Publicación:
MACHINE LEARNING-BASED MODELS FOR GESTATIONAL DIABETES MELLITUS PREDICTION BEFORE 24-28 WEEKS OF PREGNANCY: A REVIEW

dc.creatorANDRÉS IGNACIO RODRÍGUEZ MORALES
dc.date2022
dc.date.accessioned2025-01-10T15:33:42Z
dc.date.available2025-01-10T15:33:42Z
dc.date.issued2022
dc.description.abstractGESTATIONAL DIABETES MELLITUS (GDM) IS A HYPERGLYCEMIA STATE THAT IMPAIRS MATERNAL AND OFFSPRING HEALTH, SHORT AND LONG-TERM. IT IS USUALLY DIAGNOSED AT 24?28 WEEKS OF PREGNANCY (WP), BUT AT THAT TIME THE FETAL PHENOTYPE IS ALREADY ALTERED. MACHINE LEARNING (ML)-BASED MODELS HAVE EMERGED AS AN AUSPICIOUS ALTERNATIVE TO PREDICT THIS PATHOLOGY EARLIER, HOWEVER, THEY MUST BE VALIDATED IN DIFFERENT POPULATIONS BEFORE THEIR IMPLEMENTATION IN ROUTINE CLINICAL PRACTICE. THIS REVIEW AIMS TO GIVE AN OVERVIEW OF THE ML-BASED MODELS THAT HAVE BEEN PROPOSED TO PREDICT GDM BEFORE 24?28 WP, WITH SPECIAL EMPHASIS ON THEIR CURRENT VALIDATION STATE AND PREDICTIVE PER- FORMANCE. ARTICLES WERE SEARCHED IN PUBMED. MANUSCRIPTS WRITTEN IN ENGLISH AND PUBLISHED BEFORE JANUARY 1, 2022, WERE CONSIDERED. 109 ORIGINAL RESEARCH STUDIES WERE SELECTED, AND CATEGORIZED ACCORDING TO THE TYPE OF VARIABLES THAT THEIR MODELS INVOLVED: MEDICAL, I.E. CLINICAL AND/OR BIOCHEMICAL PARAMETERS; ALTERNATIVE, I.E. METABOLITES, PEPTIDES OR PROTEINS, MICRO-RIBONUCLEIC ACID MOLECULES, MICROBIOTA GENERA, OR OTHER VARIABLES THAT DID NOT FIT INTO THE FIRST CATEGORY; OR MIXED, I.E. BOTH MEDICAL AND ALTERNATIVE DATA. ONLY 8.3 % OF THE REVIEWED MODELS HAVE HAD VALIDATION IN INDEPENDENT STUDIES, WITH LOW OR MODERATE PERFORMANCE FOR GDM PREDICTION. IN CONTRAST, SEVERAL MODELS THAT LACK OF INDEPENDENT VALIDATION HAVE SHOWN A VERY HIGH PREDICTIVE POWER. THE EVALUATION OF THESE PROMISING MODELS IN FUTURE INDEPENDENT VALIDATION STUDIES WOULD ALLOW TO ASSESS THEIR PERFORMANCE ON DIFFERENT POPULATIONS, AND CONTINUE THEIR WAY TOWARDS CLINICAL IMPLEMENTATION. ONCE SETTLED, ML-BASED MODELS WOULD HELP TO PREDICT GDM EARLIER, INITIATE ITS TREATMENT TIMELY AND PREVENT ITS NEGATIVE CONSEQUENCES ON MATERNAL AND OFFSPRING HEALTH.
dc.formatapplication/pdf
dc.identifier.doi10.1016/j.artmed.2022.102378
dc.identifier.issn1873-2860
dc.identifier.issn0933-3657
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/12596
dc.languagespa
dc.publisherARTIFICIAL INTELLIGENCE IN MEDICINE
dc.relation.uri10.1016/j.artmed.2022.102378
dc.rightsPUBLICADA
dc.subjectVALIDATION EARLY PREDICTION
dc.subjectMULTIVARIATE ANALYSIS
dc.subjectMACHINE LEARNING
dc.subjectGESTATIONAL DIABETES MELLITUS
dc.subjectEARLY DETECTION
dc.titleMACHINE LEARNING-BASED MODELS FOR GESTATIONAL DIABETES MELLITUS PREDICTION BEFORE 24-28 WEEKS OF PREGNANCY: A REVIEW
dc.typeARTÍCULO DE REVISIÓN
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE CIENCIAS BASICAS
ubb.SedeCHILLÁN
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