Current medical research and opinion · 2021
Prediction and associated factors of hypothyroidism in systemic lupus erythematosus: a cross-sectional study based on multiple machine learning algorithms.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Huang Ting, Liu Siyang, Huang Jian, Li Jiarong, Liu Guixiong, Zhang Weiru, Wang Xuan
Аннотация
OBJECTIVES: The prevalence of hypothyroidism in systemic lupus erythematosus (SLE) is significantly higher than that in the common public. While SLE itself can affect multiple organs, abnormal thyroid function may exacerbate organ damage in patients with SLE. We aimed to predict abnormal thyroid function and to examine the associated factors with multiple machine learning approaches.
METHODS: In a cross-sectional study, 255 patients diagnosed with SLE at the rheumatology department in Xiangya Hospital between June 2012 and December 2016 were investigated. Feature engineering was used for filtering out principle clinical parameters, and five different machine learning methods were used to build prediction models for SLE with hypothyroidism.
RESULTS: Feature engineering selected 11 variables with which to build machine learning models. Among them, random forest modelling obtained the best prediction performance, with an accuracy rate of 88.37 and an area under the receiver operating characteristic curve of 0.772. The weights of anti-SSB antibody and anti-dsDNA antibody were 1.421 and 1.011, respectively, indicating a strong association with hypothyroidism in SLE.
CONCLUSIONS: Random Forest model performed best and is recommended for selecting vital indices and assessing clinical complications of SLE, it indicated that anti-SSB and anti-dsDNA antibodies may play principal roles in the development of hypothyroidism in SLE patients. It's feasible to build an accurate machine learning model for early diagnosis or risk factors assessment in SLE using clinical parameters, which would provide a reference for the research work of SLE in China.
METHODS: In a cross-sectional study, 255 patients diagnosed with SLE at the rheumatology department in Xiangya Hospital between June 2012 and December 2016 were investigated. Feature engineering was used for filtering out principle clinical parameters, and five different machine learning methods were used to build prediction models for SLE with hypothyroidism.
RESULTS: Feature engineering selected 11 variables with which to build machine learning models. Among them, random forest modelling obtained the best prediction performance, with an accuracy rate of 88.37 and an area under the receiver operating characteristic curve of 0.772. The weights of anti-SSB antibody and anti-dsDNA antibody were 1.421 and 1.011, respectively, indicating a strong association with hypothyroidism in SLE.
CONCLUSIONS: Random Forest model performed best and is recommended for selecting vital indices and assessing clinical complications of SLE, it indicated that anti-SSB and anti-dsDNA antibodies may play principal roles in the development of hypothyroidism in SLE patients. It's feasible to build an accurate machine learning model for early diagnosis or risk factors assessment in SLE using clinical parameters, which would provide a reference for the research work of SLE in China.