scholarly journals Measuring Group Personality with Swarm AI

2020 ◽  
Vol 2 (1) ◽  
pp. 49-68
Author(s):  
Gregg Willcox
Keyword(s):  
Team Performance ◽  
Personality Assessment ◽  
Performance Metrics ◽  
Assessment Tool ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Assessment Method ◽  
Personality Assessments ◽  
Individual Personality ◽  
Realtime System

The aggregation of individual personality assessments to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive realworld team performance. Artificial Swarm Intelligence (ASI)—a technology that enables networked teams to think together in real-time and answer questions as a unified system—promises a solution to these limitations by enabling teams to collectively complete a personality assessment, whereby the team uses ASI to converge upon answers that best represent the group’s disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) assessment both as individuals, and as a realtime system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional aggregation methods, showing at least a 91.8% increase in average correlation with the measured outcome variables, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches.

scholarly journals Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists’ input to prevent medication-related problems

2019 ◽  
Vol 28 (8) ◽  
pp. 645-656 ◽  
Author(s):  
Cathy Geeson ◽  
Li Wei ◽  
Bryony Dean Franklin
Keyword(s):  
Risk Factors ◽  
Assessment Tool ◽  
Predictive Accuracy ◽  
External Validation ◽  
Predictive Performance ◽  
Study Data ◽  
Predictive Values ◽  
Hospital Pharmacists ◽  
Potential Risk Factors ◽  
Medicines Optimisation

BackgroundMedicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services, there is a need to increase efficiency while maintaining patient safety.ObjectiveTo develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists’ input in hospital.MethodsPatients from adult medical wards at two UK hospitals were prospectively included into this cohort study. Data on medication-related problems (MRPs) were collected by pharmacists at the study sites as part of their routine daily clinical assessments. Data on potential risk factors, such as number of comorbidities and use of ‘high-risk’ medicines, were collected retrospectively. Multivariable logistic regression modelling was used to determine the relationship between risk factors and the study outcome: preventable MRPs that were at least moderate in severity. The model was internally validated and a simplified electronic scoring system developed.ResultsAmong 1503 eligible admissions, 610 (40.6%) experienced the study outcome. Eighteen risk factors were preselected for MOAT development, with 11 variables retained in the final model. The MOAT demonstrated fair predictive performance (concordance index 0.66) and good calibration. Two clinically relevant decision thresholds (ie, the minimum predicted risk probabilities to justify pharmacists’ input) were selected, with sensitivities of 90% and 66% (specificity 30% and 61%); these equate to positive predictive values of 47% and 54%, respectively. Decision curve analysis suggests that the MOAT has potential value in clinical practice in guiding decision-making.ConclusionThe MOAT has potential to predict those patients most at risk of moderate or severe preventable MRPs, experienced by 41% of admissions. External validation is now required to establish predictive accuracy in a new group of patients.

scholarly journals A New Application-Aware No-Reference Quality Assessment Method for IP Voice Services

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Péter Orosz ◽  
Tamás Tóthfalusi
Keyword(s):  
Service Quality ◽  
Performance Metrics ◽  
Assessment Method ◽  
Reference Source ◽  
User Privacy ◽  
Quality Model ◽  
Estimation Model ◽  
Full Reference ◽  
Reference Quality ◽  
The Voice

AbstractThe increasing number of Voice over LTE deployments and IP-based voice services raise the demand for their user-centric service quality monitoring. This domain’s leading challenge is measuring user experience quality reliably without performing subjective assessments or applying the standard full-reference objective models. While the former is time- and resource-consuming and primarily executed ad-hoc, the latter depends upon a reference source and processes the voice payload that may offend user privacy. This paper presents a packet-level measurement method (introducing a novel metric set) to objectively assess network and service quality online. It is accomplished without inspecting the voice payload and needing the reference voice sample. The proposal has three contributions: (i) our method focuses on the timeliness of the media traffic. It introduces new performance metrics that describe and measure the service’s time-domain behavior from the voice application viewpoint. (ii) Based on the proposed metrics, we also present a no-reference Quality of Experience (QoE) estimation model. (iii) Additionally, we propose a new method to identify the pace of the speech (slow or dynamic) as long as voice activity detection (VAD) is present between the endpoints. This identification supports the introduced quality model to estimate the perceived quality with higher accuracy. The performance of the proposed model is validated against a full-reference voice quality estimation model called AQuA, using real VoIP traffic (originated in assorted voice samples) in controlled transmission scenarios.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

Discovery of polynomial equations for regression

2004 ◽  
Vol 1 (1) ◽  
pp. 131-142
Author(s):  
Ljupčo Todorovski ◽  
Sašo Džeroski ◽  
Peter Ljubič
Keyword(s):  
Efficient Method ◽  
Regression Models ◽  
Predictive Accuracy ◽  
State Of The Art ◽  
Numerical Data ◽  
Predictive Performance ◽  
Polynomial Equations ◽  
Regression Methods ◽  
Piecewise Regression ◽  
Standard Regression

Both equation discovery and regression methods aim at inducing models of numerical data. While the equation discovery methods are usually evaluated in terms of comprehensibility of the induced model, the emphasis of the regression methods evaluation is on their predictive accuracy. In this paper, we present Ciper, an efficient method for discovery of polynomial equations and empirically evaluate its predictive performance on standard regression tasks. The evaluation shows that polynomials compare favorably to linear and piecewise regression models, induced by the existing state-of-the-art regression methods, in terms of degree of fit and complexity.

A robust framework for shoulder implant X-ray image classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Minh Thanh Vo ◽  
Anh H. Vo ◽  
Tuong Le
Keyword(s):  
Image Classification ◽  
Performance Metrics ◽  
State Of The Art ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Extraction Process ◽  
Content Type ◽  
X Ray ◽  
Robust Model ◽  
Input Dataset

PurposeMedical images are increasingly popular; therefore, the analysis of these images based on deep learning helps diagnose diseases become more and more essential and necessary. Recently, the shoulder implant X-ray image classification (SIXIC) dataset that includes X-ray images of implanted shoulder prostheses produced by four manufacturers was released. The implant's model detection helps to select the correct equipment and procedures in the upcoming surgery.Design/methodology/approachThis study proposes a robust model named X-Net to improve the predictability for shoulder implants X-ray image classification in the SIXIC dataset. The X-Net model utilizes the Squeeze and Excitation (SE) block integrated into Residual Network (ResNet) module. The SE module aims to weigh each feature map extracted from ResNet, which aids in improving the performance. The feature extraction process of X-Net model is performed by both modules: ResNet and SE modules. The final feature is obtained by incorporating the extracted features from the above steps, which brings more important characteristics of X-ray images in the input dataset. Next, X-Net uses this fine-grained feature to classify the input images into four classes (Cofield, Depuy, Zimmer and Tornier) in the SIXIC dataset.FindingsExperiments are conducted to show the proposed approach's effectiveness compared with other state-of-the-art methods for SIXIC. The experimental results indicate that the approach outperforms the various experimental methods in terms of several performance metrics. In addition, the proposed approach provides the new state of the art results in all performance metrics, such as accuracy, precision, recall, F1-score and area under the curve (AUC), for the experimental dataset.Originality/valueThe proposed method with high predictive performance can be used to assist in the treatment of injured shoulder joints.

scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

10.2196/30022 ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. e30022
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Real Time ◽  
Prediction Models ◽  
Assessment Tool ◽  
Predictive Performance ◽  
Patient Data ◽  
Adult Patients ◽  
Emergency Department Crowding ◽  
Exit Block

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

scholarly journals Predicting dengue importation into Europe, using machine learning and model-agnostic methods

2019 ◽  
Author(s):  
Donald Salami ◽  
Carla Alexandra Sousa ◽  
Maria do Rosário Oliveira Martins ◽  
César Capinha
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Transport Network ◽  
Air Transport ◽  
Health Concern ◽  
Centrality Measures ◽  
Network Centrality

ABSTRACTThe geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation.Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale.Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions.We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

scholarly journals Mind the gap: preventing circularity in missense variant prediction

2020 ◽  
Author(s):  
Stephan Heijl ◽  
Bas Vroling ◽  
Tom van den Bergh ◽  
Henk-Jan Joosten
Keyword(s):  
Real World ◽  
Performance Metrics ◽  
Model Performance ◽  
Predictive Performance ◽  
Missense Variant ◽  
Training Methods ◽  
The Real ◽  
Evaluation Scores ◽  
Evaluation Strategies ◽  
Generic Strategy

AbstractDespite advances in the field of missense variant effect prediction, the real clinical utility of current computational approaches remains rather limited. There is a large difference in performance metrics reported by developers and those observed in the real world. Most currently available predictors suffer from one or more types of circularity in their training and evaluation strategies that lead to overestimation of predictive performance. We present a generic strategy that is independent of dataset properties and algorithms used, to deal with circularity in the training phase. This results in more robust predictors and evaluation scores that accurately reflect the real-world performance of predictive models. Additionally, we show that commonly used training methods can have an adverse impact on model performance and lead to gross overestimation of true predictive performance.

scholarly journals Blue-collared Workers’ Travel Behavior Modeling using “exPlainable” Machine Learning Model: The Case of Qatar

2021 ◽  
Author(s):  
Aya Alkhereibi ◽  
Ali AbuZaid ◽  
Tadesse Wakjira
Keyword(s):  
Machine Learning ◽  
Travel Behavior ◽  
Total Population ◽  
Performance Metrics ◽  
Predictive Accuracy ◽  
Mode Choice ◽  
Behavior Modeling ◽  
Significant Feature ◽  
Machine Learning Model ◽  
Occupation Level

This paper presents a novel study on the examination of explainable machine learning (ML) technique to predict the mode choice for communities with a majority of blue-collared workers. A total of 4875 trip records for 1050 blue-collared workers have been used to predict their travel mode choices based on 11 trips and socio-economic attributes. The data used in this paper are obtained from the Ministry of Transportation and Communication (MoTC), which targeted blue-collared workers as they represent 89% of the total population in the State of Qatar. A total of four ML models are evaluated to propose the best predictive model. The four models were examined using different performance metrics. The models’ prediction results showed that the random forest (RF) model had the highest accuracy with a predictive accuracy of 0.97. Moreover, SHapley Additive exPlanation (SHAP) approach is used to investigate the significance of the input features and explain the output of the RF model. The results of SHAP analysis revealed that occupation level is the most significant feature that influences the mode choice followed by occupation section, arrival time, and arrival municipality.

scholarly journals Does Tail Label Help for Large-Scale Multi-Label Learning

2018 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Low Complexity ◽  
Tail Distribution ◽  
Prediction Time ◽  
Unseen Data ◽  
Model Size ◽  
Fast Prediction

Large-scale multi-label learning annotates relevant labels for unseen data from a huge number of candidate labels. It is well known that in large-scale multi-label learning, labels exhibit a long tail distribution in which a significant fraction of labels are tail labels. Nonetheless, how tail labels make impact on the performance metrics in large-scale multi-label learning was not explicitly quantified. In this paper, we disclose that whatever labels are randomly missing or misclassified, tail labels impact much less than common labels in terms of commonly used performance metrics (Top-$k$ precision and nDCG@$k$). With the observation above, we develop a low-complexity large-scale multi-label learning algorithm with the goal of facilitating fast prediction and compact models by trimming tail labels adaptively. Experiments clearly verify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance for state-of-the-art approaches.

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