BackgroundSince medical research based on big data has become more common, the community’s interest and effort to analyze a large amount of semistructured or unstructured text data, such as examination reports, have rapidly increased. However, these large-scale text data are often not readily applicable to analysis owing to typographical errors, inconsistencies, or data entry problems. Therefore, an efficient data cleaning process is required to ensure the veracity of such data.ObjectiveIn this paper, we proposed an efficient data cleaning process for large-scale medical text data, which employs text clustering methods and value-converting technique, and evaluated its performance with medical examination text data.MethodsThe proposed data cleaning process consists of text clustering and value-merging. In the text clustering step, we suggested the use of key collision and nearest neighbor methods in a complementary manner. Words (called values) in the same cluster would be expected as a correct value and its wrong representations. In the value-converting step, wrong values for each identified cluster would be converted into their correct value. We applied these data cleaning process to 574,266 stool examination reports produced for parasite analysis at Samsung Medical Center from 1995 to 2015. The performance of the proposed process was examined and compared with data cleaning processes based on a single clustering method. We used OpenRefine 2.7, an open source application that provides various text clustering methods and an efficient user interface for value-converting with common-value suggestion.ResultsA total of 1,167,104 words in stool examination reports were surveyed. In the data cleaning process, we discovered 30 correct words and 45 patterns of typographical errors and duplicates. We observed high correction rates for words with typographical errors (98.61%) and typographical error patterns (97.78%). The resulting data accuracy was nearly 100% based on the number of total words.ConclusionsOur data cleaning process based on the combinatorial use of key collision and nearest neighbor methods provides an efficient cleaning of large-scale text data and hence improves data accuracy.

(1) Background: Longitudinal changes in myocardial T1 relaxation time are unknown. We aimed to assess the longitudinal changes in the left ventricular (LV) myocardial T1 relaxation time and LV function. (2) Methods: Fifty asymptomatic men (mean age, 52.0 years) who underwent 1.5 T cardiac magnetic resonance imaging twice at an interval of 54 ± 21 months were included in this study. The LV myocardial T1 times and extracellular volume fractions (ECVFs) were calculated using the MOLLI technique (before and 15 min after gadolinium contrast injection). The 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk score was calculated. (3) Results: No significant differences in the following parameters were noted between the initial and follow-up assessments: LV ejection fraction (65.0 ± 6.7% vs. 63.6 ± 6.3%, p = 0.12), LV mass/end-diastolic volume ratio (0.82 ± 0.12 vs. 0.80 ± 0.14, p = 0.16), native T1 relaxation time (982 ± 36 vs. 977 ± 37 ms, p = 0.46), and ECVF (24.97 ± 2.38% vs. 25.02 ± 2.41%, p = 0.89). The following parameters decreased significantly from the initial assessment to follow-up: stroke volume (87.2 ± 13.7 mL vs. 82.6 ± 15.3 mL, p = 0.01), cardiac output (5.79 ± 1.17 vs. 5.50 ± 1.04 L/min, p = 0.01), and LV mass index (110.16 ± 22.38 vs. 104.32 ± 18.26 g/m2, p = 0.01). The 10-year ASCVD risk score also remained unchanged between the two timepoints (4.71 ± 0.19% vs. 5.16 ± 0.24%, p = 0.14). (4) Conclusion: Myocardial T1 values and ECVFs were stable over time in the same middle-aged men.

BACKGROUND Since medical research based on big data has become more common, the community’s interest and effort to analyze a large amount of semistructured or unstructured text data, such as examination reports, have rapidly increased. However, these large-scale text data are often not readily applicable to analysis owing to typographical errors, inconsistencies, or data entry problems. Therefore, an efficient data cleaning process is required to ensure the veracity of such data. OBJECTIVE In this paper, we proposed an efficient data cleaning process for large-scale medical text data, which employs text clustering methods and value-converting technique, and evaluated its performance with medical examination text data. METHODS The proposed data cleaning process consists of text clustering and value-merging. In the text clustering step, we suggested the use of key collision and nearest neighbor methods in a complementary manner. Words (called values) in the same cluster would be expected as a correct value and its wrong representations. In the value-converting step, wrong values for each identified cluster would be converted into their correct value. We applied these data cleaning process to 574,266 stool examination reports produced for parasite analysis at Samsung Medical Center from 1995 to 2015. The performance of the proposed process was examined and compared with data cleaning processes based on a single clustering method. We used OpenRefine 2.7, an open source application that provides various text clustering methods and an efficient user interface for value-converting with common-value suggestion. RESULTS A total of 1,167,104 words in stool examination reports were surveyed. In the data cleaning process, we discovered 30 correct words and 45 patterns of typographical errors and duplicates. We observed high correction rates for words with typographical errors (98.61%) and typographical error patterns (97.78%). The resulting data accuracy was nearly 100% based on the number of total words. CONCLUSIONS Our data cleaning process based on the combinatorial use of key collision and nearest neighbor methods provides an efficient cleaning of large-scale text data and hence improves data accuracy.

BACKGROUND Pulse transit time and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to use these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP. OBJECTIVE This study aims to predict systolic blood pressure and diastolic blood pressure based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models. METHODS This study was conducted on 1362 healthy men older than 18 years who visited the Samsung Medical Center. The systolic blood pressure and diastolic blood pressure were estimated using the multiple linear regression method. Models were divided into two groups based on age: younger than 60 years and 60 years or older; 200 seeds were repeated in consideration of partition bias. Mean of error, absolute error, and root mean square error were used as performance metrics. RESULTS The model divided into two age groups (younger than 60 years and 60 years and older) performed better than the model without division. The performance difference between the model using only three variables (PWV, BMI, age) and the model using 17 variables was not significant. Our final model using PWV, BMI, and age met the criteria presented by the American Association for the Advancement of Medical Instrumentation. The prediction errors were within the range of about 9 to 12 mmHg that can occur with a gold standard mercury sphygmomanometer. CONCLUSIONS Dividing age based on the age of 60 years showed better BP prediction performance, and it could show good performance even if only PWV, BMI, and age variables were included. Our final model with the minimal number of variables (PWB, BMI, age) would be efficient and feasible for predicting BP.