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Neurol."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold>Multivariable analyses (MVA) and machine learning (ML) applied on large datasets may have a high potential to provide clinical decision support in neuro-otology and reveal further avenues for vestibular research. To this end, we build base-ml, a comprehensive MVA\/ML software tool, and applied it to three increasingly difficult clinical objectives in differentiation of common vestibular disorders, using data from a large prospective clinical patient registry (DizzyReg).<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold>Base-ml features a full MVA\/ML pipeline for classification of multimodal patient data, comprising tools for data loading and pre-processing; a stringent scheme for nested and stratified cross-validation including hyper-parameter optimization; a set of 11 classifiers, ranging from commonly used algorithms like logistic regression and random forests, to artificial neural network models, including a graph-based deep learning model which we recently proposed; a multi-faceted evaluation of classification metrics; tools from the domain of \u201cExplainable AI\u201d that illustrate the input distribution and a statistical analysis of the most important features identified by multiple classifiers.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold>In the first clinical task, classification of the bilateral vestibular failure (<jats:italic>N<\/jats:italic>= 66) vs. functional dizziness (<jats:italic>N<\/jats:italic>= 346) was possible with a classification accuracy ranging up to 92.5% (Random Forest). In the second task, primary functional dizziness (<jats:italic>N<\/jats:italic>= 151) vs. secondary functional dizziness (following an organic vestibular syndrome) (<jats:italic>N<\/jats:italic>= 204), was classifiable with an accuracy ranging from 56.5 to 64.2% (k-nearest neighbors\/logistic regression). The third task compared four episodic disorders, benign paroxysmal positional vertigo (<jats:italic>N<\/jats:italic>= 134), vestibular paroxysmia (<jats:italic>N<\/jats:italic>= 49), Meni\u00e8re disease (<jats:italic>N<\/jats:italic>= 142) and vestibular migraine (<jats:italic>N<\/jats:italic>= 215). Classification accuracy ranged between 25.9 and 50.4% (Na\u00efve Bayes\/Support Vector Machine). Recent (graph-) deep learning models classified well in all three tasks, but not significantly better than more traditional ML methods. Classifiers reliably identified clinically relevant features as most important toward classification.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold>The three clinical tasks yielded classification results that correlate with the clinical intuition regarding the difficulty of diagnosis. It is favorable to apply an array of MVA\/ML algorithms rather than a single one, to avoid under-estimation of classification accuracy. Base-ml provides a systematic benchmarking of classifiers, with a standardized output of MVA\/ML performance on clinical tasks. To alleviate re-implementation efforts, we provide base-ml as an open-source tool for the community.<\/jats:p>","DOI":"10.3389\/fneur.2021.681140","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T12:45:37Z","timestamp":1627908337000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Using Base-ml to Learn Classification of Common Vestibular Disorders on DizzyReg Registry Data"],"prefix":"10.3389","volume":"12","author":[{"given":"Gerome","family":"Vivar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralf","family":"Strobl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Grill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Zwergal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed-Ahmad","family":"Ahmadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,8,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3389\/fdigh.2018.00008","article-title":"Big data as a driver for clinical decision support systems: a learning health systems perspective","volume":"5","author":"Dagliati","year":"2018","journal-title":"Front Digit Humanit."},{"key":"B2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/s40537-019-0217-0","article-title":"Big data in healthcare: management, analysis and future prospects","volume":"6","author":"Dash","year":"2019","journal-title":"J Big Data."},{"key":"B3","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1055\/s-0038-1667081","article-title":"Public and population health informatics: the bridging of big data to benefit communities","volume":"27","author":"Gamache","year":"2018","journal-title":"Yearb Med Inform."},{"key":"B4","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/s00415-019-09458-y","article-title":"Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway","volume":"266","author":"Ahmadi","year":"2019","journal-title":"J Neurol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.jelekin.2015.01.004","article-title":"Automated classification of neurological disorders of gait using spatio-temporal gait parameters","volume":"25","author":"Pradhan","year":"2015","journal-title":"J Electromyogr Kinesiol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s00415-020-09931-z","article-title":"Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders","volume":"267","author":"Ahmadi","year":"2020","journal-title":"J Neurol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s00415-020-10061-9","article-title":"Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry","volume":"267","author":"Groezinger","year":"2020","journal-title":"J Neurol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/s00415-020-10150-9","article-title":"Primary or secondary chronic functional dizziness: does it make a difference? 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