Introducing Information Extraction to Radiology Information Systems to Improve the Efficiency on Reading Reports

Background Radiology reports are a permanent record of patient's health information often used in clinical practice and research. Reading radiology reports is common for clinicians and radiologists. However, it is laborious and time-consuming when the amount of reports to be read is large. Assisting clinicians to locate and assimilate the key information of reports is of great significance for improving the efficiency of reading reports. There are few studies on information extraction from Chinese medical texts and its application in radiology information systems (RIS) for efficiency improvement. Objectives The purpose of this study was to explore methods for extracting, grouping, ranking, delivering, and displaying medical-named entities in radiology reports which can yield efficiency improvement in RISs. Methods A total of 5,000 reports were obtained from two medical institutions for this study. We proposed a neural network model called Multi-Embedding-BGRU-CRF (bidirectional gated recurrent unit-conditional random field) for medical-named entity recognition and rule-based methods for entity grouping and ranking. Furthermore, a methodology for delivering and displaying entities in RISs was presented. Results The proposed neural named entity recognition model has achieved a good F1 score of 95.88%. Entity ranking achieved a very high accuracy of 99.23%. The weakness of the system is the entity grouping approach which yield accuracy of 91.03%. The effectiveness of the overall solution was proved by an evaluation task performed by two clinicians based on the setup of actual clinical practice. Conclusions The neural model shows great potential in extracting medical-named entities from radiology reports, especially for languages, that lack lexicons and natural language processing tools. The pipeline of extracting, grouping, ranking, delivering, and displaying medical-named entities could be a feasible solution to enhance RIS functionality by information extraction. The integration of information extraction and RIS has been demonstrated to be effective in improving the efficiency of reading radiology reports.

ПРО ДОСВІД ІНТЕГРАЦІЇ РАДІОВІЗІОГРАФА DR.SUNI 1800 ТА ВІЛЬНО- РОЗПОВСЮДЖУВАНОГО ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ МІС ЕМК З ВІДКРИТИМ КОДОМ OPENDENTAL

Digital radiovisiography is widely used X-ray technology in the dentistry practice for the diagnostics currently. The brief overview of the market of the digital radiovisiographs and related radiology information systems is given. High hardware and software prices are often combined with insufficient and low-quality support services. The Dr.Suni 1800 intraoral radiovisiograph installation problems due to the outdated software are described. The abilities of the open-source free software usage in the radiology diagnoses tasks are shown. The existing methodology of the Dr.Suni 1800 intraoral radiovisiograph and the OpenDental medical information system integration is improved. An updated step-by-step integration procedure is presented. The Windows 7 (32-bit edition) compatibility is confirmed.

CT Scans in Young People in Great Britain: Temporal and Descriptive Patterns, 1993–2002

Background. Although using computed tomography (CT) can be greatly beneficial, the associated relatively high radiation doses have led to growing concerns in relation to potential associations with risk of future cancer. Very little has been published regarding the trends of CT use in young people. Therefore, our objective was to assess temporal and other patterns in CT usage among patients aged under 22 years in Great Britain from 1993 to 2002.Methods. Electronic data were obtained from the Radiology Information Systems of 81 hospital trusts within Great Britain. All included patients were aged under 22 years and examined using CT between 1993 and 2002, with accessible radiology records.Results. The number of CT examinations doubled over the study period. While increases in numbers of recorded examinations were seen across all age groups, the greatest increases were in the older patients, most notably those aged 15–19 years of age. Sixty percent of CT examinations were of the head, with the percentages varying with calendar year and patient age.Conclusions. In contrast to previous data from the North of England, the doubling of CT use was not accompanied by an increase in numbers of multiple examinations to the same individual.