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Analysis of 19.9 million publications from the PubMed/MEDLINE database using artificial intelligence methods: approaches to the generalizations of accumulated data and the phenomenon of “fake news

https://doi.org/10.17749/2070-4909/farmakoekonomika.2020.021

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Abstract

Introduction. The English-language databases PubMed/MEDLINE and Embase are valuable information resources for finding original publications in basic and clinical medicine. Currently, there are no artificial intelligence systems to evaluate the quality of these publications.

Aim. Development and testing of a system for sentiment analysis (i.e. analysis of emotional modality) of biomedical publications.

Materials and methods. The technique of analysis of the “Big data” of biomedical publications was formulated on the basis of the topological theory of sentiment analysis. Algorithms have been developed that allow for the classification of texts from 16 sentiment classes with 90% accuracy (manipulative speech, research without positive results, propaganda, falsification of results, negative personal attitude, aggressive text, negative emotional background, etc.). Based on the algorithms, a scale for assessing the sentiment quality of research (β-score) is proposed.

Results. Abstracts of 19.9 million publications registered in PubMed/MEDLINE over the past 50 years (1970–2019) were analyzed. It was shown that publications with low sentiment quality (the value of the β-score of the text is less than zero, which corresponds to the prevalence of manipulative and negative sentiments in the text) comprise only 18.5% (3.68 out of 19.9 million). The greatest values of the β-score were characterized by publications on sports medicine, systems biology, nutrition, on the use of applied mathematics and data mining in medicine. The rubrication of the entire array of publications by 27,840 headings (MESH-system of PubMed/MEDLINE) indicated an increase in the β-score by years (i.e., the positive dynamics of sentiment quality of the texts of publications) for 27,090 of the studied headings. The most intense positive dynamics was found for research in genetics, physiology, pharmacology, and gerontology. 249 headings with sharply negative dynamics of sentiment quality and with a pronounced increase in the manipulative sentiments characteristic of the tabloid press were highlighted. Separate assessments of international experts are presented that confirm the patterns identified.

Conclusion. The proposed artificial intelligence system allows a researcher to make an effective assessment of the sentiment quality of biomedical research papers, filtering out potentially inappropriate publications disguised as “evidence-based”.  

About the Authors

I. Yu. Torshin
Federal Research Center “Informatics and Management of the Russian Academy of Sciences; Moscow State University
Russian Federation

MD, PhD, Senior Researcher; Big Data Storage and Analysis Center

Scopus Author ID: 7003300274; Author ID: 54104;

WoS ResearcherID: C-7683-2018; RSCI SPIN-code: 1375-1114

44-2 Vavilova Str., Moscow 119333, Russia

1 Leninskie gory, Moscow 119991, Russia



O. A. Gromova
Federal Research Center “Informatics and Management of the Russian Academy of Sciences; Moscow State University
Russian Federation

MD, Dr Sci Med, Professor, Senior Researcher, Scientific Director; Leading Researcher, Center for Big Data Analysis

Author ID: 94901; Scopus Author ID: 7003589812;

WoS ResearcherID: J-4946-2017. RSCI SPIN-code: 6317-9833

44-2 Vavilova Str., Moscow 119333, Russia

1 Leninskie gory, Moscow 119991, Russia



L. V. Stakhovskaya
Federal Center for Cerebrovascular Pathology and Stroke
Russian Federation

MD, Dr Sci Med, Professor, director

ORCID ID: 0000-0001-6325-923 

1-10 Ostrovityanova Str., Moscow 117997, Russia

 



N. P. Vanchakova
Center for Psychosomatic Medicine at the Clinical Hospital No. 122 named after L. G. Sokolov
Russian Federation

MD, Dr Sci Med (medical psychology and psychiatry), Professor, Psychiatrist

(4 pr. Kultury, St. Petersburg, 194291, Russia

 



A. N. Galustyan
Saint-Petersburg State Pediatric Medical University
Russian Federation

MD, PhD, Associate Professor, Head of the Department of Pharmacology 

2 Litovskaya Str., St. Petersburg 194100, Russia



Zh. D. Kobalava
Peoples’ Friendship University of Russia
Russian Federation

MD, PhD, Professor, Head of the Department of Internal Medicine, Cardiology and Clinical Pharmacology

10/3 Miklukho-Maklaya Str., Moscow 117198, Russia



T. R. Grishina
Ivanovo State Medical Academy
Russian Federation
MD, Dr Sci Med, Нead of the Department of pharmacology
Aurhor ID: 113019

8 Sheremetevsky prospekt, Ivanovo 153012, Russia


A. N. Gromov
Federal Research Center “Informatics and Management of the Russian Academy of Sciences
Russian Federation

research engineer

AuthorID: 15082; Scopus Author ID: 7102053964; 

WoS ResearcherID: C-7476-2018; RSCI SPIN-code: 8034-7910

44-2 Vavilova Str., Moscow 119333, Russia



I. A. Ilovaiskaya
The State Budgetary Healthcare Institution of Moscow Area Moscows regional research clinical institute n.a. M. F. Vladimirskiy
Russian Federation
MD, Dr Sci Med, endocrinologist of the highest category, associate professor, senior researcher at the Department of Therapeutic Endocrinology
ResearcherID: I-1159-2014; Scopus Author ID: 6506067338   61/2 Shchepkina Str., Moscow 129110, Russia


V. M. Kodentsova
Federal Research Center for Nutrition and Biotechnology
Russian Federation

Dr Sci Biol, Professor, Senior Researcher, Laboratory of Vitamins and Microelements

14 Ustinsky proezd, Moscow 109240, Russia



A. G. Kalacheva
Ivanovo State Medical Academy
Russian Federation

MD, PhD, Associate Professor of the Department of Pharmacology and Clinical Pharmacology

8 Sheremetevsky prospekt, Ivanovo 153012, Russia



O. A. Limanova
Ivanovo State Medical Academy
Russian Federation
MD, PhD, Associate Professor of the Department of Pharmacology

8 Sheremetevsky prospekt, Ivanovo 153012, Russia


V. A. Maksimov
Federal State Budgetary Educational Institution of Continuing Professional Education “Russian Medical Academy of Continuing Professional Education” of the Ministry of Health of the Russian Federation
Russian Federation
MD, Dr Sci Med, gastroenterologist, Professor of the Department of Dietetics and Nutritionology

2/1 Building 1 Barrikadnaya Str., Moscow 125993, Russia


S. I. Malyavskaya
Northern State Medical University
Russian Federation
MD, Dr Sci Med, Professor, Head of Sciences
eLIBRARY ID: 6257-4400

51 Troitskiy Ave., Arkhangelsk 163000, Russia


E. V. Mozgovaya
The Research Institute of Obstetrics, Gynecology and Reproductology named after D. O. Ott; Saint-Petersburg State Pediatric Medical University
Russian Federation
MD, Dr Sci Med, Associate Professor, Head of the Obstetric Department with Perinatology; Professor, Department of Obstetrics, Gynecology and Reproductology, Faculty of Medicine
WoS ResearcherID: L-1432-2017; Author ID Scopus: 24822403200; Author ID: 386830

3 Mendeleev Line, St. Petersburg 199034, Russia


N. I. Tapilskaya
Saint-Petersburg State Pediatric Medical University; The Research Institute of Obstetrics, Gynecology and Reproductology named after D. O. Ott
Russian Federation

MD, Dr Sci Med, Professor, Leading Researcher of the Department of Assisted Reproductive Technologies

Author ID Scopus: 23013489000; WoS ResearcherID: A-7504-2016;

ID map of science: 00052162; RSCI SPIN-code: 3605-0413

2 Litovskaya Str., St. Petersburg 194100, Russia

3 Mendeleev Line, St. Petersburg 199034, Russia



K. V. Rudakov
Federal Research Center “Informatics and Management of the Russian Academy of Sciences
Russian Federation
Academician of the Russian Academy of Sciences, Scientifik Director, head of Department of Intellectual Systems MIPT. Scopus Author ID: 6603540895

44-2 Vavilova Str., Moscow 119333, Russia


V. A. Semenov
Kemerovo State Medical University
Russian Federation

MD, Dr Sci Med, Professor

22a Voroshilova Str., Kemerovo 650056, Russia



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For citation:


Torshin I.Yu., Gromova O.A., Stakhovskaya L.V., Vanchakova N.P., Galustyan A.N., Kobalava Z.D., Grishina T.R., Gromov A.N., Ilovaiskaya I.A., Kodentsova V.M., Kalacheva A.G., Limanova O.A., Maksimov V.A., Malyavskaya S.I., Mozgovaya E.V., Tapilskaya N.I., Rudakov K.V., Semenov V.A. Analysis of 19.9 million publications from the PubMed/MEDLINE database using artificial intelligence methods: approaches to the generalizations of accumulated data and the phenomenon of “fake news. FARMAKOEKONOMIKA. Modern Pharmacoeconomic and Pharmacoepidemiology. 2020;13(2):146-163. (In Russ.) https://doi.org/10.17749/2070-4909/farmakoekonomika.2020.021

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