vitjВрач и информационные технологииMedical Doctor and Information Technologies1811-01932413-5208Pirogov National Medical and Surgical Center10.25881/18110193_2023_3_44vitj-105Research ArticleОРИГИНАЛЬНЫЕ ИССЛЕДОВАНИЯORIGINAL RESEARCHМногоуровневая категоризация непрерывных переменных в задачах объяснения прогнозных оценок моделей машинного обучения в клинической медицинеMultilevel categorization of continuous variables in the tasks of explaining predictive estimates of machine learning models in clinical medicineШахгельдянК. И.ShakhgeldyanK. I.

д.т.н., доцент

Владивосток

DSc, Associate Professor

Vladivostok

carinashakh@gmail.comГельцерБ. И.GeltserB. I.

член-корр. РАН, д.м.н., профессор

г. Владивосток

Corr. Member of the RAS, DSc, Professor

Vladivostok

boris.geltser@vvsu.ruКуксинН. С.KuksinN. S.

г. Владивосток

Vladivostok

nikita.kuksin@vvsu.ruДомжаловИ. Г.DomzhalovI. G.

г. Владивосток

Vladivostok

igor@domzhalov.ruВладивостокский государственный университетРоссияVladivostok State UniversityRussian FederationДальневосточный федеральный университетРоссияFar Eastern Federal UniversityRussian Federation202327032025034457Copyright © Шахгельдян К.И., Гельцер Б.И., Куксин Н.С., Домжалов И.Г., 20252025Шахгельдян К.И., Гельцер Б.И., Куксин Н.С., Домжалов И.Г.Shakhgeldyan K.I., Geltser B.I., Kuksin N.S., Domzhalov I.G.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/105

Цель. Сравнительная оценка качества прогностических моделей внутригоспитальной летальности (ВГЛ) у больных инфарктом миокарда с подъемом сегмента ST (ИМnST) после чрескожного коронарного вмешательства (ЧКВ), разработанных на основе предикторов в непрерывной, дихотомической и многоуровневой категориальной формах.Материалы и методы. Проведено одноцентровое ретроспективное исследование, в рамках которого анализировали данные 4677 историй болезни пациентов с ИМnST после ЧКВ, находившихся на лечении в Региональном сосудистом центре г. Владивостока. Было выделено 2 группы больных: первая - 318 (6,8%) человек, умерших в стационаре, вторая — 4359 (93,2%) — с благоприятным исходом лечения. Прогностические модели ВГЛ с непрерывными переменными были разработаны методами многофакторной логистической регрессии, случайного леса и стохастического градиентного бустинга. Дихотомизация предикторов выполнялась методами поиска на сетке оптимальных точек отсечения, расчета центроидов и аддитивного объяснения Шепли (SHAP). Для многоуровневой категоризации предложено использовать объединение пороговых значений, выделенных при дихотомизации, а также ранжирование порогов отсечения с помощью весовых коэффициентов многофакторной логистической регрессии.Результаты. По результатам многоступенчатого анализа показателей клинико-функционального статуса больных ИМnST были выделены и валидированы новые предикторы ВГЛ, выполнена их категоризация и разработаны прогностические модели с непрерывными, дихотомическими и многоуровневыми категориальными переменными (AUС: 0.885-0.902). Модели, предикторы которых были выделены методом мультиметрической категоризации, не уступали по точности моделям с непрерывными переменными и имели более высокие метрики качества, чем алгоритмы с дихотомическими предикторами. Преимущество моделей с многоуровневой категоризацией предикторов заключалось в возможности объяснения и клинической интерпретации результатов прогнозирования ВГЛ.Заключение. Многоуровневая категоризация предикторов является перспективным инструментом для объяснения прогнозных оценок в клинической медицине.

Aim: Comparative assessment of the quality of predictive models of in-hospital mortality (IHM) in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary artery intervention (PCI), developed on the basis of predictors in continuous, dichotomous and multilevel categorical forms.Materials and methods: This was a single-center retrospective study analyzing data from 4677 medical records of patients with STEMI PCI who were treated at the Regional Vascular Center of Vladivostok. Two groups of patients were identified: the first consisted of 318 (6.8%) patients who died in hospital, the second — 4359 (93.2%) patients with a favorable treatment outcome. Predictive models of IHF with continuous variables were developed using multivariate logistic regression, random forest, and stochastic gradient boosting. Dichotomization of predictors was performed using grid search methods for optimal cutoff points, centroid calculation, and Shapley additive explanation (SHAP). It was proposed for multi-level categorization to use a combination of threshold values identified during dichotomization, as well as ranking cut-off thresholds using multivariate logistic regression weighting coefficients.Results: Based on the results of a multistage analysis of indicators of the clinical and functional status of STEMI patients, new predictors of IHM were identified and validated, their categorization was performed, and prognostic models with continuous, dichotomous and multilevel categorical variables were developed (AUC: 0.885-0.902). Models whose predictors were identified using the multimetric categorization method were not inferior in accuracy to models with continuous variables and had higher quality metrics than algorithms with dichotomous predictors. The advantage of models with multilevel categorization of predictors was the ability to explain and clinically interpret the results of IHM prediction.Conclusions: Multilevel categorization of predictors is a promising tool for explaining predictive scores in clinical medicine.

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