Echocardiography Population Study in Russian Federation for 4P Medicine Using Machine Learning
This article describes the study results of echocardiographic (ECHO) test data for 4P medicine applied to cardiovascular patients. Data from more than 145,000 echocardiographic tests were analyzed. One of the objectives of the study is the possibility to identify patterns and relationships in patient characteristics for more accurate appointment procedures based on the history of the disease and the individual characteristics of the patient. This is achieved by using classifications models based on machine learning methods. Early detection of disease risks and "accurate" appointment of diagnostic procedures makes a significant contribution to value-based medicine. Moreover, it was also possible to identify the classes and characteristics of patients for whom repeated diagnostic procedures are well founded. Calculation of personal risks from empirical retrospective data helps to detect the disease in early stages. Identifying patients with high risk of disease complications allow physicians to make right decisions about timely treatment, which can significantly improve the quality of treatment, and help to avoid diseases complications, optimize costs and improve the quality of medical care.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:261 |
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| Enthalten in: |
Studies in health technology and informatics - 261(2019) vom: 04., Seite 137-142 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Metsker, Oleg [VerfasserIn] |
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| Themen: |
4P medicine |
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| Anmerkungen: |
Date Completed 02.09.2019 Date Revised 02.09.2019 published: Print Citation Status MEDLINE |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM297736639 |
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| 520 | |a This article describes the study results of echocardiographic (ECHO) test data for 4P medicine applied to cardiovascular patients. Data from more than 145,000 echocardiographic tests were analyzed. One of the objectives of the study is the possibility to identify patterns and relationships in patient characteristics for more accurate appointment procedures based on the history of the disease and the individual characteristics of the patient. This is achieved by using classifications models based on machine learning methods. Early detection of disease risks and "accurate" appointment of diagnostic procedures makes a significant contribution to value-based medicine. Moreover, it was also possible to identify the classes and characteristics of patients for whom repeated diagnostic procedures are well founded. Calculation of personal risks from empirical retrospective data helps to detect the disease in early stages. Identifying patients with high risk of disease complications allow physicians to make right decisions about timely treatment, which can significantly improve the quality of treatment, and help to avoid diseases complications, optimize costs and improve the quality of medical care | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 4P medicine | |
| 650 | 4 | |a ECHO | |
| 650 | 4 | |a Russian cardiac population | |
| 650 | 4 | |a data mining | |
| 650 | 4 | |a echocardiography | |
| 650 | 4 | |a machine learning | |
| 700 | 1 | |a Yakovlev, Alexey |e verfasserin |4 aut | |
| 700 | 1 | |a Ilin, Aleksandr |e verfasserin |4 aut | |
| 700 | 1 | |a Kovalchuk, Sergey |e verfasserin |4 aut | |
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