Abstract
One of the main problems in treating stroke patients is accurate and timely triage and assessment. Not all stroke events have direct severe consequences. Full strokes are often preceded by transient ischemic attacks (TIA) or mini strokes, which exhibit signs and symptoms similar to less concerning health events, e.g., migraines. In this paper, natural language techniques are presented to process a large collection of medical narrative descriptions extracting features that can be subsequently used for automatic classification using Data Mining algorithms. We reviewed 5658 cases and analyzed the chief complaint and history of the patient illness reported at stroke rapid assessment unit (SRAU) at Victoria General Hospital (VGH). Data were collected by neurologists and stroke nurses between years 2008 and 2013. Based on a clinician-supplied list of important sign and symptom terms, we translated narrative medical text into well-codified sentences achieving an impressive agreement with a human expert. Afterwards, Data Mining algorithms were applied on codified data and obtaining not only prediction models, but also important weights for the codified terms. An extensive experimental evaluation of several classifiers is provided based on past data to predict new cases. Notably, we achieved a sensitivity of about 84 % and specificity of 64 % using support vector machines (SVM). The top terms identified by data mining algorithms were responsible for most of the prediction quality; therefore, they can be used to build a questionnaire-like, online application that can be employed as a first-line screening in triage for detecting stroke/TIA or mimic and help triage decide for the next step of treatment or discharge the patient.
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Notes
Implementing the application is out of the scope of this project, but it is under progress.
The ABCD score alone did not give us acceptable levels of sensitivity and specificity.
If the estimate is 0.8 or above, there is excellent agreement between the algorithm and the human assessment, the score between 0.6 and 0.8 is considered good agreement (Goryachev et al. 2006).
Due to IP restrictions, we cannot provide here the list of important terms and the weights we derived for them. However, the interested readers can contact Dr. Andrew Penn on how to obtain this information. Also, the front-end application is not part of this study. Again, details on the front-end application can be obtained from Dr. Andrew Penn.
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Acknowledgments
The authors would like to acknowledge Kristine Votova, Ph.D., the project manager for the SpecTRA Research Project and the Island Health clinical research team at the Stroke Rapid Assessment Unit for their support. Funding for the natural experiment in stroke care and the large-scale personalized medicine for mass spectrometry in rapid TIA triage comes from Canadian Institute of Health Research (2009–2012) and Genome Canada/BC (2013–2017).
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Sedghi, E., Weber, J.H., Thomo, A. et al. Mining clinical text for stroke prediction. Netw Model Anal Health Inform Bioinforma 4, 16 (2015). https://doi.org/10.1007/s13721-015-0090-5
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DOI: https://doi.org/10.1007/s13721-015-0090-5