When will I get out of the Hospital? Modeling Length of Stay using Comorbidity Networks

• 成果类型: Article
• 署名作者: Kalgotra, Pankush; Sharda, Ramesh
• 署名单位: Auburn University System; Auburn University; Oklahoma State University System; Oklahoma State University - Stillwater; Oklahoma State University System; Oklahoma State University - Stillwater
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2021.1990618
• 发表日期: 2021
• 页码: 1150-1184
• 关键词: diagnosis-related groups; Health disparities; similarity measure; predicting length; risk adjustment; co-morbidity; big data; mortality; IMPACT; care

摘要

A reliable and accurate estimate of the expected hospital length of stay (LOS) of a patient is important to patients, medical providers, and insurance companies. Predicting hospital Length of Stay (LOS) is a complex and ill-structured problem, driven by many factors such as a patient's individual characteristics, treatment plans, and disease-interactions. In this paper, we develop a novel model to predict the expected LOS at the time of admission by combining network science and deep learning. We propose a two-dimensional construct of latent comorbidities comprising historical and probable comorbidities that a patient does not currently manifest but could likely develop during the course of hospital stay. The probable comorbidities are derived from a network comprising relationships among diseases observed in 3.2 million patient records in hundreds of US hospitals. We employ this construct of latent comorbidities in deep learning models to predict patients' LOS using almost 10 million other patient visits belonging to various disease categories. Implementing these models and analyses required a high-performance computing (Big Data) facility. The average mean absolute percent error of our models across all categories of diseases was 29.8%, which is the best in the current state-of-the-art. Our primary contribution is in developing a generalizable method to create a predictor construct for recognizing underlying relationships through network analyses, which can then be used in a deep learning model to predict an exogenous dependent variable.