AI Article Synopsis

  • The study aimed to predict in-hospital charges for conditions like congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and diabetic ketoacidosis (DKA) using machine learning models.
  • Researchers analyzed national discharge data from 2016 to 2019 and created six different machine learning models that demonstrated solid performance in predicting costs, with R-squared values ranging from 0.615 to 0.750.
  • Key factors influencing hospital costs were identified, such as patient age, length of stay, and whether the admission was elective or not, which can help inform future efforts to reduce healthcare expenses for these conditions.

Article Abstract

Background: Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models.

Results: We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We constructed six ML models (linear regression, ridge regression, support vector machine, random forest, gradient boosting and extreme gradient boosting) to predict total in-hospital cost for admission for each condition. Our models had good predictive performance, with testing R-squared values of 0.701-0.750 (mean of 0.713) for CHF; 0.694-0.724 (mean 0.709) for COPD; and 0.615-0.729 (mean 0.694) for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures, and elective/nonelective admission.

Conclusions: ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11395859PMC
http://dx.doi.org/10.1186/s13040-024-00387-9DOI Listing

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