Predictive Models in Health Based on Machine Learning
Journal: Advances in Medicine and Engineering Interdisciplinary Research DOI: 10.32629/ameir.v2i4.2813
Abstract
Finding causality in medicine is of great interest in research, in order to generate interventions that treat or cure the disease. Most classical statistical models allow association to be inferred, and only a few designs are able to demonstrate cause and effect with an adequate methodology and solid evidence. Evidence-based medicine supports its findings in models that go from a hypothesis to search for data to prove or rule it out. This also applies to the development of predictive models to be reliable and to produce impact in clinical practice. The large amount of data stored in electronic health records and greater computational power mean that machine learning techniques can play a preponderant role in the development of new predictive analysis and recognition of unknown patterns with these modern computational models. These models, along with changing the view from data to information, are increasingly being incorporated into daily clinical practice, providing greater precision and speed for supporting decision making. The intent of this review is to provide theoretical bases and evidence of how these modern computational techniques of machine learning have allowed to achieve better results and they are being widely used. This article will review the most relevant aspects of health data science in Latin America.
Keywords
data sciences; machine learning; artificial intelligence; predictive models; clinical decision support systems; cardiac surgery; data
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[20] Baxter SL, Marks C, Kuo TT, Ohno-Machado L, Weinreb RN. Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records. Am J Ophthalmol. 2019;208:30-40. doi: 10.1016/j.ajo.2019.07.005
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[29] Wells PS. Integrated strategies for the diagnosis of venous thromboembolism. J Thromb Haemost. 2007;5 Suppl 1:41-50. doi: 10.1111/j.1538-7836.2007.02493.x
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