Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients' families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

Decline in hospitalization for childhood asthma in different air pollution regions in Taiwan, 2001-2012

This study aimed to investigate the trends in childhood asthma hospitalization in regions with differing levels of air pollution in Taiwan, 2001-2012. Joinpoint regression was used to identify significant trend changes. The hospitalization rate varied according to gender, geographic region, and age. The incidence of childhood asthma hospitalization decreased from 127.99 to 76.67 (/100,000 population), with an average annual percentage change of around -4.1%; in the Yilan region, the average air pollution concentrations were 19.92 μg/m³, 39.47 μg/m³, 25.99 ppb, 2.19 ppb, and 11.23 ppb for PM_2.5, PM₁₀, O₃, SO₂, and NO₂, respectively, which were lower than Taiwan's average values; however, the childhood asthma hospitalization rate was the highest (179.75/100,000 population). The national trend in childhood asthma hospitalization exhibited a significant decrease. The effects of air pollution on childhood asthma were greater in the higher-level air pollution regions, while less association was observed in the lower-level air pollution regions.

Liver cancer prediction in a viral hepatitis cohort: A deep learning approach

Viral hepatitis is the primary cause of liver diseases, among which liver cancer is the leading cause of death from cancer. However, this cancer is often diagnosed in the later stages, which makes treatment difficult or even impossible. This study applied deep learning (DL) models for the early prediction of liver cancer in a hepatitis cohort. In this study, we surveyed 1 million random samples from the National Health Insurance Research Database (NHIRD) to analyze viral hepatitis patients from 2002 to 2010. Then, we used DL models to predict liver cancer cases based on the history of diseases of the hepatitis cohort. Our results revealed the annual prevalence of hepatitis in Taiwan increased from 2002 to 2010, with an average annual percentage change (AAPC) of 5.8% (95% CI: 4.2-7.4). However, young people (aged 16-30 years) exhibited a decreasing trend, with an AAPC of -5.6 (95% CI: -8.1 to -2.9). The results of applying DL models showed that the convolution neural network (CNN) model yielded the best performance in terms of predicting liver cancer cases, with an accuracy of 0.980 (AUC: 0.886). In conclusion, this study showed an increasing trend in the annual prevalence of hepatitis, but a decreasing trend in young people from 2002 to 2010 in Taiwan. The CNN model may be applied to predict liver cancer in a hepatitis cohort with high accuracy.

TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings

Background

Cytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes including immunity regulation, hematopoiesis, and inflammation. As one important family of cytokines, tumor necrosis factors have association with the regulation of a various biological processes such as proliferation and differentiation of cells, apoptosis, lipid metabolism, and coagulation. The implication of these cytokines can also be seen in various diseases such as insulin resistance, autoimmune diseases, and cancer. Considering the interdependence between this kind of cytokine and others, classifying tumor necrosis factors from other cytokines is a challenge for biological scientists.

Methods

In this research, we employed a word embedding technique to create hybrid features which was proved to efficiently identify tumor necrosis factors given cytokine sequences. We segmented each protein sequence into protein words and created corresponding word embedding for each word. Then, word embedding-based vector for each sequence was created and input into machine learning classification models. When extracting feature sets, we not only diversified segmentation sizes of protein sequence but also conducted different combinations among split grams to find the best features which generated the optimal prediction. Furthermore, our methodology follows a well-defined procedure to build a reliable classification tool.

Results

With our proposed hybrid features, prediction models obtain more promising performance compared to seven prominent sequenced-based feature kinds. Results from 10 independent runs on the surveyed dataset show that on an average, our optimal models obtain an area under the curve of 0.984 and 0.998 on 5-fold cross-validation and independent test, respectively.

Conclusions

These results show that biologists can use our model to identify tumor necrosis factors from other cytokines efficiently. Moreover, this study proves that natural language processing techniques can be applied reasonably to help biologists solve bioinformatics problems efficiently.

Deep learning approaches for sleep disorder prediction in an asthma cohort

OBJECTIVE

Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined 1 million samples included in the Taiwan National Health Insurance Research Database (NHIRD) in order to predict sleep disorder in an asthma cohort from 2002-2010.

METHODS

The disease histories of the asthma patients were transferred to sequences and matrices for the prediction of sleep disorder by applying machine learning (ML) algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), and deep learning (DL) models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolution Neural Network (CNN).

RESULTS

Among 14,818 new asthma subjects in 2002, there were 4469 sleep disorder subjects from 2002 to 2010. The KNN, SVM, and RF algorithms were demonstrated to be successful sleep disorder prediction models, with accuracies of 0.798, 0.793, and 0.813, respectively (AUC: 0.737, 0.690, and 0.719, respectively). The results of the DL models showed the accuracies of the RNN, LSTM, GRU, and CNN to be 0.744, 0.815, 0.782, and 0.951, respectively (AUC: 0.658, 0.750, 0.732, and 0.934, respectively).

CONCLUSIONS

The results showed that the CNN model had the best performance for sleep disorder prediction in the asthma cohort.

The Short-Term Effects of Ambient Air Pollutants on Childhood Asthma Hospitalization in Taiwan: A National Study

This investigation determined the effects of air pollution on childhood asthma hospitalization in regions with differing air pollution levels in Taiwan over a long time period. Data of childhood hospital admissions for asthma in patients aged 0–18 years and air quality in eight regions for the period 2001–2012 in Taiwan were collected. Poisson generalized linear regression analysis was employed to identify the relative risks of hospitalization due to asthma in children associated with exposure to varying levels of air pollutants with a change in the interquartile range after adjusting for temperature and relative humidity. Particulate matter ≤2.5 μm (PM_2.5), particulate matter ≤10 μm (PM₁₀), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂), were positively associated with childhood asthma hospitalization, while O₃ was negatively associated with childhood asthma hospitalization. SO₂ was identified as the most significant risk factor. The relative risks for asthma hospitalization associated with air pollutants were higher among children aged 0–5 years than aged 6–18 years and were higher among males than females. The effects of air pollution on childhood asthma were greater in the higher-level air pollution regions, while no association was observed in the lower-level air pollution regions. These findings may prove important for policymakers involved in implementing policies to reduce air pollution.

A survey of ambulatory-treated asthma and correlation with weather and air pollution conditions within Taiwan during 2001-2010

Objective: This study of asthma was performed to evaluate annual trends in emergency department (ED) for 10 years. Weather and air pollution factors affecting asthma were also studied in order to identify the important factors and alert the public in advance. Methods: A survey of ambulatory-treated asthma patients was performed and the correlations with weather and air pollution factors examined in a cohort of one million patients in 2010. The fixed-cohort study analyzed trends, medical costs, and annual prevalence grouped by age and gender. Results: The number of asthma patients visiting EDs and non-emergency (non-ED) clinics significantly increased, with average annual percentage changes (AAPCs) of 2.3 and 4.6%, respectively. The average direct medical cost for EDs was increased significantly as compared with that of non-ED visits. Classification of asthma visits by hospital level indicated that local hospitals and others exhibited a significantly increasing trend (AAPC =15.3% [95% CI: 14.3-16.2]). The annual prevalence of asthma in males, females, and children was significantly increased (AAPCs of 1.5, 1.8, and 3.9%, respectively). Asthma patient hospitalizations were significantly correlated with temperature, humidity, and air pollution factors. Conclusions: The number of non-ED visits due to asthma increased, and the average direct medical cost for ED admissions also increased. Asthma patients tended to visit local hospitals primarily. Asthma visits by children increased, but a decrease was observed in adults. The number of hospitalized asthma patients was negatively correlated with temperature and humidity but positively correlated with the levels of PM_2.5, PM₁₀, and NO₂.

Application of a Time-Stratified Case-Crossover Design to Explore the Effects of Air Pollution and Season on Childhood Asthma Hospitalization in Cities of Differing Urban Patterns: Big Data Analytics of Government Open Data

Few studies have assessed the lagged effects of levels of different urban city air pollutants and seasons on asthma hospitalization in children. This study used big data analysis to explore the effects of daily changes in air pollution and season on childhood asthma hospitalization from 2001 to 2010 in Taipei and Kaohsiung City, Taiwan. A time-stratified case-crossover study and conditional logistic regression analysis were employed to identify associations between the risk of hospitalization due to asthma in children and the levels of air pollutants (PM_2.5, PM₁₀, O₃, SO₂, and NO₂) in the days preceding hospitalization. During the study period, 2900 children in Taipei and 1337 in Kaohsiung aged ≤15 years were hospitalized due to asthma for the first time. The results indicated that the levels of air pollutants were significantly associated with the risk of asthma hospitalization in children, and seasonal effects were observed. High levels of air pollution in Kaohsiung had greater effects than in Taipei after adjusting for seasonal variation. The most important factor was O₃ in spring in Taipei. In children aged 0-6 years, asthma was associated with O₃ in Taipei and SO₂ in Kaohsiung, after controlling for the daily mean temperature and relative humidity.

Retrospective one-million-subject fixed-cohort survey of utilization of emergency departments due to traumatic causes in Taiwan, 2001-2010

BACKGROUND

Epidemiological study was needed to evaluate trends in emergency department (ED) utilization that could be taken into account when making policy decisions regarding the delivery and distribution of medical resources.

METHODS

A retrospective fixed-cohort study of emergency medical utilization from 2001 to 2010 was performed based on one-million people sampled in 2010 in Taiwan. Focusing on traumatic cases, the annual incidences in various groups split according to sex and age were calculated, and further information regarding location of trauma and type of trauma was obtained.

RESULTS

In 2010, significantly greater proportions of male and younger subjects were visitors to EDs with a traumatic injury. During 2001-2010, the number of both traumatic cases and non-traumatic cases presenting at EDs significantly increased (average annual percentage change, AAPC 4.7 and 3.6, respectively) and a significantly greater direct medical cost associated with traumatic cases than non-traumatic cases was noted. Focusing on traumatic cases, most of these cases were directed to highest-level hospitals, accounting for 73.5-78.8 % of all traumatic cases, with a significant AAPC of 5.6. The traumatic ED visit annual incidence in males was 58.63 in 2001, which significantly increased to 69.35 per 1000 persons in 2010 (AAPC 1.5); and in females was 38.96 in 2001, which significantly increased to 50.73 per 1000 persons in 2010 (AAPC 2.5). Most of the traumatic cases treated in EDs were minor injuries, such as contusion with the skin intact, open wound of the upper limbs, open wound of the head, neck, or trunk, and other superficial injury (accounting for about 60 % of all cases). The traumatic categories of sprains/strains of joints and adjacent muscles, fractures of upper limbs, fractures of lower limbs, and fractures of the spine/trunk required greater medical resources and significantly positive AAPC values (4.3, 4.0, 4.5 and 6.8, respectively).

CONCLUSIONS

Increased ED utilization due to traumatic causes, as assessed by the annual number of cases and incidence, average direct medical cost and highest-level hospital utilization, was observed from 2001 to 2010. Orthopedic-related injuries, including soft tissue trauma of extremities and various fractures, were the categories with the greatest increase in incidence.

Epidemiological survey of orthopedic joint dislocations based on nationwide insurance data in Taiwan, 2000-2005

BACKGROUND

The epidemiology of acute orthopedic dislocations is poorly understood. A nationwide database provides a valuable resource for examining this issue in the Taiwanese population.

METHODS

A 6-year retrospective cohort study of 1,000,000 randomly-sampled beneficiaries from the year 2005 was used as the original population. Based on the hospitalized and ambulatory data, the concomitant ICD9-CM diagnosis codes and treatment codes were evaluated and classified into 8 and 3 major categories, respectively. The cases matching both inclusive criteria of dislocation-related diagnosis codes and treatment codes were defined as incident cases.

RESULTS

During 2000-2005, the estimated annual incidence (per 100,000 population) of total orthopedic dislocations in Taiwan was 42.1 (95%CI: 38.1-46.1). The major cause of these orthopedic dislocations was traffic accidents (57.4%), followed by accident falls (27.5%). The annual incidence dislocation by location was shoulder, 15.3; elbow, 7.7; wrist, 3.5; finger, 4.6; hip, 5.2; knee, 1.4; ankle, 2.0; and foot, 2.4. Approximately 16% of shoulder dislocations occurred with other concomitant fractures, compared with 17%, 53%, 16%, 76% and 52%, respectively, of dislocated elbow, wrist, hip, knee, and ankle cases. Including both simple and complex dislocated cases, the mean medical cost was US$612 for treatment of a shoulder dislocation, $504 for the elbow, $1,232 for the wrist, $1,103 for the hip, $1,888 for the knee, and $1,248 for the ankle.

CONCLUSIONS

In Taiwan, three-quarters of all orthopedic dislocations were of the upper limbs. The most common complex fracture-dislocation was of the knee, followed by the wrist and the ankle. Those usually needed a treatment combined with open reduction of fractures and resulted in a higher direct medical expenditure.

Treatment incidence of and medical utilization for hospitalized subjects with pathologic fractures in Taiwan-Survey of the 2008 National Health Insurance data

Background

Almost all studies of pathologic fractures have been conducted based on patients with tumours and hospital-based data; however, in the present study, a nationwide epidemiological survey of pathologic fractures in Taiwan was performed and the medical utilization was calculated.

Methods

All claimants of Taiwan's National Health Insurance (NHI) Program in 2008 were included in the target population of this descriptive cross-sectional study. The registration and inpatient expenditure claims data by admission of all hospitalized subjects of the target population were examined and the concomitant International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes were evaluated and classified into seven major categories of fracture.

Results

A total of 5,244 incident cases of pathologic fracture were identified from the 2008 hospitalized patient claims data. The incidence of pathologic fracture of the humerus, distal radius/ulna, vertebrae, femoral neck, other part of the femur, and tibia/fibula was 0.67, 0.08, 10.58, 1.11, 0.56, and 0.11 per 100,000 people, respectively, and patients with those fractures were hospitalized for 43.9 ± 42.9, 31.1 ± 32.9, 29. 4 ± 34.4, 43.3 ± 41.2, 42.4 ± 38.1, and 42.0 ± 32.8 days, respectively, incurring an average medical cost of US$11,049 ± 12,730, US$9,181 ± 12,115, US$6,250 ± 8,021, US$9,619 ± 8,906, US$10,646 ± 11,024, and US$9,403 ± 9,882, respectively. The percentage of patients undergoing bone surgery for pathologic fracture of the humerus, radius/ulna, vertebrae, femoral neck, other part of the femur, and tibia/fibula was 31.2%, 44.4%, 11.3%, 46.5%, 48.4%, and 52.5% respectively.

Conclusions

Comparing Taiwan to other countries, this study observed for Taiwan higher medical utilization and less-aggressive surgical intervention for patients hospitalized with pathologic fractures.

Antidiabetic effect of Gynostemma pentaphyllum tea in randomly assigned type 2 diabetic patients

The aim of the study was to investigate the antidiabetic effect of the traditional Vietnamese herb Gynostemma pentaphyllum in 24 drug-naïve type 2 diabetic patients. All patients were randomized to authenticated Gynostemma pentaphyllum tea or placebo tea, 6 g daily, during twelve weeks and received information regarding diet and exercise. Fasting plasma glucose, insulin levels, and glycosylated hemoglobin (HbA(1C)) were measured before, during, and after the treatment. Oral glucose tolerance tests were performed every four weeks. After 12-week treatment, fasting plasma glucose levels totally decreased to an extent of 3.0+/-1.8 mmol/l in the Gynostemma pentaphyllum tea group as compared to a decrease of 0.6+/-2.2 mmol/l in the control group (p<0.01). HbA(1C) levels after 12 weeks decreased approximately 2% units in the Gynostemma pentaphyllum group compared to 0.2% unit in the controls (p<0.001). Change in Homeostasis Model Assessment-Insulin Resistance between baseline and twelfth week indicated that insulin resistance decreased significantly in the Gynostemma pentaphyllum group (-2.1+/-3.0) compared with that (+1.1+/-3.3) in the control group (p<0.05). There were no hypoglycemias, or adverse effects regarding kidney and liver parameters or gastrointestinal function. In addition, lipid profiles, glucagon, cortisol levels, body measurements, and blood pressure were not different between the groups. This study shows a prompt improvement of glycemia and insulin sensitivity, and thereby provides a basis for a novel, effective, and safe approach, using Gynostemma pentaphyllum tea, to treat type 2 diabetic patients.

Screening of the hypoglycemic effect of eight Vietnamese herbal drugs

The prevalence of type 2 diabetes in developing countries is increasing. In Vietnam, several plants are thought to be useful for the treatment of type 2 diabetes. This study has been performed to screen the hypoglycemic effects of eight Vietnamese herbs used in traditional medicine for the treatment of diabetes. Blood glucose levels were measured before and several times after oral or i.p. administration of the ethanol-based plant extracts in normal mice. The extracts that reduced blood glucose both orally and i.p. were also studied in glucose tolerance tests in mice. Gynostemma pentaphyllum Makino (Cucurbitaceae) at doses of 200 and 300 mg/kg i.p. or 1500 mg/kg orally reduced blood glucose in mice (P < 0.001 for all compared to control group using NaCl 0.9%). Similarly, Anemarrhena asphodeloides Bunge (Liliaceae) at 200 and 300 mg/kg i.p. (P < 0.001 vs. control group) and 1500 mg/kg orally reduced blood glucose 4 h after administration (P < 0.001). Angiopteris evecta Forst. Hoffn. (Marattiaceae) 300 mg/kg i.p. and 1500 mg/kg orally strongly reduced blood glucose levels (P < 0.001 vs. control groups). All three extracts when dosed at 1000 mg/kg orally suppressed the rise in blood glucose in normal mice during a glucose tolerance test. We have found three herbs that reduced blood glucose and inhibited increases in blood glucose after a glucose challenge in normal mice.

Insulin Secretion is Stimulated by Ethanol Extract of Anemarrhena asphodeloides in Isolated Islet of Healthy Wistar and Diabetic Goto-Kakizaki Rats

BACKGROUND

The hypoglycemic effect of extract of Anemarrhena asphodeloides has been accounted for by the substance mangiferin which increases insulin sensitivity. The present study aimed to investigate whether an ethanol extract of Anemarrhena asphodeloides would stimulate insulin secretion and if so, further elucidate the mechanism behind this effect.

METHODS

Isolated pancreatic islets of normal Wistar rats and spontaneously diabetic Goto-Kakizaki (GK) rats were batch incubated or perifused to study effect of Anemarrhena asphodeloides extract (TH2) on insulin release.

RESULTS

At 3.3 mM glucose, 2, 4, and 8 mg/ml TH2 increased the insulin release of Wistar rat islets 2.5-, 4.1-, and 5.7-fold, respectively (p < 0.05) and of GK rat islets 1.7-, 3.0-, and 6.3-fold, respectively (p < 0.01). Similarly at 16.7 mM glucose, 2, 4 and 8 mg/ml TH2 increased insulin release of Wistar rat islets 1.5-, 2.2-, and 3.8-fold, respectively (p < 0.05) and of GK rat 2.5-, 4.2-, and 11.9-fold, respectively (p < 0.01). In perifusions of islets, TH2 also increased insulin secretion that returned to basal levels when TH2 was omitted from the perifusate. Mangiferin had no effect on insulin secretion of islets. In islets depolarized by 30 mM KCl and B-cell K-ATP channels kept open by 0.25 mM diazoxide, TH2 (8 mg/ml) further enhanced insulin secretion at 3.3 but not at 16.7 mM glucose. Pertussis toxin suppressed the insulin stimulating effect of 2 and 8 mg/ml TH2 by 35 % and 47 % (p < 0.05 and p < 0.001, respectively).

CONCLUSIONS

Ethanol extract of the roots of Anemarrhena asphodeloides contains a substance, TH2, that stimulates insulin secretion both at 3.3 and 16.7 mM glucose in islets of normal Wistar and diabetic GK rats. The mechanism behind TH2-stimulated insulin secretion involves an effect on the exocytotic machinery of the B-cell, mediated via pertussis toxin-sensitive Gi- (or Ge-) proteins.