TY - JOUR AU - Ser, Sarah E AU - Shear, Kristen AU - Snigurska, Urszula A AU - Prosperi, Mattia AU - Wu, Yonghui AU - Magoc, Tanja AU - Bjarnadottir, Ragnhildur I AU - Lucero, Robert J PY - 2023 DA - 2023/11/9 TI - Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study JO - JMIR Res Protoc SP - e48521 VL - 12 KW - big data KW - machine learning KW - data science KW - hospital-acquired condition KW - hospital induced KW - hospital acquired KW - predict KW - predictive KW - prediction KW - model KW - models KW - natural language processing KW - risk factors KW - delirium KW - risk KW - unstructured KW - structured KW - free text KW - clinical text KW - text data AB - Background: Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium. Objective: The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data. Methods: This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. Results: Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters that occurred between January 2012 to May 2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals. Conclusions: Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real time. This model has the potential to be integrated into the electronic health record and provide point-of-care decision support to prevent harm and improve quality of care. International Registered Report Identifier (IRRID): DERR1-10.2196/48521 SN - 1929-0748 UR - https://www.researchprotocols.org/2023/1/e48521 UR - https://doi.org/10.2196/48521 UR - http://www.ncbi.nlm.nih.gov/pubmed/37943599 DO - 10.2196/48521 ID - info:doi/10.2196/48521 ER -