SCH: EXP: Intelligent Clinical Decision Support with Probabilistic and Temporal EHR Modeling

临床决策支持有可能降低医疗成本并改善患者结局，同时揭示美国围绕医疗成本和实践的政策问题。 该项目旨在开发智能临床决策支持技术，以推荐最佳行动计划-包括诊断测试和医疗干预-用于治疗慢性疾病，执行多步骤和适应性治疗，以及修改长期健康习惯。为了将证据驱动的决策与既定的临床实践相结合，该研究将开发疾病不可知的人工智能技术，将来自大型电子健康记录（EHR）的联合收割机数据与人类专家的建议相结合。原型决策支持系统将在三个临床环境中进行测试-心脏病，临床抑郁症和急诊室再入院-使用现有的EHR数据集并与临床合作伙伴的领域专家进行咨询。将结果驱动和成本驱动的优化决策与当前的临床实践进行比较。这项探索性研究将为后续项目提供决策支持信息呈现，与临床工作流程和IT系统集成以及从回顾性研究过渡到临床试验的基础。 其他更广泛的影响包括人工智能的医疗应用研讨会，女性和少数民族学生将被招募并指导研究生和本科生的计算机科学研究。这项研究的技术方法建立在最先进的机器学习和人工智能方法的基础上，可以自动学习，模拟和推理患者特定的治疗计划。 这种方法必须同时具有概率性和时间性。 概率技术需要处理临床诊断和结果中的重大不确定性，就像人类临床医生一样。 需要时间技术来考虑治疗过程中未来决策的序列，而不是单个时间点的决策。 更具体地说，该项目将考虑使用统计关系学习（SRL）技术来挖掘大型电子健康记录中的概率和时间模式，这些模式将用于部分可观察马尔可夫决策过程（POMDPs），以彻底搜索最佳治疗序列。最近的结果表明，SRL在从人口统计学和生活方式观察中预测心脏骤停方面取得了上级其他机器学习方法的性能，POMDP治疗计划通过将成本降低50%并将临床抑郁症数据集的结果提高40%，优于现有的按服务收费的做法。 通过结合SRL和POMDP，特别是使用SRL来学习POMDP使用的疾病进展模型，该项目旨在进一步提高复杂治疗的推荐质量和计算可扩展性。 此外，由于EHR可能会受到有限或缺失数据的影响，因此临床决策支持工具应在必要时遵循既定的实践和专业知识。 为此，将探索将专家知识纳入SRL和POMDPs的新工作流程。 将与马什菲尔德诊所、中心石、维克森林医学院和南本德纪念医院的临床合作伙伴一起对各种疾病情况进行评价。

Clinical decision support has the potential to reduce healthcare costs and improve patient outcomes, while shedding light into policy questions surrounding healthcare costs and practices in the US. This project aims to develop intelligent clinical decision support techniques for recommending optimal action plans - including both diagnostic tests and medical interventions - for treating chronic disease, performing multi-step and adaptive treatments, and modifying long-term health habits. In an effort to integrate evidence-driven decision-making with established clinical practices, the research will develop disease-agnostic artificial intelligence techniques that combine data from large electronic health records (EHRs) with recommendations from human experts. A prototype decision support system will be tested on three clinical settings - cardiology, clinical depression, and emergency room readmission - using existing EHR datasets and consultation with domain experts from clinical partners. Outcomes-driven and cost-driven optimized decisions will be compared to current clinical practice. This exploratory research will provide the groundwork for follow-up projects in decision support information presentation, integration with clinical workflow and IT systems, and making the transition from retrospective studies to clinical trials. Other broader impacts include workshops for healthcare applications of AI, and women and minority students will be recruited and mentored in graduate and undergraduate computer science research.The technical approach of this research builds on state-of-the-art machine learning and artificial intelligence methods to automatically learn, simulate, and reason about patient-specific treatment plans. Such methods must be simultaneously probabilistic and temporal. Probabilistic techniques are needed to handle significant uncertainties in clinical diagnoses and outcomes, much like a human clinician would. Temporal techniques are needed to consider sequences of future decisions over the course of treatment, rather than decisions at single time points. More specifically, this project will consider the use of statistical relational learning (SRL) techniques to mine for probabilistic, temporal patterns in large electronic health records, and these patterns will be used in partially-observable Markov decision processes (POMDPs) that exhaustively search for optimal treatment sequences. Recent results indicate that SRL achieves superior performance to other machine learning methods in predicting cardiac arrest from demographic and lifestyle observations, and POMDP treatment plans outperform existing fee-for-service practices by reducing costs by 50% and improving outcomes by 40% on a clinical depression dataset. By combining SRL and POMDPs, specifically, using SRL to learn a disease progression model used by the POMDP, this project aims to achieve further improvements in recommendation quality and computational scalability for complex treatments. Furthermore, because EHRs may suffer from limited or missing data, clinical decision support tools should follow established practices and expert knowledge when necessary. To do so, new workflows for integrating expert knowledge into SRL and POMDPs will be explored. Evaluation will be performed on a variety of disease scenarios in conjunction with clinical partners at Marshfield Clinic, Centerstone, Wake Forest School of Medicine, and South Bend Memorial Hospital.