OBJECTIVES: Machine learning systems can considerably reduce the time and effort needed by experts to perform new systematic reviews (SRs). This study investigates categorization models, which are trained on a combination of included and commonly excluded articles, which can improve performance by identifying high quality articles for new procedures or drug SRs. METHODS: Test collections were built using the annotated reference files from 19 procedure and 15 drug systematic reviews. The classification models, using a support vector machine, were trained by the combined even data of other topics, excepting the desired topic. This approach was compared to the combination of included and commonly excluded articles with the combination of included and excluded articles. Accuracy was used for the measure of comparison. RESULTS: On average, the performance was improved by about 15% in the procedure topics and 11% in the drug topics when the classification models trained on the combination of articles included and commonly excluded, were used. The system using the combination of included and commonly excluded articles performed better than the combination of included and excluded articles in all of the procedure topics. CONCLUSIONS: Automatically rigorous article classification using machine learning can reduce the workload of experts when they perform systematic reviews when the topic-specific data are scarce. In particular, when the combination of included and commonly excluded articles is used, this system will be more effective.
Evidence-Based Medicine ; Machine Learning ; Review Literature as Topic ; Support Vector Machine

OBJECTIVES: Acquiring temporal information is important because knowledge in clinical narratives is time-sensitive. In this paper, we describe an approach that can be used to extract the temporal information found in Korean clinical narrative texts. METHODS: We developed a two-stage system, which employs an exhaustive text analysis phase and a temporal expression recognition phase. Since our target document may include tokens that are made up of both Korean and English text joined together, the minimal semantic units are analyzed and then separated from the concatenated phrases and linguistic derivations within a token using a corpus-based approach to decompose complex tokens. A finite state machine is then used on the minimal semantic units in order to find phrases that possess time-related information. RESULTS: In the experiment, the temporal expressions within Korean clinical narratives were extracted using our system. The system performance was evaluated through the use of 100 discharge summaries from Seoul National University Hospital containing a total of 805 temporal expressions. Our system scored a phrase-level precision and recall of 0.895 and 0.919, respectively. CONCLUSIONS: Finding information in Korean clinical narrative is challenging task, since the text is written in both Korean and English and frequently omits syntactic elements and word spacing, which makes it extremely noisy. This study presents an effective method that can be used to aquire the temporal information found in Korean clinical documents.
Automatic Data Processing ; Linguistics ; Medical Informatics ; Medical Records ; Multilingualism ; Pattern Recognition, Automated ; Semantics

This paper describes a web-based pulse wave information management system that applies the web solution to the pulse wave extraction and management of the patient's bio-signals. In the oriental medicine, the abnormal arterial pulse wave signals generated from the specific point of body are thought to be related to certain disease conditions of specific internal organs. Therefore, evaluating the pulse wave signals has long been used a major diagnostic means. Numerous studies have been carried out on the development of the pulse wave measuring instruments that c an simply check the one's pulse waves on the radial artery, however, fewer researches have been performed to analyze pulse waves and manage the information in association with the oriental medical information system. Recently, as the usage of instrumental pulse wave analysis is increasing in the practice of the oriental medicine, needs of the pulse wave information management system that can be interfaced with the oriental medical information system are also inc reasing. The web-based pulse wave information management system provides ea sy acc ess, analysis and management of the pulse waves at anywhere one just connects the pulse wave analyser and web browser with the server system and it can also provide the high availability of the pulse wave data. All pulse wave data were easily managed with XML based communication for interchange of the pulse wave data among the existing oriental medicine information systems.

No abstract available.
Research Design*

OBJECTIVE: CDA (Clinical Document Architecture) is a markup standard for clinical document exchange. In order to increase the semantic interoperability of documents exchange, the clinical statements in the narrative blocks should be encoded with code values. Natural language processing (NLP) is required in order to transform the narrative blocks into the coded elements in the level 3 CDA documents. In this paper, we evaluate the accuracy of text mapping methods which are based on NLP. METHODS: We analyzed about one thousand discharge summaries to know their characteristics and focused the syntactic patterns of the diagnostic sections in the discharge summaries. According to the patterns, different rules were applied for matching code values of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RESULTS: The accuracy of matching was evaluated using five-hundred discharge summaries. The precision was as follows: 86.5% for diagnosis, 61.8% for chief complaint, 62.7%, for problem list, and 64.8% for discharge medication. CONCLUSION: The text processing method based on the pattern analysis of a clinical statement can be effectively used for generating CDA entries.
Diagnosis ; Natural Language Processing ; Semantics ; Systematized Nomenclature of Medicine

No abstract available.
Financial Management* ; Writing*

No abstract available.
Prostate-Specific Antigen

Purpose: This study aimed to understand how changes in income due to the COVID-19 pandemic have affected the health behavior and mental health status of self-employed individuals. Methods: We compared the health behavior and mental health status of regular wage workers and self-employed individuals with no change in income, with that of self-employed individuals with reduced income due to the spread of COVID-19. Results Smoking status, average amount of smoking per day, changes in the amount of smoking and drinking due to COVID-19, drinking frequency per year, monthly binge drinking experiences, subjective stress, and suicidal thoughts experienced by self-employed individuals with decreased income were not only higher than those of wage workers and self-employed individuals with maintained income, but their happiness index was also lower than the latter group. Conclusion: This study suggests that the change in total household income due to COVID-19 adversely affects the health behavior and mental health status of self-employed individuals. However, COVID-19-related policies focus only on economic loss compensation, and the health behavior and mental health management for self-employed individuals is insufficient. Therefore, it is necessary to establish policies for health behavior and mental health management of self-employed individuals.

Purpose: This study uses data from the 12th~17th Korea Welfare Panel (2017~2022) to analyze changes in depression scores due to the COVID-19 outbreak and the factors that influenced depression scores according to employment type. Methods: The difference in depression scores according to employment types before COVID-19 (12th~14th) and after COVID-19 (15th~17th) was analyzed. A fixed-effect model analysis was conducted before and after the occurrence of COVID-19. Results: After the outbreak of COVID-19, job satisfaction and family life satisfaction influenced the depression scores of regular wage workers. After the outbreak of COVID-19, annual income, health status, and satisfaction with family life affected the depression scores of non-regular wage workers. After the outbreak of COVID-19, leisure life satisfaction and family relationship satisfaction influenced the depression scores of self-employed. Self-esteem played a role as a control variable in lowering the depression scores of regular and non-regular workers, but did not play a role as a control variable for self-employed. Conclusion Rather than the direct impact of infectious diseases such as COVID-19, social and economic changes resulting from policies implemented to prevent the spread affect workers' depression, and the impact varies depending on the type of employment. When implementing policies to prevent the spread of infectious diseases in the future, policies that take employment type into consideration rather than uniform policies should be prepared, and measures for mental health also need to be prepared.