with sparse representations obtained from a bag-of-words model. We observe that the learned generalized representations significantly outperform the spar…
Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules
Centre for Artificial Intelligence Driven Drug Discovery, Macao Polytechnic University（人工智能驱动药物发现中心，澳门理工学院）；Key Laboratory of Short-Range Radio Equipment Testing and Evaluation, Ministry of Industry and Information Technology Terahertz Science Application Center (TSAC), Beijing Institute of Technology（工业和信息化部短距离无线电设备测试与评估重点实验室，太赫兹科学应用中心（TSAC），北京理工大学）；Department of Critical Care Medicine, Yantai Yuhuangding Hospital, Qingdao University（重症医学科，烟台友谊医院，青岛大学）；Faculty of Education, The University of Hong Kong（教育学院，香港大学）；College of Information Engineering, Dalian University（信息工程学院，大连大学）
Purdue University（普渡大学）；Novartis（诺华）；Oxford Big Data Institute（牛津大数据研究所）
Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX (secukinumab) Ankylosing Spondylitis clinical s…
Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction
School of Information Management & Engineering, Shanghai University of Finance and Economics（上海金融学院信息管理与工程学院）；Key Laboratory of Data Intelligence and Management (Beihang University), Ministry of Industry and Information Technology, School of Economics and Management, Beihang University（北京航空航天大学数据智能与管理重点实验室）
