A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes.


Journal

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )
Titre abrégé: Artif Intell Med Conf Artif Intell Med (2005-)
Pays: Germany
ID NLM: 101750769

Informations de publication

Date de publication:
Jun 2021
Historique:
entrez: 14 7 2021
pubmed: 15 7 2021
medline: 15 7 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 34258609
doi: 10.1007/978-3-030-77211-6_59
pmc: PMC8274548
mid: NIHMS1713307
doi:

Types de publication

Journal Article

Langues

eng

Pagination

497-502

Subventions

Organisme : NICHD NIH HHS
ID : R01 HD101246
Pays : United States

Références

Paediatr Perinat Epidemiol. 2010 Sep;24(5):441-8
pubmed: 20670225
Am J Obstet Gynecol. 2015 Apr;212(4):539.e1-539.e24
pubmed: 25648779

Auteurs

Athresh Karanam (A)

The University of Texas at Dallas, USA.

Alexander L Hayes (AL)

Indiana University Bloomington, USA.

Harsha Kokel (H)

The University of Texas at Dallas, USA.

David M Haas (DM)

Indiana University Bloomington, USA.

Predrag Radivojac (P)

Northeastern University, USA.

Sriraam Natarajan (S)

The University of Texas at Dallas, USA.

Classifications MeSH