عنوان انگلیسی مقاله:
Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK
ترجمه فارسی عنوان مقاله:
اعتبارسنجی تکنیک های استخراج متن و تجزیه و تحلیل محتوا با استفاده از داده های جمع آوری شده از سیستم های نرم افزار مدیریت دامپزشکی در انگلستان
Sciencedirect - Elsevier - Preventive Veterinary Medicine, 167 (2019) 61-67: doi:10:1016/j:prevetmed:2019:02:015
Julie S. Jones-Diettea,⁎, Rachel S. Deana,1, Malcolm Cobbb, Marnie L. Brennana
Electronic patient records from practice management software systems have been used extensively in medicine
for the investigation of clinical problems leading to the creation of decision support frameworks. To date,
technologies that have been utilised for this purpose such as text mining and content analysis have not been
employed significantly in veterinary medicine.
The aim of this research was to pilot the use of content analysis and text-mining software for the synthesis and
analysis of information extracted from veterinary electronic patient records. The purpose of the work was to be
able to validate this approach for future employment across a number of practices for the purposes of practice
based research. The approach utilised content analysis (Prosuite) and text mining (WordStat) software to aggregate
the extracted text. Text mining tools such as Keyword in Context (KWIC) and Keyword Retrieval (KR)
were employed to identify specific occurrences of data across the records. Two different datasets were interrogated,
a bespoke test dataset that had been set up specifically for the purpose of the research, and a functioning
veterinary clinic dataset that had been extracted from one veterinary practice.
Across both datasets, the KWIC analysis was found to have a high level of accuracy with the search resulting
in a sensitivity of between 85.3–100%, a specificity of between 99.1–99.7%, a positive predictive value between
93.5–95.8% and a negative predictive value between 97.7–100%. The KR search, based on machine learning,
was utilised for the clinic-based dataset and was found to perform slightly better than the KWIC analysis.
This study is the first to demonstrate the application of content analysis and text mining software for validation
purposes across a number of different datasets for the purpose of search and recall of specific information
across electronic patient records. This has not been demonstrated previously for small animal veterinary epidemiological
research for the purposes of large scale analysis for practice-based research. Extension of this work
to investigate more complex diseases across larger populations is required to fully explore the use of this approach
in veterinary practice.
Keywords: Text mining | Content analysis | Veterinary practice | Practice based research