Given a deep learning model trained on data from a source site, how to deploy the model to a target hospital automatically? How to accommodate heterogeneous medical coding systems across different hospitals? Standard approaches rely on existing medical code mapping tools, which have significant practical limitations.
To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework.
We evaluate AutoMap using several deep learning models with two real-world EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction, and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further, we show that AutoMap can provide accurate mapping across coding systems. Lastly, we demonstrate that AutoMap can adapt to the two challenging scenarios: (1) mapping between completely different coding systems and (2) between completely different hospitals.
Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles
Stanford Center for Biomedical Informatics Research, Stanford University（斯坦福大学生物医学信息学研究中心）；Division of Endocrinology, Stanford University School of Medicine（斯坦福大学医学院内分泌科）；Division of Hospital Medicine, Stanford University（斯坦福大学医院医学部）
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University（清华大学临床医学院北京清华长庚医院肝胆胰中心）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；College of Electronic Information Engineering, Tongji University（同济大学电子与信息工程学院）
Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies
College of Computer Science, Zhejiang University（浙江大学计算机科学与技术学院）；School of Medicine, Zhejiang University（浙江大学医学院）；Department of Biomedical Informatics, Harvard University（哈佛大学生物医学信息学系）
Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis
Penn State University（宾夕法尼亚州立大学）；Mississippi State University（密西西比州立大学）；Northwest Missouri State University（西北密苏里州立大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Colorado State University（科罗拉多州立大学）
GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
School of Computing Science, University of Glasgow, UK（格拉斯哥大学计算机科学学院）；School of Natural and Computing Science, University of Aberdeen, UK（阿伯丁大学自然与计算科学学院）
Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization
University of Washington（华盛顿大学）；New York University（纽约大学）；Liverpool John Moores University（利物浦约翰·摩尔斯大学）；Birla Institute of Technology Pilani（比拉理工学院帕利尼分校）；Columbia University（哥伦比亚大学）
Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
The Hong Kong Polytechnic University（香港理工大学）；Hong Kong University（香港大学）；South China University of Technology（华南理工大学）；University of Toronto（多伦多大学）；Peking Union Medical College Hospital（北京地坛医院）；West China Hospital, Sichuan University（四川大学华西医院）
Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes
Department of Computer Science（计算机科学系）；Emory University（埃默里大学）；College of Public Health（公共卫生学院）；Temple University（temple大学）；School of Information（信息学院）；Florida State University（佛罗里达州立大学）；Hubert Department of Global Health（全球健康部）；Nell Hodgson Woodruff School of Nursing（Nell Hodgson Woodruff护理学院）
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm（法国国家健康与医学研究院）；Pixyl Research and Development Laboratory（Pixyl研发实验室）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Declarative Guideline Conformance Checking of Clinical Treatments: A Case Study
University of Trier（特里尔大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University Hospital Münster（明斯特大学医院）；Heinrich Heine University（海因里希·海涅大学）
The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy
Hunan University（湖南大学）；ByteDance（字节跳动）；Duke University（杜克大学）；Westlake University（西湖大学）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Macau（澳门大学）；The Ohio State University（俄亥俄州立大学）
A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties
KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
The University of New South Wales（新南威尔士大学）；University of Göttingen（哥廷根大学）；Macquarie University（麦考瑞大学）；Beijing Intelligent Decision Medical Technology Co. Ltd（北京智决医疗科技有限公司）
