Information extraction for tracking liver cancer patients' statuses: From mixture of clinical narrative report types
Journal
Telemedicine and e-Health
Journal Volume
19
Journal Issue
9
Pages
704-710
Date Issued
2013
Author(s)
Abstract
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. ? Copyright 2013, Mary Ann Liebert, Inc.
SDGs
Other Subjects
Clinical research; Clinical research eligibility criterion; Disease progression; Ehealth; Medical record; Patients' conditions; Radiology reports; Rule-based classifier; Gold; Information management; Information retrieval; Technology; Diseases; article; data mining; disease course; electronic medical record; female; health status; human; liver tumor; male; methodology; natural language processing; Taiwan; theoretical model; Data Mining; Disease Progression; Electronic Health Records; Female; Health Status; Humans; Liver Neoplasms; Male; Models, Theoretical; Natural Language Processing; Taiwan
Type
journal article