Ambient Assisted Living Framework to Assist Patient Self-Management Activities
Assisted Ambient Living (AAL) focuses on improving self-sufficiency of individuals, especially individuals living with disabilities or chronic conditions, in terms of their ability to perform activities of daily life (ADL) and self-managing their condition in a home-based setting. Activity recognition for AAL, involves inferring an individual’s ongoing activities based on personal and environment sensors. Activity recognition is a pivotal task in AAL, as it enables the AAL system to understand the context and state of the individual and then accordingly guide the individual to perform ADL or issue alerts in case of unusual activities are detected.
The objective of this project is to investigate semantic web based methods to develop a knowledge-based activity recognition framework that can detect specific activities of an individual in an AAL environment. The intent of activity recognition is to support individuals to (a) perform ADL activities to self-manage their condition through context-sensitive guidance, and (b) change unhealthy behaviours through timely personalized messages in response to observed unhealthy activities. We are investigating semantic web based methods to (i) develop high-level ontological activity models that entail contextual and temporal aspects of activities, coupled with activity related sensors, to represent activities; (ii) develop ontological models to represent an individual’s behaviour towards completion of specific activities; (iii) develop behaviour modification strategies, building on inferred activities, to assist individuals undergo behaviour modification to efficaciously self-manage their condition.
We have developed a prototype AAL system to monitor activities related to medication adherence. Medication adherence is monitored using a smart pillbox that sends events to a smartphone, and smart home sensors to detect patient movement and the use of objects (e.g. cup or glass) for taking medicines. To improve medication adherence, patients and/or their care providers are alerted in case a scheduled medication activity is not detected. Our AAL approach is non-obtrusive and it maintains the privacy of the household members as it only detects activities related to taking medications. We are investigations into extending the activity recognition model and its application to other home-based patient care activities.
Research Areas (Computer Science): Activity recognition, Semantic web, Knowledge modeling, Machine learning, Sensor data fusion
Research Areas (Health): Assisted living, Behaviour modification, Self-management, Home-based care
Project Duration: 2016 – ongoing
PRISM: Personalized Risk Investigation, Stratification and Mitigation Platform
Chronic non-communicable diseases, such as cardiovascular disease, diabetes and cancers are regarded as lifestyle-related diseases. Lifetime health is an emerging health paradigm that aims to assist individuals to achieve long-term health targets, and avoid harmful lifecycle choices to mitigate the risk of chronic diseases. Public Health Agency of Canada’s strategic directions indicate the urgent need for innovative citizen-engagement and empowerment strategies to preemptively detect and effectively prevent the onset of chronic diseases.
The PRISM platform is an innovative population health oriented lifetime health eco-system to empower Canadian to take personalized measures to prevent the onset of chronic diseases by performing periodic risk assessment, monitoring, lifestyle change to pursue lifetime health.
The PRISM platform, currently being developed, provides informative and interactive tools for individuals to (a) assess their cumulative chronic disease risk by providing their health status, and (b) pursue personalized behavioral interventions targeting lifestyle changes to mitigate the noted risk of chronic diseases. At present, PRISM assess risks for 4 chronic conditions and 8 cancers. Additionally, PRISM integrates the individual chronic disease risk scores to provide a cumulative Health Asset Score. The PRISM platform leverages artificial intelligence, data visualization and mobile health technologies, and it is accessible via web and as a mobile app.
Research Areas (Computer Science): Semantic web, Knowledge modeling, Data analytics, Data visualization
Research Areas (Health): Lifetime health, Chronic disease risk assessment, Disease prevention, Behaviour modification, Population/public health, Education and empowerment, Personalized health
Project Duration: 2017 – ongoing
BLESS: BraziLian Early Stimulation System
In Brazil, child development is a major public health problem because of the increasing number of children with microcephaly due to Zika virus, a neurological condition that leads to developmental delays and other chronic neurodevelopmental disorders. Treatment of developmental delays requires a continuous rehabilitation process. Treatment of developmental delays is pursued via Early Stimulation Programs (ESP), comprising multidisciplinary clinical and therapeutic interventions to enhance the child’s motor, cognitive, sensory, linguistic and social development. Access to ESP programs in specialized centers is always a challenge for parents, especially those living in rural areas, which results in a significant number of children not receiving the required therapies to overcome developmental delays.
The BLESS project aims to deliver personalized ESP programs to parents of children with developmental delays at their home. BLESS project is investigating the development and use of practical and effective Mobile Health (m-Health) technologies to overcome challenges faced by parents to access ESP and therapists. BLESS works on the concept of shared decision making whereby the parents and a therapist develop a personalized ESP based on the child’s needs and the parent’s efficacy to administer the program. BLESS is accessible to parents through the BLESS mobile app that (a) delivers ESP plan related material such as videos of early stimulation exercises, pamphlets about child handling, emotional coping, etc. to the parent’s mobile phone; (b) allows parents to record their child’s activities for remote child assessment; and (c) enables parents to communicate with a therapist. BLESS is being deployed in Brazil and will be evaluated for usability and health outcomes.
Research Areas (Computer Science): Knowledge-based systems, Mobile computing
Research Areas (Health): Personalized health, Patient education, Remote patient monitoring, Home-based care, Care transition services
Project Duration: 2017 – 2019
JADE: Juvenile idiopathic Arthritis Dialogue-based patient Education System
Juvenile Idiopathic Arthritis (JIA) is a life-long disease with outcomes that can include pain, prolonged use of medications, and disability. To manage their child’s condition, families often seek information to better understand the condition, therapy options, risk, and recovery trajectories. However, it is noted that families often feel overwhelmed by the volume, authenticity and format of the available patient information.
The JADE project is investigating and developing a novel, dialogue-based patient education approach that offers an interactive dialogue to discuss the issue with the user and in turn provide relevant information. In an interactive manner (human-like conversation), JADE allows the user to ask questions, seek clarifications about responses/findings, learn about the evidence supporting the response and seek alternative findings.
JADE uses an artificial intelligence based argument model to establish an interactive dialogue which uses a large volume of evidence-based digitized patient education material. JADE has implemented a comprehensive argument ontology, reasoning over the ontology allows the selection of the relevant educational material as per the discourse of the dialogue with the user who interact with JADE through a synchronous text-based dialogue interface. The interactive dialogue allows the user to explore a topic with respect to their own interests and apprehensions as opposed to being provided with a static, generic document. JADE is being evaluated at the Rheumatology Clinic at the IWK Health Centre (IWK), Halifax.
Research Areas (Computer Science): Argument theory, Semantic web, Knowledge modeling
Research Areas (Health): Patient education
Project Duration: 2018 – ongoing
PLUS: Pathology Laboratory Utilization Scorecards
The ‘Choosing Wisely Canada’ movement is promoting sustainable healthcare by optimizing the utilization of healthcare services. Pathology laboratories provide service to help with disease diagnosis and therapeutic choices. A meta-analysis of 108 studies involving 1.6 million results from 46 of the 50 most commonly ordered lab tests, found that on average 30 % of all tests are likely to be unnecessary. The question pursued in this project is whether pathology test ordered in Nova Scotia are relevant and useful for patient management?
This project is in collaboration with Department of Pathology, and its objective is to optimize laboratory utilization from a primary care perspective by: (a) informing primary care physicians about their test ordering profile to help them reduce laboratory overutilization; (b) providing laboratory managers with intelligence about laboratory utilization patterns, to help them minimize waste.
In this health data analytics project, we are investigating and developing a digital health based Pathology Laboratory Utilization Scorecard (PLUS) system that leverages advanced data analytics and data visualization techniques to analyze ‘big’ laboratory data to generate objective, descriptive and predictive laboratory utilization insights to optimize laboratory utilization. We are applying advance data analytics methods to ‘learn’ physician-specific laboratory utilization models derived from their laboratory utilization data. To display laboratory utilization, we use visual analytics techniques to display an interactive and dynamic (i) physician scorecard for physicians to self-audit their test ordering patterns; and (ii) laboratory manager dashboard to illustrate laboratory utilization for resource planning. PLUS is a web-based system that after evaluation will be used to optimize the pathology laboratory in the central zone (Halifax) that annually performs on average 15 million laboratory tests for 200,000 patients.
Research Areas (Computer Science): Big data analytics, Machine learning, Data visualization, Dashboards, Data fusion
Research Areas (Health): Pathology, Choosing wisely, Health system use optimization
Project Duration: 2017 – 2019
Engage: Behaviour Modification for Chronic Illness
Lifetime healthcare is an emerging health paradigm, aiming to assist individuals achieve health targets and avoid harmful lifestyle choices to lead a healthy life. Behavior modification is an integral element of lifetime healthcare—personalized behavior modification interventions help individuals to abandon health-compromising behaviors and adopt health-enhancing behaviors.
This behaviour modification project aims to assist chronic disease patients (such as diabetes) to achieve self-management efficacy by helping them overcome barriers to healthy lifestyles. We are developing a personalized behavior modification framework, called Engage, that (a) incorporates the Social Cognitive Theory (SCT) to generate personalized behaviour modification action plans, (b) uses consumer health devices to collect health data in a home-setting to monitor outcomes related to positive health behaviours, and (c) employs social modeling techniques to encourage individuals to pursue their behaviour modification plans.
We are investigating a knowledge-based, action-planning and community-driven approach to help patients achieve two key SCT targets—i.e. knowledge and self-efficacy to achieve positive behaviour modification. Our approach is to formulate personalized behavior modification programs as sequences of short-term action plans, targeting to overcome the individual’s perceived barriers to long-term behavior change. We are investigating semantic web methods to formally represent SCT knowledge and to digitize the behaviour modification content in terms of a high-level ontological SCT knowledge model. Reasoning over the SCT knowledge model allows to generate personalized action plans as per the patient’s health and behavioral profile. We have investigating a social modelling approach to (a) help patients select action plans by showing the success rate and experiences of similar patients within the Engage community; (b) offer motivation to patients to share the success rate of similar patients; and (c) encourage patients to connect, exchange advice and provide encouragement on action plans.
The Engage framework has been implemented and is accessible to participating patients through the Engage mobile health app and a web-based system. Engage offers an innovative approach to behavior modification that includes is personalized to an individual user but also builds on the collective experience and outcomes of a larger community of patients undergoing behaviour modification interventions.
Research Areas (Computer Science): Semantic web, Knowledge modeling, Social computing, Mobile computing
Research Areas (Health): Personalized health, Behaviour modification, Home-based care
Project Duration: 2017 – ongoing
SeDAn: SEmantics-based Data ANalytics Framework
This research is investigating plausible reasoning for semantic analytics over knowledge graphs, especially to analyze ‘big’ health datasets for question answering and hypothesis-testing.
Plausible Reasoning (PR) represents the plasticity element of human reasoning, which copes rather when confronted with incomplete data. In contrast to deductive reasoning, which reasons over a complete set of statements to infer a true statement, plausible reasoning can work with incomplete data as it derives plausible solutions by inferring the semantic relationships inherent in the data, thereby overcoming data completeness and somewhat correctness issues. Plausible reasoning is particularly akin to the clinical decision-making process of physicians, who consider the available patient information and use their tacit knowledge to infer a conclusion by discovering correlations to infer any missing information. Plausible reasoning mimics the physicians’ thinking process by inferring plausible solutions based on semantic relations inherent within the data.
The project has implemented a plausible reasoning framework—called SeDAn)—that leverages Semantic Web technologies for knowledge representation and reasoning, including the
Resource Description Framework (RDF), RDF Schema (RDFS), Web Ontology Language (OWL) and SPARQL to retrieve and manipulate the stored RDF data.
Plausible reasoning patterns developed in the project are used to answer medical questions retrieved from BioASQ challenges using standard clinical ontologies, DrugBank, Disease Ontology, and the semantic MEDLINE database as knowledge sources.
Research Areas (Computer Science): Semantic web, Knowledge graphs, Plausible reasoning, Query answering
Research Areas (Health): Health data analytics
Project Duration: 2015 – 2018