SCH: Methods for Clinical Assessment Generation

电子病历是计算机辅助临床决策支持系统的丰富资源。在CDSS方面有超过五十年的研究，产生了基于知识和机器学习的方法。然而，方法已经通过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。本研究将评估CDSS的最新方法，重点是临床评估生成的重要应用，该评估生成预测医学诊断和导致医学诊断的关键要素的总结。CDSS方法将使用美国退伍军人健康管理局（VHA）在整个美国的纵向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，特别是对罕见疾病患者的临床诊断，以及为患者确定医学专科和亚专科，来改善患者护理。这项工作将是变革性的CDSS.供应商被训练写笔记与面向问题的主观，客观，评估，和计划（SOAP）结构，其中评估可以推断出他们的主观和客观部分的SOAP笔记.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Transformer、生成预训练的Transformer、多跳推理流生成、常识知识感知对话模型、溯因常识推理和自动临床评估生成，所有这些都将使用来自美国1,200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。评价指标包括双语评价替代者和回忆型替代者。临床医生将评价每种方法的临床相关性和可解释性。我们将按照疾病类别或子类别、不同的注释类型和不同的VHA医院设施来评价方法性能。我们还将根据种族、性别、年龄和少数群体来评估方法的性能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.