Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions

Researchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions above. The solution consists of an Artificial Neural Network acquiring input features related to the considered set of opinions and other relevant attributes. The model provides the weights for minimizing the error between the expected outcome and the ground truth concerning a given phenomenon of interest. A custom loss function was defined to minimize the Mean Best Price Error (MBPE), while the evaluation of football players’ was chosen as a case study for testing the model. A custom dataset was constructed by scraping the Transfermarkt, Football Manager, and FIFA21 information sources and by computing a sentiment score through BERT, obtaining a total of 398 occurrences, of which 85% were employed for training the proposed model. The results show that the probability opinion represents the best choice in conditions of info-completeness, predicting the best price with 0.86 MBPE (0.61% of normalized error), while an arbitrary set composed of plausibility, credibility, and possibility opinions was considered for deciding successfully in info-incompleteness, achieving a confidence score of 2.47±0.188 MBPE (1.89±0.15% of normalized error). The proposed solution provided high performance in predicting the transfer cost of a football player in conditions of both info-completeness and info-incompleteness, revealing the significance of extending the feature space to opinions concerning the quantity to predict. Furthermore, the assumptions of the theoretical background were confirmed, as well as the observations found in the state of the art regarding football player evaluation.

A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.

Evaluation of Patient-Initiated Direct Care Mobile Phone–Based Teledermatology During The COVID-19 Pandemic

Background With advances in telecommunication, especially smartphones, teledermatology services offered by specialists are now being directly requested by the patients themselves. This model is known as patient-initiated, direct care teledermatology. It has been pushed to the forefront due to the COVID-19 pandemic. Objective The objectives of this study were to determine patients’ satisfaction and dermatologists’ confidence when a diagnosis was made via direct care mobile phone–based teledermatology. Methods Patients availing direct care teledermatology services during the COVID-19 pandemic at a tertiary care center were subjected to a questionnaire within 5 days of the teleconsultation to assess patient satisfaction and opinions regarding using this model during and beyond the current COVID-19 pandemic. The dermatologists rated their confidence in making the clinical diagnosis on a scale from 1-10 for every case. Results Of 437 participants, 419 (95.9%) were satisfied with this mode of teledermatology. An overwhelming majority (n=428, 97.9%) felt safe consulting the dermatologist via teleconsultation and not having to visit the hospital during the COVID-19 pandemic. In addition, 269 (61.6%) patients agreed that they would be happy to use a teledermatology service beyond the COVID-19 pandemic. The dermatologists’ confidence score in making an accurate diagnosis ranged from 3 to 10, with a mean of 9.20 (SD 1.12). Conclusions The high levels of patient satisfaction and dermatologists’ confidence scores indicate that direct care mobile phone–based teledermatology may be a useful tool in providing dermatological services in appropriate settings and its use should continue to be explored beyond the COVID-19 pandemic. Conflicts of Interest None declared.

Problem-Solving in a Pandemic: How the Creation of a New Workflow Improved the Quality and Timeliness of Remote Core Descriptions

The COVID-19 pandemic limited global travel and access to core facilities. However, by adopting an innovative remote core description workflow, potential delays to an important reservoir characterisation study were avoided and mitigated. Over c.1700ft of middle Miocene core from an Onshore well in Abu Dhabi was described using high-resolution core photos, CT scans and CCA data. Detailed (1:20ft scale) descriptions of heterogeneous, mixed lithology sediments from a gas reservoir were produced. The aim when developing the workflow was not to try and replicate the process of in-person core description, but to create a workflow that could be executed remotely, whilst maintaining technical standards. Ideally, we wanted to find a solution that also had the potential to improve the overall quality of core description, by integrating more data from the onset. The workflow used a matrix to generate a confidence score for the description of each cored interval. Factors such as core condition were considered, which highly influences the extractable core information. The confidence score was used to make decisions, such as whether an in-person review of the core was necessary, especially where core condition was below a reasonable threshold. This helped prioritise cored intervals for review, ensuring time in the core store was focused, and allowed accuracy and reliability of the remote description to be assessed. The 4-phase workflow is summarised as: Image extraction of white light (WL), ultraviolet (UV) and computed tomography (CT) core images. Digital chart creation, core-to-log shifts and sample selection: Wireline data, CCA data and core images loaded Core images used to determine core-to-log shifts Thin section, SEM and XRD samples selected Remote core description: Conducted using all core imagery, CCA and wireline data Thin section, SEM and XRD data were used to refine the description when they became available A confidence score was given to each cored interval QC and finalization: Using the results from phase 3, a selection of cored intervals for in-person review was made. Intervals included those with a poor match between remote description and petrographic data, or areas with a low confidence score. Following the review, charts were finalised and quality-checked for data export Using this workflow, ensured work on an important study could continue during the pandemic. Such an approach has continued value for future studies as it increases efficiency and accounts for more data to be considered in core description prior to viewing the core in-person; it has been used on recent studies with great success. Another benefit to this approach is that less time in the core store is required, reducing potential HSE risks and helping to manage core store availability in busy facilities.

Evaluation of Patient-Initiated Direct Care Mobile Phone–Based Teledermatology During The COVID-19 Pandemic (Preprint)

BACKGROUND With advances in telecommunication, especially smartphones, teledermatology services offered by specialists are now being directly requested by the patients themselves. This model is known as patient-initiated, direct care teledermatology. It has been pushed to the forefront due to the COVID-19 pandemic. OBJECTIVE The objectives of this study were to determine patients’ satisfaction and dermatologists’ confidence when a diagnosis was made via direct care mobile phone–based teledermatology. METHODS Patients availing direct care teledermatology services during the COVID-19 pandemic at a tertiary care center were subjected to a questionnaire within 5 days of the teleconsultation to assess patient satisfaction and opinions regarding using this model during and beyond the current COVID-19 pandemic. The dermatologists rated their confidence in making the clinical diagnosis on a scale from 1-10 for every case. RESULTS Of 437 participants, 419 (95.9%) were satisfied with this mode of teledermatology. An overwhelming majority (n=428, 97.9%) felt safe consulting the dermatologist via teleconsultation and not having to visit the hospital during the COVID-19 pandemic. In addition, 269 (61.6%) patients agreed that they would be happy to use a teledermatology service beyond the COVID-19 pandemic. The dermatologists’ confidence score in making an accurate diagnosis ranged from 3 to 10, with a mean of 9.20 (SD 1.12). CONCLUSIONS The high levels of patient satisfaction and dermatologists’ confidence scores indicate that direct care mobile phone–based teledermatology may be a useful tool in providing dermatological services in appropriate settings and its use should continue to be explored beyond the COVID-19 pandemic.

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection—an application in metabolomics studies

Background Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH. Results PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate. Conclusion PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate.

Psychometric evaluation of the 8-item Altarum Consumer Engagement (ACE) Measure™ in community-dwelling adults in Singapore

Background A valid and reliable measure is essential to assess patient engagement and its impact on health outcomes. This study aimed to examine the psychometric properties of the 8-item Altarum Consumer Engagement Measure™ (ACE Measure) among English-speaking community-dwelling adults in Singapore. Methods This cross-sectional study involved 400 randomly selected community-dwelling adults (mean age: 49.7 years, 50.0% were female, 72.3% were Chinese) who completed the English version of the 8-item ACE Measure independently. The item-level statistics were described. The internal consistency of the measure was measured by Cronbach alpha and item-rest correlations. Validity of the tool was assessed by 1) factorial validity using confirmatory factor analysis (CFA), 2) hypothesis-testing validity by correlating ACE subscales (Commitment and Navigation) with health-related outcomes, and 3) criterion validity against the Patient Activation Measure and Health Confidence Measure. Results There was no floor or ceiling effect for Commitment and Navigation subscales, and the Cronbach alpha for each subscale was 0.76 and 0.54, respectively. The two-factor structure was confirmed by CFA. In general, Commitment and Navigation subscales were positively correlated with frequency of activity participation (rho = 0.30 - 0.33) and EQ-5D visual analog scale (rho = 0.15 - 0.30). Individuals who perceived better health than peers had higher subscale scores (p < 0.01). Each subscale score had moderate and positive correlations with activation score (rho = 0.48 - 0.55) and health confidence score (rho = 0.35 - 0.47). Conclusions The two-subscale ACE Measure demonstrated good construct validity in English-speaking Singapore community-dwelling adults. Evidence in internal consistency was mixed, indicating further investigation.

Ethnobotanical Survey on Skin Whitening Prescriptions of Traditional Chinese Medicine in Taiwan

The increasing interest and demand for skin whitening products globally, particularly in Asia, have necessitated rapid advances in research on skin whitening products used in traditional Chinese medicine (TCM). Herein, we investigated 74 skin whitening prescriptions sold in TCM pharmacies in Taiwan. Commonly used medicinal materials were defined as those with a relative frequency of citation (RFC) &gt; 0.2 and their characteristics were evaluated. Correlation analysis of commonly used medicinal materials was carried out to identify the core component of the medicinal materials. Of the purchased 74 skin whitening prescriptions, 36 were oral prescriptions, 37 were external prescriptions, and one prescription could be used as an oral or external prescription. After analysis, 90 traditional Chinese medicinal materials were obtained. The Apiaceae (10%; 13%) and Leguminosae (9%; 11%) were the main sources of oral and external medicinal materials, respectively. Oral skin whitening prescriptions were found to be mostly warm (46%) and sweet (53%), while external skin whitening prescriptions included cold (43%) and bitter (29%) medicinal materials. Additionally, mainly tonifying and replenishing effects of the materials were noted. Pharmacological analysis indicated that these medicinal materials may promote wound healing, treat inflammatory skin diseases, or anti-hyperpigmentation. According to the Spearman correlation analysis on interactions among medicinal materials with an RFC &gt; 0.2 in the oral skin whitening prescriptions, Paeonia lactiflora Pall. (white) and Atractylodes macrocephala Koidz. showed the highest correlation (confidence score = 0.93), followed by Ziziphus jujuba Mill. (red) and Astragalus propinquus Schischkin (confidence score = 0.91). Seven medicinal materials in external skin whitening prescriptions with an RFC &gt; 0.2, were classified as Taiwan qī bái sàn (an herbal preparation), including Angelica dahurica (Hoffm.) Benth. &amp; Hook. f. ex Franch. &amp; Sav., Wolfiporia extensa (Peck) Ginns, Bletilla striata (Thunb.) Rchb. f., Atractylodes macrocephala Koidz., Ampelopsis japonica (Thunb.) Makino, Paeonia lactiflora Pall. (white), and Bombyx mori Linnaeus. Skin whitening prescriptions included multiple traditional Chinese medicinal materials. Despite the long history of use, there is a lack of studies concerning skin whitening products, possibly due to the complex composition of traditional Chinese medicine. Further studies are required to assess the efficacy and safety of these traditional Chinese medicinal materials for inclusion in effective, safe, and functional pharmacological products.

High-confidence structural annotation of metabolites absent from spectral libraries

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.

UpToDate versus DynaMed: a cross-sectional study comparing the speed and accuracy of two point-of-care information tools

Objective: To compare the accuracy, time to answer, user confidence, and user satisfaction between UpToDate and DynaMed (formerly DynaMed Plus), which are two popular point-of-care information tools.Methods: A crossover study was conducted with medical residents in obstetrics and gynecology and family medicine at the University of Toronto in order to compare the speed and accuracy with which they retrieved answers to clinical questions using UpToDate and DynaMed. Experiments took place between February 2017 and December 2019. Following a short tutorial on how to use each tool and completion of a background survey, participants attempted to find answers to two clinical questions in each tool. Time to answer each question, the chosen answer, confidence score, and satisfaction score were recorded for each clinical question.Results: A total of 57 residents took part in the experiment, including 32 from family medicine and 25 from obstetrics and gynecology. Accuracy in clinical answers was equal between UpToDate (average 1.35 out of 2) and DynaMed (average 1.36 out of 2). However, time to answer was 2.5 minutes faster in UpToDate compared to DynaMed. Participants were also more confident and satisfied with their answers in UpToDate compared to DynaMed.Conclusions: Despite a preference for UpToDate and a higher confidence in responses, the accuracy of clinical answers in UpToDate was equal to those in DynaMed. Previous exposure to UpToDate likely played a major role in participants’ preferences. More research in this area is recommended.