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Shan D. Expanding the dialogue: A closer look at AIH management and HCC risk. J Hepatol 2024; 81:e129-e130. [PMID: 38484914 DOI: 10.1016/j.jhep.2024.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/07/2024] [Indexed: 06/10/2024]
Affiliation(s)
- Dan Shan
- Department of Biobehavioural Sciences, Columbia University, New York, USA; Faculty of Health and Medicine, Lancaster University, Lancaster, UK.
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2
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Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024; 16:e56583. [PMID: 38646386 PMCID: PMC11031195 DOI: 10.7759/cureus.56583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.
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Affiliation(s)
- Hisham Daher
- Internal Medicine, University of Debrecen, Debrecen, HUN
| | - Sneha A Punchayil
- Internal Medicine, University Hospital of North Tees, Stockton-on-Tees, GBR
| | | | | | - Joel Jacob
- General Medicine, Diana Princess of Wales Hospital, Grimsby, GBR
| | | | - Mohammad Mansour
- General Medicine, University of Debrecen, Debrecen, HUN
- General Medicine, Jordan University Hospital, Amman, JOR
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3
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Li J, Ge M, Deng P, Wu X, Shi L, Yang Y. Withaferin A suppressed hepatocellular carcinoma progression through inducing IGF2BP3/FOXO1/JAK2/STAT3 pathway-mediated ROS production. Immunopharmacol Immunotoxicol 2024; 46:40-48. [PMID: 37671837 DOI: 10.1080/08923973.2023.2247552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/08/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVE This study aimed to investigate the underlying molecular mechanisms of Withaferin A (WA) in hepatocellular carcinoma (HCC). MATERIALS AND METHODS The gene and protein expression were analyzed using RT-qPCR and western blot, respectively. The proliferation of HCC cells was evaluated by CCK-8 assays. The migrative ability of HCC cells was measured by transwell assays. RESULTS We revealed that WA suppressed the proliferation and migration of HCC cells and inhibited IGF2BP3 (insulin like growth factor 2 mRNA binding protein 3) expression. IGF2BP3 abundance reversed the reactive oxygen species (ROS) accumulation and suppression of HCC cell proliferation and migration induced by WA. Besides, IGF2BP3 suppressed ROS production to promote the growth and migration of HCC cells. Furthermore, we found that IGF2BP3 exerted its tumor-promotive and ROS-suppressive effect on HCC cells by regulating the expression of FOXO1 (forkhead box O1). In addition, IGF2BP3-stimulated activation of JAK2 (Janus kinase 2)/STAT3 (signal transducer and activator of transcription 3) phosphorylation effectively decreased the transcription of FOXO1. FOXO1 abundance decreased the phosphorylation of JAK2 and STAT3 by increasing ROS level, forming a feedback loop for the inhibition of JAK2/STAT3 signaling activated by IGF2BP3. CONCLUSIONS WA-induced ROS inhibited HCC cell growth and migration through the inhibition of IGF2BP3 to deactivate JAK2/STAT3 signaling, resulting in increased FOXO1 expression to further stimulate ROS production and inhibit JAK2/STAT3 signaling.
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Affiliation(s)
- Jinhai Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mengchen Ge
- Department of Hepatopancreatobiliary Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Pengcheng Deng
- Department of Hepatopancreatobiliary Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Xinquan Wu
- Department of Hepatopancreatobiliary Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Longqing Shi
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yu Yang
- Department of Hepatopancreatobiliary Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China
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Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:313-331. [PMID: 37637723 PMCID: PMC10449760 DOI: 10.1007/s41666-023-00143-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 04/12/2023] [Accepted: 07/28/2023] [Indexed: 08/29/2023]
Abstract
Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem formulation, which includes defining the time windows of interest and the prediction task. Our objective was to conduct a systematic review that assessed the definition and reporting of concepts relevant to temporal clinical prediction tasks. We searched PubMed® and IEEE Xplore® databases for studies from January 1, 2010 applying machine learning models to EHR data for patient outcome prediction. Publications applying time-series methods were selected for further review. We identified 92 studies and summarized them by clinical context and definition and reporting of the prediction problem. For the time windows of interest, 12 studies did not discuss window lengths, 57 used a single set of window lengths, and 23 evaluated the relationship between window length and model performance. We also found that 72 studies had appropriate reporting of the prediction task. However, evaluation of prediction problem formulation for temporal EHR data was complicated by heterogeneity in assessing and reporting of these concepts. Even among studies modeling similar clinical outcomes, there were variations in terminology used to describe the prediction problem, rationale for window lengths, and determination of the outcome of interest. As temporal modeling using EHR data expands, minimal reporting standards should include time-series specific concerns to promote rigor and reproducibility in future studies and facilitate model implementation in clinical settings. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00143-4.
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Affiliation(s)
- Sarah Pungitore
- Program in Applied Mathematics, Department of Mathematics, 617 N Santa Rita Ave, Tucson, AZ 85721 USA
| | - Vignesh Subbian
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721-0020 USA
- Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721-0020 USA
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Wu CC, Su CH, Islam MM, Liao MH. Artificial Intelligence in Dementia: A Bibliometric Study. Diagnostics (Basel) 2023; 13:2109. [PMID: 37371004 PMCID: PMC10297057 DOI: 10.3390/diagnostics13122109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/10/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
The applications of artificial intelligence (AI) in dementia research have garnered significant attention, prompting the planning of various research endeavors in current and future studies. The objective of this study is to provide a comprehensive overview of the research landscape regarding AI and dementia within scholarly publications and to suggest further studies for this emerging research field. A search was conducted in the Web of Science database to collect all relevant and highly cited articles on AI-related dementia research published in English until 16 May 2023. Utilizing bibliometric indicators, a search strategy was developed to assess the eligibility of titles, utilizing abstracts and full texts as necessary. The Bibliometrix tool, a statistical package in R, was used to produce and visualize networks depicting the co-occurrence of authors, research institutions, countries, citations, and keywords. We obtained a total of 1094 relevant articles published between 1997 and 2023. The number of annual publications demonstrated an increasing trend over the past 27 years. Journal of Alzheimer's Disease (39/1094, 3.56%), Frontiers in Aging Neuroscience (38/1094, 3.47%), and Scientific Reports (26/1094, 2.37%) were the most common journals for this domain. The United States (283/1094, 25.86%), China (222/1094, 20.29%), India (150/1094, 13.71%), and England (96/1094, 8.77%) were the most productive countries of origin. In terms of institutions, Boston University, Columbia University, and the University of Granada demonstrated the highest productivity. As for author contributions, Gorriz JM, Ramirez J, and Salas-Gonzalez D were the most active researchers. While the initial period saw a relatively low number of articles focusing on AI applications for dementia, there has been a noticeable upsurge in research within this domain in recent years (2018-2023). The present analysis sheds light on the key contributors in terms of researchers, institutions, countries, and trending topics that have propelled the advancement of AI in dementia research. These findings collectively underscore that the integration of AI with conventional treatment approaches enhances the effectiveness of dementia diagnosis, prediction, classification, and monitoring of treatment progress.
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Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health Technology, Ming Chuan University, Taipei 333, Taiwan;
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan;
| | - Chun-Hsien Su
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan;
- Graduate Institute of Sports Coaching Science, College of Kinesiology and Health, Chinese Culture University, Taipei 11114, Taiwan
| | | | - Mao-Hung Liao
- Superintendent Office, Yonghe Cardinal Tien Hospital, New Taipei City 23148, Taiwan
- Department of Healthcare Administration, Asia Eastern University of Science and Technology, Banciao District, New Taipei City 220303, Taiwan
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Mansur A, Vrionis A, Charles JP, Hancel K, Panagides JC, Moloudi F, Iqbal S, Daye D. The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers (Basel) 2023; 15:2928. [PMID: 37296890 PMCID: PMC10251861 DOI: 10.3390/cancers15112928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA 02115, USA; (A.M.); (J.C.P.)
| | - Andrea Vrionis
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA; (A.V.); (J.P.C.)
| | - Jonathan P. Charles
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA; (A.V.); (J.P.C.)
| | - Kayesha Hancel
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | | | - Farzad Moloudi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | - Shams Iqbal
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
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Wu Z(E, Xu D, Hu PJH, Huang TS. A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients. J Am Med Inform Assoc 2023; 30:846-858. [PMID: 36794643 PMCID: PMC10114116 DOI: 10.1093/jamia/ocad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 12/26/2022] [Accepted: 01/25/2023] [Indexed: 02/17/2023] Open
Abstract
OBJECTIVE Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value. MATERIALS AND METHODS The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC). RESULTS We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods. DISCUSSION AND CONCLUSION The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.
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Affiliation(s)
- Zejian (Eric) Wu
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah, USA
| | - Da Xu
- Department of Information Systems, College of Business, California State University Long Beach, Long Beach, California, USA
| | - Paul Jen-Hwa Hu
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah, USA
| | - Ting-Shuo Huang
- Department of General Surgery, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan
- Department of Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
- Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan
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Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival. Cancers (Basel) 2022; 14:cancers14225562. [PMID: 36428655 PMCID: PMC9688689 DOI: 10.3390/cancers14225562] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018. All patients were monitored from the index date of cancer diagnosis until the event of death. Variables, including demographics, comorbidities, medications, laboratories, and patient gene tests, were used. Nine machine-learning algorithms with various modes were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 3714 patients were included. The best performance of the artificial neural network (ANN) model was achieved when integrating all variables with the AUC, accuracy, precision, recall, and F1-score of 0.89, 0.82, 0.91, 0.75, and 0.65, respectively. The most important features were cancer stage, cancer size, age of diagnosis, smoking, drinking status, EGFR gene, and body mass index. Overall, the ANN model improved predictive performance when integrating different data types.
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Zhang K, Hu B, Zhou F, Song Y, Zhao X, Huang X. Graph-based structural knowledge-aware network for diagnosis assistant. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:10533-10549. [PMID: 36032005 DOI: 10.3934/mbe.2022492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Diagnosis assistant is an effective way to reduce the workloads of professional doctors. The rich professional knowledge plays a crucial role in diagnosis. Therefore, it is important to introduce the relevant medical knowledge into diagnosis assistant. In this paper, diagnosis assistant is treated as a classification task, and a Graph-based Structural Knowledge-aware Network (GSKN) model is proposed to fuse Electronic Medical Records (EMRs) and medical knowledge graph. Considering that different information in EMRs affects the diagnosis results differently, the information in EMRs is categorized into general information, key information and numerical information, and is introduced to GSKN by adding an enhancement layer to the Bidirectional Encoder Representation from Transformers (BERT) model. The entities in EMRs are recognized, and Graph Convolutional Neural Networks (GCN) is employed to learn deep-level graph structure information and dynamic representation of these entities in the subgraphs. An interactive attention mechanism is utilized to fuse the enhanced textual representation and the deep representation of these subgraphs. Experimental results on Chinese Obstetric Electronic Medical Records (COEMRs) and open dataset C-EMRs demonstrate the effectiveness of our model.
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Affiliation(s)
- Kunli Zhang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
- Pengcheng Laboratory, Shenzhen, China
| | - Bin Hu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Feijie Zhou
- The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yu Song
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Xu Zhao
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Xiyang Huang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
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DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups. Healthcare (Basel) 2021; 9:healthcare9121632. [PMID: 34946357 PMCID: PMC8701302 DOI: 10.3390/healthcare9121632] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022] Open
Abstract
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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