Perceived quality of e‐health services: A conceptual scale development of e‐health service quality based on the C‐OAR‐SE approach

Purpose Health service quality is an important determinant for health service satisfaction and behavioral intentions. The purpose of this paper is to investigate requirements of e‐health services and to develop a measurement model to analyze the construct of “perceived e‐health service quality.” Design/methodology/approach The paper adapts the C‐OAR‐SE procedure for scale development by Rossiter. The focal aspect is the “physician‐patient relationship” which forms the core dyad in the healthcare service provision. Several in‐depth interviews were conducted in Switzerland; first with six patients (as raters), followed by two experts of the healthcare system (as judges). Based on the results and an extensive literature research, the classification of object and attributes is developed for this model. Findings The construct e‐health service quality can be described as an abstract formative object and is operationalized with 13 items: accessibility, competence, information, usability/user friendliness, security, system integration, trust, individualization, empathy, ethical conduct, degree of performance, reliability, and ability to respond. Research limitations/implications Limitations include the number of interviews with patients and experts as well as critical issues associated with C‐OAR‐SE. More empirical research is needed to confirm the quality indicators of e‐health services. Practical implications Health care providers can utilize the results for the evaluation of their service quality. Practitioners can use the hierarchical structure to measure service quality at different levels. The model provides a diagnostic tool to identify poor and/or excellent performance with regard to the e‐service delivery. Originality/value The paper contributes to knowledge with regard to the measurement of e‐health quality and improves the understanding of how customers evaluate the quality of e‐health services.

Info-active and info-passive: The new type of consumer classification

Social media platforms, that foster user generated content, have altered the ways consumers search for product related information. Conducting online searches, reading product reviews, and comparing products ratings, is becoming a more common information seeking pathway. This research demonstrates that info-active consumers are becoming less reliant on information provided by retailers or manufacturers, hence marketing generated online content may have a reduced impact on their purchasing behaviour. The results of this study indicate that beyond traditional methods of segmenting consumers, in the online context, new classifications such as info-active and info-passive would be beneficial in digital marketing. This cross-sectional, mixed-methods study is based on 43 in-depth interviews and an online survey with 500 consumers from 30 countries.

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.

The new Vancouver Chest Pain Rule using troponin as the only biomarker: An external validation study

Objectives To externally evaluate the accuracy of the new Vancouver Chest Pain Rule and to assess the diagnostic accuracy using either sensitive or highly sensitive troponin assays. Methods Prospectively collected data from 2 emergency departments (EDs) in Australia and New Zealand were analysed. Based on the new Vancouver Chest Pain Rule, low-risk patients were identified using electrocardiogram results, cardiac history, nitrate use, age, pain characteristics and troponin results at 2 hours after presentation. The primary outcome was 30-day diagnosis of acute coronary syndrome (ACS), including acute myocardial infarction, and unstable angina. Sensitivity, specificity, positive predictive values and negative predictive values were calculated to assess the accuracy of the new Vancouver Chest Pain Rule using either sensitive or highly sensitive troponin assay results. Results Of the 1635 patients, 20.4% had an ACS diagnosis at 30 days. Using the highly sensitive troponin assay, 212 (13.0%) patients were eligible for early discharge with 3 patients (1.4%) diagnosed with ACS. Sensitivity was 99.1% (95% CI 97.4-99.7), specificity was 16.1 (95% CI 14.2-18.2), positive predictive values was 23.3 (95% CI 21.1-25.5) and negative predictive values was 98.6 (95% CI 95.9-99.5). The diagnostic accuracy of the rule was similar using the sensitive troponin assay. Conclusions The new Vancouver Chest Pain Rule should be used for the identification of low risk patients presenting to EDs with symptoms of possible ACS, and will reduce the proportion of patients requiring lengthy assessment; however we recommend further outpatient investigation for coronary artery disease in patients identified as low risk.

Adult total wellness: group differences based on sitting time and physical activity level

Background An increasing body of evidence associates a high level of sitting time with poor health outcomes. The benefits of moderate to vigorous-intensity physical activities to various aspects of health are now well documented; however, individuals may engage in moderate-intensity physical activity for at least 30 minutes on five or more days of the week and still exhibit a high level of sitting time. This purpose of this study was to examine differences in total wellness among adults relative to high/low levels of sitting time combined with insufficient/sufficient physical activity (PA). The construct of total wellness incorporates a holistic approach to the body, mind and spirit components of life, an approach which may be more encompassing than some definitions of health. Methods Data were obtained from 226 adult respondents (27 ± 6 years), including 116 (51%) males and 110 (49%) females. Total PA and total sitting time were assessed with the International Physical Activity Questionnaire (IPAQ) (short-version). The Wellness Evaluation of Lifestyle Inventory was used to assess total wellness. An analysis of covariance (ANCOVA) was utilised to assess the effects of the sitting time/physical activity group on total wellness. A covariate was included to partial out the effects of age, sex and work status (student or employed). Cross-tabulations were used to show associations between the IPAQ derived high/low levels of sitting time with insufficient/sufficient PA and the three total wellness groups (i.e. high level of wellness, moderate wellness and wellness development needed). Results The majority of the participants were located in the high total sitting time and sufficient PA group. There were statistical differences among the IPAQ groups for total wellness [F (2,220) = 32.5 (p <0.001)]. A Chi-square test revealed a significant difference in the distribution of the IPAQ categories within the classification of wellness [χ2 (N = 226) = 54.5, p < .001]. One-hundred percent (100%) of participants who self-rated as high total sitting time/insufficient PA were found in the wellness development needed group. In contrast, 72% of participants who were located in the low total sitting time/sufficient PA group were situated in the moderate wellness group. Conclusion Many participants who meet the physical activity guidelines, in this sample, sit for longer periods of time than the median Australian sitting time. An understanding of the effects of the enhanced PA and reduced sitting time on total wellness can add to the development of public health initiatives. Keywords: IPAQ; The Wellness Evaluation of Lifestyle (WEL); Sedentary lifestyle

Evidence-based recommendations for the diagnosis of ankylosing spondylitis: Results from the Australian 3E initiative in rheumatology

• As part of the 3E program, we conducted a systematic literature review and gathered consensus from 23 practising Australian rheumatologists to develop guidelines for early identification of ankylosing spondylitis and specialist referral. • In three rounds of break-out sessions followed by discussion and voting, the specialist panel addressed three questions related to diagnosis of ankylosing spondylitis: In individuals with back pain, what are the early clinical features that suggest ankylosing spondylitis? How useful is imaging in identifying early ankylosing spondylitis? Based on which clinical features should a general practitioner refer a patient to a rheumatologist for further evaluation? • The panel agreed on six recommendations related to the three questions: 1a. Early clinical features to suggest ankylosing spondylitis include inflammatory back pain and age at symptom onset < 45 years. 1b. The absence of symptomatic response to an appropriate course of non-steroidal anti-inflammatory drugs makes the diagnosis of ankylosing spondylitis less likely. 1c. Raised inflammatory markers are supportive, but their absence does not rule out the diagnosis of ankylosing spondylitis. 2a. Despite low sensitivity to detect changes of early ankylosing spondylitis, plain radiographs of the pelvis and spine are appropriate initial imaging techniques. 2b. Magnetic resonance imaging is a useful imaging modality for detecting early changes of ankylosing spondylitis. 3. Individuals with inflammatory back pain should be referred to a rheumatologist for further evaluation. • Effective dissemination and implementation of these recommendations are important to standardise the approach to early diagnosis of ankylosing spondylitis.

Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Morphology-based interslice interpolation on manual segmentations of joint bones and muscles in MRI

This paper presents a validation study on the application of a novel interslice interpolation technique for musculoskeletal structure segmentation of articulated joints and muscles on human magnetic resonance imaging data. The interpolation technique is based on morphological shape-based interpolation combined with intensity based voxel classification. Shape-based interpolation in the absence of the original intensity image has been investigated intensively. However, in some applications of medical image analysis, the intensity image of the slice to be interpolated is available. For example, when manual segmentation is conducted on selected slices, the segmentation on those unselected slices can be obtained by interpolation. We proposed a two- step interpolation method to utilize both the shape information in the manual segmentation and local intensity information in the image. The method was tested on segmentations of knee, hip and shoulder joint bones and hamstring muscles. The results were compared with two existing interpolation methods. Based on the calculated Dice similarity coefficient and normalized error rate, the proposed method outperformed the other two methods.

Gene-based pleiotropy across migraine with aura and migraine without aura patient groups

Introduction: It is unclear whether patients diagnosed according to International Classification of Headache Disorders criteria for migraine with aura (MA) and migraine without aura (MO) experience distinct disorders or whether their migraine subtypes are genetically related. Aim: Using a novel gene-based (statistical) approach, we aimed to identify individual genes and pathways associated both with MA and MO. Methods: Gene-based tests were performed using genome-wide association summary statistic results from the most recent International Headache Genetics Consortium study comparing 4505 MA cases with 34,813 controls and 4038 MO cases with 40,294 controls. After accounting for non-independence of gene-based test results, we examined the significance of the proportion of shared genes associated with MA and MO. Results: We found a significant overlap in genes associated with MA and MO. Of the total 1514 genes with a nominally significant gene-based p value (pgene-based ≤ 0.05) in the MA subgroup, 107 also produced pgene-based ≤ 0.05 in the MO subgroup. The proportion of overlapping genes is almost double the empirically derived null expectation, producing significant evidence of gene-based overlap (pleiotropy) (pbinomial-test = 1.5 × 10–4). Combining results across MA and MO, six genes produced genome-wide significant gene-based p values. Four of these genes (TRPM8, UFL1, FHL5 and LRP1) were located in close proximity to previously reported genome-wide significant SNPs for migraine, while two genes, TARBP2 and NPFF separated by just 259 bp on chromosome 12q13.13, represent a novel risk locus. The genes overlapping in both migraine types were enriched for functions related to inflammation, the cardiovascular system and connective tissue. Conclusions: Our results provide novel insight into the likely genes and biological mechanisms that underlie both MA and MO, and when combined with previous data, highlight the neuropeptide FF-amide peptide encoding gene (NPFF) as a novel candidate risk gene for both types of migraine.

A flexible hierarchical approach for facial age estimation based on multiple features

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Pheo-type: A diagnostic gene-expression assay for the classification of pheochromocytoma and paraganglioma

Context: Pheochromocytomas and paragangliomas (PPGLs) are heritable neoplasms that can be classified into gene-expression subtypes corresponding to their underlying specific genetic drivers. Objective: This study aimed to develop a diagnostic and research tool (Pheo-type) capable of classifying PPGL tumors into gene-expression subtypes that could be used to guide and interpret genetic testing, determine surveillance programs, and aid in elucidation of PPGL biology. Design: A compendium of published microarray data representing 205 PPGL tumors was used for the selection of subtype-specific genes that were then translated to the Nanostring gene-expression platform. A support vector machine was trained on the microarray dataset and then tested on an independent Nanostring dataset representing 38 familial and sporadic cases of PPGL of known genotype (RET, NF1, TMEM127, MAX, HRAS, VHL, and SDHx). Different classifier models involving between three and six subtypes were compared for their discrimination potential. Results: A gene set of 46 genes and six endogenous controls was selected representing six known PPGL subtypes; RTK1–3 (RET, NF1, TMEM127, and HRAS), MAX-like, VHL, and SDHx. Of 38 test cases, 34 (90%) were correctly predicted to six subtypes based on the known genotype to gene-expression subtype association. Removal of the RTK2 subtype from training, characterized by an admixture of tumor and normal adrenal cortex, improved the classification accuracy (35/38). Consolidation of RTK and pseudohypoxic PPGL subtypes to four- and then three-class architectures improved the classification accuracy for clinical application. Conclusions: The Pheo-type gene-expression assay is a reliable method for predicting PPGL genotype using routine diagnostic tumor samples.