Konferenzpaper | 2012 | Peer reviewed

An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques

Autor(en)

Gaspers, Judith ; Thiele, Kristina ; Cimiano, Philipp

; Foltz, Anouschka ; Stenneken, Prisca ; Tscherepanow, Marko

Titel des Konferenzbandes

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Name der Konferenz

2nd ACM SIGHIT International Health Informatics Symposium

DOI

10.1145/2110363.2110389

ISBN-13

9781450313667

URL

https://pub.uni-bielefeld.de/record/2329837

Weitere Details

Sprache

englisch

Darstellungsform

Text

Seitenbereich

209 - 218

Schlagwörter

Machine learning , aphasia , cognitivecommunication disorder , word generation tasks

Reliably distinguishing patients with verbal impairment due to brain damage, e.g. aphasia, cognitive communication disorder (CCD), from healthy subjects is an important challenge in clinical practice. A widely-used method is the application of word generation tasks, using the number of correct responses as a performance measure. Though clinically well-established, its analytical and explanatory power is limited. In this paper, we explore whether additional features extracted from task performance can be used to distinguish healthy subjects from aphasics or CCD patients. We considered temporal, lexical, and sublexical features and used machine learning techniques to obtain a model that minimizes the empirical risk of classifying participants incorrectly. Depending on the type of word generation task considered, the exploitation of features with state-of-the-art machine learning techniques outperformed the predictive accuracy of the clinical standard method (number of correct responses). Our analyses confirmed that number of correct responses is an adequate measure for distinguishing aphasics from healthy subjects. However, our additional features outperformed the traditional clinical measure in distinguishing patients with CCD from healthy subjects: The best classification performance was achieved by excluding number of correct responses. Overall, our work contributes to the challenging goal of distinguishing patients with verbal impairments from healthy subjects.

Zitierstile

Gaspers J, Thiele K, Cimiano P, Foltz A, Stenneken P, Tscherepanow M. An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. ACM; 2012: 209-218.

In die Zwischenablage einfügen

Gaspers, J., Thiele, K., Cimiano, P., Foltz, A., Stenneken, P., & Tscherepanow, M. (2012). An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 209-218. ACM. https://doi.org/10.1145/2110363.2110389

In die Zwischenablage einfügen

Gaspers, Judith, Thiele, Kristina, Cimiano, Philipp, Foltz, Anouschka, Stenneken, Prisca, and Tscherepanow, Marko. 2012. “An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques”. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 209-218. ACM.

In die Zwischenablage einfügen

Gaspers, J., Thiele, K., Cimiano, P., Foltz, A., Stenneken, P., and Tscherepanow, M. (2012). “An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques” in Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (ACM), 209-218.

In die Zwischenablage einfügen

Gaspers, J., et al., 2012. An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. ACM, pp. 209-218.

In die Zwischenablage einfügen

J. Gaspers, et al., “An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques”, Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, ACM, 2012, pp.209-218.

In die Zwischenablage einfügen

Gaspers, J., Thiele, K., Cimiano, P., Foltz, A., Stenneken, P., Tscherepanow, M.: An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. p. 209-218. ACM (2012).

In die Zwischenablage einfügen

Gaspers, Judith, Thiele, Kristina, Cimiano, Philipp, Foltz, Anouschka, Stenneken, Prisca, and Tscherepanow, Marko. “An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques”. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. ACM, 2012. 209-218.

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