English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Objective: To determine a predictive model of preeclampsia according to the consumption of macronutrients through machine learning in a hospital in Lima, 2019. Material and methods: Analytical study of cases and controls where 120 pregnant women attended at the Sergio Bernales National Hospital participated. Preeclampsia was evaluated according to the hospital clinical record and the consumption of macronutrients in calories ingested from lipids, carbohydrates and proteins. Predictive cut-off points of preeclampsia were generated using a supervised machine learning model. Results: Patients who had preeclampsia had lower carbohydrate consumption (Me = 1004.1 cal) and higher consumption of lipids (Me = 1869.9 cal) and proteins (Me = 684.5 cal). Of these there was only significant variation in carbohydrates and lipids (p <0.05). Using the decision tree method, it was determined that consuming less than or equal to 1743.57cal of lipids or 1463.2cal per day reduces the probability of presenting preeclampsia (p <0.05). Conclusion: The consumption of lipids and carbohydrates, measured in calories, at certain values allows to predict the presence of preeclampsia through a machine learning model. Key word: Pre-eclampsia, macronutrients, predictive model (Source: MeSH NLM).

Modelo predictivo de preeclampsia según el consumo de macronutrientes mediante aprendizaje automático en un hospital de Lima, 2019