Previously submitted to: JMIR Medical Informatics (no longer under consideration since Jun 01, 2019)
Date Submitted: Apr 5, 2019
Open Peer Review Period: Apr 8, 2019 - Jun 1, 2019
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Automated outlier Identification for Validation (AIV) of Stroke Outcomes Using Multiple Large Repositories
Background:
Introduction: Researchers commonly use the Modified Rankin Scale (mRS) and the Barthel Index (BI) to assess a patient’s clinical outcome after stroke. These are potential targets in machine learning models for stroke outcome prediction.
Objective:
The objective of this study was to evaluate density-based outlier detection methods on mRS and BI assessments.
Methods:
We trained three density-based outlier detection methods including density-based spatial clustering of applications (DBSCAN), hierarchical DBSCAN (HDBSCAN) and local outlier factor (LOF) based on data obtained from a nationwide prospective stroke registry in Taiwan. The testing of each method was done by using four different NINDS stroke datasets.
Results:
The DBSCAN achieved a high performance across all mRS values (the highest average accuracy was 99.2±0.7 at mRS-4 and the lowest average accuracy was 92.0±4.6 at mRS-3). The LOF also achieved similar performance but the HDBSCAN needs further improvement.
Conclusions:
The evaluation results showed that promising density-based outlier detection methods are promising for validation of stroke outcome measures during the study data clean up processes to improve completion of clinical data for increased data quality and research analyses.
Citation
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