E-learning recommender system dataset.

Mandarine Academy is an Ed-Tech company that specializes in innovative corporate training techniques such as personalized Massive Open Online Courses (MOOCs), web conferences, etc. With more than 550K users spread across 100 active e-learning platforms. The company creates online pedagogical content (videos, quizzes, documents, etc.) on daily basis to support the digitization of work environments and to keep up with current trends. Mandarine Academy provided us with access to Mooc.office365-training.com. A publicly available MOOC in both French and English versions to conduct research on recommender systems in online learning environments. Mandarine Academy collects user feedback using two types of ratings: Explicit (Like Button, Social share, Bookmarks), and Implicit (Watch Time, Page View). Unfortunately, explicit ratings are underutilized. Most users avoid the burden of stating their preferences explicitly. To address this, we shift our attention to implicit interactions, which generate more data that can be significant in some cases. Implicit Ratings are what constitute Mandarine Academy Recommender System (MARS) Dataset. We believe that the degree of viewing has an impact on the overall impression, for this reason, we applied changes to the implicit data and made a part of it similar to the explicit rating format found in other known datasets (e.g., Movielens). This paper presents two real-world dataset variations that consist of 89,000 explicit ratings and 276,000 implicit ratings. Data was collected starting early 2016 until late 2021. Chosen users had rated at least one item. To protect their privacy, sensitive information has been removed. To the best of our knowledge, this is the first publicly available real-world dataset of E-Learning recommendations in both French and English with mixed ratings (implicit and explicit), allowing the research community to focus on pre-and post-COVID-19 behavior in online learning.

The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based "white-box" classification algorithm for medical data.

BACKGROUND: Multi-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), before or during their stay. METHODS: Applied to a data set of 48,945 hospital stays (including known cases of carriage) with up to 16,325 attributes per stay, MOCA-I generated alert rules for risk of carriage or infection. A risk score was then computed from each stay according to the triggered rules.Recall and precision curves were plotted. RESULTS: The classification can be focused on specifically detecting high risk of having a positive test, or identifying large numbers of at-risk patients by modulating the risk score cut-off level. For a risk score above 0.85,recall (sensitivity) is 62 % with 69 % precision (confidence) for MDR bacteria, recall is 58 % with 88 % precision for MRSA. In addition, MOCA-I identifies 38 and 21 cases of previously unknown MDR and MRSA respectively. CONCLUSIONS: MOCA-I generates medically pertinent alert rules. This classification algorithm can be used to detect patients with high risk of testing positive to MDR bacteria (including MRSA). Classification can be modulated by appropriately setting the risk score cut-off level to favor specific detection of small numbers of patients at very high risk or identification of large numbers of patients at risk. MOCA-I can thus contribute to more adapted treatments and preventive measures from admission, depending on the clinical setting or management strategy.

Remote Monitoring of Cardiac Implantable Devices: Ontology Driven Classification of the Alerts.

The number of patients that benefit from remote monitoring of cardiac implantable electronic devices, such as pacemakers and defibrillators, is growing rapidly. Consequently, the huge number of alerts that are generated and transmitted to the physicians represents a challenge to handle. We have developed a system based on a formal ontology that integrates the alert information and the patient data extracted from the electronic health record in order to better classify the importance of alerts. A pilot study was conducted on atrial fibrillation alerts. We show some examples of alert processing. The results suggest that this approach has the potential to significantly reduce the alert burden in telecardiology. The methods may be extended to other types of connected devices.

Personalized and automated remote monitoring of atrial fibrillation.

AIMS: Remote monitoring of cardiac implantable electronic devices is a growing standard; yet, remote follow-up and management of alerts represents a time-consuming task for physicians or trained staff. This study evaluates an automatic mechanism based on artificial intelligence tools to filter atrial fibrillation (AF) alerts based on their medical significance. METHODS AND RESULTS: We evaluated this method on alerts for AF episodes that occurred in 60 pacemaker recipients. AKENATON prototype workflow includes two steps: natural language-processing algorithms abstract the patient health record to a digital version, then a knowledge-based algorithm based on an applied formal ontology allows to calculate the CHA2DS2-VASc score and evaluate the anticoagulation status of the patient. Each alert is then automatically classified by importance from low to critical, by mimicking medical reasoning. Final classification was compared with human expert analysis by two physicians. A total of 1783 alerts about AF episode >5 min in 60 patients were processed. A 1749 of 1783 alerts (98%) were adequately classified and there were no underestimation of alert importance in the remaining 34 misclassified alerts. CONCLUSION: This work demonstrates the ability of a pilot system to classify alerts and improves personalized remote monitoring of patients. In particular, our method allows integration of patient medical history with device alert notifications, which is useful both from medical and resource-management perspectives. The system was able to automatically classify the importance of 1783 AF alerts in 60 patients, which resulted in an 84% reduction in notification workload, while preserving patient safety.

Comparing Drools and ontology reasoning approaches for telecardiology decision support.

Implantable cardioverter defibrillators can generate numerous alerts. Automatically classifying these alerts according to their severity hinges on the CHA2DS2VASc score. It requires some reasoning capabilities for interpreting the patient's data. We compared two approaches for implementing the reasoning module. One is based on the Drools engine, and the other is based on semantic web formalisms. Both were valid approaches with correct performances. For a broader domain, their limitations are the number and complexity of Drools rules and the performances of ontology-based reasoning, which suggests using the ontology for automatically generating a part of the Drools rules.

Is virtual reality effective to motivate and raise interest in phobic children toward therapy? A clinical trial study of in vivo with in virtuo versus in vivo only treatment exposure.

OBJECTIVE: The first objective of this study was to assess if a combined treatment with mostly virtual reality-based (in virtuo) exposure increases phobic children's motivation toward therapy compared to children who only receive in vivo exposure. Another objective was the assessment of motivation as a predictor of treatment outcome. METHOD: Thirty-one DSM-IV-diagnosed arachnophobic participants aged from 8 to 15 years were randomly assigned to 1 of 2 treatment conditions: in vivo exposure alone or in virtuo plus in vivo exposure. Measures of motivation were taken at pretest and at the end of each part of the treatment; some other measures were taken at each session. The "Why Are You in Therapy?" questionnaire for children was the target measure of motivation and the main variable in the study. Outcome measures were taken at pretest, at the end of each part of the treatment, and at the 6-month follow-up. This study was conducted between September 2006 and March 2007. RESULTS: The results showed that children who received in virtuo exposure did not show a higher level of motivation toward their treatment than those who received in vivo exposure, but statistically significant interactions were found for both parts of the treatment. Multiple regression analysis confirmed that motivation was a significant predictor of outcome (P < .01), especially extrinsic integrated motivation. Participants in the combined treatment were significantly more phobic before beginning treatment, but both treatments appeared successful (P < .001). CONCLUSIONS: In this study, the use of virtual reality did not increase motivation toward psychotherapy. At the end of the second part of therapy, all participants were comparably efficient in facing a live tarantula. These results bear important clinical implications concerning how to use virtual reality with children and concerning motivation of children toward therapy in general. They are discussed in the light of how to present in virtuo therapy to children.

Activism among older women in Quebec, Canada: changing the world after age 65.

The aim of this article is to examine senior women's involvement experience in Quebec, Canada. Study results are based on a qualitative methodology, and shed light on family history and continuity in the involvement trajectory, diversity in terms of group type and involvement practices, and gender differences in involvement. The meaning that older women attribute to their involvement is also addressed, including attitudes toward feminism. The discussion highlights the extent to which older women's involvement has been marked by their life trajectories, above and beyond their age. In sum, their involvement in the private and public spheres is quite impressive.

Why and according to what consultation profiles do female sex workers consult health care professionals? A study conducted in Laval, Québec.

We carried out a study to understand help-seeking behavior among female sex workers in order to bring adequate health care and services to this population at risk for sexually transmitted infection (STI) and human immunodeficiency virus (HIV) transmissions. Data were collected by means of questionnaires, focus groups, and in-depth individual interviews. Analysis reveals that the respondents are familiar with and have access to the health care system. Over 80% claimed to have consulted a health professional during the preceding 12 months. Gynecological, psychosocial, respiratory, digestive, and drug addiction problems were the most frequent. Only a third of the respondents received care and services related to STIs. Data are displayed as three consultation profiles, one of which only tends to foster continuity and comprehensive health care, including screening and treatment of STIs.

Effectiveness of virtual reality exposure in the treatment of arachnophobia using 3D games.

Buying or creating a virtual reality (VR) software is very costly. A less expensive alternative could be to modify already existing 3D computer games. The goal of this study is to assess the effectiveness of in virtuo exposure in the treatment of arachnophobia using modified 3D games. Participants were 10 women and 1 man. Virtual worlds were created using the game editor of a 3D computer game (Half-Life), modified to offer graduals hierarchies of fearful stimuli (spiders). Analyses revealed significant improvement between pre and post results on the behavioral avoidance test, the Spider Beliefs Questionnaire, and perceived self-efficacy. These promising results suggest that therapy using virtual reality exposure via a modified computer game is useful in the treatment of arachnophobia.