AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

• Published in: Journal of clinical medicine Vol. 10; no. 4; p. 766
• Main Authors: Majnarić, Ljiljana Trtica, Babič, František, O'Sullivan, Shane, Holzinger, Andreas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.02.2021; MDPI
• Subjects: Aging; artificial intelligence; Big Data; chronic diseases; Chronic illnesses; Clinical medicine; Cognition & reasoning; Cognitive ability; Data analysis; Dementia; Disease; Electronic health records; Frailty; Hypotheses; machine learning; Mental disorders; Metabolism; multimorbidity; Older people; Patients; population aging; Review; Statistical methods; multimorbidity; population aging; machine learning; chronic diseases; artificial intelligence
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm10040766

Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.