OBJECTIVES: Clinical discharge summaries provide valuable information about patients' clinical history, which is helpful for the realization of intelligent healthcare applications. The documents tend to take the form of separate segments based on temporal or topical information. If a patient's clinical history can be seen as a consecutive sequence of clinical events, then each temporal segment can be seen as a snapshot, providing a certain clinical context at a specific moment. This study aimed to demonstrate a temporal segmentation method of Korean clinical narratives for identifying textual snapshots of patient history as a proof-of-a-concept. METHODS: Our method uses pattern-based segmentation to approximate human recognition of the temporal or topical shifts in clinical documents. We utilized rheumatic patients' discharge summaries and transformed them into sequences of constituent chunks. We built 97 single pattern functions to denote whether a certain chunk has attributes that indicate that it can be a segment boundary. We manually defined the relationships between the pattern functions to resolve multiple pattern matchings and to make a final decision. RESULTS: The algorithm segmented 30 discharge summaries and processed 1,849 decision points. Three human judges were asked whether they agreed with the algorithm's prediction, and the agreement percentage on the judges' majority opinion was 89.61%. CONCLUSIONS: Although this method is based on manually constructed rules, our findings demonstrate that the proposed algorithm can achieve fairly good segmentation results, and it may be the basis for methodological improvement in the future.
Delivery of Health Care ; Electronic Health Records ; Humans ; Methods ; Natural Language Processing ; Pattern Recognition, Automated ; Rheumatic Diseases

BACKGROUND/AIMS: Recent studies have shown an association of epicardial fat thickness with diabetes and hypertension (HTN) in asymptomatic populations. However, there is lack of information as to whether there is similar association between pericoronary adipose tissue (PAT) and HTN in the patients who have acute or chronic illness. METHODS: This study included 214 nonobese patients hospitalized with acute or chronic noncardiogenic illness. PAT thicknesses were measured from fat tissues surrounding left and right coronary arteries in enhanced, chest computed tomography scans, yielding the maximal PAT value from left and right coronary arteries was used for analysis. Baseline data from hypertensive (n = 81) and normotensive (n = 133) patients were collected and compared. RESULTS: PAT is positively correlated with age (r = 0.377, p <0.001), body mass index (BMI; r = 0.305, p < 0.001), systolic blood pressure (r = 0.216, p = 0.001), and total cholesterol (r = 0.200, p = 0.006). The hypertensive group was older (69.58 ± 11.69 years vs. 60.29 ± 14.98 years), and had higher PAT content (16.30 ± 5.37 mm vs. 13.06 ± 5.58 mm) and BMI (23.14 ± 3.32 kg/m² vs. 20.96 ± 3.28 kg/m²) than the normotensive group (all p < 0.001). Multivariate analysis showed that age (odds ratio [OR], 2.193; p = 0.016), PAT thickness (OR, 1.065; p = 0.041), and BMI (25 ≤ BMI < 30 kg/m² ; OR, 6.077; p = 0.001) were independent risk factors for HTN. CONCLUSIONS: In nonobese patients with noncardiogenic acute or chronic illness, PAT thickness is independently correlated with HTN, age, and BMI.
Adipose Tissue* ; Blood Pressure ; Body Mass Index ; Cholesterol ; Chronic Disease* ; Coronary Vessels ; Humans ; Hypertension* ; Multidetector Computed Tomography ; Multivariate Analysis ; Risk Factors ; Thorax