An NLP-Enhanced Approach to Test Comorbidities Risk Scoring Based on Unstructured Health Data for Hospital Readmissions Prediction

Tahir Hameed, Haris Khan, Saad Khan, Mutahira Khalid, Asim Abbas, Syed Bukhari

2023

Abstract

Hospital readmissions have emerged as a key healthcare quality indicator since the passing of the Affordable Care Act in 2010. It is easier to predict the readmission risk of patients without complications, but comorbidities, such as diabetes and cardiovascular diseases, make it difficult to accurately assess the readmission risk. 30-days hospital readmissions (30DRA) risk models typically rely on demographic, socioeconomic, and medical variables from structured data, such as diagnosis, vitals, lab reports, and comorbidities, etc. Comorbidity indices help in assessing overall disease burden by accounting for the disease codes in electronic health records (EHRs). With the advent of natural language processing (NLP), there is a potential to extract additional health related variables including the possibility of gleaning additional disease codes for comorbidities in unstructured portion of the EHRs, such as clinical notes, medical history, and discharge summaries. Whereas NLP has been applied heavily in healthcare information systems, to the best of our knowledge, there is no research that identifies comorbidities from unstructured clinical texts. This paper employs a Bidirectional Encoder Representation from Transfer (BERT) deep learning technique to predict additional comorbid conditions in the unstructured portions of EHRs and evaluates the effectiveness in comorbidity scoring. Comorbidity scores based on the NLP-predicted comorbidity codes (predicted) were compared against the scores calculated from codes identified by the health providers (diagnosed), and also against a combination of the two (diagnosed and predicted). We find NLP is effective in improving the accuracy of comorbidity calculations, that in turn could improve predictive power of AI models for hospital readmissions and mortality predictions. It is among the first papers employing NLP to predict ICD-10 codes from unstructured EHRs for comorbidity index calculations.

Paper Citation

in Harvard Style

Hameed T., Khan H., Khan S., Khalid M., Abbas A. and Bukhari S. (2023). An NLP-Enhanced Approach to Test Comorbidities Risk Scoring Based on Unstructured Health Data for Hospital Readmissions Prediction. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH, ISBN 978-989-758-631-6, pages 649-659. DOI: 10.5220/0011944500003414

in Bibtex Style

@conference{cch23,
author={Tahir Hameed and Haris Khan and Saad Khan and Mutahira Khalid and Asim Abbas and Syed Bukhari},
title={An NLP-Enhanced Approach to Test Comorbidities Risk Scoring Based on Unstructured Health Data for Hospital Readmissions Prediction},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH,},
year={2023},
pages={649-659},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011944500003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH,
TI - An NLP-Enhanced Approach to Test Comorbidities Risk Scoring Based on Unstructured Health Data for Hospital Readmissions Prediction
SN - 978-989-758-631-6
AU - Hameed T.
AU - Khan H.
AU - Khan S.
AU - Khalid M.
AU - Abbas A.
AU - Bukhari S.
PY - 2023
SP - 649
EP - 659
DO - 10.5220/0011944500003414