Application of machine learning methods in the problem of determining the level of risks during pregnancy
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Abstract
В период пандемии большое развитие получило направление медицинских дистанционных консультаций. Для оптимизации времени ожидания полезна функция, реализующая определение предварительной оценки состояния человека на основе ответов на простые вопросы о самочувствии. Целью данной работы является исследование методов машинного обучения на данных из электронных медицинских карт для применения результатов в системе удаленных консультаций беременных женщин.
During the pandemic, the direction of medical remote consultations was greatly developed. To optimize the waiting time, a function is useful that implements the determination of a preliminary assessment of a person's condition based on answers to simple questions about well-being. The purpose of this work is to analyze the application of machine learning methods on data from electronic medical records for the application of the results in the system of remote consultations for pregnant women.
During the pandemic, the direction of medical remote consultations was greatly developed. To optimize the waiting time, a function is useful that implements the determination of a preliminary assessment of a person's condition based on answers to simple questions about well-being. The purpose of this work is to analyze the application of machine learning methods on data from electronic medical records for the application of the results in the system of remote consultations for pregnant women.
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Машинное обучение, электронные медицинские карты, оценка перинатальных рисков, классификация, k-ближайших соседей, метод опорных векторов, классификация, случайный лес, логистическая регрессия, Machine learning, electronic health records, perinatal risk assessment, word2vec, fasttext, BertEmbeddings, classification, k-nearest neighbors, support vector machines, random forest, logistic regression