ArticleAmerican journal of public health2017
Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.
Article in American journal of public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine Learning-Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study.Journal of medical Internet research · 2026Article
- Multidimensional predictors of preterm birth risk among black and white primiparous women in the U.S.: insights from machine learning.BMC pregnancy and childbirth · 2025Article
- Black-white differences in chronic stress exposures to predict preterm birth: interpretable, race/ethnicity-specific machine learning model.BMC pregnancy and childbirth · 2024Article
- Study of machine learning techniques for outcome assessment of leptospirosis patients.Scientific reports · 2024Article
- Predicting the drop out from the maternal, newborn and child healthcare continuum in three East African Community countries: application of machine learning models.BMC medical informatics and decision making · 2023Article
- Emergency department use and Artificial Intelligence in Pelotas: design and baseline results.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2023Article
- Leveraging Data and Digital Health Technologies to Assess and Impact Social Determinants of Health (SDoH): a State-of-the-Art Literature Review.Online journal of public health informatics · 2021Article
- Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review.Frontiers in bioengineering and biotechnology · 2021Review
- Towards neonatal mortality risk classification: A data-driven approach using neonatal, maternal, and social factors.Informatics in medicine unlocked · 2020Article
- Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study.PloS one · 2019Article
- A Machine Learning Approach to Identify NIH-Funded Applied Prevention Research.American journal of preventive medicine · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
objectivesTo evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services.
methodsWe used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection.
resultsMachine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable.
conclusionsOur analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions.
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