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This study assesses a machine learning (ML) neonatal risk tool in Kenya. Findings are expected to support integrating ML for reducing neonatal mortality in low- and middle-income countries.

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Area of Science:

  • Public Health
  • Machine Learning
  • Neonatal Care

Background:

  • Neonatal mortality is a significant challenge in low- and middle-income countries (LMICs), especially in sub-Saharan Africa.
  • Existing triage mechanisms and clinical tools often fall short in identifying high-risk neonates promptly.
  • A prior ML model showed high accuracy (AUC > 0.80) in predicting neonatal mortality using 11 parameters, with birth weight being the strongest predictor.

Purpose of the Study:

  • To evaluate the feasibility, applicability, and usability of ML-based neonatal risk predictor variables in Kenyan healthcare facilities.
  • To inform potential adoption of the tool, aligning with national health priorities and Sustainable Development Goal 3.2.

Main Methods:

  • A mixed-methods feasibility study using a preimplementation approach in 3 Kenyan health facilities.
  • Phased implementation: Phase 1 (key informant interviews), Phase 2 (4-month paper-based tool integration), Phase 3 (post-intervention surveys).
  • Qualitative and quantitative data collection to assess integration, workflow disruption, user experience, and barriers.

Main Results:

  • The neonatal risk predictors are anticipated to show strong contextual feasibility for integration into existing triage systems.
  • The tool is expected to enable early identification of high-risk neonates within 48 hours for timely clinical decisions.
  • Usability assessments are projected to reveal positive user experience and acceptability among healthcare workers.

Conclusions:

  • The study is expected to yield actionable evidence for translating ML-based risk prediction into routine clinical practice in LMICs.
  • Successful implementation can contribute to reducing neonatal mortality rates.
  • This aligns with global targets, specifically Sustainable Development Goal target 3.2.