Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel admission to discharge task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources. We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines. A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.
Convolutional Neural Networks for Medical Diagnosis from Admission Notes
Petuum Inc.（Petuum 公司）；Department of Psychiatry, University of Pittsburgh Medical Center（匹兹堡大学医学中心精神科）
$\textbf{Objective}$开发一种自动诊断系统，该系统仅使用电子健康记录（EHR）中的文本入院信息，并通过及时且经统计学验证的决策工具协助临床医生。希望该工具可以用于减少误诊。$\textbf{Materials and Methods}$我们使用来自MIMIC-III的真实世界临床记录，这是一个免费提供的数据集，由2001年至2012年期间在贝斯以色列女执事医疗中心重症监护室住院的4万多名患者的临床数据组成。我们提出了一个卷积神经网络模型，从非结构化文本输入中学习语义特征，并自动预测初次出院诊断。$\textbf{Results}$提出的模型在10种最常见的疾病类别上实现了96.11%的总体准确性和80.48%的加权F1评分值，在加权F1评分方面显著优于4个强基线模型至少12.7%。$\textbf{Discussion}$实验结果表明，CNN模型适用于在存在复杂、噪声和非结构化临床数据的情况下支持诊断决策，同时使用比其他传统深度网络模型更少的层和参数。$\textbf{Conclusion}$我们的模型证明了从非结构化的临床笔记中代表复杂的医学有意义的特征的能力，以及对常见误诊的常见疾病的预测能力。它可以很容易地在临床环境中使用，以提供及时和统计证明的决策支持。$\textbf{Keywords}$卷积神经网络、文本分类、出院诊断预测、来自EHR的入院信息。
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）
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Illinois Chicago Peoria（伊利诺伊大学芝加哥分校皮奥里亚分校）
Carnegie Mellon University（卡内基梅隆大学）；Swedish Medical Group（瑞典医疗集团）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）
HERMES: Contrast-Aware Knowledge Graph Reasoning from Clinical Notes for Patient Outcome Prediction
Business AI Lab, College of Technology, National Economics University（国民经济大学技术学院商业人工智能实验室）；Hanoi University of Science and Technology（河内理工大学）；A2I Lab, Phenikaa School of Computing, Phenikaa University（Phenikaa大学Phenikaa计算学院A2I实验室）；Department of Head and Neck Surgery, Vietnam National Cancer Hospital & Hanoi Medical University（越南国家癌症医院头颈外科与河内医科大学）；VNPT AI, VNPT Group（VNPT集团VNPT AI）
CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction
Laboratory for Emerging Intelligence, University of California, San Diego（新兴智能实验室，加州大学圣地亚哥分校）；Department of Dermatology, University of California, San Diego（皮肤科系，加州大学圣地亚哥分校）；Elsevier
DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginations
Laboratory for Emerging Intelligence, University of California, San Diego（新兴智能实验室，加州大学圣地亚哥分校）；Department of Dermatology, University of California, San Diego（皮肤科系，加州大学圣地亚哥分校）；University of Chicago（芝加哥大学）；Elsevier（艾尔斯特出版社）
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学院）；School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）；Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院）
