Machine Learning Clinical Order Recommendations for Specialty Consultation Care

专科咨询护理的机器学习临床医嘱建议

基本信息

批准号：
10265158
负责人：
JONATHAN H. CHEN
金额：
$ 39.43万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-25 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10265158
关键词：
Active Learning Acute Address Adopted Adoption Algorithms Caring Clinical Clinical Data Clinical Medicine Complex Computerized Medical Record Consult Consultations Data Data Reporting Data Science Diagnosis Diagnostic tests Electronic Mail Endocrinology Environment Evaluation Event Feedback Foundations Future Goals Graph Healthcare Healthcare Systems Hematology Human Human Resources Hyperthyroidism Individual Industry Information Retrieval Inpatients Knowledge Knowledge Discovery Learning Licensing Machine Learning Manuals Medical Methods Modeling Modern Medicine Monitor Outcome Outpatients Patient-Focused Outcomes Patients Pattern Persons Pharmaceutical Preparations Physicians Primary Health Care Psychological reinforcement Recommendation Records Reproducibility Research Resolution Resources Running Specialist Suggestion System Test Result Testing Thrombocytopenia Time Training Transcend Vision Visit Work base clinical care clinical decision support clinical practice clinical predictors convolutional neural network crowdsourcing data streams design experience individual patient innovation iterative design medical specialties mortality novel open source outcome forecast personalized decision personalized predictions predictive modeling prototype statistical learning statistics tool usability

Active Learning Acute Address Adopted Adoption Algorithms Caring Clinical Clinical Data Clinical Medicine Complex Computerized Medical Record Consult Consultations Data Data Reporting Data Science Diagnosis Diagnostic tests Electronic Mail Endocrinology Environment Evaluation Event Feedback Foundations Future Goals Graph Healthcare Healthcare Systems Hematology Human Human Resources Hyperthyroidism Individual Industry Information Retrieval Inpatients Knowledge Knowledge Discovery Learning Licensing Machine Learning Manuals Medical Methods Modeling Modern Medicine Monitor Outcome Outpatients Patient-Focused Outcomes Patients Pattern Persons Pharmaceutical Preparations Physicians Primary Health Care Psychological reinforcement Recommendation Records Reproducibility Research Resolution Resources Running Specialist Suggestion System Test Result Testing Thrombocytopenia Time Training Transcend Vision Visit Work base clinical care clinical decision support clinical practice clinical predictors convolutional neural network crowdsourcing data streams design experience individual patient innovation iterative design medical specialties mortality novel open source outcome forecast personalized decision personalized predictions predictive modeling prototype statistical learning statistics tool usability

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项目摘要

Summary: Machine Learning Clinical Order Recommendations for Specialty Consultation Care A future vision of clinical decision support must transcend constraints in scalability, maintainability, and adaptability. The shortage of 100,000 physicians by 2030 reflects unmet (and unlimited) demand for the scarcest healthcare resource, clinical expertise. Over 25 million in the US alone have deficient access to medical specialty care, with delays contributing to 20% higher mortality. There is no quality without access. Our goal is to develop a radically different paradigm for outpatient specialty consultations by inductively learning clinical workups embedded in clinical data. We focus on predicting the concrete clinical orders for medications and diagnostic tests that result from specialty consultations. This can power a tier of fully automated guides that will enable clinicians to initiate care that would otherwise await in-person specialty visits, opening access for more patients. The major scientific barriers are advances in data science and decision support methods for collating clinical knowledge, with continuous improvement through clinical experience, crowdsourcing, and machine learning. Our innovative approach is inspired by collaborative filtering algorithms that power “Customers like you also bought this...” recommender systems with the scalability to answer unlimited queries, maintainability through statistical learning, and adaptability to respond to evolving clinical practices. Our team uniquely combines expertise in clinical medicine, electronic medical records, clinical decision support, statistics and machine learning to enhance medical specialty consultations through aims that seek to: (1) Develop methods to generate clinical decision support by predicting the clinical orders that will result from Endocrinology and Hematology specialty consultations; (2) Evaluate and iteratively design clinical collaborative filtering prototypes based on clinical user input on usability and acceptability; and (3) Determine which consult clinical order patterns are associated with better results through reinforcement learning and causal inference frameworks. Completion of these aims will yield a sustained, powerful impact on clinical information retrieval and knowledge discovery for synthesizing clinical practices from real-world data. By addressing grand challenges in clinical decision support, adoption of these methods will fulfill a vision that empowers clinicians to practice to the top of their license, making healthcare more scalable in reach, responsiveness, and reproducibility

摘要：机器学习临床订单专业咨询护理建议对临床决策支持的未来愿景必须超越可伸缩性，可维护性和适应性。到2030年到2030年的100,000名医生的短缺反映了未满足的（无限）的需求 Scarcest医疗保健资源，临床专业知识。仅在美国，就有超过2500万医学专业护理，延迟死亡率增加了20％。没有访问就没有质量。我们的目标是通过归纳为门诊专业咨询的根本不同的范式学习嵌入临床数据中的临床检查。我们专注于预测混凝土临床订单专业咨询导致的药物和诊断测试。这可以充分利用自动指南将使临床医生能够开始护理，否则将等待现场专业访问，开放更多患者。主要科学障碍是数据科学和决策支持方法的进步临床知识，通过临床经验，众包和机器的持续改进学习。我们的创新方法的灵感来自协作过滤算法，以“客户喜欢的客户您还会购买此...”推荐系统具有可扩展性，可以回答无限的查询，可维护性通过统计学习和适应能够应对不断发展的临床实践的能力。我们的团队独特地结合了临床医学，电子病历，临床决定的专业知识支持，统计和机器学习，以通过试图以下目的来增强医学专业咨询：（1）通过预测将由内分泌学和血液学专业咨询；（2）评估和迭代设计的临床合作根据临床用户对可用性和可接受性的输入过滤原型；（3）确定哪个咨询临床顺序模式通过增强学习和因果推断与更好的结果有关框架。这些目标的完成将对临床信息检索和知识产生持续，有力的影响从现实世界数据中综合临床实践的发现。通过解决临床方面的巨大挑战决策支持，采用这些方法将实现一个愿景，使临床医生能够练习到顶部他们的许可证，使医疗保健在触及，响应能力和可重复性方面更具可扩展性