{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:12:06Z","timestamp":1784391126487,"version":"3.55.0"},"reference-count":157,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Cardiovascular diseases (CVDs) remain the leading causes of morbidity, mortality, and healthcare expenditures, presenting substantial challenges for hospitals operating under Diagnosis-Related Group (DRG) payment models. Recent advances in deep learning offer new strategies for optimizing CVD management to meet cost control objectives. This review synthesizes the roles of deep learning in CVD diagnosis, treatment planning, and prognostic modeling, emphasizing applications that reduce unnecessary diagnostic imaging, predict high-cost complications, and optimize the utilization of critical resources like ICU beds. By analyzing medical images, forecasting adverse events from patient data, and dynamically optimizing treatment plans, deep learning offers a data-driven strategy to manage high-cost procedures and prolonged hospital stays within DRG budgets. Deep learning offers the potential for earlier risk stratification and tailored interventions, helping mitigate the financial pressures associated with DRG reimbursements. Effective integration requires multidisciplinary collaboration, robust data governance, and transparent model design. Real-world evidence, drawn from retrospective studies and large clinical registries, highlights measurable improvements in cost control and patient outcomes; for instance, AI-optimized treatment strategies have been shown to reduce estimated mortality by 3.13%. However, challenges\u2014such as data quality, regulatory compliance, ethical issues, and limited scalability\u2014must be addressed to fully realize these benefits. Future research should focus on continuous model adaptation, multimodal data integration, equitable deployment, and standardized outcome monitoring to validate both clinical quality and financial return on investment under DRG metrics. By leveraging deep learning\u2019s predictive power within DRG frameworks, healthcare systems can advance toward a more sustainable model of high-quality, cost-effective CVD care.<\/jats:p>","DOI":"10.3389\/frai.2025.1580445","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T05:33:42Z","timestamp":1756704822000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models"],"prefix":"10.3389","volume":"8","author":[{"given":"Haohao","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"De","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"587","DOI":"10.3390\/healthcare12242587","article-title":"Federated learning in smart healthcare: a comprehensive review on privacy, security, and predictive analytics with IoT integration","volume":"12","author":"Abbas","year":"2024","journal-title":"Healthcare"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1038\/s41591-022-01894-0","article-title":"Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis","volume":"28","author":"Adams","year":"2022","journal-title":"Nat. Med."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e008437","DOI":"10.1161\/CIRCEP.120.008437","article-title":"Artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea","volume":"13","author":"Adedinsewo","year":"2020","journal-title":"Circ. Arrhythm. Electrophysiol."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10799-023-00407-w","article-title":"Healthcare professionals satisfaction and AI-based clinical decision support system in public sector hospitals during health crises: a cross-sectional study","volume":"26","author":"Ahmad","year":"2023","journal-title":"Inf. Technol. Manag."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1093\/database\/baaa010","article-title":"Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine","volume":"2020","author":"Ahmed","year":"2020","journal-title":"Database"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"276","DOI":"10.3390\/medicina59071276","article-title":"Machine learning for early prediction of sepsis in intensive care unit (ICU) patients","volume":"59","author":"Alanazi","year":"2023","journal-title":"Medicina"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"215","DOI":"10.3390\/diagnostics12123215","article-title":"Ensemble learning based on hybrid deep learning model for heart disease early prediction","volume":"12","author":"Almulihi","year":"2022","journal-title":"Diagnostics"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"e35307","DOI":"10.2196\/35307","article-title":"Predicting 30-day readmission risk for patients with chronic obstructive pulmonary disease through a federated machine learning architecture on findable, accessible, interoperable, and reusable (FAIR) data: development and validation study","volume":"10","author":"Alvarez-Romero","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1002\/hec.4479","article-title":"The long-run effects of diagnosis related group payment on hospital lengths of stay in a publicly funded health care system: evidence from 15 years of micro data","volume":"31","author":"Aragon","year":"2022","journal-title":"Health Econ."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","article-title":"Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram","volume":"25","author":"Attia","year":"2019","journal-title":"Nat. Med."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"207","DOI":"10.3390\/info11040207","article-title":"Ensemble deep learning models for heart disease classification: a case study from Mexico","volume":"11","author":"Baccouche","year":"2020","journal-title":"Information"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"32639","DOI":"10.1007\/s11042-021-11176-5","article-title":"A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)","volume":"80","author":"Baral","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.compbiomed.2019.04.027","article-title":"Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs","volume":"109","author":"Barton","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1377\/hlthaff.2014.0041","article-title":"Big data in health care: using analytics to identify and manage high-risk and high-cost patients","volume":"33","author":"Bates","year":"2014","journal-title":"Health Aff."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e56","DOI":"10.1161\/CIR.0000000000000659","article-title":"Heart disease and stroke statistics-2019 update: a report from the American Heart Association","volume":"139","author":"Benjamin","year":"2019","journal-title":"Circulation"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1007\/s10729-020-09522-4","article-title":"Personalized treatment for coronary artery disease patients: a machine learning approach","volume":"23","author":"Bertsimas","year":"2020","journal-title":"Health Care Manag. Sci."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.jacr.2023.01.002","article-title":"Addressing the challenges of implementing artificial intelligence tools in clinical practice: principles from experience","volume":"20","author":"Bizzo","year":"2023","journal-title":"J. Am. Coll. Radiol."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/s10741-022-10283-1","article-title":"Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review","volume":"28","author":"Bjerken","year":"2023","journal-title":"Heart Fail. Rev."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"e20250207","DOI":"10.62675\/2965-2774.20250207","article-title":"Frequency, financial impact, and factors associated with cost outliers in intensive care units: a cohort study in Belgium","volume":"37","author":"Bruyneel","year":"2025","journal-title":"Crit. Care Sci."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/bmj.f3197","article-title":"Diagnosis related groups in Europe: moving towards transparency, efficiency, and quality in hospitals?","volume":"346","author":"Busse","year":"2013","journal-title":"BMJ"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s12916-024-03273-7","article-title":"Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review","volume":"22","author":"Cai","year":"2024","journal-title":"BMC Med."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2025.110843","article-title":"MARIA: a multimodal transformer model for incomplete healthcare data","volume":"2412","author":"Caruso","year":"2024","journal-title":"Arxiv"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1007\/s11547-020-01290-z","article-title":"Cardiac-CT and cardiac-MR cost-effectiveness: a literature review","volume":"125","author":"Centonze","year":"2020","journal-title":"Radiol. Med."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"7339","DOI":"10.1109\/JIOT.2023.3325822","article-title":"Federated learning for healthcare applications","volume":"11","author":"Chaddad","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1056\/NEJMp1702071","article-title":"Machine learning and prediction in medicine-beyond the peak of inflated expectations","volume":"376","author":"Chen","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1038\/s41551-023-01115-0","article-title":"A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring","volume":"9","author":"Chen","year":"2023","journal-title":"Nat. Biomed. Eng."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"102444","DOI":"10.1016\/j.media.2022.102444","article-title":"Recent advances and clinical applications of deep learning in medical image analysis","volume":"79","author":"Chen","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"108121","DOI":"10.1016\/j.compbiomed.2024.108121","article-title":"Multi-modal learning for inpatient length of stay prediction","volume":"171","author":"Chen","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"e27798","DOI":"10.2196\/27798","article-title":"Predicting the mortality and readmission of in-hospital cardiac arrest patients with electronic health records: a machine learning approach","volume":"23","author":"Chi","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.healthpol.2019.10.005","article-title":"Reducing low value services in surgical inpatients in Taiwan: does diagnosis-related group payment work?","volume":"124","author":"Chien","year":"2020","journal-title":"Health Policy"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1186\/s13054-023-04609-0","article-title":"Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards","volume":"27","author":"Cho","year":"2023","journal-title":"Crit. Care"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"30116","DOI":"10.1038\/s41598-024-80268-7","article-title":"A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system","volume":"14","author":"Choi","year":"2024","journal-title":"Sci. Rep."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"359","DOI":"10.3390\/ijerph192316359","article-title":"Artificial intelligence implementation in healthcare: a theory-based scoping review of barriers and facilitators","volume":"19","author":"Chomutare","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"103518","DOI":"10.1016\/j.jbi.2020.103518","article-title":"Endpoint prediction of heart failure using electronic health records","volume":"109","author":"Chu","year":"2020","journal-title":"J. Biomed. Inform."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/10255842.2020.1821192","article-title":"Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks","volume":"24","author":"Cinar","year":"2021","journal-title":"Comput. Methods Biomech. Biomed. Engin."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.surg.2024.03.051","article-title":"Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations","volume":"176","author":"Cruz","year":"2024","journal-title":"Surgery"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"e58278","DOI":"10.2196\/58278","article-title":"Evaluating a natural language processing-driven, AI-assisted international classification of diseases, 10th revision, clinical modification, coding system for diagnosis related groups in a real hospital environment: algorithm development and validation study","volume":"26","author":"Dai","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"e16","DOI":"10.1161\/CIR.0000000000000985","article-title":"Mechanical complications of acute myocardial infarction: a scientific statement from the American Heart Association","volume":"144","author":"Damluji","year":"2021","journal-title":"Circulation"},{"key":"ref39","first-page":"6878","article-title":"Leveraging AI and machine learning for enhanced preventive care and chronic disease management in health insurance plans","volume":"13","author":"Danda","year":"2024","journal-title":"Front. Health Inform."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1136\/bmjhci-2024-101292","article-title":"Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome","volume":"32","author":"Demandt","year":"2025","journal-title":"BMJ Health Care Inform."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s10916-024-02043-5","article-title":"Transformer models in healthcare: a survey and thematic analysis of potentials, shortcomings and risks","volume":"48","author":"Denecke","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref42","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.jacc.2018.11.053","article-title":"PCI and CABG for treating stable coronary artery disease: JACC review topic of the week","volume":"73","author":"Doenst","year":"2019","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref43","doi-asserted-by":"crossref","DOI":"10.1109\/EMBC53108.2024.10781564","article-title":"Reinforcement learning for heart failure treatment optimization in the intensive care unit","author":"Drudi","year":"2024"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1269704","DOI":"10.3389\/fpubh.2024.1269704","article-title":"Based on knowledge capital value for disease cost accounting of diagnosis related groups","volume":"12","author":"Duan","year":"2024","journal-title":"Front. Public Health"},{"key":"ref45","first-page":"1","article-title":"Enhancing operational efficiency in healthcare with AI-powered management","author":"Dubey","year":"2023"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1016\/j.jacc.2012.08.1011","article-title":"Ventricular arrhythmia after cardiac surgery: incidence, predictors, and outcomes","volume":"60","author":"El-Chami","year":"2012","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"185676","DOI":"10.1109\/ACCESS.2020.3030031","article-title":"A new real time clinical decision support system using machine learning for critical care units","volume":"8","author":"El-Ganainy","year":"2020","journal-title":"IEEE Access"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1186\/s12910-024-01151-8","article-title":"Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals' perspectives","volume":"25","author":"Elgin","year":"2024","journal-title":"BMC Med. Ethics"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1016\/j.jacc.2023.06.045","article-title":"Clinical pathway for coronary atherosclerosis in patients without conventional modifiable risk factors: JACC state-of-the-art review","volume":"82","author":"Figtree","year":"2023","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10916-024-02104-9","article-title":"Identifying facilitators and barriers to implementation of AI-assisted clinical decision support in an electronic health record system","volume":"48","author":"Finkelstein","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref52","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/j.bja.2019.07.025","article-title":"Deep-learning model for predicting 30-day postoperative mortality","volume":"123","author":"Fritz","year":"2019","journal-title":"Br. J. Anaesth."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s13561-024-00550-2","article-title":"Patients' health care resources utilization and costs estimation across cardiovascular risk categories: insights from the LATINO study","volume":"14","author":"Gavina","year":"2024","journal-title":"Health Econ. Rev."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1007\/s10741-019-09811-3","article-title":"Cost-effectiveness of coronary artery bypass graft and percutaneous coronary intervention compared to medical therapy in patients with coronary artery disease: a systematic review","volume":"24","author":"Gholami","year":"2019","journal-title":"Heart Fail. Rev."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41746-019-0216-8","article-title":"Deep learning interpretation of echocardiograms","volume":"3","author":"Ghorbani","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref56","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1097\/QMH.0b013e3181ccbcc3","article-title":"The evolution of diagnosis-related groups (DRGs): from its beginnings in case-mix and resource use theory, to its implementation for payment and now for its current utilization for quality within and outside the hospital","volume":"19","author":"Goldfield","year":"2010","journal-title":"Qual. Manag. Health Care"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"441","DOI":"10.2105\/AJPH.79.4.441","article-title":"Improving the homogeneity of diagnosis-related groups (DRGs) by using clinical laboratory, demographic, and discharge data","volume":"79","author":"Goldman","year":"1989","journal-title":"Am. J. Public Health"},{"key":"ref58","doi-asserted-by":"publisher","first-page":"100139","DOI":"10.1016\/j.acepjo.2025.100139","article-title":"National Cost Savings from use of artificial intelligence guided echocardiography in the assessment of intermediate-risk patients with Syncope in the emergency department","volume":"6","author":"Goldsmith","year":"2025","journal-title":"J. Am. Coll. Emerg. Physic. Open"},{"key":"ref59","doi-asserted-by":"publisher","first-page":"104652","DOI":"10.1016\/j.jbi.2024.104652","article-title":"Cost prediction for ischemic heart disease hospitalization: interpretable feature extraction using network analysis","volume":"154","author":"Gong","year":"2024","journal-title":"J. Biomed. Inform."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1093\/ejcts\/ezx039","article-title":"Postoperative atrial fibrillation following cardiac surgery: a persistent complication","volume":"52","author":"Greenberg","year":"2017","journal-title":"Eur. J. Cardiothorac. Surg."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1111\/aas.13507","article-title":"New-onset postoperative atrial fibrillation after heart surgery","volume":"64","author":"Gudbjartsson","year":"2020","journal-title":"Acta Anaesthesiol. Scand."},{"key":"ref62","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s12911-022-01774-0","article-title":"Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study","volume":"22","author":"Guo","year":"2022","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2302.04355","article-title":"Meddiff: generating electronic health records using accelerated denoising diffusion model","volume":"2302","author":"He","year":"2023","journal-title":"Arxiv"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1038\/s41591-022-01895-z","article-title":"Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing","volume":"28","author":"Henry","year":"2022","journal-title":"Nat. Med."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.amjsurg.2017.09.035","article-title":"DRG migration: a novel measure of inefficient surgical care in a value-based world","volume":"215","author":"Hughes","year":"2018","journal-title":"Am. J. Surg."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.3390\/healthcare9121632","article-title":"Deep DRG: performance of artificial intelligence model for real-time prediction of diagnosis-related groups","volume":"9","author":"Islam","year":"2021","journal-title":"Healthcare (Basel)"},{"key":"ref67","doi-asserted-by":"publisher","first-page":"106998","DOI":"10.1016\/j.compbiomed.2023.106998","article-title":"Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: a review","volume":"160","author":"Jafari","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref68","doi-asserted-by":"publisher","first-page":"100379","DOI":"10.1016\/j.ajpc.2022.100379","article-title":"Medicine 2032: the future of cardiovascular disease prevention with machine learning and digital health technology","volume":"12","author":"Javaid","year":"2022","journal-title":"Am. J. Prev. Cardiol."},{"key":"ref69","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1038\/s41746-024-01316-0","article-title":"A primer on reinforcement learning in medicine for clinicians","volume":"7","author":"Jayaraman","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref70","doi-asserted-by":"publisher","first-page":"698","DOI":"10.3390\/su141811698","article-title":"Modeling conceptual framework for implementing barriers of AI in public healthcare for improving operational excellence: experiences from developing countries","volume":"14","author":"Joshi","year":"2022","journal-title":"Sustainability"},{"key":"ref71","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1109\/RBME.2022.3142058","article-title":"Hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions","volume":"16","author":"Kadem","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref72","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/s41746-022-00649-y","article-title":"Machine learning for real-time aggregated prediction of hospital admission for emergency patients","volume":"5","author":"King","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref73","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1038\/s41591-018-0213-5","article-title":"The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care","volume":"24","author":"Komorowski","year":"2018","journal-title":"Nat. Med."},{"key":"ref74","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1093\/eurheartj\/ehz056","article-title":"Deep learning for cardiovascular medicine: a practical primer","volume":"40","author":"Krittanawong","year":"2019","journal-title":"Eur. Heart J."},{"key":"ref75","doi-asserted-by":"publisher","first-page":"e188332","DOI":"10.1001\/jamanetworkopen.2018.8332","article-title":"Association of the Swiss Diagnosis-Related Group Reimbursement System with Length of stay, mortality, and readmission rates in hospitalized adult patients","volume":"2","author":"Kutz","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref76","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.amjcard.2022.01.012","article-title":"Healthcare expenditure associated with polypharmacy in older adults with cardiovascular diseases","volume":"169","author":"Kwak","year":"2022","journal-title":"Am. J. Cardiol."},{"key":"ref77","doi-asserted-by":"publisher","first-page":"15109","DOI":"10.1038\/s41598-023-40343-x","article-title":"Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection","volume":"13","author":"Laghari","year":"2023","journal-title":"Sci. Rep."},{"key":"ref78","doi-asserted-by":"publisher","first-page":"217","DOI":"10.3803\/EnM.2020.35.2.217","article-title":"Effects of cardiovascular risk factor variability on health outcomes","volume":"35","author":"Lee","year":"2020","journal-title":"Endocrinol. Metab."},{"key":"ref79","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1038\/s41746-022-00625-6","article-title":"Multi-center validation of machine learning model for preoperative prediction of postoperative mortality","volume":"5","author":"Lee","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref80","doi-asserted-by":"publisher","first-page":"e035425","DOI":"10.1161\/JAHA.124.035425","article-title":"Using machine learning to predict outcomes following transfemoral carotid artery stenting","volume":"13","author":"Li","year":"2024","journal-title":"J. Am. Heart Assoc."},{"key":"ref81","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1186\/s12913-023-09686-z","article-title":"Impact of diagnosis-related-group (DRG) payment on variation in hospitalization expenditure: evidence from China","volume":"23","author":"Li","year":"2023","journal-title":"BMC Health Serv. Res."},{"key":"ref82","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/s41746-021-00474-9","article-title":"Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes","volume":"4","author":"Liu","year":"2021","journal-title":"NPJ Digit Med"},{"key":"ref83","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/s41598-022-27211-w","article-title":"A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection","volume":"13","author":"Liu","year":"2023","journal-title":"Sci. Rep."},{"key":"ref84","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.3390\/jpm11111149","article-title":"An artificial intelligence-based alarm strategy facilitates management of acute myocardial infarction","volume":"11","author":"Liu","year":"2021","journal-title":"J. Pers. Med."},{"key":"ref85","doi-asserted-by":"publisher","first-page":"e18477","DOI":"10.2196\/18477","article-title":"Reinforcement learning for clinical decision support in critical care: comprehensive review","volume":"22","author":"Liu","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref86","doi-asserted-by":"publisher","first-page":"467","DOI":"10.3109\/23256176.2013.877696","article-title":"Influence of fill in of homepage of medical record on diagnosis-related groups data quality","volume":"1","author":"Liu","year":"2014","journal-title":"Chinese Med. Rec."},{"key":"ref87","doi-asserted-by":"publisher","first-page":"e0245177","DOI":"10.1371\/journal.pone.0245177","article-title":"Recurrent disease progression networks for modelling risk trajectory of heart failure","volume":"16","author":"Lu","year":"2021","journal-title":"PLoS One"},{"key":"ref88","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable machine-learning predictions for the prevention of hypoxaemia during surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"ref89","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z. Med. Phys."},{"key":"ref90","first-page":"1077","article-title":"Deep stable representation learning on electronic health records","author":"Luo","year":"2022"},{"key":"ref91","doi-asserted-by":"publisher","first-page":"337","DOI":"10.3390\/bioengineering11040337","article-title":"The role of AI in hospitals and clinics: transforming healthcare in the 21st century","volume":"11","author":"Maleki Varnosfaderani","year":"2024","journal-title":"Bioengineering"},{"key":"ref92","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1136\/bmj.n1747","article-title":"Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study","volume":"374","author":"Marafino","year":"2021","journal-title":"BMJ"},{"key":"ref93","doi-asserted-by":"publisher","first-page":"e0140874","DOI":"10.1371\/journal.pone.0140874","article-title":"Predictors of high profit and high deficit outliers under Swiss DRG of a tertiary care center","volume":"10","author":"Mehra","year":"2015","journal-title":"PLoS One"},{"key":"ref94","first-page":"1002","article-title":"Ensemble deep learning models for accurate prediction of cardiovascular disease risk: a comparative analysis","author":"Midhun","year":"2023"},{"key":"ref95","doi-asserted-by":"publisher","first-page":"2333392816647892","DOI":"10.1177\/2333392816647892","article-title":"Review of diagnosis-related group-based financing of hospital care","volume":"3","author":"Mihailovic","year":"2016","journal-title":"Health Serv. Res. Manag. Epidemiol."},{"key":"ref96","doi-asserted-by":"publisher","first-page":"104990","DOI":"10.1016\/j.healthpol.2023.104990","article-title":"The end of an era? Activity-based funding based on diagnosis-related groups: a review of payment reforms in the inpatient sector in 10 high-income countries","volume":"141","author":"Milstein","year":"2024","journal-title":"Health Policy"},{"key":"ref97","first-page":"231","article-title":"Associations between surgical wound infectious and clinical profile in patients undergoing cardiac surgery","volume":"11","author":"Miranda","year":"2021","journal-title":"Am. J. Cardiovasc. Dis."},{"key":"ref98","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/s41746-023-00889-6","article-title":"Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches","volume":"6","author":"Mittermaier","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref99","doi-asserted-by":"publisher","first-page":"193","DOI":"10.3390\/jimaging10080193","article-title":"Revolutionizing cardiac imaging: a scoping review of artificial intelligence in echocardiography, CTA, and cardiac MRI","volume":"10","author":"Moradi","year":"2024","journal-title":"J Imaging"},{"key":"ref100","doi-asserted-by":"publisher","first-page":"3904","DOI":"10.1093\/eurheartj\/ehab544","article-title":"Artificial intelligence in the diagnosis and management of arrhythmias","volume":"42","author":"Nagarajan","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref101","first-page":"58","article-title":"Enhancing heart disease prediction through a heterogeneous ensemble DL models","author":"Naidu","year":"2024"},{"key":"ref102","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1007\/s10462-024-10876-2","article-title":"A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction","volume":"57","author":"Nasarudin","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref103","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1016\/j.jacc.2023.09.818","article-title":"Can machine learning aid the selection of percutaneous vs surgical revascularization?","volume":"82","author":"Ninomiya","year":"2023","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref104","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of population","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Sci. Transl. Med."},{"key":"ref105","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1136\/bmjhci-2024-101120","article-title":"Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics","volume":"31","author":"Pavuluri","year":"2024","journal-title":"BMJ Health Care Inform."},{"key":"ref106","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1109\/JBHI.2021.3098662","article-title":"Sequence to sequence ECG cardiac rhythm classification using convolutional recurrent neural networks","volume":"26","author":"Pokaprakarn","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref107","doi-asserted-by":"publisher","first-page":"116450","DOI":"10.1016\/j.neuroimage.2019.116450","article-title":"Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan","volume":"208","author":"Pomponio","year":"2020","journal-title":"NeuroImage"},{"key":"ref108","doi-asserted-by":"publisher","first-page":"e0000057","DOI":"10.1371\/journal.pdig.0000057","article-title":"Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19","volume":"1","author":"Pyrros","year":"2022","journal-title":"PLoS Digit. Health"},{"key":"ref109","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.clinbiochem.2023.01.002","article-title":"Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: a pre-implementation study","volume":"113","author":"Rabbani","year":"2023","journal-title":"Clin. Biochem."},{"key":"ref110","doi-asserted-by":"publisher","first-page":"5366","DOI":"10.1007\/s10489-021-02696-6","article-title":"Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data","volume":"52","author":"Rai","year":"2021","journal-title":"Appl. Intell."},{"key":"ref111","first-page":"1","article-title":"Hybrid CNN-LSTM model for automatic prediction of cardiac arrhythmias from ECG big data","author":"Rai","year":"2020"},{"key":"ref112","first-page":"1","article-title":"Human-algorithmic interaction using a large language model-augmented artificial intelligence clinical decision support system","author":"Rajashekar","year":"2024"},{"key":"ref113","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref114","doi-asserted-by":"publisher","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref115","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1707.01836","article-title":"Cardiologist-level arrhythmia detection with convolutional neural networks","volume":"1707","author":"Rajpurkar","year":"2017","journal-title":"Arxiv"},{"key":"ref116","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","article-title":"Deep learning for health informatics","volume":"21","author":"Ravi","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref117","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","article-title":"The future of digital health with federated learning","volume":"3","author":"Rieke","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref118","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref119","first-page":"1","article-title":"P-transformer: a prompt-based multimodal transformer architecture for medical tabular data","volume":"2303","author":"Ruan","year":"2023","journal-title":"Arxiv"},{"key":"ref120","doi-asserted-by":"publisher","first-page":"100599","DOI":"10.1016\/j.jacadv.2023.100599","article-title":"Risk factors for heart failure readmission after cardiac surgery","volume":"2","author":"Sabe","year":"2023","journal-title":"JACC Adv."},{"key":"ref121","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1186\/s40001-024-02044-7","article-title":"Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models","volume":"29","author":"Sadr","year":"2024","journal-title":"Eur. J. Med. Res."},{"key":"ref122","first-page":"1","article-title":"Prediction of hospital readmission using federated learning","author":"Sazdov","year":"2023"},{"key":"ref123","doi-asserted-by":"publisher","first-page":"107514","DOI":"10.1016\/j.pharmthera.2020.107514","article-title":"Epigenetic-sensitive pathways in personalized therapy of major cardiovascular diseases","volume":"210","author":"Schiano","year":"2020","journal-title":"Pharmacol. Ther."},{"key":"ref124","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.1001\/jama.2015.5387","article-title":"Association of a bundled intervention with surgical site infections among patients undergoing cardiac, hip, or knee surgery","volume":"313","author":"Schweizer","year":"2015","journal-title":"JAMA"},{"key":"ref125","first-page":"99","article-title":"The human body is a black box","author":"Sendak","year":"2020"},{"key":"ref126","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1038\/s41746-019-0148-3","article-title":"Artificial intelligence and machine learning in clinical development: a translational perspective","volume":"2","author":"Shah","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref127","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1136\/heartjnl-2017-311198","article-title":"Machine learning in cardiovascular medicine: are we there yet?","volume":"104","author":"Shameer","year":"2018","journal-title":"Heart"},{"key":"ref128","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.ijcard.2022.05.023","article-title":"Machine learning based model for risk prediction after ST-elevation myocardial infarction: insights from the North India ST elevation myocardial infarction (NORIN-STEMI) registry","volume":"362","author":"Shetty","year":"2022","journal-title":"Int. J. Cardiol."},{"key":"ref129","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1038\/s41598-023-27418-5","article-title":"Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks","volume":"13","author":"Shickel","year":"2023","journal-title":"Sci. Rep."},{"key":"ref130","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1007\/s11606-020-06394-w","article-title":"From code to bedside: implementing artificial intelligence using quality improvement methods","volume":"36","author":"Smith","year":"2021","journal-title":"J. Gen. Intern. Med."},{"key":"ref131","doi-asserted-by":"publisher","first-page":"106821","DOI":"10.1016\/j.cmpb.2022.106821","article-title":"Deep learning-based automatic segmentation of images in cardiac radiography: a promising challenge","volume":"220","author":"Song","year":"2022","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref132","doi-asserted-by":"publisher","first-page":"506","DOI":"10.21037\/acs.2018.05.17","article-title":"Coronary artery bypass grafting (CABG) vs. percutaneous coronary intervention (PCI) in the treatment of multivessel coronary disease: quo vadis?-a review of the evidences on coronary artery disease","volume":"7","author":"Spadaccio","year":"2018","journal-title":"Ann. Cardiothorac. Surg."},{"key":"ref133","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1017\/S1744133123000208","article-title":"Promoting the systematic use of real-world data and real-world evidence for digital health technologies across Europe: a consensus framework","volume":"18","author":"Srivastava","year":"2023","journal-title":"Health Econ. Policy Law"},{"key":"ref134","doi-asserted-by":"publisher","first-page":"101717","DOI":"10.1016\/j.jmir.2024.101717","article-title":"A multidisciplinary team and multiagency approach for AI implementation: a commentary for medical imaging and radiotherapy key stakeholders","volume":"55","author":"Stogiannos","year":"2024","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"ref135","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1007\/s42979-022-01598-9","article-title":"Hybrid CNN and LSTM network for heart disease prediction","volume":"4","author":"Sudha","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref136","doi-asserted-by":"publisher","first-page":"306","DOI":"10.3390\/s24196306","article-title":"An arrhythmia classification model based on a CNN-LSTM-SE algorithm","volume":"24","author":"Sun","year":"2024","journal-title":"Sensors"},{"key":"ref137","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-024-10453-2","article-title":"Revolutionizing cardiovascular health: integrating deep learning techniques for predictive analysis of personal key indicators in heart disease","volume":"37","author":"Talaat","year":"2024","journal-title":"Neural Comput. Applic."},{"key":"ref138","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1093\/jamia\/ocad045","article-title":"Graph convolutional network-based fusion model to predict risk of hospital acquired infections","volume":"30","author":"Tariq","year":"2023","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref139","first-page":"52","article-title":"Cost of single-vessel and multivessel coronary drug-eluting stenting: comparison to the DRG funding level","volume":"6","author":"Varani","year":"2005","journal-title":"Ital. Heart J."},{"key":"ref140","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.xjon.2022.04.017","article-title":"Parsimonious machine learning models to predict resource use in cardiac surgery across a statewide collaborative","volume":"11","author":"Verma","year":"2022","journal-title":"JTCVS Open"},{"key":"ref141","doi-asserted-by":"publisher","first-page":"e254","DOI":"10.1161\/CIR.0000000000000950","article-title":"Heart disease and stroke Statistics-2021 update: a report from the American Heart Association","volume":"143","author":"Virani","year":"2021","journal-title":"Circulation"},{"key":"ref142","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2402.00955","article-title":"Fair EHR-CLP: towards fairness-aware clinical predictions with contrastive learning in multimodal electronic health records","volume":"252","author":"Wang","year":"2024","journal-title":"Arxiv"},{"key":"ref143","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s12911-024-02493-4","article-title":"Early prediction of sudden cardiac death risk with nested LSTM based on electrocardiogram sequential features","volume":"24","author":"Wang","year":"2024","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref144","doi-asserted-by":"publisher","first-page":"1187299","DOI":"10.3389\/fcomp.2023.1187299","article-title":"Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review","volume":"5","author":"Wang","year":"2023","journal-title":"Front. Comput. Sci."},{"key":"ref145","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.jcmg.2022.10.022","article-title":"National Trends in coronary artery disease imaging: associations with health care outcomes and costs","volume":"16","author":"Weir-McCall","year":"2023","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref146","doi-asserted-by":"publisher","first-page":"594971","DOI":"10.3389\/fdgth.2021.594971","article-title":"Success factors of artificial intelligence implementation in healthcare","volume":"3","author":"Wolff","year":"2021","journal-title":"Front. Digit. Health"},{"key":"ref147","doi-asserted-by":"publisher","first-page":"665464","DOI":"10.3389\/fmed.2021.665464","article-title":"Artificial intelligence for clinical decision support in Sepsis","volume":"8","author":"Wu","year":"2021","journal-title":"Front. Med."},{"key":"ref148","doi-asserted-by":"publisher","first-page":"e0195024","DOI":"10.1371\/journal.pone.0195024","article-title":"Readmission prediction via deep contextual embedding of clinical concepts","volume":"13","author":"Xiao","year":"2018","journal-title":"PLoS One"},{"key":"ref149","first-page":"1","article-title":"Privacy-preserving heterogeneous federated learning for sensitive healthcare data","volume":"2406","author":"Xu","year":"2024","journal-title":"Arxiv"},{"key":"ref150","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1109\/JRFID.2024.3392682","article-title":"Individual medical costs prediction methods based on clinical notes and DRGs","volume":"8","author":"Yang","year":"2024","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref151","doi-asserted-by":"publisher","first-page":"e338","DOI":"10.2196\/jmir.7994","article-title":"Encouraging physical activity in patients with diabetes: intervention using a reinforcement learning system","volume":"19","author":"Yom-Tov","year":"2017","journal-title":"J. Med. Internet Res."},{"key":"ref152","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2302.10261","article-title":"Deep reinforcement learning for cost-effective medical diagnosis","volume":"2302","author":"Yu","year":"2023","journal-title":"Arxiv"},{"key":"ref153","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1093\/eurjcn\/zvae031","article-title":"Machine learning-based 30-day readmission prediction models for patients with heart failure: a systematic review","volume":"23","author":"Yu","year":"2024","journal-title":"Eur. J. Cardiovasc. Nurs."},{"key":"ref154","first-page":"238","article-title":"Impacts of hospital payment based on diagnosis related groups (DRGs) with global budget on resource use and quality of care: a case study in China","volume":"48","author":"Yuan","year":"2019","journal-title":"Iran. J. Public Health"},{"key":"ref155","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1186\/s12882-021-02459-y","article-title":"Effect of clinical decision support systems on clinical outcome for acute kidney injury: a systematic review and meta-analysis","volume":"22","author":"Zhao","year":"2021","journal-title":"BMC Nephrol."},{"key":"ref156","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/s41436-019-0667-y","article-title":"Systematic review of the evidence on the cost-effectiveness of pharmacogenomics-guided treatment for cardiovascular diseases","volume":"22","author":"Zhu","year":"2020","journal-title":"Genet. Med."},{"key":"ref157","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s12913-020-4957-5","article-title":"The effects of diagnosis-related groups payment on hospital healthcare in China: a systematic review","volume":"20","author":"Zou","year":"2020","journal-title":"BMC Health Serv. Res."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1580445\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T05:33:46Z","timestamp":1756704826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1580445\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":157,"alternative-id":["10.3389\/frai.2025.1580445"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1580445","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"article-number":"1580445"}}