Lucie Tvrdá: Missing Data Could Themselves Be Used as Predictors of Stroke Outcomes
Lucie Tvrdá, PhD Researcher at University of Glasgow, College of Medical, Veterinary and Life Sciences, shared on LinkedIn about a recent article she and her colleagues co-authored, adding:
”Happy to share that our paper ‘Early Prediction of Adverse Stroke Outcomes Using Nonclinical Factors and Missing Data: A Machine Learning Study’ is now published with open access in Cerebrovascular Diseases!
Missing clinical information is a common problem for predictive models using routinely collected healthcare data.
As it turns out, missingness itself could be used as a predictor of stroke outcomes!
Big thanks to all co-authors from my Glasgow team and from RES-Q+ including Kalliopi Mavromati, Stelios L., Katryna C., Esra Zihni, Prof. John D. Kelleher and Terry Quinn.”
Title: Early Prediction of Adverse Stroke Outcomes Using Nonclinical Factors and Missing Data: A Machine Learning Study
Authors: Lucie Tvrda, Kalliopi Mavromati, Stelios Lamprou, Katryna Cisek, Esra Zihni, John D. Kelleher, Terence J. Quinn
Read the Full Article on Cerebrovascular Diseases
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