Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques are being used to support clinical decision making (CDS). Heretofore, there has not been a review of ways in which research in machine learning and other types of data-driven techniques can contribute effectively to clinical care and the types of support they can bring to clinicians. In this paper, we consider ways in which two data driven domains - machine learning and data visualizations - can contribute to the next generation of clinical decision support systems. We review the literature regarding the ways heuristic knowledge, machine learning, and visualization are - and can be - applied to three types of CDS. There has been substantial research into the use of predictive modeling for alerts, however current CDS systems are not utilizing these methods. Approaches that leverage interactive visualizations and machine-learning inferences to organize and review patient data are gaining popularity but are still at the prototype stage and are not yet in use. CDS systems that could benefit from prescriptive machine learning (e.g., treatment recommendations for specific patients) have not yet been developed. We discuss potential reasons for the lack of deployment of data-driven methods in CDS and directions for future research.
Cluster-Based Random Forest Visualization and Interpretation
University of Cologne（科隆大学）；Maastricht University（马斯特里赫特大学）；Leipzig University（莱比锡大学）；ScaDS.AI Dresden/Leipzig（德累斯顿/莱比锡ScaDS.AI）；Eindhoven University of Technology（埃因霍温理工大学）
Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems
University of Coimbra（科英布拉大学）；Centre for Informatics and Systems of University of Coimbra（科英布拉大学信息学与系统中心）；Coimbra Institute of Engineering (ISEC), Polytechnic of Coimbra（科英布拉理工学院科英布拉工程学院（ISEC））
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
University of Maine（缅因大学）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Duke University（杜克大学）；University of Rochester（罗切斯特大学）；University of Iowa（爱荷华大学）
CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents
University of Georgia（佐治亚大学）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；University of Chicago（芝加哥大学）
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
The University of Manchester（曼彻斯特大学）；CRUK Manchester Institute（英国癌症研究中心曼彻斯特研究所）；Idiap Research Institute（Idiap研究所）
Imitation learning for clinical decision support in pediatric ECMO
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Technische Universität Darmstadt（德累斯顿技术大学）；Swarthmore College（斯沃斯里学院）；University of Texas Southwestern Medical Center（德克萨斯西南医学中心）
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）
MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
School of Information, Computer；Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand 1
