Cargando…
Extracting New Temporal Features to Improve the Interpretability of Undiagnosed Type 2 Diabetes Mellitus Prediction Models
Type 2 diabetes mellitus (T2DM) often results in high morbidity and mortality. In addition, T2DM presents a substantial financial burden for individuals and their families, health systems, and societies. According to studies and reports, globally, the incidence and prevalence of T2DM are increasing...
Autores principales: | Kocbek, Simon, Kocbek, Primož, Gosak, Lucija, Fijačko, Nino, Štiglic, Gregor |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950921/ https://www.ncbi.nlm.nih.gov/pubmed/35330368 http://dx.doi.org/10.3390/jpm12030368 |
Ejemplares similares
-
A Review of Mortality Risk Prediction Models in Smartphone Applications
por: Fijačko, Nino, et al.
Publicado: (2021) -
Sentiment Analysis of Social Media Users’ Emotional Response to Sudden Cardiac Arrest During a Football Broadcast
por: Fijačko, Nino, et al.
Publicado: (2023) -
Building interpretable models for polypharmacy prediction in older chronic patients based on drug prescription records
por: Kocbek, Simon, et al.
Publicado: (2018) -
Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data
por: Kocbek, Primoz, et al.
Publicado: (2019) -
Early detection of type 2 diabetes mellitus using machine learning-based prediction models
por: Kopitar, Leon, et al.
Publicado: (2020)