Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning
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- 作者：Michael Jungheim ; M.D.a ; jungheim.michael@mh-hannover.de" class="auth_mail" title="E-mail the corresponding author ; Andre Busche ; Ph.D.b ; c ; busche@ismll.uni-hildesheim.de" class="auth_mail" title="E-mail the corresponding author ; Simone Miller ; B.A. Linguisticsa ; miller.simone@mh-hannover.de" class="auth_mail" title="E-mail the corresponding author ; Nicolas Schilling ; Dipl.Math.c ; schilling@ismll.uni-hildesheim.de" class="auth_mail" title="E-mail the corresponding author ; Lars Schmidt-Thieme ; Ph.D.c ; schmidt-thieme@ismll.uni-hildesheim.de" class="auth_mail" title="E-mail the corresponding author ; Martin Ptok ; M.D. ; Ph.D.a ; ptok.martin@mh-hannover.de" class="auth_mail" title="E-mail the corresponding author
- 关键词：High resolution manometry ; Upper esophageal sphincter ; Deglutition ; Dysphagia ; Restitution time ; Machine learning model
- 刊名：Physiology & Behavior
- 出版年：2016
- 出版时间：15 October 2016
- 年：2016
- 卷：165
- 期：Complete
- 页码：413-424
- 全文大小：1623 K
文摘
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Physiological data for the restitution time of the upper esophageal sphincter are provided.
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The restitution time is determined by swallowing specialists and objectively by a Machine Learning model.
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The implemented Machine Learning model is partly independent of the input of the investigators.
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The methods provided can be a basis for a complete automatic evaluation of high resolution manometry data in the future.