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Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation

Bose, Eliezer; Maganti, Sasank; Bowles, Kathryn H.; Brueshoff, Bonnie L.; Monsen, Karen A.

doi: 10.1097/NNR.0000000000000315

Background Public health nurses (PHNs) engage in home visiting services and documentation of care services for at-risk clients. To increase efficiency and decrease documentation burden, it would be useful for PHNs to identify critical data elements most associated with patient care priorities and outcomes. Machine learning techniques can aid in retrospective identification of critical data elements.

Objective We used two different machine learning feature selection techniques of minimum redundancy–maximum relevance (mRMR) and LASSO (least absolute shrinkage and selection operator) and elastic net regularized generalized linear model (glmnet in R).

Methods We demonstrated application of these techniques on the Omaha System database of 205 data elements (features) with a cohort of 756 family home visiting clients who received at least one visit from PHNs in a local Midwest public health agency. A dichotomous maternal risk index served as the outcome for feature selection.

Application Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero, and generalized linear model applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero, and generalized linear model applied on them achieved the highest accuracy of 95.5% on a held-out test set.

Discussion Feature selection techniques show promise toward reducing public health nursing documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing PHNs’ focus on client-specific and targeted interventions in the delivery of care.

Eliezer Bose, PhD, BEng, APRN, ACNP-BC, is Assistant Professor, University of Texas at Austin School of Nursing.

Sagank Maganti, MS, B-Tech, is Research Assistant, University of Minnesota Carlson School of Computer Science and Engineering, Minneapolis.

Kathryn H. Bowles, PhD, RN, FAAN, FACMI, is Professor, University of Pennsylvania School of Nursing, Philadelphia.

Bonnie L. Brueshoff, DNP, RN, PHN, is Public Health Director at Dakota County, Minneapolis, Minnesota.

Karen A. Monsen, PhD, RN, FAAN, is Associate Professor, University of Minnesota School of Nursing, Minneapolis.

Accepted for publication June 3, 2018.

This research work has adhered to strict ethical conduct of research and was approved by the University of Minnesota Institutional Review Board.

The authors have no conflicts of interest to report.

Corresponding author: Eliezer Bose, PhD, BEng, APRN, ACNP-BC, University of Texas at Austin School of Nursing, 1710 Red River St., Austin, TX 78712 (e-mail:

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