Many areas of research are characterised by the deluge of large-scale highly-dimensional time-series data. However, using the data available for prediction and decision making is hampered by the current lag in our ability to uncover and quantify true interactions that explain the outcomes.We are interested in areas such as intensive care medicine, which are characterised by i) continuous monitoring of multivariate variables and non-uniform sampling of data streams, ii) the outcomes are generally governed by interactions between a small set of rare events, iii) these interactions are not necessarily definable by specific values (or value ranges) of a given group of variables, but rather, by the deviations of these values from the normal state recorded over time, iv) the need to explain the predictions made by the model. Here, while numerous data mining models have been formulated for outcome prediction, they are unable to explain their predictions.
We present a model for uncovering interactions with the highest likelihood of generating the outcomes seen from highly-dimensional time series data. Interactions among variables are represented by a relational graph structure, which relies on qualitative abstractions to overcome non-uniform sampling and to capture the semantics of the interactions corresponding to the changes and deviations from normality of variables of interest over time. Using the assumption that similar templates of small interactions are responsible for the outcomes (as prevalent in the medical domains), we reformulate the discovery task to retrieve the most-likely templates from the data.
A Unified AI Approach for Continuous Monitoring of Human Health and Diseases from Intensive Care Unit to Home with Physiological Foundation Models (UNIPHY+)
Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care
Imperial College London（伦敦帝国理工学院）；Harvard–MIT Division of Health Sciences & Technology（哈佛-麻省理工学院健康科学与技术部）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Philips Healthcare（飞利浦医疗）；University of Maryland, School of Pharmacy（马里兰大学药学院）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）；Data Science Institute（数据科学研究所）
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model
University of Florida（佛罗里达大学）；Stanford University（斯坦福大学）
LUME-DBN: Full Bayesian Learning of DBNs from Incomplete data in Intensive Care
Department of Informatics, Systems and Communication, University of Milano-Bicocca（米兰-比科卡大学信息学、系统与通信系）；Department of Science and Engineering, University of Groningen（格罗宁根大学科学与工程系）；BReCHS – Bicocca Research Centre in Health Services（比科卡健康服务研究中心）；Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano-Bicocca, Vedano al Lambro (MB), Italy（比科卡生物信息学、生物统计学与生物成像中心-B4，米兰-比科卡大学，维达诺阿尔兰布罗（MB），意大利）
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation
New Mexico Institute of Mining and Technology（新墨西哥矿业技术学院）；Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Tennessee Health Science Center（田纳西大学健康科学中心）；Illinois State University（伊利诺伊州立大学）
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes
Columbia University（哥伦比亚大学）；Flinders University（弗林德斯大学）；University of Melbourne（墨尔本大学）；Technical University of Munich（慕尼黑工业大学）；Prince Charles Hospital Brisbane（布里斯班查尔斯王子医院）；Edith Cowan University（伊迪丝·考恩大学）；Monash University（莫纳什大学）；Massachusetts Institute of Technology（麻省理工学院）
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks
Harvard-MIT Health Sciences and Technology（哈佛-麻省理工学院健康科学与技术）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；Columbia University（哥伦比亚大学）
A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
Department of Electrical and Computer Engineering（电气与计算机工程系）；Queen’s University（女王大学）；School of Computing（计算机学院）；Smith School of Business（商学院）；Departments of Emergency Medicine and Critical Care Medicine（急诊医学与重症医学系）；School of Computing and the Department of Electrical and Computer Engineering（计算机学院和电气与计算机工程系）；Department of Surgery（外科系）；Departments of Medicine and Critical Care Medicine（医学与重症医学系）；University of British Columbia（不列颠哥伦比亚大学）
Video object detection for privacy-preserving patient monitoring in intensive care
ZHAW School of Engineering（苏黎世应用科技大学工程学院）；University Hospital Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；Charité - University Medicine Berlin（柏林夏里特医学院）；European Centre for Living Technology（欧洲生命技术中心）
Developing and Evaluating an AI-Assisted Prediction Model for Unplanned Intensive Care Admissions following Elective Neurosurgery using Natural Language Processing within an Electronic Healthcare Record System
University College London（伦敦大学学院）；National Hospital for Neurology and Neurosurgery（国立神经学与神经外科医院）
Heart Rate Variability and Respiration Signal as Diagnostic Tools for Late Onset Sepsis in Neonatal Intensive Care Units
INSERM（法国国家健康与医学研究院）；Université de Rennes 1（雷恩第一大学）；CHU Rennes（雷恩大学医院）；Southeast University（东南大学）；Centre de Recherche en Information Biomédicale sino-français（中法生物医学信息研究中心）；University of California, San Francisco（加利福尼亚大学旧金山分校）
Autonomous Agent-Orchestrated Digital Twins (AADT): Leveraging the OpenClaw Framework for State Synchronization in Rare Genetic Disorders
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia（费城儿童医院雷蒙德·G·佩雷尔曼细胞与分子治疗中心）；Applied Mathematics and Computational Science Graduate Program, University of Pennsylvania（宾夕法尼亚大学应用数学与计算科学研究生项目）；Bioengineering Graduate Program, University of Pennsylvania（宾夕法尼亚大学生物工程研究生项目）；Department of Biomedical Informatics, Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心生物医学信息学系）；Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院病理学与实验医学系）
