Explainable haemoglobin deferral predictions using machine learning models: Interpretation and consequences for the blood supply.

ABSTRACT:

Background and objectives

Accurate predictions of haemoglobin (Hb) deferral for whole-blood donors could aid blood banks in reducing deferral rates and increasing efficiency and donor motivation. Complex models are needed to make accurate predictions, but predictions must also be explainable. Before the implementation of a prediction model, its impact on the blood supply should be estimated to avoid shortages.

Materials and methods

Donation visits between October 2017 and December 2021 were selected from Sanquin's database system. The following variables were available for each visit: donor sex, age, donation start time, month, number of donations in the last 24 months, most recent ferritin level, days since last ferritin measurement, Hb at nth previous visit (n between 1 an

SUBMITTER: Vinkenoog M 

PROVIDER: S-EPMC9826045 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature