Personalized Predictions for Glaucoma Progression Using Artificial Intelligence for Electronic Health Records

使用电子健康记录人工智能对青光眼进展进行个性化预测

• 批准号: 10191911
• 负责人: Sophia Ying Wang
• 金额: $ 25.42万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10191911
• 关键词: Adherence; Advisory Committees; Affect; Algorithms; Area; Artificial Intelligence; Blindness; Calibration; Characteristics; Clinic; Clinical; Clinical Data; Code; Communities; Complex; Computers; Data; Data Sources; Databases; Development; Disease; Documentation; Electronic Health Record; Ensure; Evaluation; Future; Glaucoma; Goals; Health; Image; Individual; Informatics; Intervention; Machine Learning; Measurement; Medicine; Mentorship; Methods; Mission; Modeling; Names; Natural Language Processing; Operative Surgical Procedures; Ophthalmic examination and evaluation; Ophthalmology; Outcome; Patient Care; Patients; Pattern; Performance; Pharmaceutical Preparations; Phenotype; Physicians; Physiologic Intraocular Pressure; Process; Public Health; Readability; Recording of previous events; Registries; Research; Research Personnel; Scientist; Structure; Techniques; Technology; Testing; Text; Translating; Treatment outcome; Variant; Vertebral column; Veterans Health Administration; Vision; Work; base; biomedical informatics; career; clinical data warehouse; clinically relevant; cohort; data sharing; demographics; evidence base; high risk; improved; individualized medicine; innovation; insight; intelligent algorithm; large scale data; medication compliance; optimal treatments; personalized medicine; personalized predictions; precision medicine; prediction algorithm; predictive modeling; prevent; prospective; structured data; success; support tools; tool

Project Summary/Abstract: Glaucoma is the leading cause of irreversible blindness, affecting over 60 million people worldwide. Glaucoma patients vary widely in their presentation, with some retaining long-term disease stability, and others progressing quickly to vision loss. If glaucoma patients at highest risk of progression could be identified early, clinicians could better personalize their treatment approaches. Many clinical factors that affect glaucoma progression, such as intraocular pressure, treatment history, and medication adherence, are documented within the free-text notes of the electronic health records (EHR) and are not in large-scale administrative claims databases. Recent advances in artificial intelligence (AI) and natural language processing (NLP) have enabled the integration of the rich and complex EHR data into highly accurate predictive algorithms for health outcomes in medicine and surgery. We hypothesize that we can extend these AI and NLP techniques to build predictive algorithms for glaucoma progression that outperform traditional models reliant on only administrative features. The goal of this project is to build and evaluate predictive algorithms for glaucoma progression using large-scale EHR data, while developing Dr Wang's expertise in AI and NLP, advancing her career as an independent clinician scientist. Aim 1 focuses on using the structured clinical data within the EHR, which are numeric or coded and readily machine-readable, to build baseline machine learning models predicting glaucoma progression requiring surgery. Aim 2 focuses on using and augmenting clinical named entity recognition tools to integrate information from EHR free text into AI models predicting glaucoma progression to surgery. Aim 3 focuses on understanding, explaining, and evaluating the performance of AI algorithms in a real-world prospective setting, by evaluating their performance on key subpopulations, their reliance on key features, and investigating potential areas of bias in a new cohort of glaucoma patients. This proposal is innovative in developing AI-based predictive algorithms for glaucoma progression using numeric and textual clinical data uniquely available in the EHR. The tools and methods Dr Wang will build and evaluate will substantially impact the ophthalmology field by enabling evidence-based tailoring of treatment approaches to patients' unique clinical characteristics, a step towards precision medicine. Furthermore, the careful evaluation of AI predictive algorithms on a new cohort of patients will provide insights into their performance on key subpopulations and reliance on key features, which is critical to advancing our understanding of possible limitations of deploying AI in the clinical workflow. Dr. Wang's career and research will advance under the primary mentorship of Dr. Tina Hernandez-Boussard, a national leader in informatics and expert in using NLP on EHR to improve patient care. Her outstanding Advisory Committee, including clinician-investigators Drs. Pershing, Stein, Chang, and Goldberg, will ensure Dr. Wang's success in becoming an independent clinician-investigator integrating ophthalmology and informatics.

项目摘要/摘要：青光眼是不可逆致盲的主要原因，影响6000多万人 世界各地的人们。青光眼患者的表现差异很大，有些人会长期患病。 稳定，其他进展迅速，导致视力丧失。如果进展风险最高的青光眼患者 如果及早发现，临床医生可以更好地个性化他们的治疗方法。许多临床因素 影响青光眼进展的因素，如眼压、治疗史和药物依从性 记录在电子健康记录(EHR)的自由文本说明中，并且不是大规模的 管理索赔数据库。人工智能(AI)和自然语言的最新进展 处理(NLP)使丰富和复杂的电子病历数据能够集成到高度准确的 医学和外科领域健康结果的预测算法。我们假设我们可以延长这些时间 AI和NLP技术，以构建优于传统青光眼进展的预测算法 仅依赖管理功能的型号。该项目的目标是建立和评估预测性 使用大规模EHR数据的青光眼进展算法，同时发展王博士的 在人工智能和NLP方面的专业知识，推动了她作为一名独立临床医生科学家的职业生涯。目标1侧重于 使用电子病历内的结构化临床数据，这些数据是数字或编码的，并且机器可读，以 建立预测青光眼进展需要手术的基线机器学习模型。目标2侧重于 使用和增强临床命名实体识别工具，将电子病历自由文本中的信息集成到人工智能中 预测青光眼进展到手术的模型。目标3侧重于理解、解释和 通过评估人工智能算法的性能，评估它们在真实世界预期环境中的性能 关于关键的亚群，他们对关键特征的依赖，以及在新的队列中调查潜在的偏见领域 青光眼患者。该方案在开发基于人工智能的预测算法方面具有创新性 使用电子病历中唯一可用的数字和文本临床数据进行青光眼进展。这些工具 王博士将建立和评估的方法将通过使 以循证为基础的治疗方法适应患者独特的临床特征，是迈向 精准医学。此外，AI预测算法在新的患者队列上的仔细评估 将深入了解他们在关键亚群上的表现以及对关键功能的依赖，这一点至关重要 有助于增进我们对在临床工作流程中部署人工智能可能存在的限制的理解。王医生的 职业和研究将在Tina Hernandez-Boussard博士的主要指导下向前发展，Tina Hernandez-Boussard博士是一名 信息学领域的领导者和使用电子病历上的NLP来改善患者护理的专家。她杰出的忠告 包括临床医生兼调查人员潘兴、斯坦、张和戈德伯格博士在内的委员会将确保王医生的 成功地成为集眼科和信息学为一体的独立临床医生和研究人员。