Модели объяснения диагноза как элемент интеллектуальных систем диагностики в медицине: краткий обзор

В работе рассмотрены наиболее важные и эффективные подходы и модели объяснения и интерпретации результатов диагностики, получаемых с использованием интеллектуальных систем диагностики. Необходимость их использования обусловлена тем, что сама интеллектуальная система диагностики является «черным ящиком» и для врача важно не только получить диагноз пациента, но и понять, почему получен такой диагноз, какие элементы информации о пациенте наиболее значимы с точки зрения диагноза. Приведены обзоры основных подходов к объяснению предсказаний моделей машинного обучения в целом и применительно к медицине. Показано, как различная исходная информация о пациенте влияет на выбор моделей объяснения. Рассмотрены модели при наличии визуальной и табличной информации. Также рассмотрены модели объяснения примерами. Цель работы — обзор основных моделей объяснения и их зависимости от вида информации о пациенте.

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