Authors:
Tahir Hameed
1
;
Haris Khan
2
;
Saad Khan
2
;
Mutahira Khalid
2
;
Asim Abbas
3
and
Syed Bukhari
3
Affiliations:
1
Girard School of Business, Merrimack College, North Andover, MA, 01824, U.S.A.
;
2
School of Electrical Engineering and Computer Science, NUST, H-12, Islamabad, Pakistan
;
3
Division of Computer Science, St. John’s University, Queens, NY 11439, U.S.A.
Keyword(s):
Comorbidities, Hospital Readmissions, Clinical Decision Support Systems, Natural Language Processing, Bert, Deep Learning.
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.
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