Search

Pain management must be done systematically according to the type of pain and its severity level in order to obtain optimal results. Therefore it is necessary to have an expert in pain management, who is equipped with a variety of pain management knowledge. In Indonesia there is no specific profession or license to become a pain management specialist. In this article the author tries to explain what is pain management specialist from various references abroad.

Background The ETDRS stereoscopic seven-field (7F) has been a standard imaging and grading protocol for assessment of diabetic retinopathy (DR) severity score in many clinical trials. To the best of our knowledge, the comparison between montage and stereoscopic 7F has not been reported in the literature. Therefore, the main purpose of this study is to compare agreement between montage and stereoscopic seven-field (7F) photographs in the assessment of DR severity. Methods Stereoscopic 7F photographs were captured from subjects with DR. Montages of monoscopic 7F images were created using Adobe Photoshop CS6 Extended©. The best quality image of each stereo pair was selected and placed on a 150 × 125-inch canvas field according to the standard location from field 1 to 7. All the fields were aligned following the vessels and overlaid using the built-in blending tool. The resulting montage was utilized for grading and compared with grading on stereoscopic 7F photographs. Three independent graders were asked to assess DR severity on stereoscopic 7F photographs and montage. Severity level agreement between stereo 7F and montage was cross-tabulated and the agreement of DR severity levels between stereoscopic 7-field images and montage was analyzed using κ intergrader agreement; statistical significance was set at p Results A total of 50 eyes were included in the study. There was a substantial agreement between stereoscopic 7F and montage (κ = 0.745, κweighted = 0.867) in assessment of DR severity. Of 50 eyes, 80% of the cases showed complete agreement, and 100% of the cases had agreement within one-step. There was a moderate agreement among graders, and κ-value ranged from 0.4705 to 0.5803. Conclusion In this study, we found a substantial agreement in assessing DR severity score employing non-stereoscopic montage and stereoscopic 7F photographs.

Voluntary wheel running (VWR) behaviour is a sensitive indicator of disturbed wellbeing and used for the assessment of individual experimental severity levels in laboratory mice. However, monitoring individual VWR performance usually requires single housing, which itself might have a negative effect on wellbeing. In consideration of the 3Rs principle, VWR behaviour was evaluated under group-housing conditions. To test the applicability for severity assessment, this readout was evaluated in a dextran sodium sulphate (DSS) induced colitis model. For continuous monitoring, an automated system with integrated radio-frequency identification technology was used, enabling detection of individual VWR. After a 14-day adaptation period mice demonstrated a stable running performance. Analysis during DSS treatment in combination with repeated facial vein phlebotomy and faecal sampling procedure resulted in significantly reduced VWR behaviour during the course of colitis and increased VWR during disease recovery. Mice submitted to phlebotomy and faecal sampling but no DSS treatment showed less reduced VWR but a longer-lasting recovery. Application of a cluster model discriminating individual severity levels based on VWR and body weight data revealed the highest severity level in most of the DSS-treated mice on day 7, but a considerable number of control mice also showed elevated severity levels due to sampling procedures alone. In summary, VWR sensitively indicated the course of DSS colitis severity and the impact of sample collection. Therefore, monitoring of VWR is a suitable method for the detection of disturbed wellbeing due to DSS colitis and sampling procedure in group-housed female laboratory mice. Federal Ministry of Economics and Energy, "Zentrales Innovationsprogramm Mittelstand (ZIM) Deutsche Forschungsgemeinschaft https://doi.org/https://dx.doi.org/10.13039/501100001659

Context: The severity level attribute of a bug report is considered one of the most critical variables for planning evolution and maintenance in Free/Libre Open Source Software. This variable measures the impact the bug has on the successful execution of the software system and how soon a bug needs to be addressed by the development team. Both business and academic community have made an extensive investigation towards the proposal methods to automate the bug report severity prediction. Objective: This paper aims to provide a comprehensive mapping study review of recent research efforts on automatically bug report severity prediction. To the best of our knowledge, this is the first review to categorize quantitatively more than ten aspects of the experiments reported in several papers on bug report severity prediction. Method: The mapping study review was performed by searching four electronic databases. Studies published until December 2017 were considered. The initial resulting comprised of 54 papers. From this set, a total of 18 papers were selected. After performing snowballing, more nine papers were selected. Results: From the mapping study, we identified 27 studies addressing bug report severity prediction on Free/Libre Open Source Software. The gathered data confirm the relevance of this topic, reflects the scientific maturity of the research area, as well as, identify gaps, which can motivate new research initiatives. Conclusion: The message drawn from this review is that unstructured text features along with traditional machine learning algorithms and text mining methods have been playing a central role in the most proposed methods in literature to predict bug severity level. This scenario suggests that there is room for improving prediction results using state-of-the-art machine learning and text mining algorithms and techniques.

Background Priority setting in healthcare can be guided by both efficiency and equity principles. The latter principle is often explicated in terms of disease severity and, for example, defined as absolute or proportional shortfall. These severity operationalizations do not explicitly consider patients' age, even though age may be inextricably related to severity and an equity-relevant characteristic. Objective This study examines the relative strength of societal preferences for severity and age for informing allocation decisions in healthcare. Methods We elicited preferences for severity and age in a representative sample of the public in The Netherlands (N = 1025) by applying choice tasks and person-trade-off tasks in a design in which severity levels and ages varied both separately and simultaneously between patient groups. We calculated person trade-off ratios and, in addition, applied ordinary least squares regression models to aid interpretation of the ratios when both severity and age varied. Results Respondents attached a higher weight (median of ratios: 2.46-3.50) to reimbursing treatment for relatively more severely ill and younger patients when preferences for both were elicited separately. When preferences were elicited simultaneously, respondents attached a higher weight (median of ratios: 1.98 and 2.42) to reimbursing treatment for relatively younger patients, irrespective of patients’ severity levels. Ratios varied depending on severity level and age and were generally higher when the difference in severity and age was larger between groups. Conclusions Our results suggest that severity operationalizations and equity weights based on severity alone may not align with societal preferences. Adjusting decision-making frameworks to reflect age-related societal preferences should be considered.

Tuberculosis (TB) is a disease caused by a bacterium called Mycobacterium tuberculosis. Basically there are two types of TB, latent and pulmonary TB. It symptoms include persistent cough for more than two weeks, cough with blood, night sweat, tiredness, fever, chest pain, lymph node enlargement. It treatment depends on severity level such as mild, moderate, severe and very severe and this can be obtained from TB diagnosis such as chest x-ray image, nucleic acid amplification test and culture and sensitivity. The main aim of this work is to determine severity level of TB using image processing techniques. Chest x-ray (CXR) images of TB patients were obtained from Google image. Firstly, the CXR images were enhanced from Red Green Blue (RGB) to gray image (GI) form. Secondly, GI were decomposed, convolved, compressed and filtered these processing is called image degradation. Thirdly, These GI were restored and binarized. Threshold value ≥ 53 was obtained to classify the severity level of the infected image Furthermore, average error was calculated and system performance obtained. This work revealed that image 1, 2, 6 and 11 has a total pixel value 11067, 6735, 9256 and 3894 respectively ≥ 53 which indicates that the % of the infected areas as 99.62%, 99.52%, 96.70% and 82.78% respectively which shows the severity level are very severe. While the image 3, 4, 5, 8 and 9 has a percentage of infected areas 73.13%, 72.90%, 76.80%, and 76.84% respectively which indicate the severity level as severe. Image 7 has a percentage of infected area 51.21 which shows the severity level as moderate. This research work revealed that image processing is a suitable technique for determine the severity level of TB.

This study examines Korean’s complaint speech act in which 204 subjects participated in survey that elicited their responses from ten complaint situations. As somewhat anticipated, they opted out 31.3% of the total responses in survey questionnaire and used only 4.74 words on average on a complaint situation, which means that they complained very shortly. Gender difference is found in which men used more words than women. Age group difference is also found in which the lower age group used more words than the older age group. Considering social factors, the study finds that relatives used less words than other social groups including strangers, acquaintances, and friends, and the respondents used more words when social obligation was more explicit than implicit. For severity level on a 1 - 5 scale, their severity level in the study is 2.54 on average which indicates that they complained in the middle of indirect and mitigated on the severity scale. Gender difference is not found but age group difference is found for severity level in which the 21-29 age group’s severity level is higher than age group over 30 at p < .05 level. Considering social factors for severity level, the study also finds that severity level is the greatest for friends and the weakest for relatives. For social status, severity level is the greatest for equals and the weakest for younger speakers. And for social obligation, severity level is found greater when social obligation was more explicit than implicit. They scarcely used softeners and intensifiers in the study.

The paper addresses experimental characterization of instant shape of paraffin grain with predefined structural discontinuity in process of combustion and its possible influence on structural integrity of hybrid propellant motor. The one-port paraffin grains with and without artificial crack were subjected to combustion in a hybrid propellant test-motor and considerable discrepancies of temperature on external surface of the non-cracked and the cracked grains were detected. Elevated temperature on the external surface of cracked grain was caused by fast evolution of the crack in direction of motor’s casing during combustion process. Thus, it was shown that crack formation causes a local overheating of structural casing that may subsequently jeopardize structural integrity of the motor.

BACKGROUND Central sensitization (CS) is found in patients with musculoskeletal disorders and is related to clinical symptoms, including pain-related disability. The Central Sensitization Inventory (CSI) has been developed for patients who are at risk of symptoms related to CS, and CSI severity levels are suggested for clinical interpretation of the CSI score. However, the longitudinal relationship between CSI severity and pain-related disability is unclear in primary care. In this study, we investigated the association between CSI severity levels and the profiles of patients with musculoskeletal disorders as well as the longitudinal relationship between CSI severity levels and pain-related disability in primary care settings. METHODS A total of 553 patients were assessed using CSI, EuroQol-5 dimension (EQ5D), and Brief Pain Inventory (BPI). Of the 553 patients, 150 patients were reassessed at the 3-month follow-up. Patients were grouped into three severity levels according to baseline CSI score: subclinical, mild, and moderate to higher level. RESULTS As the CSI severity levels increased, the clinical symptoms tended to worsen on cross-sectional analysis (p

In the current era, research on automated knowledge extraction from Chronic Obstructive Pulmonary Disease (COPD) images is growing rapidly. COPD becomes a highly prevalent disease that impacts both patients and healthcare system. In various medical applications, image classification algorithms are used to predict the disease severity that can help in early diagnosis and decision-making process. Also, for large scale and complex medical images, machine learning techniques are more efficient,accuracy and reliable. Traditional image classification models such as Naïve Bayesian, Neural Networks, SVM, Regression models. etc are used to classify the image using the annotated ROI and image texture features. These models are used as a diagnostic tool in analyzing the COPD and disease prediction. These models are not applicable to classify the COPD using the disease severity level. Also, the accuracy and false positive rate of existing classification models is still far from satisfactory, due to lack of feature extraction and noise handling methods. Therefore, developing an effective classification model for predicting the severity of the COPD using features derived from CT images is a challenge task.In this paper, an ensemble feature selection based classification model was developed, using images features extracted from COPD patients’ CT scan images, to classify disease into “Severity level ” and “Normal level” categories, representing their riskof suffering a COPD disease. We applied five different classifier methods and three state-of-the-art ensemble classifiers to the COPD dataset and validated their performance in terms of F-measure and false positive rate. We found that proposed feature selection based ensemble classifier (F-measure 0.957) had the highest average accuracy for COPD classification.

Study design Descriptive study. Objective To demonstrate Euro Quality of Life 5-dimensional 5-level (EQ-5D-5L) by severity level at the acute stage and discharge in patients with traumatic spinal cord injury (SCI). Setting Spinal Injuries Center, Fukuoka, Japan. Methods Patients with traumatic SCI who completed the EQ-5D-5L instrument at the acute stage and discharge were divided into four groups according to severity (severity group G1; C1-C4 ASIA Impairment Scale (AIS) A, B, and C, G2; C5-C8 AIS A, B, and C, G3; T1-S5 AIS A, B, and C, G4; all AIS D). All data were extracted from the Japan single-center study for spinal cord injury database (JSSCI-DB). Results Data were collected from 139 individuals at the acute stage and 164 individuals at discharge. In the comparison of utility score by severity in acute stage and discharge, G3 was significantly higher in discharge (0.325 versus 0.580). Utility scores by degree of severity were significantly higher in group G4 than those of G1, G2, and G3 at the acute stage. Utility scores at discharge were significantly higher in groups G3 and G4 than in G1, and there was a significant difference between groups G4 and G2. Conclusions At each stage, the utility scores of group G4 were significantly higher than those of groups G1 and G2. Altogether, the utility scores for hospitalized patients with traumatic SCI that were indicated in this study will serve as basic data that can be used while performing spinal regeneration medical procedures in the future.

Background: Autism is a multisystem neurodevelopmental disturbance characterized by stereotyped behaviors, restricted interests, as well as impaired communication and social interactions.Aim of Study: The current study examined the relationship between upper limb coordination, as measured by Bruininks-Oseretsky Test of Motor Proficiency 2nd Edition (BOT-2), and autism severity, as measured by the Childhood Autism Rating Scale (CARS).Subjects and Methods: Thirty (30) children with a diag-nosis of Autism Spectrum Disorder (ASD) were evaluated using the CARS and then tested for upper limb coordination using (BOT-2), Statistical analysis was then conducted to examine the relationship between autism severity and upper limb coordination.Results: The results of the present study showed that there was a negative correlation between CARS score and upper limb after adjustment for age, weight, height, gender and year of birth.Conclusion: Evidence suggests that upper limb coordina-tion in children with ASD is related to the severity of the disorder. Further research is needed to determine the extent and consistency of the motor impairments and possible treat-ments.

Background Studies to inform feeding interventions on neonatal units are lacking. This study aims to compare gestational age and medical history as methods of identifying infants at higher risk of developing feeding difficulties by 40 weeks gestational age. Method A retrospective case note analysis was conducted on a level 3 neonatal unit. Infants were given a severity category based on gestational age at birth and medical history. Feeding outcomes were analysed for sensitivity, specificity, and positive and negative predictive values. Results 233 infants were included. Medical history at severity level 2 provided a high level of sensitivity (84.62%) and specificity (87.92%) for feeding difficulties at 40 weeks gestational age. Conclusion Medical history can be used as a simple method to identify infants at highest risk of feeding difficulties prior to commencing oral feeding, enabling the targeting of preventative therapeutic interventions and caseload prioritisation.

A calibrated mechanistic-empirical model was developed to analyze top-down cracking (TDC) performance with actual traffic load and thermal stress. The traffic-induced and thermal-induced J-integral were employed in the Paris’ law with fracture parameters A ′ and n ′ estimated using asphalt mixture properties. Number of days for initiated TDC to reach the medium severity level was then calculated mechanistically, which was related to field performance for calibration purposes. It was observed that when TDC reached a critical length, the TDC length diminished and area of fatigue cracking increased. An associated computer program was also developed, which normally completed a 30-year performance analysis within 1 min.

Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims to explore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system. This study is a cross-sectional study, where 36 children with ASD were recruited and classified into two groups: mild and severe ASD (18 children in each). Three calculated outcomes identified brain complexity of mild and severe ASD groups: averaged MSE values, MSE topographical cortical representation, and MSE curve plotting. Averaged MSE values of children with mild ASD were higher than averaged MSE value in children with severe ASD in right frontal (0.37 vs. 0.22, respectively, p = 0.022), right parietal (0.31 vs. 0.13, respectively, p = 0.017), left parietal (0.37 vs. 0.17, respectively, p = 0.018), and central cortical area (0.36 vs. 0.21, respectively, p = 0.026). In addition, children with mild ASD showed a clear and more increase in sample entropy values over increasing values of scale factors than children with severe ASD. Obtained data showed different brain complexity (MSE) features, values and topographical representations in children with mild ASD compared with those with severe ASD. As a result of this, MSE could serve as a sensitive method for identifying the severity level of ASD.

[EN] Objectives: This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets. Methods: The database used contains the information of 156,811 inhabitants in a Valencian Community health district in 2013. The variables were: age, sex, CRG's main health statuses, severity level, and healthcare expenditure. The two-part models were used for predicting healthcare expenditure. From the coefficients of the selected model, the relative weights of each group were calculated to set a case-mix in each health district. Results: Models based on multimorbidity-related variables better explained integrated healthcare expenditure. In the first part of the two-part models, a logit model was used, while the positive costs were modelled with a log-linear OLS regression. An adjusted R-2 of 46-49% between actual and predicted values was obtained. With the weights obtained by CRG, the differences found with the case-mix of each health district proved most useful for budgetary purposes. Conclusions: The expenditure models allowed improved budget allocations between health districts by taking into account morbidity, as opposed to budgeting based solely on population size., This work was supported by "Instituto de Salud Carlos III - Ministerio de Economia y Competitividad" and the European Union (FEDER funds) - FIS PI12/00037.

The study was conducted to know whether bulk inflow of Non Emergency Calls (NECs) acts as an independent predictor for depression in call handlers of Rescue 1122, Punjab, Pakistan. Forty five (45) call handlers were recruited from evening shift of 9 districts. Similarly, same-sized control group was made out of field rescuers. The groups were compared for rate and severity level of depression using Beck’s Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996). Probable predictors (except workplace stress) for depression in call handlers were evaluated through risk estimates. For workplace stress, a purposefully developed close- ended Workplace Stress Questionnaire including two subscales i.e. Non Emergency call and Control Room Environment of 30 items each was administrated. Twenty nine (64.4%) subjects of study group reported depression on BDI. Consequently, the same group had significantly higher mean depression score than control (18.2 vs. 12.6; p = .00). The scores also showed insignificant association with any of the probable predictors (demographic variables) of the call attendees. The respondents perceived more occupational stress against NECs. The findings attract the attention of authorities towards the severity of the concern.

Leucocytosis, marked increase in the number of white blood cells (WBC) is a known physiological response to trauma. In recent times, several studies have asserted the absence of this response in Africans. In view of this, current study investigated the existence of, and prognostic implications of post-traumatic leucocytosis amongst adult Nigerians with acute musculoskeletal (MSK) trauma. Two hundred and twenty three (223) adult male and females (MSK traumatized) and fifty apparently healthy volunteers (adults) were ethically recruited from the National Orthopaedic Hospital, Enugu, regional centre for trauma, orthopaedic, burns and plastic surgery in south-east Nigeria. Using the Leishman’s stained blood smear technique, leucocyte profiles [Neutrophil, Lymphocytes, Basophils, Eosinophils and Monocyte counts] were obtained for each participant. In all case, Age, Gender and duration of hospitalization were also obtained. Following careful analysis, study found, using one way analysis of variance (ANOVA), a statistically significant increase (p < .05) in acutely traumatized subjects; with adults of ages 20 to 49 years constituting the majority (70%). A statistically significant lymphopenia was also observed in test population, with Pearson Product Moment Correlation proving positive for higher levels of WBC counts. A negative correlation was also seen for Neutrophils and lymphocyte counts, implicating the Neutrophil-Lymphocyte Stress Factor (NLSF). We recommend the exploration of the NLSF for prognosis of Leucocytosis in Africans.

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma seve...

With the publication of DSM-5, clinical assessment of Autism Spectrum Disorder (ASD) has begun to follow a new dimensional framework which includes new severity specifiers. Little research ...

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features.Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods.All statistical comparisons exhibited a significant difference (p 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informative.

Basal Stem Rot (BSR) is the most destructive disease of oil palm in Malaysia. White-rot fungus Ganoderma is the causal pathogen of BSR disease leading to significant economic loss to the oil palm industry. Early disease detection is crucial to control the disease and to reduce the disease effects on the yield. Terrestrial Laser Scanning (TLS) is an active imaging method which is contact-free, precise, cost-effective and user-friendly. It provides accurate information on tree dimensions and morphology, which relates to plant development and health. This study proposed an image processing technique using the point clouds, ground input data taken from the terrestrial laser scanner that has capability to get a top view image of the tree. The objective of this study was to analyze the oil palm inter-frond angles (measured in degrees) using top view image of oil palm tree at four different severity levels of infection. From the results, mean degree of frond angle in each level showed significant relationship with severity level of BSR with high correlation, R2 values of 0.96. Results from Analysis of Variance (ANOVA) for frond angles exhibited significant differences between all levels of infection with p-value less than 0.0001.

Severity of rice disease is an important indicator for farmers to plan appropriate treatments for protecting diseases which may give damages to rice paddy fields. This paper proposes an estimation method of rice-disease severity level using deep convolutional neural network. Since rice-disease severity level is the ratio between rice disease area and whole area in a rice leaf, candidate boundaries of rice disease are detected, and those boundaries are classified into a level out of rice-leaf disease levels which are early, middle, and final stages. All classified boundaries of levels are calculated with total area, and finally ratio with the whole leaf area is obtained. To evaluate performance of the proposed method, experiments with 2,500 images and 5,000 disease boundaries including five disease types have been performed, and results reveal 96.40%, 96.40%, and 96.56% accuracy for early, middle, and final stages, respectively.

Background Through improvements in trauma care there has been a decline in injury mortality, as more people survive severe trauma. Patients who survive severe trauma are at risk of long-term disabilities which may place a high economic burden on society. The purpose of this study was to estimate the health care and productivity costs of severe trauma patients up to 24 months after sustaining the injury. Furthermore, we investigated the impact of injury severity level on health care utilization and costs and determined predictors for health care and productivity costs. Methods This prospective cohort study included adult trauma patients with severe injury (ISS≥16). Data on in-hospital health care use, 24-month post-hospital health care use and productivity loss were obtained from hospital registry data and collected with the iMTA Medical Consumption and Productivity Cost Questionnaire. The questionnaires were completed 1 week and 1, 3, 6, 12 and 24 months after injury. Log-linked gamma generalized linear models were used to investigate the drivers of health care and productivity costs. Results In total, 174 severe injury patients were included in this study. The median age of participants was 55 years and the majority were male (66.1%). The mean hospital stay was 14.2 (SD = 13.5) days. Patients with paid employment returned to work 21 weeks after injury. In total, the mean costs per patient were €24,760 with in-hospital costs of €11,930, post-hospital costs of €7,770 and productivity costs of €8,800. Having an ISS ≥25 and lower health status were predictors of high health care costs and male sex was associated with higher productivity costs. Conclusions Both health care and productivity costs increased with injury severity, although large differences were observed between patients. It is important for decision-makers to consider not only in-hospital health care utilization but also the long-term consequences and associated costs related to rehabilitation and productivity loss.

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include ’No Referable Diabetic Macular Edema Grade (DME)’ and ’Referable DME’ while five categories consist of ‘Proliferative diabetic retinopathy’, ‘Severe’, ‘Moderate’, ‘Mild’, and ‘No diabetic retinopathy’. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.

Background Overcrowding of the Emergency Department is rapidly becoming a global challenge and a major source of concern for emergency physicians. The desire to improve Emergency Department throughput requires novel approaches to patient flow. Materials and methods We conducted a prospective and cluster-randomized study, to evaluate the impact in patient outcomes of a new patient flow based on Point-of-Care Testing (POCT). A total of 380 Emergency Severity Level-3 patients were enrolled and studied in two different groups, interventional arm (laboratory analyses performed on POCT analyzers implemented in the Emergency Department) or control arm (central laboratory). The primary outcome was the Emergency Department length of stay. Secondary outcome included the time to first medical intervention, the laboratory turnaround time and the time to disposition decision. Readmission within the 7 days after discharge was also calculated. Results Length of stay significantly decreased by 88.50 min (from 247.00 to 158.50), time to disposition decision by 89.00 min (from 192.00 to 103.00) and laboratory turnaround time by 67.11 min (from 89.84 to 22.73) in the POCT group. No increase in readmission was found. Conclusion Our strategy based on POCT represents a good approach to optimize patient flow in the Emergency Department and it should be seen as a starting point for further studies focusing on improving throughput.

This study aimed to assess physical and physiological seed quality attributes and seed vigor in different hybrids presenting different corn stunt complex severity levels, molicutes and Maize rayado fino virus (MRFV). This was performed through a multivariate approach, in order to select high vigor seeds and more disease-tolerant hybrids, as well as identify the main vigor variables. Seeds from three corn hybrids (H1, H2 and H3) obtained from seed production fields were assessed, each presenting three levels of corn stunt complex ear severity (healthy, medium and high). Seed quality was assessed using physical and physiological quality attributes. The data were subjected to an analysis of variance and, when significant, assessed through factor analysis, Scott–Knott test (p

OBJECTIVE To establish a visual assessment scale for the severity of dark circles' pigmented and vascular type in Chinese women. MATERIALS AND METHOD A total of 269 healthy Chinese women from Shanghai with different degrees of dark circles', both pigmented and vascular types, were evaluated by visual assessment. Photographs of their dark circles were analyzed by image analysis. RESULTS The visual assessment evaluation on classification and severity showed a favorable agreement between the successive measured results. Significant differences from very slight to severe dark circles for pigmented type and vascular type were observed. The severity level by visual assessment was significantly positively correlated with ΔE values while negatively correlated with ΔL values (P

Nurses play a key role as the first line of service for patients with medical conditions and injuries in the emergency department (ED), which includes assessing patients for sepsis. The researchers evaluated tools to examine the performance of the Simple Clinical Score (SCS) and the Rapid Emergency Medicine Score (REMS) to predict sepsis severity and mortality among sepsis patients in the ED. A retrospective survey was performed, selecting participants by using a purposive sampling method, and including the medical records of all patients diagnosed with sepsis admitted to the ED at Singburi Hospital, Thailand. Data were analysed using the ROC curve and the Area Under Curve (AUC) to calculate the accuracy of each patient’s mortality prediction. A total of 225 patients diagnosed with sepsis was identified, with a mortality rate of 59.11% after admission to the medical service and intensive care unit. The AUC analysis showed that the accuracy of the model generated from the REMS (88.6%) was higher than that of the SCS (76.7%). The authors also recommend that key variables identified in this research should be used to develop screening and assessment tools for sepsis in the context of the ED.

Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery. The loss of fine control over muscles that assist the movement of lips, vocal chords, tongue and diaphragm results in abnormal speech delivery. One can assess the severity level of dysarthria by analyzing the intelligibility of speech spoken by an individual. Continuous intelligibility assessment helps speech language pathologists not only study the impact of medication but also allows them to plan personalized therapy. It helps the clinicians immensely if the intelligibility assessment system is reliable, automatic, simple for (a) patients to undergo and (b) clinicians to interpret. Lack of availability of dysarthric data has resulted in development of speaker dependent automatic intelligibility assessment systems which requires patients to speak a large number of utterances. In this paper, we propose (a) a cost minimization procedure to select an optimal (small) number of utterances that need to be spoken by the dysarthric patient, (b) four different speaker independent intelligibility assessment systems which require the patient to speak a small number of words, and (c) the assessment score is close to the perceptual score that the Speech Language Pathologist (SLP) can relate to. The need for small number of utterances to be spoken by the patient and the score being relatable to the SLP benefits both the dysarthric patient and the clinician from usability perspective., 29 pages, 2 figures, Computer Speech & Language 2021

The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.

Objectives To assess the esthetic impact of anterior occlusal conditions and malocclusion severity levels. Materials and Methods A population-based cross-sectional study of 700 adolescents aged 15 to 19 years was conducted. The Oral Aesthetic Subjective Impact Scale (OASIS) was used to evaluate the subjective esthetic impact of malocclusion. The Dental Aesthetic Index (DAI) criteria were used to diagnose the anterior occlusal characteristics in isolation and the severity levels of malocclusion. The variables with P < .20 in the individual analyses were tested in multiple logistic regression models, and those with P < .10 remained in the model. The adjusted odds ratio (OR) was estimated with a 95% confidence interval (CI). Results Of the adolescents, 42% showed negative self-perception of malocclusion. In addition, 15.4% of adolescents had severe malocclusion (DAI 3) and 18.9% very severe malocclusion (DAI 4). Crowding and spacing were shown to be 2.90 (CI: 2.06–4.09) and 2.53 (CI: 1.65–3.86) times, respectively, more likely to cause a negative esthetic impact in adolescents (P < .05). In addition, adolescents with orthodontic treatment need (DAI 2, 3, and 4) were more likely to report a negative esthetic impact (P < .05). Conclusions Anterior crowding and spacing are the conditions that most influence the esthetic concern of adolescents. Adolescents with very severe malocclusion and higher orthodontic treatment need are more likely to report a negative esthetic impact.

Hydroclimatic extremes such as droughts and floods triggered by human-induced climate change are causing severe damage in the Nile River Basin (NRB). These hydroclimatic extremes are not well studied in a holistic approach in NRB. In this study, the Gravity Recovery and Climate Experiment (GRACE) mission and its Follow on mission (GRACE-FO) derived indices and other standardized hydroclimatic indices are computed for developing monitoring and evaluation methods of flood and drought. We evaluated extreme hydroclimatic conditions by using GRACE/GRACE-FO derived indices such as water storage deficits Index (WSDI); and standardized hydroclimatic indices (i.e., Palmer Drought Severity Index (PDSI) and others). This study showed that during 1950–2019, eight major floods and ten droughts events were identified based on standardized-indices and GRACE/GRACE-FO-derived indices. Standardized-indices mostly underestimated the drought and flood severity level compared to GRACE/GRACE-FO derived indices. Among standardized indices PDSI show highest correlation (r2 = 0.72) with WSDI. GRACE-/GRACE-FO-derived indices can capture all major flood and drought events; hence, it may be an ideal substitute for data-scarce hydro-meteorological sites. Therefore, the proposed framework can serve as a useful tool for flood and drought monitoring and a better understanding of extreme hydroclimatic conditions in NRB and other similar climatic regions.

This study aimed to analyze the responsiveness of the 5STS test among stroke patients and to estimate the MCIDs (minimal clinically important differences) for different severity levels of community ambulation and stages of recovery. The 5STS and comparator instruments (gait speed and Functional Ambulatory Category (FAC)) were evaluated at baseline. These measures were repeated at 4 (Stage 1) and 8 weeks (Stage 2), together with the Global Rating of Change (GROC). The MCIDs were calculated with two anchor-based methods using the GROC as the external criterion. Responsiveness to change for the 5STS was estimated analyzing the correlation with changes in the two comparator instruments and their capacity to discriminate improvement. For the 5STS test, while the MCIDs of the limited community ambulators were similar in the two stages (around 3 s), those of the household ambulators decreased from 1.9 s to 0.72 s. Spearman’s rho coefficients showed an acceptable correlation between changes in 5STS and changes for both the FAC and gait speed changes in both stages of recovery. Our study revealed that the 5STS is responsive to functional changes in patients with stroke and that their degree of severity and stage of recovery influence the MCID values of the 5STS.

Objective: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study is to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis has been extended to observe behavior of wheeze sounds in different datasets. Method: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) has been calculated from normalized power spectrum. Subsequently, multivariate analysis has been performed for analysis. Result: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level ? = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ????2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples ? = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ????2 = 0.386-0.568. Conclusion: The results demonstrate that severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics. Keywords: Asthma, Breath Sounds, Wheeze Detection, Airway Obstruction, Severity Level Continuous...

Zusammenfassung Ziel der Studie Untersuchung soziodemografischer und störungsbezogener Determinanten zur Inanspruchnahme ärztlicher oder psychologischer Hilfe bei Depressionen im Lebensverlauf. Methodik In der bevölkerungsbasierten SHIP-Trend-1-Erhebung (2016–2019) wurden N = 490 Personen mit Lebenszeitdiagnosen depressiver Episoden und die Inanspruchnahme professioneller Hilfe erfasst. Geschlecht, sozioökonomischer Status und störungsbezogene Variablen wurden in altersstratifizierten logistischen Regressionsmodellen als Prädiktoren berücksichtigt. Ergebnisse Männer und Personen im höheren Lebensalter berichteten weniger Inanspruchnahme von Hilfe als Frauen und jüngere Menschen. In unterer und mittlerer Altersgruppe war die Inanspruchnahme mit Störungsschwere assoziiert, in der oberen mit körperlichen Ursachen. Schlussfolgerung Die Inanspruchnahme professioneller Hilfe war in den verschiedenen Altersgruppen mit unterschiedlichen Prädiktoren assoziiert. Latente Variablen wie altersgruppenspezifische Erfahrungen, Werte und Lebensstile könnten diese Unterschiede erklären.

Fetal alcohol spectrum disorder (FASD) is a biomedical disorder that places an individual at high risk for intellectual disability (ID) or developmental disability (DD). Yet in some legal settings, people with FASD are denied legal protections or entitlements because of a mistaken belief that the disorder has low severity. Commonly, this misunderstanding reflects a view of FASD as the functional equivalent of attention-deficit/hyperactivity disorder (ADHD). This view also reflects the fact that FASD typically is not diagnosed in childhood and because of the near-universal co-occurrence of attentional problems, affected individuals commonly are diagnosed with ADHD, which masks the underlying FASD. In this chapter, we develop a model for establishing disability severity and compare the relative severity of FASD with other disorders, concluding that FASD is much more severe than ADHD and comparable in severity to ID. We also use this model to assess individual disability severity, finding that while ADHD as a whole is a low-severity disorder, there are a minority of people within that category who have a high-severity disability and very possibly have undiagnosed FASD. Implications of these findings for forensic practice are explored, with emphasis on the importance of basing severity determination on an evaluee’s competence profile and support needs rather than diagnostic label history.

Due to advancement in technology moveability of people increase with increase in number of vehicles. This may lead to occurrence of accidents. Accidents are very crucial problem which damage human lives, infrastructure and economy of nation. . It has been recorded that in road accidents near about 13 peoples are dying per hour. According to road safety management the accidental black spots are those spots were road traffic accidents were happened repeatedly. If we considered the Global Road Safety Report (2015) given by World Health Organisation (WHO), in India more than 2, 00,000 deaths are recorded because of accidents. To overcome this problem fuzzy logic is better solution. The Problems which are complex in nature can be solved by using Fuzzy logic. This study highlights a fuzzy logic would be a best option to identify accidental black spot. By using Fuzzy Multicriteria Decision Making (FMCDM) and Fuzzy Rule Based System (FRBS) approaches we can calculate Accidental Black Spot Severity Index (ABSSI) and Accidental Black Spot Severity Level (ABSSL) of that particular spot respectively. The Accidental Black Spots can be further ranked on the basis of their severity index and can be classified like highly severe, severe, just severe and not severe on the basis of level of Severity.

Today, diabetes is one of the most prevalent, chronic, and deadly diseases in the world owing to some complications. If accurate early diagnosis is feasible, the risk factor and incidence of diabetes may be greatly decreased. Diabetes prediction is stable and reliable, since there are only minimal labelling evidence and outliers found in the datasets of diabetes. Numerous works coped with diabetes disease prediction and provided the solution. But the existing methods proffered low accuracy detection and consumed more training time. So, this paper proposed an OWDANN algorithm for diabetes mellitus disease prediction and severity level estimation. The proposed system mainly consists of two phases, namely, disease prediction and severity level estimation phase. In the disease prediction phase, the preprocessing is performed for the Pima dataset. Then, the features are extracted from the preprocessed data, and finally, the classification step is performed by using OWDANN. In the severity level estimation phase, the diabetes positive dataset is preprocessed first. Then, the features are extracted, and lastly, the severity level is predicted using GDHC. The extensive experimental results showed that the proposed system outperforms with 98.97% accuracy, 94.98% sensitivity, 95.62% specificity, 97.02% precision, 93.84% recall, 9404% f-measure, 0.094% FDR, and 0.023% FPR compared with the state-of-the-art methods.

Depression is a severe psychological disorder that is experienced by a significant number of individuals across the globe. It greatly changes the way one thinks, triggering a constant decline in mood. Studies have shown that gender can act as a good indicator of depression. In this paper, we analyse the effects of gender information in the estimation of depression. We have carried out different experiments on the benchmark data set named Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). Concretely, we discovered that a) gender information substantially improves the performance of depression severity estimation, and b) adversarially learning to predict the depression score distributed by gender improves the performance of depression severity estimation.

Diagnosis and evaluation of Parkinson’s disease (PD) by clinicians is normally dependent on several established clinical criteria. Measuring the severity level according to these criteria depends heavily on the doctor’s expertise, which is subjective and inefficient. Hence, the current work aims to provide a quantitative and comprehensive evaluation in order to improve the diagnosis and assess the severity of PD using deep learning approaches. In the current study, a machine learning based method is proposed to automatically diagnose the PD severity from handwritten exams, by extracting the information from static and dynamic tests. A hybrid model for prediction is suggested by fusing convolutional neural network (CNN) and long-short term memory (LSTM). The suggested hybrid model (CNN + LSTM) obtained accuracies of 85.5% and 99.3% using the ParkinsonHW and HandPD datasets, respectively. We conclude that the quantitative evaluation provided by our model may be considered a helpful tool in the clinical detection of Parkinson’s disease.

Acoustic Emission (AE) technology is widely used in many industrial applications because it can provide the key information from the analysis of damage signals produced in the materials and the structures. The results from the analysis can be taken out, such as the source localization, damage modes and severity level of the material. The characteristics of AE signals can be varied with the different distances to the damage sources and with the effects of amplitude-frequency response of AE sensors, the structure types and the frequency dispersion. The material damage evaluation based on the analysis of AE signals can be unreliable. In this paper, AE signals were generated by hypervelocity impact simulation tests due to these signals without the effects of AE sensors, degree of coupling and acquisition system, and can be used to analysis structural effects on the characteristics of those signals. The correlations among the signals from different propagation distances were computed with algorithm of probability statistics. The results of correlation coefficient variations were presented. The conclusions can be used to analyses the precision of damage localization and evaluate the reasonability of failure modes in the materials and the structures.

Yellow rust disease caused by Puccinia striiformis f. sp. tritici, a pathogen in wheat, results in significant losses in wheat production worldwide due to its high destructive property. On the other side, yellow rust can be taken under control by growing resistant cultivars, by the application of fungicides, and by the use of appropriate cultural practices. Thus, it is crucial to detect the disease at an early stage. The current study offers to use computerized models in determining the infection type of yellow rust disease in wheat. Herein, a deep convolutional neural networks-based model, named Yellow-Rust-Xception, was proposed. The model inputs the wheat leaf image and classifies it as no disease, resistant, moderately resistant, moderately susceptible, or susceptible according to the rust severity, i.e. percentage. The convolutional neural networks, a state-of-art approach, have layered structures those inspired by the human brain and able to learn discriminative features from data automatically; thus networks performance match and even surpass humans in task-specific applications, a newly developed dataset containing yellow rust-infected wheat leaf images, was used to train, validate, and test Yellow-Rust-Xception, in result, the test accuracy was 91%. Thus, Yellow-Rust-Xception can be used in determining wheat yellow rust and its severity level.

Older pedestrians are vulnerable on the streets and at significant risk of injury or death when involved in crashes. Pedestrians&rsquo, safety is critical for roadway agencies to consider and improve, especially older pedestrians aged greater than 65 years old. To better protect the older pedestrian group, the factors that contribute to the older crashes need to be analyzed deeply. Traditional modeling approaches such as Logistic models for data analysis may lead to modeling distortions due to the independence assumptions. In this study, Extreme Gradient Boosting (XGBoost), is used to model the classification problem of three different levels of severity of older pedestrian traffic crashes from crash data in Colorado, US. Further, Shapley Additive explanations (SHAP) are implemented to interpret the XGBoost model result and analyze each feature&rsquo, s importance related to the levels of older pedestrian crashes. The interpretation results show that the driver characteristic, older pedestrian characteristics, and vehicle movement are the most important factors influencing the probability of the three different severity levels. Those results investigate each severity level&rsquo, s correlation factors, which can inform the department of traffic management and the department of road infrastructure to protect older pedestrians by controlling or managing some of those significant features.

Traumatic brain injury (TBI) in the military rose to prominence in the early 2000s and was called the signature injury of the wars in Iraq and Afghanistan. Although generally adhering to commonly used academic definitions, the Department of Defense (DoD) differs in the use of imaging findings in the determination of TBI severity; any computed tomography (CT) abnormalities raise the severity level to at least moderate ( Table 49.1 ). 2 Using these definitions, the US military reported about 380,000 TBI among military servicemembers between 2000 and 201810; current incidence rates are about 17,000 per year. Over 80% of brain injuries in active duty personnel are classified as mild. Prevalence studies estimated that approximately 20% of those who deployed to Iraq or Afghanistan sustained a TBI. 9