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

青光眼是导致不可逆失明的主要原因，影响超过6000万人 世界各地的人们。青光眼患者的临床表现差异很大，有些患者的病情长期存在 稳定，其他人迅速发展为视力丧失。如果进展风险最高的青光眼患者可以 早期发现，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，如眼内压、治疗史和药物依从性， 记录在电子健康记录（EHR）的自由文本注释中，并且不是大规模的 行政索赔数据库。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EHR数据集成到高度准确的 医学和外科健康结果的预测算法。我们假设我们可以扩展这些 AI和NLP技术构建优于传统青光眼进展的预测算法 仅依赖于管理功能的模型。该项目的目标是建立和评估预测 使用大规模EHR数据进行青光眼进展的算法，同时开发Wang博士的 在人工智能和NLP的专业知识，推进她的职业生涯作为一个独立的临床科学家。目标1侧重于 使用EHR中的结构化临床数据，这些数据是数字或编码的，易于机器读取， 建立基线机器学习模型，预测需要手术的青光眼进展。目标2侧重于 使用和增强临床命名实体识别工具，将EHR自由文本中的信息集成到AI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AI算法的性能，在现实世界的前瞻性环境中评估AI算法的性能 关键亚群，他们对关键特征的依赖，以及调查新队列中的潜在偏倚领域 青光眼患者。该提案在开发基于AI的预测算法方面具有创新性， 使用EHR中唯一可用的数字和文本临床数据评估青光眼进展。的工具 王博士将建立和评估的方法将通过使眼科领域能够 以证据为基础的治疗方法，以适应患者独特的临床特征， 精准医疗此外，人工智能预测算法对新患者队列的仔细评估 将深入了解他们在关键亚群上的表现以及对关键特征的依赖，这一点至关重要 进一步了解在临床工作流程中部署AI的可能局限性。王医生的 职业和研究将在国家科学院院长Tina Hernanove-Boussard博士的主要指导下取得进展。 信息学领导者，在EHR上使用NLP来改善患者护理方面的专家。优秀的咨询 委员会，包括临床研究人员潘兴博士，斯坦，张和戈德堡，将确保王博士的 成功地成为一个独立的临床研究人员整合眼科学和信息学。

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.