Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study.

Timely management of Chronic Obstructive Pulmonary Disease (COPD) exacerbations can improve recovery and reduce the risk of hospitalization. Digital therapeutics are digital interventions, based on best evidence, designed to provide home-based, patient-centered and pervasive self-management support to patients. Digital therapeutics can be effectively used to offer personalized and explainable self-management and behaviour modification resources to patients to reduce the burden of COPD, especially the prevention of acute COPD exacerbations. The functionalities of COPD specific digital therapeutics for self-management need to be grounded in clinical evidence and behavioral theories, in keeping with the self-management needs of COPD patients and their care providers. In this paper, we report the functionalities of a COPD digital therapeutic mobile application based on a needs analysis qualitative study involving both COPD patients and physicians, and, based on the study's finding, we present a knowledge-driven digital therapeutic for COPD self-management.

Characterizing Cluster-Based Frailty Phenotypes in a Multicenter Prospective Cohort of Kidney Transplant Candidates.

Frailty is associated with a higher risk of death among kidney transplant candidates. Currently available frailty indices are often based on clinical impression, physical exam or an accumulation of deficits across domains of health. In this paper we investigate a clustering based approach that partitions the data based on similarities between individuals to generate phenotypes of kidney transplant candidates. We analyzed a multicenter cohort that included several features typically used to determine an individual's level of frailty. We present a clustering based phenotyping approach, where we investigated two clustering approaches-i.e. neural network based Self-Organizing Maps (SOM) with hierarchical clustering, and KAMILA (KAy-means for MIxed LArge data sets). Our clustering results partition the individuals across 3 distinct clusters. Clusters were used to generate and study feature-level phenotypes of each group.

Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians.

BACKGROUND: Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools. METHODS: A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians' current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI. RESULTS: The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, 'automated charting/report generation', 'clinical prediction rules' and 'monitoring vitals with early-warning detection' were the top items. When ranking by physician work-activities, 'AI-tools for documentation', 'AI-tools for computer use' and 'AI-tools for triaging patients' were the top items. For secondary outcomes, EPs indicated AI was 'likely' (43.1%) or 'extremely likely' (43.7%) to be able to complete the task of 'documentation' and indicated either 'a-great-deal' (32.8%) or 'quite-a-bit' (39.7%) of potential for AI in EM. Further, EPs were either 'strongly' (48.5%) or 'somewhat' (39.8%) interested in AI for EM. CONCLUSIONS: Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.

A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support.

OBJECTIVE: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans. MATERIALS AND METHODS: Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods. RESULTS: Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support. Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods. DISCUSSION: The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS. CONCLUSION: We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).

Validation of a flow-cytometry-based red blood cell antigen phenotyping method.

BACKGROUND AND OBJECTIVES: Current manual and automated phenotyping methods are based on visual detection of the antigen-antibody interaction. This approach has several limitations including the use of large volumes of patient and reagent red blood cells (RBCs) and antisera to produce a visually detectable reaction. We sought to determine whether the flow cytometry could be developed and validated to perform RBC phenotyping to enable a high-throughput method of phenotyping using comparatively miniscule reagent volumes via fluorescence-based detection of antibody binding. MATERIALS AND METHODS: RBC phenotyping by flow cytometry was performed using monoclonal direct typing antisera (human IgM): anti-C, -E, -c, -e, -K, -Jka , -Jkb and indirect typing antisera (human IgG): anti-k, -Fya , -Fyb , -S, -s that are commercially available and currently utilized in our blood transfusion services (BTS) for agglutination-based phenotyping assays. RESULTS: Seventy samples were tested using both flow-cytometry-based-phenotyping and a manual tube standard agglutination assay. For all the antigens tested, 100% concordance was achieved. The flow-cytometry-based method used minimal reagent volume (0.5-1 µl per antigen) compared with the volumes required for manual tube standard agglutination (50 µl per antigen) CONCLUSION: This study demonstrates the successful validation of flow-cytometry-based RBC phenotyping. Flow cytometry offers many benefits compared to common conventional RBC phenotyping methods including high degrees of automation, quantitative assessment with automated interpretation of results and extremely low volumes of reagents. This method could be used for high-throughput, low-cost phenotyping for both blood suppliers and hospital BTS.

Specifications and feasibility of technology-based self-management of COPD: An exploratory qualitative study with patients and providers.

This article outlines the findings of a study that looked at the self-management needs of Chronic Obstructive Pulmonary Disease (COPD) patients and the feasibility of an eHealth intervention. This study found that patient self-monitoring is sub-optimal. Patients want the technology to include record keeping, feedback, the integration of biomedical and environmental data, exacerbation detection, and the ability to connect with providers. Health leaders could benefit from this information by working with their suppliers to eliminate system and technology barriers, and ensuring that technology is interactive, personalized and easy to use.

A Knowledge Graph of Mechanistic Associations Between COVID-19, Diabetes Mellitus, and Chronic Kidney Disease.

We present an automated knowledge synthesis and discovery framework to analyze published literature to identify and represent underlying mechanistic associations that aggravate chronic conditions due to COVID-19. Our literature-based discovery approach integrates text mining, knowledge graphs and medical ontologies to discover hidden and previously unknown pathophysiologic relations, dispersed across multiple public literature databases, between COVID-19 and chronic disease mechanisms. We applied our approach to discover mechanistic associations between COVID-19 and chronic conditions-i.e. diabetes mellitus and chronic kidney disease-to understand the long-term impact of COVID-19 on patients with chronic diseases. We found several gene-disease associations that could help identify mechanisms driving poor outcomes for COVID-19 patients with underlying conditions.

Using Interactive Visual Analytics to Optimize Blood Products Inventory at a Blood Bank.

Blood products and their derivatives are perishable commodities that require an efficient inventory management to ensure both a low wastage rate and a high product availability rate. To optimize blood product inventory, Blood Transfusion Services (BTS) need to reduce wastage by avoiding outdates and improving availability of different blood products. We took a blood product lifecycle approach and used advanced visualization techniques to design and develop a highly interactive web-based dashboard to audit retrospective data and consequently, to identify and learn from procedural inefficiencies based on analysis of transactional data. We present pertinent scenarios to show how the blood transfusion staff can use the dashboard to investigate blood product lifecycles so as to probe transition sequence patterns that led to wastage as a means to discover causes of procedural inefficiencies in the BTS.

Semantic knowledge modeling and evaluation of argument Theory to develop dialogue based patient education systems for chronic disease Self-Management.

BACKGROUND: To improve understanding as well as uptake of health educational material, it should be tailored to informational needs, offer intuitive modes of interaction, and present credible evidence for health claims. Dialogue systems go some way in meeting these requirements, as they emulate interactive and intuitive person-to-person communication. However, most works do not offer a formal model nor modelling process to structure dialogue content, and do not focus on ensuring credibility. METHODS: We propose an Extended Model of Argument (EMA) dialogue model and modelling process to support educational dialogue systems. In this dialogue model, computerized arguments directly offer evidence for health claims. EMA further offers "dialogue by design", where argument structures and interrelations are dynamically leveraged to offer dialogues, instead of relying on predefining discourse flows. We implemented an EMA-based dialogue education system for Juvenile Idiopathic Arthritis (JIA). We performed a qualitative evaluation with JIA health experts involving a Cognitive Walkthrough and Semi-Structured Interview. We applied Directed Content Analysis using categories from the O'Grady framework, and coded sub-themes within those categories using Grounded Theory. RESULTS: We identified 6 sub-themes within the participant feedback pertaining to Quality, Credibility, and Utility. Participants attached strong importance to credibility and found the dialogue system to be a flexible educational tool. Some participants suggested sorting educational items by importance, and presenting only salient knowledge associations to reduce dialogue complexity. CONCLUSION: Overall, our qualitative evaluation confirmed the following: the ability of EMA to offer credible and appropriate dialogues; and, in general, the utility of dialogue systems to educate JIA patients and their families. In future work, we will revise the system based on evaluation feedback, and perform a more extensive evaluation with patients and caregivers.

Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning.

In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks-these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.

Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data.

BACKGROUND: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. OBJECTIVE: To understand and analyze public sentiments towards social distancing as articulated in Twitter textual data. METHODS: Twitter data specific to Canada and texts comprising social distancing keywords were extrapolated, followed by utilizing the SentiStrength tool to extricate sentiment polarity of tweet texts. Thereafter, the support vector machine (SVM) algorithm was employed for sentiment classification. Evaluation of performance was measured with a confusion matrix, precision, recall, and F1 measure. RESULTS: This study resulted in the extraction of a total of 629 tweet texts, of which, 40% of tweets exhibited neutral sentiments, followed by 35% of tweets showed negative sentiments and only 25% of tweets expressed positive sentiments towards social distancing. The SVM algorithm was applied by dissecting the dataset into 80% training and 20% testing data. Performance evaluation resulted in an accuracy of 71%. Upon using tweet texts with only positive and negative sentiment polarity, the accuracy increased to 81%. It was observed that reducing test data by 10% increased the accuracy to 87%. CONCLUSION: Results showed that an increase in training data increased the performance of the algorithm.

Building a Knowledge Graph Representing Causal Associations Between Risk Factors and Incidence of Breast Cancer.

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. 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.

Ontology-Based Personalized Cognitive Behavioural Plans for Patients with Mild Depression.

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.

Using Interactive Visual Analytics to Optimize in Real-Time Blood Products Inventory at a Blood Bank.

Blood products and their derivatives are perishable commodities that require an efficient inventory management to ensure both a low wastage rate and a high product availability rate. To optimize blood product inventory, blood transfusion services need to reduce wastage by avoiding outdates and improve availability of different blood products. We used advance visualization techniques to design and develop a highly interactive real-time web-based dashboard to monitor the blood product inventory and the on-going blood unit transactions in near-real-time based on analysis of transactional data. Blood transfusion staff use the dashboard to locate units with specific characteristics, investigate the lifecycle of the units, and efficiently transfer units between facilities to minimize outdates.

A Knowledge Graph of Mechanistic Associations Between COVID-19, Diabetes Mellitus and Kidney Diseases.

This paper proposes an automated knowledge synthesis and discovery framework to analyze published literature to identify and represent underlying mechanistic associations that aggravate chronic conditions due to COVID-19. We present a literature-based discovery approach that integrates text mining, knowledge graphs and ontologies to discover semantic associations between COVID-19 and chronic disease concepts that were represented as a complex disease knowledge network that can be queried to extract plausible mechanisms by which COVID-19 may be exacerbated by underlying chronic conditions.

Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases.

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework.

Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual's indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual's discrete semantic location; i.e., it is the concrete type of an individual's location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual's discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study.

BACKGROUND: Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. OBJECTIVE: This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. METHODS: We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. RESULTS: Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm's performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). CONCLUSIONS: Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model's performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients' outcomes.

Providing Comorbid Decision Support via the Integration of Clinical Practice Guidelines at Execution-Time by Leveraging Medical Linked Open Datasets.

Clinical Decision Support Systems (CDSS) utilize computerized Clinical Practice Guidelines (CPG) to deliver evidence-based care recommendations. However, when dealing with comorbidity (i.e., patients with multiple conditions), disease-specific CPG often interact in adverse ways (e.g., drug-drug, drug-disease interactions), and may involve redundant elements as well (e.g., repeated care tasks). To avoid adverse interactions and optimize care, current options involve the static, a priori integration of comorbid CPG by replacing or removing therapeutic tasks. Nevertheless, many aspects are relevant to a clinically safe and efficient integration, and these may change over time-task delays, test outcomes, and health profiles-which are not taken into account by static integrations. Moreover, in case of comorbidity, clinical practice often demands nuanced solutions, based on current health profiles. We propose an execution-time approach to safely and efficiently cope with comorbid conditions, leveraging knowledge from medical Linked Open Datasets to aid during CIG integration.