SCH: Methods for Clinical Assessment Generation

电子病历是计算机辅助临床决策支持系统(CDSS)的丰富资源。在CDSS方面有50多年的研究，产生了基于知识的方法和机器学习方法。然而，不同的方法已经被不同的数据集或在不同的临床应用中进行了评估，很难比较不同方法的性能。这项研究将评估CDSS的最新方法，重点是临床评估生成的重要应用，它预测医疗诊断并总结导致医疗诊断的关键要素。CDSS方法将使用美国退伍军人健康管理局(VHA)在整个美利坚合众国的纵向EHR数据进行实施和评估。临床评估生成将通过生成与临床相关的案例研究来帮助医学教育。临床评估生成将通过协助初级保健医生进行临床诊断，特别是对罕见疾病的患者进行临床诊断，并通过确定他们的患者的医学专科和子专科，来改善患者的护理。这项工作将在CDSS中具有变革性。提供者接受培训，以编写具有面向问题的主观、客观、评估和计划(Soap)结构的笔记，其中评估可以从他们的主观和客观部分推断。临床评估生成可以被认为是文本到文本或图形到文本生成的应用。最先进的方法包括文本到文本传输转换器、生成性预训练转换器、多跳推理流程生成、常识知识感知对话模型、引导性常识推理和自动临床评估生成，所有这些方法都将使用来自美国1200多家医疗机构的800多万VHA患者的纵向EHR进行临床评估生成评估。评估指标包括双语评估、后备学习和面向回忆的后备学习。临床医生将评估每种方法的临床相关性和可解释性。我们将根据疾病类别或子类别、不同的笔记类型和不同的VHA医院设施来评估方法的性能。我们还将根据种族、性别、年龄和少数群体来评估方法的表现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Electronic health records (EHRs) are rich resources for computer-assisted clinical decision support systems (CDSS). There are over five decades of research in CDSS, resulting in methods in knowledge-based and machine-learning approaches. However, methods have been evaluated by different datasets or in different clinical applications, it is difficult to compare the performance of different methods. This study will evaluate state-of-the-art methods for CDSS, focusing on the important application of clinical assessment generation, which predicts medical diagnoses and summaries of the key elements that lead to the medical diagnoses. The CDSS methods will be implemented and evaluated using the longitudinal EHR data from the US Veterans Health Administration (VHA) across the entire United States of America. Clinical assessment generation will help medical education by generating clinically relevant case studies. Clinical assessment generation will improve patient care by assisting primary care physicians with clinical diagnoses, especially for patients with rare diseases, and by identifying medical specialties and subspecialties for their patients. This work will be transformative in CDSS.Providers are trained to write notes with a problem-oriented subjective, objective, assessment, and plan (SOAP) structure, where assessments can be inferred from their subjective and objective sections of the SOAP notes. Clinical assessment generation can be considered as an application of text-to-text or graph-to-text generation. State of the art methods includes text-to-text transfer transformer, generative pre-trained transformer, generation with multihop reasoning flow, commonsense knowledge aware conversational model, abductive commonsense reasoning, and automated clinical assessment generation, all of which will be evaluated for clinical assessment generation using over 8 million VHA patients’ longitudinal EHRs from over 1,200 healthcare facilities in the US. The evaluation metrics include bilingual evaluation understudy and Recall­Oriented Understudy for Gisting Evaluation. Clinicians will evaluate the clinical relevance and interpretability of each method. We will evaluate method performance by disease categories or subcategories, by different note types, and by different VHA hospital facilities. We will also evaluate method performance by race, gender, age, and minority groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.