Standardized Database of 12-Lead Electrocardiograms with a Common Standard for the Promotion of Cardiovascular Research: KURIAS-ECG.

Objectives: Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases.

A pre-trained BERT for Korean medical natural language processing.

With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain.

Prognostic Role of Circulating Tumor Cells in the Pulmonary Vein, Peripheral Blood, and Bone Marrow in Resectable Non-Small Cell Lung Cancer.

Background: Studies of the prognostic role of circulating tumor cells (CTCs) in early-stage non-small cell lung cancer (NSCLC) are still limited. This study investigated the prognostic power of CTCs from the pulmonary vein (PV), peripheral blood (PB), and bone marrow (BM) for postoperative recurrence in patients who underwent curative resection for NSCLC.

Methods: Forty patients who underwent curative resection for NSCLC were enrolled.

A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation.

Background: The fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages.

Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.

Pathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword extraction for pathology reports is necessary to summarize the informative text and reduce intensive time consumption.

Diagnostic performance of initial serum albumin level for predicting in-hospital mortality among aspiration pneumonia patients.

Purpose: The predictive value of serum albumin in adult aspiration pneumonia patients remains unknown.

Methods: Using data collected during a 3-year retrospective cohort of hospitalized adult patients with aspiration pneumonia, we evaluated the predictive value of serum albumin level at ED presentation for in-hospital mortality.

Results: 248 Patients were enrolled; of these, 51 cases died (20.

Clinical year in review 2014: critical care medicine.

Severe sepsis is the most common cause of death among critically ill patients in non-coronary intensive care units. In 2002, the guideline titled "Surviving Sepsis Campaign" was published by American and European Critical Care Medicine to decrease the mortality of severe sepsis and septic shock patients, which has been the basis of the treatment for those patients. After the first revised guidelines were published on 2008, the most current version was published in 2013 based on the updated literature of until fall 2012.