Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia

Herdiantri Sufriyana, MD; Yu-Wei Wu, PhD; Emily Chia-Yu Su, PhD

EBioMedicine (Apr 2020)

Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia

Herdiantri Sufriyana, MD,
Yu-Wei Wu, PhD,
Emily Chia-Yu Su, PhD

Affiliations

Herdiantri Sufriyana, MD: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Department of Medical Physiology, College of Medicine, University of Nahdlatul Ulama Surabaya, Surabaya 60237, Indonesia
Yu-Wei Wu, PhD: Department of Medical Physiology, College of Medicine, University of Nahdlatul Ulama Surabaya, Surabaya 60237, Indonesia; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan
Emily Chia-Yu Su, PhD: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 11031, Taiwan; Corresponding author at: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan

Journal volume & issue: Vol. 54

Abstract

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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. Keywords: Artificial intelligence, Preeclampsia, Clinical prediction rule, Machine learning, Natural language processing, Health insurance dataset

Published in EBioMedicine

ISSN: 2352-3964 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Medicine: Medicine (General)
Website: http://www.journals.elsevier.com/ebiomedicine/

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