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    題名: Artificial intelligence-assisted prediction of preeclampsia: development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia
    作者: 蘇家玉
    Herdiantri Sufriyana, Yu-Wei Wu, Emily Chia-Yu Su
    貢獻者: 醫學資訊研究所
    日期: 2020-04
    上傳時間: 2025-03-31 10:51:53 (UTC+8)
    摘要: Abstract
    Background: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.

    Methods: The BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) from all women with one pregnancy. The dataset provided 95 features consisting of demographic variables and medical histories started from 24 months to event and ended by delivery as the event. Six algorithms were compared by area under the receiver operating characteristics curve (AUROC) with a subgroup analysis by time to the event. We compared our model to similar prediction models from systematically reviewed studies. In addition, we conducted a text mining analysis based on natural language processing techniques to interpret our modeling results.

    Findings: The best model consisted of 17 predictors extracted by a random forest algorithm. Nine?12 months to the event was the period that had the best AUROC in external validation by either geographical (0.88, 95% confidence interval (CI) 0.88-0.89) or temporal split (0.86, 95% CI 0.85-0.86). We compared this model to prediction models in seven studies from 869 records in PUBMED, EMBASE, and SCOPUS. This model outperformed the previous models in terms of the precision, sensitivity, and specificity in all validation sets.

    Interpretation: Our low-cost model improved preliminary prediction to decide pregnant women that will be predicted by the models with high specificity and advanced predictors.

    Funding: This work was supported by grant no. MOST108-2221-E-038-018 from the Ministry of Science and Technology of Taiwan.
    關聯: EBioMedicine. 2020 Apr; 54: 102710
    描述: 【111-1 升等】臺北醫學大學教師升等專門著作
    職別:專任
    送審等級:教授
    著作送審
    資料類型: article
    顯示於類別:[教師升等送審著作] 111

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