• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• Journal of Oriental Medical Thermology
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• v.3 no.1
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• pp.6-14
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• 2004

Purpose: We intended to research the relations between abdominal temperature and primary dysmenorrhea severity. Methods: We selected the 95 primary dysmenorrhea patients by means of screening test (first screening test-inquiry, second screening test-clinical test, additionally Waist-to-Hip ratio (WHR) by Inbody 2.0). We measured 4 points abdominal temperature (Chon-jung(CV17), Chung-wan(CV12), Kwan-won(CV4), Chung-guk(CV3)) by DITI (DOREX Inc., USA). And then we checked the difference of temperature $({\Delta}T)$ between CV17 and CV4 / CV17 and CV3 / CV12 and CV4 / CV12 and CV3. After that, we compared ${\Delta}T$ with primary dysmenorrhea severity evaluated by multidimensional scales (verbal rating scale modified from the one devised by Bibe roglu & Berhrman(VRS; B&B), multidimensional verbal rating scale by Andersch & Milsom(MVRS)). In dysmenorrhea severity, we standardized scale score and 3-group-severity by score (mild, moderate, severe). For statistics, we used Pearson correlations and Spearman's rho correlations, SPSS 11.0 for windows. Results: In case of MVRS, MVRS score and 3-group-severity were not correlated to ${\Delta}T$. In case of VRS; B&B, VRS; B&B score was correlated to ${\Delta}T$ (CV12 and CV4 / CV12 and CV3) and 3-group-severity was correlated to ${\Delta}T$ (CV12 and CV3). Statistically they showed significant result (p<0.05). So we can consider that ${\Delta}T$ (CV12 and CV3) and the primary dysmenorrhea severity by VRS; B&B are most correlated. Conclusion: The primary dysmenorrhea patients showed that severity by VRS; B&B was connected with ${\Delta}T$ (CV12 and CV3). So we can consider DITI as primary dysmenorrhea evaluation instrument and must further research measurement points for the exact primary dysmenorrhea evaluation by DITI.

• Clinical and Experimental Reproductive Medicine
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• v.47 no.3
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• pp.233-234
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• 2020

• Microbiology and Biotechnology Letters
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• v.42 no.1
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• pp.82-88
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• 2014

Atopic dermatitis (AD) is a common chronic inflammatory disease preceding the development of allergic disorders. The aim of this study was to evaluate the effect of Sargassum micracanthum ethanol extract (SMEE) on AD. AD was induced by spreading 2,4-dinitrochlorobenzen (DNCB) on the back sides of BALB/c mice. The efficacy of SMEE was tested by observing the skin clinical severity score, proliferations of Raw 264.7 cells and the secretion of cytokines and IgE. The secretion of IL-4, and IgE was significantly decreased by SMEE in a dose dependent manner, while IFN-${\gamma}$ was increased. In addition, SMEE alleviated the AD symptoms better when compared to the positive controls. In conclusion, these results suggest that SMEE has an inhibitory effect on AD, and may serve as a useful biomaterial for the development of cosmeceuticals.

• Journal of The Korean Society of Clinical Toxicology
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• v.12 no.1
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• pp.14-21
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• 2014

Purpose: The objective of this study was to develop a new scoring tool that is comprehensively applicable and predicts fatality within 24 h of intoxication. Methods: This was a cohort study conducted in two emergency medical centers from 2011 to 2012. We identified factors associated with severe/fatality. Through a discriminant analysis, we devised the aBIG (age, Base deficit, Infection, and Glasgow coma scale) score. To compare the ability of aBIG to predict intoxication severity with that of previous scoring systems such as APACHE II, MODS, SAPS IIe, and SOFA, we determined the receiver operating characteristic curves of each variable in predicting severe-to-fatal toxicity. Results: Compared with the mild/moderate toxicity group (n=211), the severe/fatal group (n=143) had higher incidences of metabolic acidosis, infection, serious mental change, QTc prolongation and hepato-renal failure. Age, base deficit, infection-WBC count, and Glasgow Coma Scale were independently associated with severe/fatal poisoning. These variables were combined into the poisoning "aBIG" score [$0.28{\times}$Age group+$0.38{\times}WBC$ count/$10^3+0.52{\times}$Base deficit+$0.64{\times}$(15-GCS)], which were each calculated to have an area under the curve of 0.904 (95% confidence interval: 0.868-0.933). The aBIG poisoning score had an equivalent level of severity predictability as APACHE II and a superior than MODS, SOFA, and SAPS IIe. Conclusion: We developed a simplified scoring system using the four variables of age, base deficit, infected leukocytosis, and GCS. The poisoning aBIG score was a simple method that could be performed rapidly on admission to evaluate severity of illness and predict fatal severity in patients with acute intoxications.

• Journal of Trauma and Injury
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• v.18 no.2
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• pp.141-147
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• 2005

Purpose: Recently, The new Injury Severity Score (NISS) has become a more accurate predictor of mortality than the traditional Injury Severity Score (ISS) in the trauma population. Trauma Score Injury Severity Score (TRISS) method, regarded as the gold standard for mortality prediction in trauma patients, still contains the ISS as an essential factor within its formula. The purpose of this study was to determine whether a simple modification of the TRISS by replacing the ISS with the NISS would improve the prediction of in-hospital mortality in a trauma population with blunt head and neck trauma. Objects and Methods: The study population consisted of 641 patients from a regional emergency medical center in Kyoungsangnam-do. Demographic data, clinical information, the final diagnosis, and the outcome for each patient were collected in a retrospective manner. the ISS, NISS, TRISS, and modified TRISS were calculated for each patients. The discrimination and the calibration of the ISS, NISS, modified TRISS and conventional TRISS models were compared using receiver operator characteristic (ROC) curves, areas under the ROC curve (AUC) and Hosmer-Lemeshow statistics. Results: The AUC of the ISS, NISS, modified TRISS, and conventional TRISS were 0.885, 0.941, 0.971, and 0.918 respectively. Statistical differences were found between the ISS and the NISS (p=0.008) and between the modified TRISS and the conventional TRISS (p=0.009). Hosmer-Lemeshow chi square values were 13.2, 2.3, 50.1, and 13.8, respectively; only the conventional TRISS failed to achieve the level of and an excellent calibration model (p<0.001). Conclusion: The modified TRISS is a more accurate predictor of in-hospital mortality than the conventional TRISS in a trauma population of blunt head and neck trauma.

• Korean Journal of Acupuncture
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• v.29 no.4
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• pp.623-633
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• 2012

Objectives : Fatigue is a common and distressing symptom that is a concern for cancer patients. It has a decisive effect on quality of life. The purpose of this study was to examine the feasibility of clinical trial to evaluate of efficacy and safety of acupuncture on cancer related fatigue of lung cancer patients. Methods : Total lung cancer 9 patients complained of fatigue were treated by acupuncture twice a week for four weeks(8 times in total). Evaluation of the severity of fatigue was measured by FSS(Fatigue Severity Score). In visit 1, 10, we checked FSS. For check safety of acupuncture treatment, we did blood test. Results : After 4 weeks of acupuncture treatment, the FSS was significantly decreased from $4.92{\pm}1.06$ to $3.74{\pm}1.37$(p=0.008). And the level of hemoglobin was significantly increased from 10.87 g/dl to 12.01 g/dl(p=0.014). No other lab measures indicated any significant differences between before and after acupuncture treatment. Conclusions : This study suggests that acupuncture treatment will be beneficial for lung cancer patients to improve the fatigue severity. And acupuncture treatment is safe method for lung cancer patients. A large-scale study to confirm efficacy and safety of acupuncture is needed.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1045-1054
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• 2011

During clinical trials a researcher is frequently able to observe a disease symptom in a subject as well as a severity score for those who experienced a symptom after a fixed length of treatment. The traditional method to evaluate a decreasing trend in proportion, when there is an intrinsic order in the treatment groups (for example control and two or more treatment groups) is a Cochran-Armitage test, while the method to evaluate a decreasing trend in continuous non-normal data is a Jonckheere-Tersptra test. The Cochran-Armitage test emphasizes the dichotomous data of symptom occurrence and the Jonckheere-Tersptra test emphasizes the continuous non-normal data of severity symptom scores. In this paper we propose new test statistics that consider the combined evidence from a symptom occurrence and disease severity score. We illustrate these methods with example data of schizophrenic inpatients that demonstrated antipsychotic-drug induced constipation. A small-scale simulation is conducted to compare the new trend tests with other trend tests.

• Anxiety and mood
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• v.11 no.2
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• pp.136-142
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• 2015

Objective : Subjective tinnitus is defined as an acoustic sensation perceived without any external source. Patients with tinnitus have often experienced psychological distress and low quality of life. However, the connection between psychiatric characteristics and tinnitus has not yet been established. The purpose of this study was to investigate the relationship of psychiatric characteristics with tinnitus severity. Method : A total of 124 patients with tinnitus were included in the study. Demographic and clinical characteristics were retrospectively collected in the otolaryngology clinic. The self-reported scales including the Tinnitus Handicap Inventory, the Hospital Anxiety and Depression Scale and the Anxiety Sensitivity Index were used in this study. Results : The Pearson product moment correlation revealed significant correlations between tinnitus severity and psychiatric characteristics including depression (r=0.26, p<0.01), anxiety (r=0.38, p<0.01), anxiety sensitivity (r=0.27, p<0.01). After controlling for age, sex and awareness of tinnitus, tinnitus severity was positively correlated with anxiety (partial r=0.39, p<0.01) and anxiety sensitivity (partial r=0.28, p<0.01). These correlations remained significant in male but not in female patients. Conclusion : Results of the present study indicated that not only anxiety symptoms but also anxiety sensitivity could relate to tinnitus severity. Our findings have implications for the understanding of the psychiatric characteristics in patients with tinnitus.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.200-208
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• 2017

The purpose of this study was to examine the relationship of the severity, depression and to identify factors influencing quality of sleep in Restless Legs Syndrome(RLS) patients. A total of 303 consecutive RLS patients were assessed by self questionnaires and participant's medical records were reviewed for obtaining their clinical information. The collected data were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation, and multiple regression. The quality of sleep was positively correlated with symptoms(r=. 21, p<.001) and depression(r=. 37, p<.001). The results of multiple regression analysis showed that significant variables influencing the quality of sleep were depression(${\beta}=.35$, p<.001), age(${\beta}=.21$, p<.001), and severity(${\beta}=.15$, p=.005). The explanation power of this regression model was 21.0% and it was statistically significant. As a result, to improve their sleep quality, the nursing interventions are required for RLS patients who have the depression, age, and severity.