• Asian-Australasian Journal of Animal Sciences
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• v.18 no.6
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• pp.780-786
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• 2005

This study presents a non-video, non-invasive, automatic, on-site monitoring system the system employs ultrasonic transducers to detect behavior in sows before, during and after parturition. An ultrasonic transmitting/receiving (T/R) circuit of 40 kHz was mounted above a conventional parturition bed. The T/R units use ultrasonic time-of-flight (TOF) ranging technology to measure the height of the confined sows at eight predetermined locations. From this data, three momentary postures of the sow are determined, characterized as standing-posture (SP), lateral-lying-posture (LLP) and sitting posture (STP). By examining the frequencies of position switch Stand-Up-Sequence (SUS) between standing-posture (SP), lateral-lying-posture (LLP) and sitting-posture (STP) rate can be determined for the duration of the sow' confinement. Three experimental pureblooded Landrace sows undergoing normal gestation were monitored for the duration of confinement. In agreement with common observation, the sows exhibited increased restlessness as parturition approached. Analysis of the data collected in our study showed a distinct peak in Stand-Up-Sequence (SUS, i.e. the transition from lying laterally to standing up ) and sitting-posture (STP) rate approximately 12 h prior to parturition, the observed peak being 5 to 10 times higher than observed on any other measurement day. It is concluded that the presented methodology is a robust, low-cost, lowlabor method for the continuous remote monitoring of sows and similar large animals for parturition and other behavior. It is suggested that the system could be applied to automatic prediction of sow parturition, with automatic notification of remote management personnel so human attendance at birth could reduce rates of sow and piglet mortality. The results of this study provide a good basis for enhancing automation and reducing costs in large-scale sow husbandry and have applications in the testing of various large mammals for the effects of medications, diets, genetic modifications and environmental factors.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5571-5575
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• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

• Journal of Preventive Medicine and Public Health
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• v.48 no.1
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• pp.1-9
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• 2015

The purpose of the present review is to give an overview of the association between alcohol intake and the risk of developing cancer. Two large-scale expert reports; the World Cancer Research Fund (WCRF)/American Institute of Cancer Research (AICR) report from 2007, including its continuous update project, and the International Agency for Research of Cancer (IARC) monograph from 2012 have extensively reviewed this association in the last decade. We summarize and compare their findings, as well as relate these to the public health impact, with a particular focus on region-specific drinking patterns and disease tendencies. Our findings show that alcohol intake is strongly linked to the risk of developing cancers of the oral cavity, pharynx, larynx, oesophagus, colorectum (in men), and female breast. The two expert reports diverge on the evidence for an association with liver cancer and colorectal cancer in women, which the IARC grades as convincing, but the WCRF/AICR as probable. Despite these discrepancies, there does, however, not seem to be any doubt, that the Population Attributable Fraction of alcohol in relation to cancer is large. As alcohol intake varies largely worldwide, so does, however, also the Population Attributable Fractions, ranging from 10% in Europe to almost 0% in countries where alcohol use is banned. Given the World Health Organization's prediction, that alcohol intake is increasing, especially in low- and middle-income countries, and steadily high in high-income countries, the need for preventive efforts to curb the number of alcohol-related cancers seems growing, as well as the need for taking a region- and gender-specific approach in both future campaigns as well as future research. The review acknowledges the potential beneficial effects of small doses of alcohol in relation to ischaemic heart disease, but a discussion of this lies without the scope of the present study.

• Journal of Trauma and Injury
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• v.32 no.1
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• pp.26-31
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• 2019

Purpose: Rhabdomyolysis (RB) is a syndrome characterized by the decomposition of striated muscles and leakage of their contents into the bloodstream. Acute kidney injury (AKI) is the most significant and serious complication of RB and is a major cause of mortality in patients with RB. Severe RB (creatine kinase [CK] ${\geq}5,000$) has been associated with AKI. However, early prediction is difficult because CK can reach peak levels 1-3 days after the trauma. Hence, the aim of our study was to identify predictors of severe RB using initial patient information and parameters. Methods: We retrospectively analyzed 1,023 blunt trauma patients admitted to a single tertiary hospital between August 2011 and March 2018. Patients with previously diagnosed chronic kidney disease were excluded from the study. RB and severe RB were defined as a CK level ${\geq}1,000U/L$ and ${\geq}5,000U/L$, respectively. The diagnosis of AKI was based on RIFLE criteria. Results: The overall incidence of RB and severe RB was 31.3% (n=320) and 6.2% (n=63), respectively. On multivariable analysis, male sex (odds ratio [OR] 3.78, 95% confidence interval [CI] 1.43 to 10.00), initial base excess (OR 0.85, 95% CI 0.80 to 0.90), initial CK (OR 2.07, 95% CI 1.67 to 2.57), and extremity abbreviated injury scale score (OR 1.78, 95% CI 1.39 to 2.29) were found to predict severe RB. The results of receiver operating characteristic analysis showed that the best cutoff value for the initial serum CK level predictive of severe RB was 1,494 U/L. Conclusions: Male patients with severe extremity injuries, low base excess, and initial CK level >1,500 U/L should receive vigorous fluid resuscitation.

• Kosin Medical Journal
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• v.33 no.3
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• pp.337-346
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• 2018

Objectives: Serum procalcitonin (PCT) is a specific biomarker that rises after bacterial infection, and levels of PCT are known to correlate with the severity and mortality of patients with pneumonia and sepsis. However, the usefulness of PCT levels in acute pyelonephritis is unknown. This study aimed to evaluate the effectiveness of using the PCT level as a predictive test for bacteremia in acute pyelonephritis. Methods: Between January 2012 and June 2013, 140 patients diagnosed with acute pyelonephritis were admitted to Haeundae Paik Hospital. Serum PCT, C-reactive protein (CRP), and white blood cell (WBC) levels at pre- and post- treatment were measured. Blood and urine cultures were obtained from all patients. The levels of PCT, CRP, and WBCs were each compared between the blood culture-positive and blood culture-negative groups to assess their effectiveness in predicting bacteremia. Results: Pre-treatment PCT level was 0.77 ng/mL (95% CI: 0.42-1.60 ng/mL) in the blood culture-negative group and 4.89 ng/mL (95% CI: 2.88-9.04 ng/mL) in the blood culture-positive group, and the increase between the two groups was statistically significant. The area under the receiver operating characteristic curve of PCT level for prediction of bacteremia was 0.728. A cut-off value of 1.23 ng/mL indicated a sensitivity of 79.0 % and specificity of 60.0 % for PCT level. Conclusions: Serum PCT level is a useful predictive test for bacteremia in acute pyelonephritis. Through the early detection of bacteremia, serum PCT level can help estimate the prognosis and predict complications such as sepsis.

• Journal of Korean Neurosurgical Society
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• v.65 no.4
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• pp.582-590
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• 2022

Objective : Hydrocephalus after intracerebral hemorrhage (ICH) is known to be related to poor prognosis and mortality. We analyzed predictors of permanent hydrocephalus in the patients with surgically treated supratentorial ICH. Methods : From 2004 to 2019, a total of 414 patients with surgically treated primary supratentorial ICH were included. We retrospectively analyzed age, sex, preexisting hypertension and diabetes, location and volume of ICH, presence and severity of intraventricular hemorrhage (IVH), and type of surgery. Results : Forty patients (9.7%) required shunt surgery. Concomitant IVH was higher in the 'shunt required' group (92.5%) than in the 'shunt not required' group (67.9%) (p=0.001). IVH severity was worse in the 'shunt required' group (13.5 vs. 7.5, p=0.008). Craniectomy (47.5%) was significantly high in the 'shunt required' group. According to multivariable analysis, the presence of an IVH was 8.1 times more frequent and craniectomy was 8.6 times more frequent in the 'shunt required' group. In the comparison between craniotomy and craniectomy group, the presence of an IVH was related with a 3.9 times higher (p=0.033) possibility and craniectomies rather than craniotomies with a 7-times higher possibility of shunt surgery (p<0.001). Within the craniectomy group, an increase in the craniectomy area by 1 cm² was correlated with a 3.2% increase in the possibility of shunt surgery (odds ratio, 1.032; 95% confidence interval, 1.005-1.061; p=0.022). Conclusion : Presence of IVH, the severity of IVH and decompressive craniectomy were related to the development of shunt dependent hydrocephalus in the patients with ICH. The increasing size of craniectomy was related with increasing rate of shunt requirement.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.21-28
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• 2022

All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Tuberculosis and Respiratory Diseases
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• v.45 no.1
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• pp.57-67
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• 1998

Purpose : This study was undertaken to determine the preoperative predictors of mortality and morbidity after lung cancer resection. Method: During the period from October 1, 1995 to August 31, 1996, a prospective study was conducted in 92 lung resection candidates diagnosed as lung cancer. For preoperative predictors of nonpulmonary factors, we considered age, sex, weight loss, hematocrit, serum albumin, EKG and concomitant illness, and for those of pulmonary factors, smoking history, presence of pneumonia, dyspnea scale(1 to 4), arterial blood gas analysis with room air breathing, routine pulmonary function test. And predicted postoperative(ppo) pulmonary factors such as PPO-$FEV_1$, ppo-diffusing capacity(DLco), predicted postoperative product(PPP) of ppo-$FEV_1%{\times}ppo$-DLco% and ppo-maximal $O_2$ uptake($VO_2$max) were also considered. Results: There were 78 men and 14 women with a median age of 62 years(range 42 to 82) and a mean $FEV_1$ of $2.37\pm0.06L$. Twenty nine patients had a decreased $FEV_1$ less than 2.0L. Pneumonectomy was performed in 26 patients, bilobectomy in 12, lobectomy in 54. Pulmonary complications developed in 10 patients, cardiac complications in 9, other complications(empyema, air leak, bleeding) in 11, and 16 patients were managed in intensive care unit for more than 48hours. Three patients died within 30 days after operation. The ppo-$VO_2$max was less than 10ml/kg/min in these three patients, but its statistical significance could not be determined due to small number of patients. In multivariate analysis, the predictor related to postoperative death was weight loss(p<0.05), and as for pulmonary complications, weight loss, dyspnea scale, ppo-DLco and extent of resection(p<0.05). Conclusions: Based on this study, preoperative nonpulmonary factors such as weight loss and dyspnea scale are more important than the pulmonary factors in the prediction of postoperative mortality and/or morbodity in lung resection candidates, but exercise pulmonary fuction test may be useful Our study suggests that ppo-$VO_2$max value less than 10ml/kg/min is associated with death after lung cancer resection but further studies are needed to validate this result.