Negative / Null Result ReportOpen accessMedicine· cited by 164

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

Hendrikus J. A. van Os; Lucas A. Ramos; Adam Hilbert; Matthijs van Leeuwen; Marianne A.A. van Walderveen; Nyika D. Kruyt; Diederik W.J. Dippel; Ewout W. Steyerberg · 2018 · Frontiers in Neurology

WASTE classifies this as Negative / Null Result Report · AI classification, approximate

The study found no significant effect — useful as a negative control or null benchmark for your own design.

Abstract

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO

Abstract by Hendrikus J. A. van Os; Lucas A. Ramos; Adam Hilbert; Matthijs van Leeuwen; Marianne A.A. van Walderveen; Nyika D. Kruyt; Diederik W.J. Dippel; Ewout W. Steyerberg, Frontiers in Neurology (2018) — licensed CC BY 4.0.

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