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DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups
Open accessAbstract
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.
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Previous studies have showcased advancements in DRGs classification accuracy through various machine-learning algorithms and deep neural networks.
“Previous studies have showcased advancements in DRGs classification accuracy through various machine-learning algorithms10 and deep neural networks11.”
From DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients (2024) · cites this paper as [11]
These studies focused on using structured data as input variables instead of clinical text.
“These studies focused on using structured data as input variables instead of clinical text.”
From DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients (2024) · cites this paper as [11]
Previous studies have underscored the effectiveness of employing diverse machine learning algorithms and deep neural networks for DRG prediction within healthcare systems outside the United States.
“Previous studies have underscored the effectiveness of employing diverse machine learning algorithms and deep neural networks for DRG prediction within healthcare systems outside the United States10,11.”
From DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients (2024) · cites this paper as [11]
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Cites
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Validation of the national health insurance research database with ischemic stroke cases in Taiwan
Cheng, C.-L., Kao, Y., Lin, S.-J., Lee, C.-H., & Lai, M. (2011) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.2087 · Cited by 1,242
Hsing, A. W., & Ioannidis, J. P. A. (2015) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.3540 · Cited by 580
Busse, R., Geißler, A., Aaviksoo, A., Cots, F., Häkkinen, U., Kobel, C., Mateus, C., Or, Z., O’Reilly, J., Serdén, L., et al. (2013) · PubMed · doi:10.14279/depositonce-9402 · Cited by 506
Muka, T., Imo, D., Jaspers, L., Colpani, V., Chaker, L., van der Lee, S. J., Mendis, S., Chowdhury, R., Bramer, W. M., Falla, A., et al. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-014-9984-2 · Cited by 374
Minsky, M., & Papert, S. A. (2017) · The MIT Press eBooks · doi:10.7551/mitpress/11301.001.0001 · Cited by 330
Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods
Ijaz, M. F., Attique, M., & Son, Y. (2020) · Sensors · doi:10.3390/s20102809 · Cited by 270
Mathauer, I., & Wittenbecher, F. (2013) · Bulletin of the World Health Organization · doi:10.2471/blt.12.115931 · Cited by 238
Schwartz, A. L., Chernew, M. E., Landon, B. E., & McWilliams, J. M. (2015) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.4525 · Cited by 165
Review of Diagnosis-Related Group-Based Financing of Hospital Care
Mihailovic, N., Kocic, S., & Jakovljevic, M. (2016) · Health Services Research and Managerial Epidemiology · doi:10.1177/2333392816647892 · Cited by 138
Cheng, P., Gilchrist, A., Robinson, K. M., & Paul, L. J. (2009) · Health Information Management Journal · doi:10.1177/183335830903800105 · Cited by 128
A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification
Mandal, M., Singh, P. K., Ijaz, M. F., Shafi, J., & Sarkar, R. (2021) · Sensors · doi:10.3390/s21165571 · Cited by 98
Low-value care in Australian public hospitals: prevalence and trends over time
Badgery‐Parker, T., Pearson, S., Chalmers, K., Brett, J., Scott, I. A., Dunn, S., Onley, N., & Elshaug, A. G. (2018) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008338 · Cited by 97
Busse, R., Geissler, A., Aaviksoo, A., Cots, F., Hakkinen, U., Kobel, C., Mateus, C., Or, Z., O'Reilly, J., Serden, L., et al. (2013) · BMJ · doi:10.1136/bmj.f3197 · Cited by 94
A Study of Clinical Coding Accuracy in Surgery
Nouraei, S. A. R., Hudovsky, A., Frampton, A. E., Mufti, U., White, N. B., Wathen, C. G., Sandhu, G., & Darzi, A. W. (2014) · Annals of Surgery · doi:10.1097/sla.0000000000000851 · Cited by 86
Rashid, S. A. Z. A., Nur, A. M., Puteh, S. E. W., & Aljunid, S. M. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-2843-1 · Cited by 67
Economic assessment of the use of the sFlt-1/PlGF ratio test to predict preeclampsia in Germany
Schlembach, D., Hund, M., Schröer, A., & Wolf, C. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3406-1 · Cited by 67
Kutz, A., Gut, L., Ebrahimi, F., Wagner, U., Schüetz, P., & Müeller, B. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.8332 · Cited by 67
Machine Learning Approaches for Early DRG Classification and Resource Allocation
Gartner, D., Kolisch, R., Neill, D. B., & Padman, R. (2015) · INFORMS Journal on Computing · doi:10.1287/ijoc.2015.0655 · Cited by 58
Liu, J., Capurro, D., Nguyen, A., & Verspoor, K. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00474-9 · Cited by 50
Colla, C. H., Morden, N. E., Sequist, T. D., Mainor, A. J., Li, Z., & Rosenthal, M. B. (2017) · Health Services Research · doi:10.1111/1475-6773.12665 · Cited by 48
Nouraei, S. A. R., Virk, J. S., Hudovsky, A., Wathen, C. G., Darzi, A. W., & Parsons, D. (2015) · Journal of Public Health · doi:10.1093/pubmed/fdv041 · Cited by 45
Jung, Y. W., Pak, H., Lee, I., & Kim, E. H. (2018) · Yonsei Medical Journal · doi:10.3349/ymj.2018.59.4.539 · Cited by 43
Hu, W.-Y., Yeh, C., Shiao, A., & Tu, T. (2015) · Journal of the Chinese Medical Association · doi:10.1016/j.jcma.2015.06.012 · Cited by 40
Documentation and coding of medical records in a tertiary care center: a pilot study
Farhan, J., Al-Jummaa, S., Alrajhi, A. A., Al-Rayes, H., & Alnasser, A. (2005) · Annals of Saudi Medicine · doi:10.5144/0256-4947.2005.46 · Cited by 38
Wu, S.-W., Pan, Q., & Chen, T. (2020) · World Journal of Clinical Cases · doi:10.12998/wjcc.v8.i12.2484 · Cited by 37
Multi‐GRU prediction system for electricity generation's planning and operation
Li, W., Logenthiran, T., & Woo, W. L. (2018) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2018.6081 · Cited by 36
Ayub, S. S., Scali, S. T., Richter, J., Huber, T. S., Beck, A. W., Fatima, J., Berceli, S. A., Upchurch, G. R., Arnaoutakis, D. J., Back, M. R., et al. (2018) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2018.04.027 · Cited by 30
Liang, C.-W., Yang, H., Islam, M. M., Nguyen, P., Feng, Y.-T., Hou, Z. Y., Huang, C., Poly, T. N., & Li, Y. (2021) · JMIR Cancer · doi:10.2196/19812 · Cited by 29
Self-citationProducing Evidence to Reduce Low-Value Care
Howard, D. H., & Gross, C. P. (2015) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.5453 · Cited by 23
Kim, S. J., Han, K., Kim, W., Kim, S. J., & Park, E. (2017) · Health Services Research · doi:10.1111/1475-6773.12749 · Cited by 23
Gout drugs use and risk of cancer: A case-control study
Yang, H., Nguyễn, P. A., Islam, M. M., Huang, C., Poly, T. N., Iqbal, U., & Li, Y. (2018) · Joint Bone Spine · doi:10.1016/j.jbspin.2018.01.008 · Cited by 23
Self-citationYang, H., Islam, M. M., Nguyen, P., Wang, C., Poly, T. N., Huang, C., & Li, Y. (2021) · JMIR Public Health and Surveillance · doi:10.2196/21401 · Cited by 18
Self-citationThe impact of different surgical procedures on hypoparathyroidism after thyroidectomy
Chen, K.-C., Iqbal, U., Nguyen, P.-A., Hsu, C.-H., Huang, C.-L., Hsu, Y. E., Atique, S., Islam, M. M., Li, Y., & Jian, W. (2017) · Medicine · doi:10.1097/md.0000000000008245 · Cited by 17
Self-citationViral warts (Human Papilloma Virus) as a potential risk for breast cancer among younger females
Atique, S., Hsieh, C.-H., Hsiao, R.-T., Iqbal, U., Nguyen, P., Islam, M. M., Li, Y., Hsu, C., Chuang, T., & Syed-Abdul, S. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.024 · Cited by 15
Self-citationReading selectively via Binary Input Gated Recurrent Unit
Li, Z., Wang, P., Lu, H., & Cheng, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/705 · Cited by 15
Bayati, M., Mehrolhassani, M. H., & Yazdi‐Feyzabadi, V. (2019) · Cost Effectiveness and Resource Allocation · doi:10.1186/s12962-019-0197-0 · Cited by 15
A decision support system for diagnosis related groups coding
Lee, M.-T., Su, Z., Hou, Y., Liao, H.-C., & Lian, J.-D. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.09.016 · Cited by 12
Chien, T., Lee, Y., & Wang, H. (2019) · BMC Health Services Research · doi:10.1186/s12913-019-4417-2 · Cited by 10
Improved diagnosis-medication association mining to reduce pseudo-associations
Wang, C., Nguyễn, P. A., Li, Y., Islam, M. M., Poly, T. N., Tran, Q., Huang, C., & Yang, H. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106181 · Cited by 8
Self-citationReduced hospital revenue due to error code diagnosis in the implementation of INA-CBGs
Maryati, W., Yuliani, N., Susanto, A., Wannay, A. O., & Justika, A. I. (2021) · International Journal of Public Health Science (IJPHS) · doi:10.11591/ijphs.v10i2.20690 · Cited by 6
Flagged first, then by citations
3 of the 46 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv.
- Zafirah, 2017, Incidence of clinical coding errors and implications on casemix reimbursement in a teaching hospital in Malaysia, Malays. J. Public Health Med., 17, 19
- Pongpirul, 2013, Hospital manipulations in the DRG system: A systematic scoping review, Asian Biomed., 7, 301
Cited by
Sorted by citations
DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients
Wang, H., Gao, C., Dantona, C., Hull, B., & Sun, J. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00989-3 · Cited by 99
“Previous studies have underscored the effectiveness of employing diverse machine learning algorithms and deep neural networks for DRG prediction within healthcare systems outside the United States (Gartner et al. 2015; Islam et al. 2021).”
InfluentialRogers, P., Boussina, A. E., Shashikumar, S. P., Wardi, G., Longhurst, C., & Nemati, S. (2022) · Journal of Medical Internet Research · doi:10.2196/43486 · Cited by 13
Yuan, B., & Quan, L. (2023) · BMC Health Services Research · doi:10.1186/s12913-023-10299-9 · Cited by 9
Chen, H., Zeng, Y., & Cai, D. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1580445 · Cited by 10
Individual Medical Costs Prediction Methods Based on Clinical Notes and DRGs
Yang, C., Hu, X., Zhu, Q., Tu, Q., Geng, H., Xu, J., Liu, Z., Wang, Y., & Wang, J. (2024) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2024.3392682 · Cited by 6
Achieving Robust Medical Coding in DRGs Systems: Innovative Actions Adopted in Greece
Platis, C. G., Papaioannou, L., Sideri, P., Messaropoulos, P., Chalkias, K., & Kontodimopoulos, N. (2024) · Healthcare · doi:10.3390/healthcare12171782 · Cited by 8
Su, C., Su, J., Li, X., Jin, X., Wang, Z., Liu, J., & Wen, H. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1648606 · Cited by 3
DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups
Hajialigol, D., Kaknes, D., Barbour, T. B., Yao, D., North, C. L., Sun, J., Liem, D. A., & Wang, X. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.34 · Cited by 2
DRGs grouping Prediction Method based on Semantics Information of Clinical Notes
Yang, C., Hu, X., Xu, J., Liu, Z., Geng, H., Zhu, Q., Tu, Q., Wang, Y., & Wang, J. (2023) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi59677.2023.10365467 · Cited by 2
Wang, H., Luo, L., Ma, D., Xie, Z., & Fang, Y. (2025) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580251389813 · Cited by 1
A Regression Analysis for Predicting Surgical Complications
Madani, A. Z. B. A., Pauzi, N. H. B. M., Radzi, N. I. B. A., Tarmizi, W. N. A. B. W., Wani, S., & Olowolayemo, A. (2023) · International Journal on Perceptive and Cognitive Computing · doi:10.31436/ijpcc.v9i1.369 · Cited by 1
Lu, A.-T., Liou, C.-S., Lai, C.-H., Shain, B.-T., Li, M.-T., Sun, C.-Y., Kao, H.-Y., Dai, H.-J., & Tsai, M. (2024) · JMIR Human Factors · doi:10.2196/59961 · Cited by 1
Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network
Chiang, Y.-C., Hsieh, Y.-C., Lu, L.-C., & Ou, S.-Y. (2023) · Healthcare · doi:10.3390/healthcare11111598 · Cited by 1
Rogers, P., Boussina, A. E., Shashikumar, S. P., Wardi, G., Longhurst, C., & Nemati, S. (2022) · doi:10.2196/preprints.43486
Lu, A.-T., Liou, C.-S., Lai, C.-H., Shain, B.-T., Li, M.-T., Sun, C.-Y., Kao, H.-Y., Dai, H.-J., & Tsai, M. (2024) · doi:10.2196/preprints.59961
Data Augmentation Techniques for Chinese Disease Name Normalization
Cui, W., Fu, X., Liu, S., Gu, M., Liu, X., Wu, J., & King, I. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821986
Guidelines for Hospital Managers Implementing DRGs
Panagiotopoulos, P., & Maniadakis, N. (2026) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2026.161014
Artificial intelligence models for lung cancer risk prediction using electronic health records
Islam, M. M., & Okere, A. N. (2026) · Health Sciences Review · doi:10.1016/j.hsr.2026.100269
Self-citationJi, D., Huang, M., & Wang, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1913896
Metadata
- Authors
- Islam, M. M., Li, G.-H., Poly, T. N., & Li, Y.-C. (.
- Affiliations
- Taipei Medical University (TW)
- AESOP Technology, Songshan District, Taipei 105, Taiwan
- Wan Fang Hospital (TW)
- Corresponding author
- Li, Yu-Chuan (Jack)
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2021-11-25
- Volume
- 9
- Issue
- 12
- Pages
- 1632
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC8701302
- OpenAlex
- W3217307465
- Cited by
- 19
- References
- 46
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 6 · 2023: 5 · 2022: 2
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- diagnosis-related groups, medical coding, inpatient reimbursement, gated recurrent unit, artificial neural networks, deep learning, hospital performance, medical resource utilization
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