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Young unhealthy weight treatments: news, opinions, as well as data.
Cognitive Behavioural Therapy (CBT) is an action-oriented psychotherapy that combines cognitive and behavioural techniques for psychosocial treatment for depression, and is considered by many to be the golden standard in psychotherapy. More recently, computerized CBT (CCBT) has been deployed to help increase availability and access to this evidence-based therapy. In this vein, a CBT ontology, as a shared common understanding of the domain, can facilitate the aggregation, verification, and operationalization of computerized CBT knowledge. Moreover, as opposed to black-box applications, ontology-enabled systems allow recommended, evidence-based treatment interventions to be traced back to the corresponding psychological concepts. We used a Knowledge Management approach to synthesize and computerize CBT knowledge from multiple sources into a CBT ontology, which allows generating personalized action plans for treating mild depression, using the Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We performed a formative evaluation of the CBT ontology in terms of its completeness, consistency, and conciseness.This paper explores the use of semantic- and evidence-based biomedical knowledge to build the RiskExplorer knowledge graph that outlines causal associations between risk factors and chronic disease or cancers. The intent of this work is to offer an interactive knowledge synthesis platform to empower health-information-seeking individuals to learn about and mitigate modifiable risk factors. Our approach analyzes biomedical text (from PubMed abstracts), Semantic Medline database, evidence-based semantic associations, literature-based discovery, and graph database to discover associations between risk factors and breast cancer. Our methodological framework involves (a) identifying relevant literature on specified chronic diseases or cancers, (b) extracting semantic associations via knowledge mining tool, (c) building rich semantic graph by transforming semantic associations to nodes and edges, (d) applying frequency-based methods and using semantic edge properties to traverse the graph and identify meaningful multi-node NCD risk paths. click here Generated multi-node risk paths consist of a source node (representing the source risk factor), one or more intermediate nodes (representing biomedical phenotypes), a target node (representing a chronic disease or cancer), and edges between nodes representing meaningful semantic associations. The results demonstrate that our methodology is capable of generating biomedically valid knowledge related to causal risk and protective factors related to breast cancer.High importance of the remote health monitoring (RHM) especially during and after the pandemic is accentuated in this article. It is displayed that by this way it is possible to revealing automatically situations "suspicious" from the COVID-19 illness view point for some concrete patients and also to keep up with possible complications after the illness. Another great opportunity of the RHM is connected with investigation of the vaccines. RHM allows reveal cases of the complications among the patients as well as positive and negative consequences of the vaccination. The general depersonalized data should be gathered and analyzed to research the regularities of the COVID-19 sickness rate and the complications frequency and also of the safety and effectiveness of the vaccines. It is proposed to combine advantages of RHM for medical care for concrete patients with opened opportunities for research to reveal important general normality connected with correlation between COVID-19 complications and peculiarities of various chronic diseases and specific medical statements. Telemedicine platform intended for creation of RHM systems is described. It has gone through the pilot running in Russian medical institutions and now presented good results have been achieved.HIV Pre-Exposure Prophylaxis (PrEP) is effective in Men who have Sex with Men (MSM), and is reimbursed by the social security in France. Yet, PrEP is underused due to the difficulty to identify people at risk of HIV infection outside the "sexual health" care path. We developed and validated an automated algorithm that re-uses Electronic Health Record (EHR) data available in eHOP, the Clinical Data Warehouse of Rennes University Hospital (France). Using machine learning methods, we developed five models to predict incident HIV infections with 162 variables that might be exploited to predict HIV risk using EHR data. We divided patients aged 18 or more having at least one hospital admission between 2013 and 2019 in two groups cases (patients with known HIV infection in the study period) and controls (patients without known HIV infection and no PrEP in the study period, but with at least one HIV risk factor). Among the 624,708 admissions, we selected 156 cases (incident HIV infection) and 761 controls. The best performing model for identifying incident HIV infections was the combined model (LASSO, Random Forest, and Generalized Linear Model) AUC = 0.88 (95% CI 0.8143-0.9619), specificity = 0.887, and sensitivity = 0.733 using the test dataset. The algorithm seems to efficiently identify patients at risk of HIV infection.Vaccination information is needed at individual and at population levels, as it is an important part of public health measures. In Finland, a vaccination data structure has been developed for centralized information services that include patient access to information. Harmonization of data with national vaccination registry is ongoing. New requirements for vaccination certificates have emerged because of COVID-19 pandemic. We explore, what is the readiness of Finnish development of vaccination data structures and what can be learned from Finnish harmonization efforts in order to accomplish required level of interoperability.Ambient assisted living (AAL) technologies aim at increasing an individual's safety at home by early recognizing risks or events that might otherwise harm the individual. A clear definition of safety in the context of AAL is still missing and facets of safety still have to be shaped. The objective of this paper is to characterize the facets of AAL-related safety, to identify opportunities and challenges of AAL regarding safety and to identify open research issues in this context. Papers reporting aspects of AAL-related safety were selected in a literature search. link2 Out of 395 citations retrieved, 28 studies were included in the current review. Two main facets of safety were identified user safety and system safety. System safety concerns an AAL system's reliability, correctness and data quality. User safety reflects impact on physical and mental health of an individual. Privacy, data safety and security issues, sensor quality and integration of sensor data, as well as technical failures of sensors and systems are reported challenges. To conclude, there is a research gap regarding methods and metrics for measuring user and system safety in the context of AAL technologies.This paper presents a complex application for rehabilitation of patients with first and second stage rheumatoid arthritis (RA). The application contains a module for the doctor, for the kinetotherapist, and a module as a game matching the symptoms for each stage of RA. The purpose of this application is to achieve the rehabilitation of the RA hand with support of digital technology and multimodal interaction leap motion, serious gaming, and neuronal networks. The neural network offers support for patients to perform the exercises at home classifying the correct movement with accuracy of 95%. During the development of the application, various challenges were encountered in terms of populating the database, raising the cubes within the game related to second stage of RA, and the implementation of the neural network. The application was tested by a group of students, resulting in the fact that the degree of mental stress, fatigue in the fingers, wrists and physical exertion were insignificant in most cases.The Covid-19 pandemic has globally introduced a new crisis with severe consequences and led to a series of pandemic-related containment measures, including social distancing and self-isolation may cause significant impact on mental health. This study describes a social care initiative that was actualized during the Covid-19 outbreak with regard to the potential benefits in older adults' quality of life through the use of the Integrated Healthcare System Long Lasting Memories Care (LLM Care), and specifically the web-based cognitive training software. Online questionnaires, assessing various psychosocial and mental health domains, were distributed to 28 older adults before and after the interaction with the software aiming at evaluating the potential positive effect and usability of cognitive training software. Overall, the study demonstrates that the interaction with the web-based cognitive training software during the pandemic plays a significant role in maintaining mental health among older people, through improvements in well-being.Timely access to care is a persistent challenge for health care systems. Providing the right care to the right patient at the right time is important to reduce inappropriate use and improve the performance of healthcare services. The complexity of accessing primary care contributes to the high usage of emergency rooms for not-urgent conditions. Many digital tools try to offer a better access to care for patients and reduce ER overuse. This environmental scan of the digital tools available in Quebec identifies those digital tools and some of their limitations. The results reveal the complexity of mobilizing digital tools in the healthcare sector and highlight the need for all stakeholders to work together to enhance access to care.This paper proposes an approach and demonstrates its application for cross-border exchange of ePrescriptions in the European Union. A business process model of the main use case for exchange of prescription content in the eHealth Digital Service Infrastructure is created and analyzed. The novelty in this approach is the proposed encoding of the basic dataset in a Quick Response (QR) code in terms of an XML scheme that is independent of clinical models or proprietary database structures. It allows to inverse the dataflow control in the chain of message exchanges between Dispenser and National Contact Points. The proposed inversion of control positions the citizen with the QR code of the prescription in the center of that chain of message exchanges between the main actors of the business process. The independent format of content representation in the QR code allows the actors in the message exchange to auto-populate data in their registers when the medicine is dispensed. Initial results are reported and reveal the advantages of embedding prescription details in QR code employing a common independent XML scheme.Smart home systems create new opportunities for patient care. link3 In this paper, a role model is created for the different groups of people involved in the care process of an occupant. Based on a systematic literature review seven roles were identified. A second literature review deals with the topic Feedback of Smart Home Systems. Combining both reviews visualization proposals were created and are presented for two of the roles. The role model is adapted to German health system but could be transformed for different countries. To confirm the results an evaluation of role model and visualization proposal should be done in collaboration with possible users of smart homes.
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