Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.

Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report.

Community-based management of acute malnutrition for infants under 6 months of age is safe and effective: analysis of operational data.

Objective: To assess the effectiveness of outpatient management with ready-to-use and supplementary foods for infants under 6 months (u6m) of age who were unable to be treated as inpatients due to social and economic barriers.

Design: Review of operational acute malnutrition treatment records.

Setting: Twenty-one outpatient therapeutic feeding clinics in rural Malawi.