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Article

DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups

1
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
2
AESOP Technology, Songshan District, Taipei 105, Taiwan
3
International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
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Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
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Department of Dermatology, Wan Fang Hospital, Taipei 116, Taiwan
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TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Mahmoud Elkhodr, Omar Darwish, Belal Alsinglawi and Daniele Giansanti
Healthcare 2021, 9(12), 1632; https://doi.org/10.3390/healthcare9121632
Received: 21 October 2021 / Revised: 17 November 2021 / Accepted: 22 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Emerging Technologies in Health Informatics and Management)
Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients’ age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance. View Full-Text
Keywords: deep learning; artificial intelligence; diagnosis-related groups; hospital expenditure deep learning; artificial intelligence; diagnosis-related groups; hospital expenditure
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MDPI and ACS Style

Islam, M.M.; Li, G.-H.; Poly, T.N.; Li, Y.-C. DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups. Healthcare 2021, 9, 1632. https://doi.org/10.3390/healthcare9121632

AMA Style

Islam MM, Li G-H, Poly TN, Li Y-C. DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups. Healthcare. 2021; 9(12):1632. https://doi.org/10.3390/healthcare9121632

Chicago/Turabian Style

Islam, Md. M., Guo-Hung Li, Tahmina N. Poly, and Yu-Chuan Li 2021. "DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups" Healthcare 9, no. 12: 1632. https://doi.org/10.3390/healthcare9121632

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