{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T16:34:27Z","timestamp":1786466067177,"version":"3.56.0"},"reference-count":98,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Artificial Intelligence (AI) methodologies have profoundly influenced healthcare research, particularly in chronic disease management and public health. This paper provides a comprehensive state-of-the-art review of AI\u2019s applications across diabetes, cancer, epidemiology, and mortality prediction. The analysis highlights advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) that enable robust predictive models and decision support systems, leading to significant clinical and public health outcomes. The study examines predictive modeling, pattern recognition, and decision support applications, addressing their respective challenges and potential in real-world healthcare settings. Emphasis is placed on the emerging role of explainable AI (XAI), multimodal data fusion, and privacy-preserving techniques such as federated learning, which aim to enhance interpretability, robustness, and ethical compliance. This paper underscores the vital role of interdisciplinary collaboration and adaptive AI systems in creating resilient, scalable, and patient-centric healthcare solutions.<\/jats:p>","DOI":"10.3390\/computers14040143","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T05:28:07Z","timestamp":1744262887000},"page":"143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A State-of-the-Art Review of Artificial Intelligence (AI) Applications in Healthcare: Advances in Diabetes, Cancer, Epidemiology, and Mortality Prediction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9295-5917","authenticated-orcid":false,"given":"Mariano","family":"Vargas-Santiago","sequence":"first","affiliation":[{"name":"Secretaria de Ciencia, Humanidades, Tecnolog\u00eda e Inovaci\u00f3n (Secihti-IXM), Ciudad de M\u00e9xico 03940, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2586-9402","authenticated-orcid":false,"given":"Diana Assaely","family":"Le\u00f3n-Velasco","sequence":"additional","affiliation":[{"name":"Departamento de Sistemas, Universidad Aut\u00f3noma Metropolitana, Unidad Azcapotzalco, Ciudad de M\u00e9xico 02128, Mexico"},{"name":"Science Department, Instituto Tecnol\u00f3gico y de Estudios Superiores de Monterrey, Ciudad de M\u00e9xico 14380, Mexico"},{"name":"Escuela Superior de Apan, Universidad Aut\u00f3noma del Estado de Hidalgo, Hidalgo 43920, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5390-9301","authenticated-orcid":false,"given":"Christian Efra\u00edn","family":"Maldonado-Sifuentes","sequence":"additional","affiliation":[{"name":"Secretaria de Ciencia, Humanidades, Tecnolog\u00eda e Inovaci\u00f3n (Secihti-IXM), Ciudad de M\u00e9xico 03940, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7468-9603","authenticated-orcid":false,"given":"Liliana","family":"Chanona-Hernandez","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Escuela Superior de Ingenier\u00eda Mec\u00e1nica y El\u00e9ctrica, Unidad Zacatenco, Ciudad de M\u00e9xico 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"ref_1","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_4","first-page":"2","article-title":"Machine Learning Models for Cancer Type Classification with Unstructured Data","volume":"24","year":"2020","journal-title":"Comput. Sist."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2015","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_6","first-page":"716","article-title":"Artificial intelligence in cancer imaging: Opportunities and challenges","volume":"290","author":"Bi","year":"2019","journal-title":"Radiology"},{"key":"ref_7","first-page":"4","article-title":"Symbolic Learning Using Brain Programming for the Recognition of Leukemia Images","volume":"25","author":"Sossa","year":"2021","journal-title":"Comput. Sist."},{"key":"ref_8","first-page":"1","article-title":"Lightweight CNN for Detecting Microcalcifications Clusters in Digital Mammograms","volume":"28","year":"2024","journal-title":"Comput. Sist."},{"key":"ref_9","first-page":"1287","article-title":"Applying NLP to clinical notes for improved mortality prediction","volume":"30","author":"Yang","year":"2023","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_10","first-page":"2","article-title":"Mental Illness Classification on Social Media Texts Using Deep Learning and Transfer Learning","volume":"28","author":"Arif","year":"2024","journal-title":"Comput. Sist."},{"key":"ref_11","first-page":"212","article-title":"Potential and limitations of artificial intelligence in healthcare: Data bias and model interpretation","volume":"38","author":"Davenport","year":"2019","journal-title":"Health Aff."},{"key":"ref_12","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","first-page":"1","article-title":"Automatic Selection of Multi-View Learning Techniques and Views for Pattern Recognition in Electroencephalogram Signals","volume":"27","author":"Morales","year":"2023","journal-title":"Comput. Sist."},{"key":"ref_14","first-page":"351","article-title":"Multimodal AI for diabetic retinopathy detection integrating clinical and imaging data","volume":"28","author":"Zhang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, privacy-preserving and federated machine learning in medical imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_16","first-page":"3","article-title":"SensMask: An Intelligent Mask for Assisting Patients during COVID-19 Emergencies","volume":"25","author":"Sarkar","year":"2021","journal-title":"Comput. Sist."},{"key":"ref_17","first-page":"129177","article-title":"Deep learning for blood glucose prediction: A systematic review","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1089\/pop.2018.0129","article-title":"Transforming diabetes care through artificial intelligence: The future is here","volume":"22","author":"Rivo","year":"2019","journal-title":"Popul. Health Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.amjmed.2020.03.033","article-title":"Artificial intelligence: The future for diabetes care","volume":"133","author":"Ellahham","year":"2020","journal-title":"Am. J. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101213","DOI":"10.1016\/j.xcrm.2023.101213","article-title":"Artificial intelligence in diabetes management: Advancements, opportunities, and challenges","volume":"4","author":"Guan","year":"2023","journal-title":"Cell Rep. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Khalifa, M., and Albadawy, M. (2024). Artificial intelligence for clinical prediction: Exploring key domains and essential functions. Comput. Methods Programs Biomed. Update, 5.","DOI":"10.1016\/j.cmpbup.2024.100148"},{"key":"ref_22","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_23","first-page":"530","article-title":"Convolutional neural networks in diabetic retinopathy detection: A review of recent advances","volume":"23","author":"Li","year":"2021","journal-title":"Diabetes Technol. Ther."},{"key":"ref_24","first-page":"216","article-title":"Transformers vs. CNNs in diabetic retinopathy classification: Comparative study","volume":"42","author":"Matsoukas","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","unstructured":"Kim, M., Park, J., Yoo, Y., Choi, H., and Lee, J. (2023). Transfer learning in diabetic retinopathy detection for low-resource settings. Comput. Methods Programs Biomed., 230."},{"key":"ref_26","first-page":"102469","article-title":"GAN-based data augmentation for diabetic retinopathy detection","volume":"88","author":"Liu","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_27","first-page":"034004","article-title":"Grad-CAM: Visual explanations for deep learning models in diabetic retinopathy detection","volume":"9","author":"Selvaraju","year":"2022","journal-title":"J. Med. Imaging"},{"key":"ref_28","first-page":"225","article-title":"Telemedicine for diabetic retinopathy screening: A review of AI-based applications","volume":"30","author":"Xu","year":"2024","journal-title":"Telemed. e-Health"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Abramoff","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"ref_30","first-page":"04012","article-title":"Ethics and equity in AI-based diabetic retinopathy detection","volume":"14","author":"Elsharkawy","year":"2024","journal-title":"J. Glob. Health"},{"key":"ref_31","unstructured":"Yu, H., Wang, Z., and Li, J. (2021). Artificial intelligence in diabetes management: Focusing on personalized medicine. J. Diabetes Res., 2021."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1097\/ACI.0000000000001015","article-title":"Artificial intelligence and machine learning for anaphylaxis algorithms","volume":"24","author":"Miller","year":"2024","journal-title":"Curr. Opin. Allergy Clin. Immunol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., and Colen, R.R. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-69250-1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gupta, Y., Srivastava, V., and Singh, R.K. (2025). AI-enhanced patient-centric clinical trial design. Proceedings of the AIP Conference Proceedings, AIP Publishing.","DOI":"10.1063\/5.0247858"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e36388","DOI":"10.2196\/36388","article-title":"Evaluation and mitigation of racial bias in clinical machine learning models: Scoping review","volume":"10","author":"Huang","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"54310","DOI":"10.1109\/ACCESS.2021.3071301","article-title":"Deep learning in medical ultrasound image analysis: A review","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e10775","DOI":"10.2196\/10775","article-title":"Artificial intelligence for diabetes management and decision support: Literature review","volume":"20","author":"Contreras","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tyler, N.S., and Jacobs, P.G. (2020). Artificial intelligence in decision support systems for type 1 diabetes. Sensors, 20.","DOI":"10.3390\/s20113214"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e40259","DOI":"10.2196\/40259","article-title":"The effectiveness of wearable devices using artificial intelligence for blood glucose level forecasting or prediction: Systematic review","volume":"25","author":"Ahmed","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_41","first-page":"248","article-title":"IoT and wearable technologies for diabetes management: A comprehensive review","volume":"23","author":"Reddy","year":"2021","journal-title":"Diabetes Technol. Ther."},{"key":"ref_42","first-page":"216","article-title":"Machine learning for predictive modelling based on big data from diabetes management system","volume":"68","author":"Shaikhina","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"ref_43","first-page":"91","article-title":"Data mining techniques in the detection of diabetes complications","volume":"176","author":"Rivas","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_44","unstructured":"Wang, J., Liu, Y., Zhou, Y., and Liu, S. (2020). Personalized treatment planning in diabetes management with machine learning. Comput. Biol. Med., 118."},{"key":"ref_45","first-page":"294","article-title":"Machine learning models for real-time glucose monitoring in diabetes patients","volume":"43","author":"Tison","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_46","first-page":"1291","article-title":"Risk prediction models in diabetes management using ensemble learning","volume":"67","author":"Le","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_47","first-page":"1078","article-title":"Automated extraction and analysis of diabetic patient data from EHRs using NLP techniques","volume":"12","author":"Johnson","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_48","first-page":"e12902","article-title":"Deep learning for lifestyle and diet impact analysis in diabetic patients","volume":"21","author":"Rana","year":"2019","journal-title":"J. Med. Internet Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1136\/gutjnl-2022-327211","article-title":"Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa","volume":"71","author":"Waljee","year":"2022","journal-title":"Gut"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e214708","DOI":"10.1001\/jamanetworkopen.2021.4708","article-title":"Estimated projection of US cancer incidence and death to 2040","volume":"4","author":"Rahib","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hsieh, M.H., Sun, L.M., Lin, C.L., Hsieh, M.J., Hsu, C.Y., and Kao, C.H. (2019). The performance of different artificial intelligence models in predicting breast cancer among individuals having type 2 diabetes mellitus. Cancers, 11.","DOI":"10.3390\/cancers11111751"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1007\/s11831-021-09648-w","article-title":"A systematic review of artificial intelligence techniques in cancer prediction and diagnosis","volume":"29","author":"Kumar","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"T\u0103taru, O.S., Vartolomei, M.D., Rassweiler, J.J., Virgil, O., Lucarelli, G., Porpiglia, F., Amparore, D., Manfredi, M., Carrieri, G., and Falagario, U. (2021). Artificial intelligence and machine learning in prostate cancer patient management\u2014Current trends and future perspectives. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020354"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.3748\/wjg.v26.i36.5408","article-title":"Artificial intelligence in gastric cancer: Application and future perspectives","volume":"26","author":"Niu","year":"2020","journal-title":"World J. Gastroenterol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hunter, B., Hindocha, S., and Lee, R.W. (2022). The role of artificial intelligence in early cancer diagnosis. Cancers, 14.","DOI":"10.3390\/cancers14061524"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.matpr.2022.01.071","article-title":"Study of deep learning techniques for medical image analysis: A review","volume":"56","author":"Singhal","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kim, H.E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M.E., and Ganslandt, T. (2022). Transfer learning for medical image classification: A literature review. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00793-7"},{"key":"ref_58","first-page":"431","article-title":"Genomic Medicine and the Role of Machine Learning","volume":"70","author":"Friedman","year":"2019","journal-title":"Annu. Rev. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1002\/mp.12625","article-title":"Deep reinforcement learning for automated radiation adaptation in lung cancer","volume":"44","author":"Tseng","year":"2017","journal-title":"Med. Phys."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Blanco-Gonzalez, A., Cabezon, A., Seco-Gonzalez, A., Conde-Torres, D., Antelo-Riveiro, P., Pineiro, A., and Garcia-Fandino, R. (2023). The role of AI in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals, 16.","DOI":"10.3390\/ph16060891"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1093\/ehjci\/jeac147","article-title":"A framework of deep learning networks provides expert-level accuracy for the detection and prognostication of pulmonary arterial hypertension","volume":"23","author":"Diller","year":"2022","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Thakur, R.S., Chatterjee, S., Yadav, R.N., and Gupta, L. (2023). Medical image denoising using convolutional neural networks. Digital Image Enhancement and Reconstruction, Elsevier.","DOI":"10.1016\/B978-0-32-398370-9.00012-3"},{"key":"ref_63","first-page":"159","article-title":"Real-time epidemic tracking and forecasting using artificial intelligence","volume":"5","author":"Zhang","year":"2019","journal-title":"J. Health Inform. Res."},{"key":"ref_64","first-page":"4605","article-title":"AI for pandemic preparedness and infectious disease surveillance: Predicting outbreaks, modeling transmission, and optimizing public health interventions","volume":"5","author":"Ali","year":"2024","journal-title":"Int. J. Res. Publ. Rev."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1200\/CCI.20.00101","article-title":"Application of artificial intelligence methods to pharmacy data for cancer surveillance and epidemiology research: A systematic review","volume":"4","author":"Grothen","year":"2020","journal-title":"JCO Clin. Cancer Inform."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.imed.2022.10.003","article-title":"Application of big data and artificial intelligence in epidemic surveillance and containment","volume":"3","author":"Jiao","year":"2023","journal-title":"Intell. Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41586-024-08564-w","article-title":"Artificial intelligence for modelling infectious disease epidemics","volume":"638","author":"Kraemer","year":"2025","journal-title":"Nature"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Anjaria, P., Asediya, V., Bhavsar, P., Pathak, A., Desai, D., and Patil, V. (2023). Artificial intelligence in public health: Revolutionizing epidemiological surveillance for pandemic preparedness and equitable vaccine access. Vaccines, 11.","DOI":"10.3390\/vaccines11071154"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"03000605231159335","DOI":"10.1177\/03000605231159335","article-title":"Artificial intelligence in public health: The potential of epidemic early warning systems","volume":"51","author":"MacIntyre","year":"2023","journal-title":"J. Int. Med. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1161\/STROKEAHA.120.031960","article-title":"Epidemiological surveillance of the impact of the COVID-19 pandemic on stroke care using artificial intelligence","volume":"52","author":"Nogueira","year":"2021","journal-title":"Stroke"},{"key":"ref_71","first-page":"1","article-title":"Real-time Bayesian networks for disease surveillance and outbreak detection","volume":"147","author":"Neill","year":"2019","journal-title":"Epidemiol. Infect."},{"key":"ref_72","first-page":"2145","article-title":"Using machine learning to predict infectious disease outbreaks: A systematic review","volume":"16","author":"Choi","year":"2017","journal-title":"J. Infect. Dis."},{"key":"ref_73","first-page":"e275","article-title":"Deep learning for influenza trend prediction: A case study using social media data","volume":"20","author":"Wang","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_74","unstructured":"Jean, N., and Burke, M. (2019). Semi-automated convolutional neural networks for malaria risk mapping using satellite data. PLoS ONE, 14."},{"key":"ref_75","first-page":"24","article-title":"Tracking COVID-19 using natural language processing on social media data: An overview","volume":"4","author":"Lampos","year":"2021","journal-title":"Nat. Digit. Med."},{"key":"ref_76","unstructured":"Smigielski, W., Rees, E., Patel, N., Zhou, S., and Tatem, A.J. (2020). Real-time integration of multimodal data sources for outbreak detection. BMC Public Health, 20."},{"key":"ref_77","first-page":"047004","article-title":"Data-driven models for environmental surveillance and zoonotic disease prediction","volume":"128","author":"Rakki","year":"2020","journal-title":"Environ. Health Perspect."},{"key":"ref_78","unstructured":"Chen, L., Huang, W., Cheng, Y., Wu, C.H., and Li, J. (2020). Tracking COVID-19 spread with real-time adaptive learning models. Sci. Rep., 10."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.70445\/gtst.1.1.2025.1-14","article-title":"AI in Predictive Healthcare Analytics: Forecasting Disease Outbreaks and Patient Outcomes","volume":"1","author":"Bacha","year":"2025","journal-title":"Glob. Trends Sci. Technol."},{"key":"ref_80","first-page":"315","article-title":"Federated learning for mortality prediction across hospitals: A privacy-preserving approach","volume":"28","author":"Xu","year":"2024","journal-title":"J. Biomed. Health Inform."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Harrell, F.E. (2001). Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, Springer.","DOI":"10.1007\/978-1-4757-3462-1"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e222008","DOI":"10.1148\/radiol.222008","article-title":"AI-based CT body composition identifies myosteatosis as key mortality predictor in asymptomatic adults","volume":"307","author":"Nachit","year":"2023","journal-title":"Radiology"},{"key":"ref_83","first-page":"188","article-title":"Predicting tomorrow\u2019s Ailments: How AI\/ML Is Transforming Disease Forecasting","volume":"1","author":"Reddy","year":"2021","journal-title":"ESP J. Eng. Technol. Adv."},{"key":"ref_84","unstructured":"Choi, E., Schuetz, A., Stewart, W.F., and Sun, J. (2018). Medical event prediction using recurrent neural networks and sequences of health records. Sci. Rep., 8."},{"key":"ref_85","first-page":"102037","article-title":"Predicting mortality risk using convolutional neural networks on chest X-rays and clinical data","volume":"71","author":"Liu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_86","unstructured":"Wang, H., Zhang, J., Li, Y., and Huang, Y. (2023). Ensemble learning for mortality prediction in multi-institutional datasets. J. Biomed. Inform., 146."},{"key":"ref_87","first-page":"2613","article-title":"Interpretable survival models with survival trees for predicting mortality risk","volume":"41","author":"Lee","year":"2022","journal-title":"Stat. Med."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Ye, J., Yao, L., Shen, J., Janarthanam, R., and Luo, Y. (2020). Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01318-4"},{"key":"ref_89","first-page":"102431","article-title":"Interpretability in deep learning models for mortality prediction in healthcare","volume":"136","author":"Sun","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_90","first-page":"252","article-title":"Integrating IoT data for real-time mortality risk prediction in wearable devices","volume":"11","author":"Green","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_91","first-page":"341","article-title":"Adaptive learning models for ICU mortality prediction with evolving patient data","volume":"52","author":"Chen","year":"2024","journal-title":"Crit. Care Med."},{"key":"ref_92","first-page":"892","article-title":"Genomics and AI for predicting mortality risk: A review of recent advances","volume":"13","author":"Li","year":"2023","journal-title":"J. Pers. Med."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Sarantopoulos, A., Mastori Kourmpani, C., Yokarasa, A.L., Makamanzi, C., Antoniou, P., Spernovasilis, N., and Tsioutis, C. (2024). Artificial Intelligence in Infectious Disease Clinical Practice: An Overview of Gaps, Opportunities, and Limitations. Trop. Med. Infect. Dis., 9.","DOI":"10.3390\/tropicalmed9100228"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, D., Zhang, X., and Zhang, X. (2024). Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Front. Microbiol., 15.","DOI":"10.3389\/fmicb.2024.1449844"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Chen, S., Yu, J., Chamouni, S., Wang, Y., and Li, Y. (2024). Integrating machine learning and artificial intelligence in life-course epidemiology: Pathways to innovative public health solutions. BMC Med., 22.","DOI":"10.1186\/s12916-024-03566-x"},{"key":"ref_96","unstructured":"Kwak, G.H., and Hui, P. (2019). DeepHealth: Review and challenges of artificial intelligence in health informatics. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1007\/s11205-018-1837-z","article-title":"Electronic health record breaches as social indicators","volume":"141","author":"Koczkodaj","year":"2019","journal-title":"Soc. Indic. Res."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"73","DOI":"10.12913\/22998624\/195690","article-title":"Text mining analysis of over 392 million compromised healthcare records","volume":"19","author":"Koczkodaj","year":"2025","journal-title":"Adv. Sci. Technol. Res. J."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:12:02Z","timestamp":1760029922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,10]]},"references-count":98,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["computers14040143"],"URL":"https:\/\/doi.org\/10.3390\/computers14040143","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,10]]}}}