Athresh Karanam, Alexander L Hayes, Harsha Kokel, David M Haas, Predrag Radivojac, Sriraam Natarajan
{"title":"A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes.","authors":"Athresh Karanam, Alexander L Hayes, Harsha Kokel, David M Haas, Predrag Radivojac, Sriraam Natarajan","doi":"10.1007/978-3-030-77211-6_59","DOIUrl":null,"url":null,"abstract":"<p><p>Qualitative influence statements are often provided a priori to guide learning; we answer a challenging reverse task and automatically extract them from a learned probabilistic model. We apply our Qualitative Knowledge Extraction method toward early prediction of gestational diabetes on clinical study data. Our empirical results demonstrate that the extracted rules are both interpretable and valid.</p>","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"12721 ","pages":"497-502"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274548/pdf/nihms-1713307.pdf","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-030-77211-6_59","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/6/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Qualitative influence statements are often provided a priori to guide learning; we answer a challenging reverse task and automatically extract them from a learned probabilistic model. We apply our Qualitative Knowledge Extraction method toward early prediction of gestational diabetes on clinical study data. Our empirical results demonstrate that the extracted rules are both interpretable and valid.