Machine learning for social services: A study of prenatal case management in Illinois

I. Pan, L.B. Nolan, R.R. Brown, R. Khan, P. Van Der Boor, D.G. Harris and R. Ghani

American Journal of Public Health, 2017, vol. 107, issue 6, 938-944

Abstract: Objectives. To 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. Methods. We 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. Results. Machine 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. Conclusions. Our 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:aph:ajpbhl:10.2105/ajph.2017.303711_9

DOI: 10.2105/AJPH.2017.303711

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