Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Tsinghua University（清华大学）；Institute of Brain and Cognitive Sciences, Tsinghua University（清华大学脑与认知科学研究院）；Chinese PLA General Hospital（中国人民解放军总医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteriorations such as acute kidney injury, acute myocardial infarction, or circulatory failure. DL models have proven to be powerful tools for various tasks but come with the cost of lacking interpretability and limited generalizability, hindering their clinical applications. To develop a practical EWS system applicable to various outcomes, we propose causally-informed explainable early prediction model, which leverages causal discovery to identify the underlying causal relationships of prediction and thus owns two unique advantages: demonstrating the explicit interpretation of the prediction while exhibiting decent performance when applied to unfamiliar environments. Benefiting from these features, our approach achieves superior accuracy for 6 different critical deteriorations and achieves better generalizability across different patient groups, compared to various baseline algorithms. Besides, we provide explicit causal pathways to serve as references for assistant clinical diagnosis and potential interventions. The proposed approach enhances the practical application of deep learning in various medical scenarios.
Improving Representation Learning of Complex Critical Care Data with ICU-BERT
Fraunhofer AICOS（弗劳恩霍夫应用信息与通信技术研究所）；LibPhys；NOVA School of Science and Technology（NOVA科技学院）；University of Delaware（特拉华大学）；Fraunhofer SCAI（弗劳恩霍夫智能分析与信息系统研究所）；University of Bonn（波恩大学）
Universidad Politécnica de Madrid（马德里理工大学）；Centre national de la recherche scientifique (CNRS)（法国国家科学研究中心）；Interdisciplinary Institute for Neuroscience (IINS)（跨学科神经科学研究所）；University of Bordeaux（波尔多大学）；Mechanobiology Institute（机械生物学研究所）；National University of Singapore（新加坡国立大学）
Microscopy images often suffer from high levels of noise, which can hinder further analysis and interpretation. Content-aware image restoration (CARE) methods have been proposed to address this issue, but they often require large amounts of training data and suffer from over-fitting. To overcome these challenges, we propose a novel framework for few-shot microscopy image denoising. Our approach combines a generative adversarial network (GAN) trained via contrastive learning (CL) with two structure preserving loss terms (Structural Similarity Index and Total Variation loss) to further improve t…
Resilient Contrastive Pre-training under Non-Stationary Drift
Australian Artificial Intelligence Institute (AAII)（澳大利亚人工智能研究所）；Faulty of Engineering and Information Technology（工程与信息技术学院）；University of Technology Sydney（悉尼技术大学）
Bakar Computational Health Sciences Institute, University of California, San Francisco（加州大学旧金山分校巴卡尔计算健康科学研究所）
Clinical data for ambulatory care, which accounts for 90% of the nations healthcare spending, is characterized by relatively small sample sizes of longitudinal data, unequal spacing between visits for each patient, with unequal numbers of data points collected across patients. While deep learning has become state-of-the-art for sequence modeling, it is unknown which methods of time aggregation may be best suited for these challenging temporal use cases. Additionally, deep models are often considered uninterpretable by physicians which may prevent the clinical adoption, even of well performing …
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；Peking university（北京大学）；Georgia Institute of Technology（佐治亚理工学院）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Harvard Medical School（哈佛医学院）；Stanford University（斯坦福大学）；Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）
Electronic health records (EHR) offer unprecedented opportunities for in-depth clinical phenotyping and prediction of clinical outcomes. Combining multiple data sources is crucial to generate a complete picture of disease prevalence, incidence and trajectories. The standard approach to combining clinical data involves collating clinical terms across different terminology systems using curated maps, which are often inaccurate and/or incomplete. Here, we propose sEHR-CE, a novel framework based on transformers to enable integrated phenotyping and analyses of heterogeneous clinical datasets witho…
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
CSAIL, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Harvard Medical School（哈佛医学院）；University of California, Berkeley（加州大学伯克利分校）
Path-specific effects for pulse-oximetry guided decisions in critical care
Columbia University（哥伦比亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mila & Université de Montréal（Mila与蒙特利尔大学）；Google DeepMind（谷歌DeepMind）；Purdue University（普渡大学）
Towards Foundation Models for Critical Care Time Series
ETH Zurich（苏黎世联邦理工学院）；University Children’s Hospital Zurich（苏黎世大学儿童医院）；University of Zurich（苏黎世大学）；University of Luzern（卢塞恩大学）；Zurich University of Applied Sciences (ZHAW)（苏黎世应用科技大学）；University of Bern（伯尔尼大学）
Growing Data；BioMedical Informatics center, Faculty of Medicine, Chiang Mai University（清迈大学医学院生物医学信息中心）；Faculty of Medicine, Chiang Mai University（清迈大学医学院）；Faculty of Engineering, Chiang Mai University（清迈大学工程学院）；Biomedical Engineering Institute, Chiang Mai University（清迈大学生物医学工程研究所）；Maharaj Nakhon Chiang Mai hospital（玛哈拉吉那空清迈医院）；IT department of Maharaj Nakhon Chiang Mai hospital（玛哈拉吉那空清迈医院信息技术部）
Clinical coding is an administrative process that involves the translation of diagnostic data from episodes of care into a standard code format such as ICD10. It has many critical applications such as billing and aetiology research. The automation of clinical coding is very challenging due to data sparsity, low interoperability of digital health systems, complexity of real-life diagnosis coupled with the huge size of ICD10 code space. Related work suffer from low applicability due to reliance on many data sources, inefficient modelling and less generalizable solutions. We propose a novel colla…
