References

vitj

Врач и информационные технологии

Medical Doctor and Information Technologies

1811-01932413-5208

Pirogov National Medical and Surgical Center

10.25881/18110193_2024_1_28

vitj-41

Research Article

ОБЗОРЫ

REVIEWS

Ошибки в данных реальной клинической практики: обзор литературы

Errors in real-world data: a review

Ермакова

Н. А.

Ermakova

N. A.

ermakova_na@rsmu.ru

Гусев

А. В.

Gusev

A. V.

к.т.н.

PhD

agusev@webiomed.ru

Реброва

О. Ю.

Rebrova

O. Yu.

д.м.н.

DSc

rebrova_oyu@rsmu.ru

ФГАОУ ВО РНИМУ им. Н.И. ПироговаРоссияPirogov Russian National Research Medical UniversityRussian Federation

ФГБУ «Центральный научно-исследовательский институт организации и информатизации здравоохранения» Минздрава РоссииРоссияFederal Research Institute for Health Organization and InformaticsRussian Federation

ФГАОУ ВО РНИМУ им. Н.И. Пирогова; ГНЦ РФ ФГБУ «НМИЦ эндокринологии» Минздрава РоссииРоссияPirogov Russian National Research Medical UniversityRussian Federation

2024

01082024

012843

2024

Ермакова Н.А., Гусев А.В., Реброва О.Ю.

Ermakova N.A., Gusev A.V., Rebrova O.Y.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://www.vit-j.ru/jour/article/view/41

В последнее время возрастает интерес к использованию больших данных реальной клинической практики для разработки систем искусственного интеллекта в интересах врачебной практики – моделей диагностики заболеваний и состояний и прогноза их течения. При этом качество этих данных обычно невысоко из-за допускаемых ошибок при вводе, неоптимальной архитектуры информационных систем, отсутствия стандартизации и др. В обзоре рассмотрены критерии надежности данных реальной практики, наиболее часто встречающиеся проблемы и способы их устранения: оценка соответствия набора данных дизайну разрабатываемой модели, выявление и удаление дублирующих записей в наборах данных, обработка пропущенных значений, обнаружение и обработка выпадающих значений, выявление и обработка несогласованности в данных. Делается вывод о том, что требуется дальнейшее развитие методик создания наборов данных на основе реальной клинической практики в части повышения их качества, так как наличие ошибок в них может приводить к снижению качества создаваемых моделей машинного обучения для диагностики и прогнозирования

There is increasing interest in using big data of real clinical practice to develop artificial intelligence systems for diagnostic and predictive models of diseases and conditions. At the same time, the quality of this data is usually low due to errors during input, suboptimal architecture of information systems, lack of standardization, etc. The review examines criteria for the reliability of real-world data, the most common problems, and ways to eliminate them: assessing the compliance of the data set with the design of the model being developed, identifying, and removing duplicate records in data sets, handling missing values, detecting, and handling outliers, identifying and handling inconsistencies in data. We conclude that further development of methods for creating data sets based on real-world data is required in terms of improving their quality, can lead to lower quality of the created machine learning models for diagnosis and prognosis

реальная клиническая практикакачество данныхпропущенные значенияпротиворечиявыпадающие значениядублирующие записиэлектронные медицинские картыискусственный интеллект

Real-world datadata qualitymissing valuescontradictionsoutliersduplicate entrieselectronic health recordartificial intelligence

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Национальный стандарт РФ ГОСТ Р 59921.5-2022 «Системы искусственного интеллекта в клинической медицине. Часть 5. Требования к структуре и порядку применения набора данных для обучения и тестирования алгоритмов».

-Nacional’nyj standart RF GOST R 59921.5-2022 «Sistemy iskusstvennogo intellekta v klinicheskoj medicine. CHast’ 5. Trebovaniya k strukture i poryadku primeneniya nabora dannyh dlya obucheniya i testirovaniya algoritmov». (In Russ.)

The authors declare that there are no conflicts of interest present.