: The present study aims to analyze driving style and latent driving behavior typically at intersections where various driving habits show up. To this end, 6 different scenarios were simulated and data on the gaze of the drivers were analyzed using topic modeling. Their driving styles (topics) latent in the driver’s driving behaviors (words) following a driving scenario (document) were analyzed by using the latent dirichlet allocation of topic modeling, the most frequently used in discovering latent topics in documents generally made up of words. For the study, six participants in their twenties were selected whose driver licenses were more than a year old. They were asked to drive in a virtual reality simulator, while wearing a head mounted display capable of tracking their gazes. The experimental results showed that the less experienced the drivers were, the more frequently and longer they gazed at the navigation and the speed instrument panel and repeated the start and stop. On the other hand, the more experienced the drivers were, the more they gazed briefly at the objects within the car, maintained speed after glancing at the most distant objects, and applied braking only when necessary.
: In hospitals the need to have devices connected and accessible remotely is increasing, in order to continuously monitor patients. This need also arose for mechanical ventilators. In this paper, we introduce the first prototype of a mobile application to connect remotely to Milano Ventilatore Meccanico, a mechanical ventilator developed during COVID-19 pandemic. We show the process adopted to design and develop the first prototype in Android.
: Medical fraud and waste is a costly problem for health insurers. Growing volumes and complexity of data add challenges for detection, which data mining and machine learning may solve. We introduce a framework for incorporating domain knowledge (through the use of the claim ontology), learning claim contexts and provider roles (through topic modelling), and estimating repeated, costly behaviours (by comparison of provider costs to expected costs in each discovered context). When applied to orthopaedic surgery claims, our models highlighted both known and novel patterns of anomalous behaviour. Costly behaviours were ranked highly, which is useful for effective allocation of resources when recovering potentially fraudulent or wasteful claims. Further work on incorporating context discovery and domain knowledge into fraud detection algorithms on medical insurance claim data could improve results in this field.
: Cancer is still a fatal disease in many cases, despite intensive research into prevention, treatment and follow-up. In this context, an important parameter is the stage of the cancer. The TNM/UICC classification is an important method to describe a cancer. It dates back to the surgeon Pierre Denoix and is an important prognostic factor for patient survival. Unfortunately, despite its importance, the TNM/UICC classification is often poorly documented in cancer registries. The aim of this work is to investigate the possibility of predicting UICC stages using statistical learning methods based on cancer registry data. Data from the Cancer Registry Clinic Arlesheim (CRCA) were used for this analysis. It contains a total of 5,305 records of which 1,539 cases were eligible for data analysis. For prediction classification and regression trees, random forests, gradient tree boosting and logistic regression are used as statistical methods for the problem at hand. As performance measures Mean misclassification error (mmce), area under the receiver operating curve (AUC) and Cohen’s kappa are applied. Misclassification rates were in the range of 28.0% to 30.4%. AUCs ranged between 0.73 and 0.80 and Cohen kappa showed values between 0.39 and 0.44 which only show a moderate predictive performance. However, with only 1,539 records, the data set considered here was significantly lower than those of larger cancer registries, so that the results found here should be interpreted with caution.
: To monitor patients’ well-being and evaluate the efficacy of digital health intervention, patients are required to regularly respond to standardised surveys. Responding to a large number of questionnaires is effortful and may discourage mHealth app users from engaging with the intervention. Gamification might reduce the burden of self-reporting. However, researchers have adopted various approaches to the personalisation of gamification design: ranking of game elements by the user, Hexad Gamification User Types classification (G) and selection of preferred design mockups (MU) . In this paper we report on a small population study involving 54 healthy participants aged 17 to 60, and investigate if these alternative approaches lead to the same design choices. We find that different evaluation approaches lead to different choices of gamification elements. We suggest to use game element ranking in combination with mockup selection. Hexad player classification might be less useful in the context of mHealth applications design.
Digital health contributes to health promotion by empowering the user with the holistic view of their health.Health promotion is to enable the user to take control over their health.The availability of wearables has contributed to the shift in healthcare, that is more connected, predictive, and proactive.Proactive in healthcare is to predict and prevent a situation, beforehand.This shift in healthcare puts the user in charge of most healthrelated decisions.Innovative technologies like AI already contribute to the cause by applying reasoning and negotiation to the collected health data to provide timely interventions to the user.The availability of realtime data from sensors that the user wears all the time allows more opportunities with new health insights.One such prospect is the use of digital twins, which provides personalization and precision.Digital twins also allow risk-free modelling for more accurate outcomes.A user digital twin is not just a virtual replica, but it combines all the factors that can impact the user.The context of the user is a prominent factor in healthcare.The paper establishes the need for digital twins in health promotion.In this paper, a Fit-twin is presented that mimics a user with wearables and the user context as input.The Fit-twin is implemented using Azure digital twins, Fitbit charge, and local context API.This allows one-way communication between the user and the Fit-twin.The outcome is a user digital twin that can be used for health promotion by applying predictive capabilities.
: This paper addresses the development of the serious game PHOBOS, a virtual reality exposure therapy game for the treatment of blood-injection-injury phobia, also known as hemophobia. The virtual reality game which incorporates biometric sensors was upgraded from a 2018 version to perform usability tests to get the game ready for clinical trials. With this project we expect to contribute to the development of a framework that can be used by physiologists in the treatment of their patients with hemophobia.
This paper aims to assess how the top-funded digital health companies in T1DM can create value for customers and which implications this has in terms of scalability.Med tech companies, academia, and policymakers should be able to make better strategic decisions based on the findings provided.Companies were identified using a leading venture capital database, PitchBook.Our analysis revealed that 50% of the thirty top-funded companies pursue a Layer Player strategy to generate value for T1DM patients.We recommend that companies in T1DM focus more on automated services such as conversational agents to improve scalability.In terms of scalability, many companies have room for improvement by increasingly relying on automated services, among other things.
Background: Out of hospital cardiac arrest (OHCA) causes close to 400,000 deaths every year in North America, and it is also a leading cause of death among young athletes.OHCA is a treatable medical condition, and the patient's survival chances can be increased if immediate treatment is provided to the patient.However, non-treatment of the patient leads to a dramatic decline in survival chances at 10% per minute.Currently, various technologies are being used, and many more are being researched to reduce the time to provide early treatment to the patient.Objective: This survey focuses on summarizing various available technologies for use during OHCA.This survey focuses on evaluating technologies used in each step of the OHCA process.Methods: In this survey, articles were searched using the term "ohca" on Google Scholar and more than 18,000 articles were found.The articles were further filtered using keywords for each stage of the OHCA process (2,128).For each step, articles were filtered again using author developed method to select articles relevant to each technology for each step of the OHCA process (339).Finally, articles were manually filtered by authors based on their relevance and 112 articles were used in this survey.Results: The technologies that exist today work independently and are not linked with the other steps of the OHCA process.Integration between these technologies could help in reducing time and increase the survival chances of the patient.Also, if it is found that some of the proposed solutions are experimental in nature and not meant to be used in the real-world OHCA scenarios.
: In this research, we evaluate medical case retrieval for AD on the bases of descriptors generated by combining different modalities (Magnetic Resonance Imaging (MRI) markers, Fluorodeoxy-glucose Positron Emission Tomography (FDG-PET) based measures, Cerebrospinal Fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype and age as risk factors). We investigated whether they would provide complementary information aiming to improve medical case retrieval for AD. According to the obtained results, we concluded that this approach outperformed the retrieval results in the current reported research by gaining MAP value of 0.98 yet providing an efficient medical case retrieval for AD and keeping low dimensional feature vector.
