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Lucie Tvrdá: Missing Data Could Themselves Be Used as Predictors of Stroke Outcomes
Lucie Tvrdá/LinkedIn
Mar 27, 2026, 17:39

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

Lucie Tvrdá: Missing Data Could Themselves Be Used as Predictors of Stroke Outcomes

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Cerebrovascular Diseases Esra Zihni Health Hematology Hemostasis Hemostasis News Hemostasis Today John D. Kelleher Kalliopi Mavromati Katryna Cisek Lucie Tvrda Machine Learning Medicine Missing Data ML Nonclinical Factors Stelios Lamprou stroke Terence J. Quinn thrombosis
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