Predictive Modeling in Health Informatics: A Review of Applications in Population and Personalized Health
Keywords:
Predictive modeling, health informatics, machine learning, electronic health records, data quality.Abstract
With the help of predictive modeling, health informatics has found new ways to predict both individual and population health results. This review reviews important techniques in data science, for example, statistical analysis, machine learning, and deep learning, and the varied types of data those models depend on, for instance, electronic health records, genomic data, and social determinants of health. The study looks at the use of such models in following diseases in the community and providing best treatment for individuals. Even though efforts in AI are successful, there are still obstacles like poor data, biased algorithms, a lack of explanation, and issues concerning people’s privacy and fairness. Dealing with these barriers is necessary for safe and successful use of models. Moving forward with more technical progress and teamwork among many areas, predictive modeling may lead to improved decisions, results, and a healthcare system that treats everyone better and individually. It clearly explains what the subject has achieved till now and how it might affect the future.
References
Malik MM, Abdallah S, Ala’raj M. Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review. Annals of Operations Research. 2018 Nov;270(1):287-312.
Huang JD, Wang J, Ramsey E, Leavey G, Chico TJ, Condell J. Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review. Sensors. 2022; 22(20):8002.
Hassler AP, Menasalvas E, García-García FJ, Rodríguez-Mañas L, Holzinger A. Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome. BMC medical informatics and decision making. 2019 Dec;19:1-7.
Hasan ME, Islam MJ, Islam MR, Chen D, Sanin C, Xu G. Applications of Artificial Intelligence for Health Informatics: A Systematic Review. Journal home: http. 2023 Dec; 4(2):19-46.
Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes LE, Dou D. From personalized medicine to population health: a survey of mHealth sensing techniques. IEEE Internet of Things Journal. 2022 Mar 22; 9(17):15413-34.
Hyysalo J, Dasanayake S, Hannu J, et al. Smart mask–wearable IoT solution for improved protection and personal health. Internet Things. 2022; 18:100511.
Sharafoddini A, Dubin JA, Lee J. Patient similarity in prediction models based on health data: a scoping review. JMIR medical informatics. 2017 Mar 3;5(1):e6730.
Fan K, Zhao Y. Mobile health technology: a novel tool in chronic disease management. Intell Med. 2022; 2(1):41-47.
Mei J, Xu E, Hao B, Zhang Y, Yu Y, Li S. Translational Health Informatics from Risk Prediction Modeling to Risk Assessment Service. In2019 IEEE International Conference on Healthcare Informatics (ICHI) 2019 Jun 10 (pp. 1-2). IEEE.
Fang R, Pouyanfar S, Yang Y, Chen SC, Iyengar SS. Computational health informatics in the big data age: a survey. ACM Computing Surveys (CSUR). 2016 Jun 14;49(1):1-36.
Busnatu T, Niculescu AG, Bolocan A, et al. Clinical applications of artificial intelligence—an updated overview. J Clin Med. 2022; 11(8):2265.
Amin P, Anikireddypally NR, Khurana S, Vadakkemadathil S, Wu W. Personalized health monitoring using predictive analytics. In2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService) 2019 Apr 4 (pp. 271-278). IEEE.
Swapna M, Viswanadhula UM, Aluvalu R, Vardharajan V, Kotecha K. Bio-signals in medical applications and challenges using artificial intelligence. J Sens Actuator Netw. 2022; 11(1):17.
Bajeh AO, Abikoye OC, Mojeed HA, Salihu SA, Oladipo ID, Abdulraheem M, Awotunde JB, Sangaiah AK, Adewole KS. Application of computational intelligence models in IoMT big data for heart disease diagnosis in personalized health care. InIntelligent IoT systems in personalized health care 2021 Jan 1 (pp. 177-206). Academic Press.
Eskofier BM, Klucken J. Predictive models for health deterioration: Understanding disease pathways for personalized medicine. Annual Review of Biomedical Engineering. 2023 Jun 8; 25(1):131-56.
Viceconti M, Hunter P, Hose R. Big data, big knowledge: big data for personalized healthcare. IEEE journal of biomedical and health informatics. 2015 Feb 24; 19(4):1209-15.
Cronin RM, Jimison H, Johnson KB. Personal health informatics. InBiomedical informatics: computer applications in health care and biomedicine 2021 Jun 1 (pp. 363-389). Cham: Springer International Publishing.
Babarinde AO, Ayo-Farai O, Maduka CP, Okongwu CC, Sodamade O. Data analytics in public health, A USA perspective: A review. World Journal of Advanced Research and Reviews. 2023; 20(3):211-24.
Tang A, Woldemariam S, Roger J, Sirota M. Translational bioinformatics to enable precision medicine for all: elevating equity across molecular, clinical, and digital realms. Yearbook of medical informatics. 2022 Aug;31(01):106-15.
Wang P, Lin Z, Yan X, et al. A wearable ECG monitor for deep learning based real-time cardiovascular disease detection. arXiv preprint arXiv:2201.10083. 2022.
Atwany MZ, Sahyoun AH, Yaqub M. Deep learning techniques for diabetic retinopathy classification: a survey. IEEE Access. 2022; 10:28642-28655.
Hasan N, Bao Y. Understanding current states of machine learning approaches in medical informatics: a systematic literature review. Health and Technology. 2021 May; 11(3):471-82.
Xu J, Glicksberg BS, Su C, Walker P, Bian J, Wang F. Federated learning for healthcare informatics. Journal of healthcare informatics research. 2021 Mar; 5:1-9.
Wollenstein-Betech S, Cassandras CG, Paschalidis IC. Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: hospitalizations, mortality, and the need for an ICU or ventilator. International Journal of Medical Informatics. 2020 Oct 1; 142:104258.
Sevakula RK, Au-Yeung WTM, Singh JP, Heist EK, Isselbacher EM, Armoundas AA. State-of-the-art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system.J Am Heart Assoc. 2020; 9(4):e013924.
Song YT, Qin J. Metaverse and personal healthcare. Procedia Computer Science. 2022 Jan 1; 210:189-97.
Ramachandran KK. POPULATION HEALTH MANAGEMENT THROUGH PREDICTIVE ANALYTICS. Journal ID.; 3721:5412.
Bhatt C, Kumar I, Vijayakumar V, Singh KU, Kumar A. The state of the art of deep learning models in medical science and their challenges. Multimed Syst. 2021; 27(4):599-613.
Bianchi V, Bassoli M, Lombardo G, Fornacciari P, Mordonini M, De Munari I. IoT wearable sensor and deep learning: an integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet Things J. 2019; 6(5):8553-8562.
Pearson TA, Califf RM, Roper R, Engelgau MM, Khoury MJ, Alcantara C, Blakely C, Boyce CA, Brown M, Croxton TL, Fenton K. Precision health analytics with predictive analytics and implementation research: JACC state-of-the-art review. Journal of the American College of Cardiology. 2020 Jul 21; 76(3):306-20.
Simpao AF, Ahumada LM, Gálvez JA, Rehman MA. A review of analytics and clinical informatics in health care. Journal of medical systems. 2014 Apr; 38:1-7.
Sawyer J. Wearable Internet of Medical Things sensor devices, artificial intelligence-driven smart healthcare services, and personalized clinical care in COVID-19 telemedicine. Am J Med Res. 2020; 7(2):71-77.
Fu J, Wang H, Na R, Jisaihan A, Wang Z, Yuko O. Recent advancements in digital health management using multi-modal signal monitoring. Math Biosci Eng. 2023; 20(3):5194-5222
Osamika D, Adelusi BS, Kelvin-Agwu MC, Mustapha AY, Forkuo AY, Ikhalea N. A Comprehensive Review of Predictive Analytics Applications in US Healthcare: Trends, Challenges, and Emerging Opportunities.
Feldman K, Davis D, Chawla NV. Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration. Journal of biomedical informatics. 2015 Oct 1; 57:377-85.
Evans RS. Electronic health records: then, now, and in the future. Yearbook of medical informatics. 2016;25(S 01):S48-61.
Hu J, Perer A, Wang F. Data driven analytics for personalized healthcare. Healthcare Information Management Systems: Cases, Strategies, and Solutions. 2016:529-54.
Wu Q, Chen X, Zhou Z, and Zhang J. Fedhome: cloud-edge based personalized federated learning for in-home health monitoring. IEEE Trans Mob Comput. 2020; 21(8):2818-2832.
Guo L, Sim G, Matuszewski B. Inter-patient ECG classification with convolutional and recurrent neural networks. Biocybern Biomed Eng. 2019; 39(3):868-879.
Mulani J, Heda S, Tumdi K, Patel J, Chhinkaniwala H, Patel J. Deep reinforcement learning based personalized health recommendations. Deep learning techniques for biomedical and health informatics. 2020:231-55.
Garoufis C, Zlatintsi A, Filntisis P, et al. Towards unsupervised subject-independent speech-based relapse detection in patients with psychosis using variational autoencoders. In: IEEE; 2022:175–179.
Wan TT, Gurupur VP, Tanik MM. Design and evaluation of integrated healthcare informatics. Journal of Integrated Design and Process Science. 2017 Nov 22;21(3):1-5.
Paganelli AI, Mondéjar AG, da Silva AC, et al. Real-time data analysis in health monitoring systems: a comprehensive systematic literature review. J Biomed Inform. 2022; 127:104009
Rane N, Choudhary S, Rane J. towards Autonomous Healthcare: Integrating Artificial Intelligence (AI) for Personalized Medicine and Disease Prediction. Available at SSRN 4637894. 2023 Nov 9.
Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep learning for health informatics. IEEE journal of biomedical and health informatics. 2016 Dec 29; 21(1):4-21.
Li K, Urteaga I, Shea A, Vitzthum VJ, Wiggins CH, Elhadad N. A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking. Journal of the American Medical Informatics Association. 2022 Jan 1;29(1):3-11.
