Dual-View Single-Shot Multibox Detector at Urban Intersections: Settings and Performance Evaluation

The explosion of artificial intelligence methods has paved the way for more sophisticated smart mobility solutions. In this work, we present a multi-camera video content analysis (VCA) system that exploits a single-shot multibox detector (SSD) network to detect vehicles, riders, and pedestrians and triggers alerts to drivers of public transportation vehicles approaching the surveilled area. The evaluation of the VCA system will address both detection and alert generation performance by combining visual and quantitative approaches. Starting from a SSD model trained for a single camera, we added a second one, under a different field of view (FOV) to improve the accuracy and reliability of the system. Due to real-time constraints, the complexity of the VCA system must be limited, thus calling for a simple multi-view fusion method. According to the experimental test-bed, the use of two cameras achieves a better balance between precision (68%) and recall (84%) with respect to the use of a single camera (i.e., 62% precision and 86% recall). In addition, a system evaluation in temporal terms is provided, showing that missed alerts (false negatives) and wrong alerts (false positives) are typically transitory events. Therefore, adding spatial and temporal redundancyincreases the overall reliability of the VCA system

A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations

Despite the growing availability of artificial intelligence models for predicting type 2 diabetes, there is still a lack of personalized approaches to quantify minimum viable changes in biomarkers that may help reduce the individual risk of developing disease. The aim of this article is to develop a new method, based on counterfactual explanations, to generate personalized recommendations to reduce the one-year risk of type 2 diabetes. Ten routinely collected biomarkers extracted from Electronic Medical Records of 2791 patients at low risk and 2791 patients at high risk of type 2 diabetes were analyzed. Two regions characterizing the two classes of patients were estimated using a Support Vector Data Description classifier. Counterfactual explanations (i.e., minimal changes in input features able to change the risk class) were generated for patients at high risk and evaluated using performance metrics (availability, validity, actionability, similarity, and discriminative power) and a qualitative survey administered to seven expert clinicians. Results showed that, on average, the requested minimum viable changes implied a significant reduction of fasting blood sugar, systolic blood pressure, and triglycerides and a significant increase of high-density lipoprotein in patients at risk of diabetes. A significant reduction in body mass index was also recommended in most of the patients at risk, except in females without hypertension. In general, greater changes were recommended in hypertensive patients compared to non-hypertensive ones. The experts were overall satisfied with the proposed approach although in some cases the proposed recommendations were deemed insufficient to reduce the risk in a clinically meaningful way. Future research will focus on a larger set of biomarkers and different comorbidities, also incorporating clinical guidelines whenever possible. Development of additional mathematical and clinical validation approaches will also be of paramount importance

Evaluation of mobile apps for treatment of patients at risk of developing gestational diabetes

This study evaluates mobile apps using a theory-based evaluation framework to discover their applicability for patients at risk of gestational diabetes. This study assessed how well the existing mobile apps on the market meet the information and tracking needs of patients with gestational diabetes and evaluated the feasibility of how to integrate these apps into patient care. A search was conducted in the Apple iTunes and Google Play store for mobile apps that contained keywords related to the following concepts of nutrition: diet, tracking, diabetes, and pregnancy. Evaluation criteria were developed to assess the mobile apps on five dimensions. Overall, the apps scored well on education and information functions and scored poorly on engagement functions. There are few apps that provide comprehensive evidence-based educational content, tracking tools, and integration with electronic health records. This study demonstrates the need to develop apps that have comprehensive content, tracking tools, and ability to bidirectionally share data

Comparative Assessment of Physiological Responses to Emotional Elicitation by Auditory and Visual Stimuli

: The study of emotions through the analysis of the induced physiological responses gained increasing interest in the past decades. Emotion-related studies usually employ films or video clips, but these stimuli do not give the possibility to properly separate and assess the emotional content provided by sight or hearing in terms of physiological responses. In this study we have devised an experimental protocol to elicit emotions by using, separately and jointly, pictures and sounds from the widely used International Affective Pictures System and International Affective Digital Sounds databases. We processed galvanic skin response, electrocardiogram, blood volume pulse, pupillary signal and electroencephalogram from 21 subjects to extract both autonomic and central nervous system indices to assess physiological responses in relation to three types of stimulation: auditory, visual, and auditory/visual. Results show a higher galvanic skin response to sounds compared to images. Electrocardiogram and blood volume pulse show different trends between auditory and visual stimuli. The electroencephalographic signal reveals a greater attention paid by the subjects when listening to sounds compared to watching images. In conclusion, these results suggest that emotional responses increase during auditory stimulation at both central and peripheral levels, demonstrating the importance of sounds for emotion recognition experiments and also opening the possibility toward the extension of auditory stimuli in other fields of psychophysiology. Clinical and Translational Impact Statement- These findings corroborate auditory stimuli's importance in eliciting emotions, supporting their use in studying affective responses, e.g., mood disorder diagnosis, human-machine interaction, and emotional perception in pathology

Evaluation of Machine Learning Algorithms and Explainability Techniques to Detect Hearing Loss From a Speech-in-Noise Screening Test

Purpose: The aim of this study was to analyze the performance of multivariate machine learning (ML) models applied to a speech-in-noise hearing screening test and investigate the contribution of the measured features toward hearing loss detection using explainability techniques. Method: Seven different ML techniques, including transparent (i.e., decision tree and logistic regression) and opaque (e.g., random forest) models, were trained and evaluated on a data set including 215 tested ears (99 with hearing loss of mild degree or higher and 116 with no hearing loss). Post hoc explainability techniques were applied to highlight the role of each feature in predicting hearing loss. Results: Random forest (accuracy = .85, sensitivity = .86, specificity = .85, precision = .84) performed, on average, better than decision tree (accuracy = .82, sensitivity = .84, specificity = .80, precision = .79). Support vector machine, logistic regression, and gradient boosting had similar performance as random forest. According to post hoc explainability analysis on models generated using random forest, the features with the highest relevance in predicting hearing loss were age, number and percentage of correct responses, and average reaction time, whereas the total test time had the lowest relevance. Conclusions: This study demonstrates that a multivariate approach can help detect hearing loss with satisfactory performance. Further research on a bigger sample and using more complex ML algorithms and explainability techniques is needed to fully investigate the role of input features (including additional features such as risk factors and individual responses to low-/high-frequency stimuli) in predicting hearing loss.publishedVersionPeer reviewe

Automated Characterization of Mobile Health Apps' Features by Extracting Information From the Web: An Exploratory Study

Purpose: The aim of this study was to test the viability of a novel method for automated characterization of mobile health apps. Method: In this exploratory study, we developed the basic modules of an automated method, based on text analytics, able to characterize the apps' medical specialties by extracting information from the web. We analyzed apps in the Medical and Health & Fitness categories on the U.S. iTunes store. Results: We automatically crawled 42,007 Medical and 79,557 Health & Fitness apps' webpages. After removing duplicates and non-English apps, the database included 80,490 apps. We tested the accuracy of the automated method on a subset of 400 apps. We observed 91% accuracy for the identification of apps related to health or medicine, 95% accuracy for sensory systems apps, and an average of 82% accuracy for classification into medical specialties. Conclusions: These preliminary results suggested the viability of automated characterization of apps based on text analytics and highlighted directions for improvement in terms of classification rules and vocabularies, analysis of semantic types, and extraction of key features (promoters, services, and users). The availability of automated tools for app characterization is important as it may support health care professionals in informed, aware selection of health apps to recommend to their patients

Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records.

BackgroundSystemic inflammatory response syndrome (SIRS) and sepsis are the most common causes of in-hospital death. However, the characteristics associated with the improvement in the patient conditions during the ICU stay were not fully elucidated for each population as well as the possible differences between the two.GoalThe aim of this study is to highlight the differences between the prognostic clinical features for the survival of patients diagnosed with SIRS and those of patients diagnosed with sepsis by using a multi-variable predictive modeling approach with a reduced set of easily available measurements collected at the admission to the intensive care unit (ICU).MethodsData were collected from 1,257 patients (816 non-sepsis SIRS and 441 sepsis) admitted to the ICU. We compared the performance of five machine learning models in predicting patient survival. Matthews correlation coefficient (MCC) was used to evaluate model performances and feature importance, and by applying Monte Carlo stratified Cross-Validation.ResultsExtreme Gradient Boosting (MCC = 0.489) and Logistic Regression (MCC = 0.533) achieved the highest results for SIRS and sepsis cohorts, respectively. In order of importance, APACHE II, mean platelet volume (MPV), eosinophil counts (EoC), and C-reactive protein (CRP) showed higher importance for predicting sepsis patient survival, whereas, SOFA, APACHE II, platelet counts (PLTC), and CRP obtained higher importance in the SIRS cohort.ConclusionBy using complete blood count parameters as predictors of ICU patient survival, machine learning models can accurately predict the survival of SIRS and sepsis ICU patients. Interestingly, feature importance highlights the role of CRP and APACHE II in both SIRS and sepsis populations. In addition, MPV and EoC are shown to be important features for the sepsis population only, whereas SOFA and PLTC have higher importance for SIRS patients