Rule-based Cervical Spine Defect Classification Using Medical Narratives.
Classifying the defects occurring at the cervical spine provides the basis for surgical treatment planning and therapy recommendation. This process requires evidence from patient records. Further, the degree of a defect needs to be encoded in a standardized from to facilitate data exchange and multimodal interoperability. In this paper, a concept for automatic defect classification based on information extracted from textual data of patient records is presented. In a retrospective study, the classifier is applied to clinical documents and the classification results are evaluated.
Innovation Center of Computer Assisted Surgery, University of Leipzig, Germany.
Yihan
Y
Deng
Nourosurgical Department, University Hospital Leipzig, Germany.
Mathias Jacob
MJ
Groll
Innovation Center of Computer Assisted Surgery, University of Leipzig, Germany.
Kerstin
K
Denecke
D000465:Algorithms; D064229:Biological Ontologies; D002574:Cervical Vertebrae; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records; D006801:Humans; D051188:Knowledge Bases; D000069550:Machine Learning; D033262:Narration; D009323:Natural Language Processing; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013130:Spinal Stenosis / Q000145:classification / Q000175:diagnosis*; D009626:Terminology as Topic