Sheharyar Raza: How Machine Learning Could Transform Blood Donation
Sheharyar Raza, Transfusion Medicine Specialist at Sunnybrook Health Sciences Center and Internal Medicine Physician at Unity Health Toronto, shared a post on LinkedIn about a recent article he and his colleagues co-authored, published in Transfusion Medicine Reviews, adding:
“Can machine learning be applied to solve everyday problems in blood donation?
In our article in Transfusion Medicine Reviews, Ruchika, Na and I review a representative subset of studies that use machine learning methods to advance donor-related blood services.
Although the field is at a nascent stage, we identified strong articles solving important problems in blood donation.
A team featuring the late Mart Janssen studied prediction of vasovagal reactions from facial temperature profiles; a team with Katja van den Hurk and Mikko Arvas predicted hemoglobin-based donor deferral in a multi-national cohort; Alton Russell‘s team used ML to predict iron deficiency after blood donation, among others.
Editorializing a bit, I have noticed organizational leadership is often understandably dazzled by generative AI and overlook analytical machine learning, which can arguably provide sharp, purpose-built, low-cost, and higher value solutions for a much larger set of problems faced in daily operations, usually at lower cost.
Generative AI still likely has a place as the human interface between ML outputs and lay users in many such scenarios, however, ML can often do much of the heavy lifting, and both technologies can be used in series or parallel.
The article is a bit technical, though we hope lay readers still find useful insights within.”
Title: Machine Learning for Donor Safety and Engagement: An Analytical Assessment
Authors: Sheharyar Raza, Na Li, Ruchika Goel
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