ArticleInformatics in medicine unlocked2020
Towards neonatal mortality risk classification: A data-driven approach using neonatal, maternal, and social factors.
Article in Informatics in medicine unlocked, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.Population health metrics · 2025Article
- A case-based reasoning system for neonatal survival and LOS prediction in neonatal intensive care units: a development and validation study.Scientific reports · 2023Article
- Prediction of neonatal deaths in NICUs: development and validation of machine learning models.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infant mortality is an important health measure in a population as a crude indicator of the poverty and socioeconomic level. It also shows the availability and quality of health services and medical technology in a specific region. Although improvements have been observed in the last decades, the implementation of actions to reduce infant mortality is still a concern in many countries. To address such an important problem, this paper proposes a new support decision approach to classify newborns according to their neonatal mortality risk. Using features related to mother, newborn, and socio-demographic, we model the problem using a data-driven classification model able to provide the probability of a newborn dying until
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.