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Machine learning based on laboratory data for disease prediction

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Objective: to review domestic and foreign literature on the issue of machine learning methods applied in medical information systems (MIS), to analyze the accuracy and efficiency of the technologies under study, their advantages and disadvantages, the possibilities of implementation in clinical practice.

Material and methods. The literature search was performed in the PubMed/MEDLINE databases covering the period from 2000 to 2020 (using groups of keyphrases: "machine learning", "laboratory data", "clinical events", "prediction diseases"), CyberLeninka ("machine learning", "laboratory data", "clinical events", "prediction diseases" Russian keyphrases combinations) and Papers With Code ("clinical events", "prediction diseases", "electronic health record"). After reviewing the full text of 30 literature sources that met the selection criteria, the 19 most relevant articles were selected.

Results. An analysis of sources that describe the application of artificial intelligence techniques to obtain predictive analytics, taking into account information about patients, such as demographic, anamnestic, and laboratory data, the data of instrumental studies, information about existing and former diseases available in MIS, was performed. The existing ways of predicting adverse medical outcomes using machine learning methods were considered. Information about the significance of the used laboratory data for constructing high-precision predictive mathematical models is presented.

Conclusion. Implementation of machine learning algorithms in MIS seems to be a promising tool for effective prediction of adverse medical events for wide application in real clinical practice. It corresponds to the global trend in the development of personalized medicine based on the calculation of individual risk. There is an increase in the activity of research in the field of predicting noncommunicable diseases using artificial intelligence technologies.

About the Authors

A. V. Gusev
Russian Federation

Aleksandr V. Gusev – PhD (Engineering), Business Development Director

Scopus Author ID: 57222273391

RSCI SPIN-code: 9160-7024

17 Varkaus Emb., Republic of Karelia, Petrozavodsk, 185031

R. E. Novitskiy
Russian Federation

Roman E. Novitskiy – Director General

Scopus Author ID: 57222272806

RSCI SPIN-code: 8309-1740

17 Varkaus Emb., Republic of Karelia, Petrozavodsk, 185031

A. A. Ivshin
Petrozavodsk State University
Russian Federation

Aleksandr A. Ivshin – MD, PhD, Chief of Chair of Obstetrics and Gynecology, Dermatovenerology

Scopus Author ID: 57222275843

RSCI SPIN-code: 8196-6605

33 Lenin Ave., Republic of Karelia, Petrozavodsk, 185910

A. A. Alekseev
Russian Federation

Aleksandr A. Alekseev – Specialist 

17 Varkaus Emb., Republic of Karelia, Petrozavodsk, 185031


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For citation:

Gusev A.V., Novitskiy R.E., Ivshin A.A., Alekseev A.A. Machine learning based on laboratory data for disease prediction. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2021;14(4):581-592. (In Russ.)

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