Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis
Publication Date
February 14, 2018
Authors
Yizhao Ni, Kathleen Alwell, Charles J. Moomaw, Daniel Woo, et al
Volume
13
Issue
2
Pages
e0192586
DOI
https://dx.plos.org/10.1371/journal.pone.0192586
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0192586
Scopus
85042157386
Mendeley
http://www.mendeley.com/research/towards-phenotyping-stroke-leveraging-data-largescale-epidemiological-study-detect-stroke-diagnosis
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Mendeley | Further Information

{"title"=>"Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis", "type"=>"journal", "authors"=>[{"first_name"=>"Yizhao", "last_name"=>"Ni", "scopus_author_id"=>"56029964100"}, {"first_name"=>"Kathleen", "last_name"=>"Alwell", "scopus_author_id"=>"6508079369"}, {"first_name"=>"Charles J.", "last_name"=>"Moomaw", "scopus_author_id"=>"7004407885"}, {"first_name"=>"Daniel", "last_name"=>"Woo", "scopus_author_id"=>"7103062797"}, {"first_name"=>"Opeolu", "last_name"=>"Adeoye", "scopus_author_id"=>"6507210644"}, {"first_name"=>"Matthew L.", "last_name"=>"Flaherty", "scopus_author_id"=>"57200376883"}, {"first_name"=>"Simona", "last_name"=>"Ferioli", "scopus_author_id"=>"12792103600"}, {"first_name"=>"Jason", "last_name"=>"Mackey", "scopus_author_id"=>"36137501400"}, {"first_name"=>"Felipe De Los Rios", "last_name"=>"La Rosa", "scopus_author_id"=>"57200678472"}, {"first_name"=>"Sharyl", "last_name"=>"Martini", "scopus_author_id"=>"8602092000"}, {"first_name"=>"Pooja", "last_name"=>"Khatri", "scopus_author_id"=>"7004042183"}, {"first_name"=>"Dawn", "last_name"=>"Kleindorfer", "scopus_author_id"=>"8239856900"}, {"first_name"=>"Brett M.", "last_name"=>"Kissela", "scopus_author_id"=>"6603414786"}], "year"=>2018, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"620704875", "doi"=>"10.1371/journal.pone.0192586", "issn"=>"19326203", "scopus"=>"2-s2.0-85042157386", "sgr"=>"85042157386", "isbn"=>"1111111111"}, "id"=>"eb99c7c7-6a78-31e8-9fe0-17e968c03381", "abstract"=>"Objective: 1) To develop a machine learning approach for detecting stroke cases and subtypes from hospitalization data, 2) to assess algorithm performance and predictors on real-world data collected by a large-scale epidemiology study in the US; and 3) to identify directions for future development of high-precision stroke phenotypic signatures.; Materials and Methods: We utilized 8,131 hospitalization events (ICD-9 codes 430-438) collected from the Greater Cincinnati/Northern Kentucky Stroke Study in 2005 and 2010. Detailed information from patients' medical records was abstracted for each event by trained research nurses. By analyzing the broad list of demographic and clinical variables, the machine learning algorithms predicted whether an event was a stroke case and, if so, the stroke subtype. The performance was validated on gold-standard labels adjudicated by stroke physicians, and results were compared with stroke classifications based on ICD-9 discharge codes, as well as labels determined by study nurses.; Results: The best performing machine learning algorithm achieved a performance of 88.57%/93.81%/92.80%/93.30%/89.84%/98.01% (accuracy/precision/recall/F-measure/area under ROC curve/area under precision-recall curve) on stroke case detection. For detecting stroke subtypes, the algorithm yielded an overall accuracy of 87.39% and greater than 85% precision on individual subtypes. The machine learning algorithms significantly outperformed the ICD-9 method on all measures (P value<0.001). Their performance was comparable to that of study nurses, with better tradeoff between precision and recall. The feature selection uncovered a subset of predictive variables that could facilitate future development of effective stroke phenotyping algorithms.; Discussion and Conclusions: By analyzing a broad array of patient data, the machine learning technologies held promise for improving detection of stroke diagnosis, thus unlocking high statistical power for subsequent genetic and genomic studies.; ", "link"=>"http://www.mendeley.com/research/towards-phenotyping-stroke-leveraging-data-largescale-epidemiological-study-detect-stroke-diagnosis", "reader_count"=>4, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Lecturer > Senior Lecturer"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Lecturer > Senior Lecturer"=>2}, "reader_count_by_subject_area"=>{"Medicine and Dentistry"=>2, "Neuroscience"=>1, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>1}}, "group_count"=>0}

Scopus | Further Information

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