Approach to machine learning for extraction of real-world data variables from electronic health records

Abstract

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI’s ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability.Methods: We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (e.g., clinician notes, radiology reports, lab reports, etc.) to output a set of structured variables required for RWD analysis. This research used a nationwide electronic health record (EHR)-derived database. Models were selected based on performance. Variables curated with an approach using ML extraction are those where the value is determined solely based on an ML model (i.e. not confirmed by abstraction), which identifies key information from visit notes and documents. These models do not predict future events or infer missing information.Results: We developed an approach using NLP and ML for extraction of clinically meaningful information from unstructured EHR documents and found high performance of output variables compared with variables curated by manually abstracted data. These extraction methods resulted in research-ready variables including initial cancer diagnosis with date, advanced/metastatic diagnosis with date, disease stage, histology, smoking status, surgery status with date, biomarker test results with dates, and oral treatments with dates.Conclusion: NLP and ML enable the extraction of retrospective clinical data in EHR with speed and scalability to help researchers learn from the experience of every person with cancer.

Genomic Profiles and Clinical Outcomes of Penile Squamous Cell Carcinoma With Elevated Tumor Mutational Burden

“… 22 Patient smoking status was extracted by natural language processing of electronic health record documents. 23 In this cohort, overall survival (OS) with routine clinical treatment was calculated from start of treatment in the metastatic setting to death from any cause, and patients without a record of mortality were right censored at the date of their last clinic visit or structured activity. Because patients could not enter the database until a CGP report was delivered, OS risk intervals were left truncated to the date of report to account for immortal time.…”

Analyzing FDA's approaches to using electronic health record and medical claims data to support regulatory decision making

“…Традиционные методы, такие как ручной просмотр карт пациентов, требуют много времени и ресурсов, что ограничивает число пациентов, доступных для исследования. ИИ может эффективно обрабатывать большие объёмы данных, позволяя исследователям получить доступ к более широкой популяции пациентов и более эффективно генерировать доказательства, полученные на основе данных РКП [6].…”

“…Плюсы использования ИИ для оценки и извлечения данных ЭМК [6]: • Эффективность и масштабируемость: методы ИИ позволяют быстро и точно обрабатывать большие объёмы неструктурированных данных, обеспечивая эффективное извлечение клинически значимой информации. Такая масштабируемость позволяет исследователям анализировать данные большего числа пациентов, что ведёт к получению более надёжных и репрезентативных доказательств.…”