OBJECTIVE: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. MATERIALS AND METHODS: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and the classifier, the gold-standard annotations and answers were developed manually, and the results of the implemented system were compared with the gold standard. RESULTS: The IE model achieved an F-score from 92.40% to 99.59%, and the classifier achieved accuracy from 96.15% to 100%. CONCLUSIONS: The application was successfully applied to the various types of narrative clinical reports. It might be applied to the key extraction for other types of cancer patients.
OBJECTIVE: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. MATERIALS AND METHODS: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancerpatients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and the classifier, the gold-standard annotations and answers were developed manually, and the results of the implemented system were compared with the gold standard. RESULTS: The IE model achieved an F-score from 92.40% to 99.59%, and the classifier achieved accuracy from 96.15% to 100%. CONCLUSIONS: The application was successfully applied to the various types of narrative clinical reports. It might be applied to the key extraction for other types of cancerpatients.
Authors: Joeky T Senders; Aditya V Karhade; David J Cote; Alireza Mehrtash; Nayan Lamba; Aislyn DiRisio; Ivo S Muskens; William B Gormley; Timothy R Smith; Marike L D Broekman; Omar Arnaout Journal: JCO Clin Cancer Inform Date: 2019-04
Authors: Seyedmostafa Sheikhalishahi; Riccardo Miotto; Joel T Dudley; Alberto Lavelli; Fabio Rinaldi; Venet Osmani Journal: JMIR Med Inform Date: 2019-04-27