Risk stratification of new-onset psychiatric disorders using clinically distinct traumatic brain injury phenotypes.

Background: Patients with traumatic brain injury (TBI) constitute a highly heterogeneous population, with varying risks for New-onset Psychiatric Disorders (NPDs). The objectives of this study were to identify TBI phenotypes and determine how NPDs differ among these phenotypes.

Methods: Hospitalized TBI patients from 2003 to 2019 were obtained from the provincial trauma registry.

Ensemble Clustering to Generate Phenotypes of Kidney Transplant Donors and Recipients.

In this paper we investigate the generation of phenotypes for kidney transplant donors and recipients to assist with decision making around organ allocation. We present an ensemble clustering approach for multi-type data (numerical and categorical) using two different clustering approaches-i.e.

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.

Predicting Urgent Dialysis at Ambulance Transport to the Emergency Department Using Machine Learning Methods.

Hemodialysis patients frequently require ambulance transport to the hospital for dialysis. Some patients require urgent dialysis (UD) within 24 hours of transport to hospital to avoid morbidity and mortality. UD is not available in all hospitals; therefore, predicting patients who need UD prior to hospital transport can help paramedics with destination planning.

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.

Spatial Hotspots and Sociodemographic Profiles Associated With Traumatic Brain Injury in Nova Scotia.

Traumatic brain injury (TBI) is a leading cause of death and disability, primarily caused by falls and motor vehicle collisions (MVCs). Although many TBIs are preventable, there is a notable lack of studies exploring the association of geographically defined TBI hotspots with social deprivation. Geographic information systems (GIS) can be used to identify at-risk neighborhoods (hotspots) for targeted interventions.

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.

Multiview Clustering to Identify Novel Kidney Donor Phenotypes for Assessing Graft Survival in Older Transplant Recipients.

Key Points: An unsupervised machine learning clustering algorithm identified distinct deceased kidney donor phenotypes among older recipients. Recipients of certain donor phenotypes were at a relatively higher risk of all-cause graft loss even after accounting for recipient factors. The use of unsupervised clustering to support kidney allocation systems may be an important area for future study.

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.

Improving Mental Health Literacy and Reducing Psychological Problems Among Teachers in Zambia: Protocol for Implementation and Evaluation of a Wellness4Teachers Email Messaging Program.

Background: Primary, basic, secondary, and high school teachers are constantly faced with increased work stressors that can result in psychological health challenges such as burnout, anxiety, and depression, and in some cases, physical health problems. It is presently unknown what the mental health literacy levels are or the prevalence and correlates of psychological issues among teachers in Zambia. It is also unknown if an email mental messaging program (Wellness4Teachers) would effectively reduce burnout and associated psychological problems and improve mental health literacy among teachers.

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.

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.

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.

Towards an Adaptive Clinical Transcription System for In-Situ Transcribing of Patient Encounter Information.

Electronic patient charts are essential for follow-up and multi-disciplinary care, but either take up an exorbitant amount of time during the patient encounter using a key-stroke entry system, or suffer from poor recall when made long after the encounter. Transcribing in-situ, natural dictations by the clinician, recorded during the encounter, with minimal workflow impact, is a promising solution. However, human transcription requires significant manual resources, whereas automated transcription currently lacks the accuracy for specialized clinical language.

Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases.

Chronic exposure to environmental arsenic has been linked to a number of human diseases affecting multiple organ systems, including cancer. The greatest concern for chronic exposure to arsenic is contaminated groundwater used for drinking as it is the main contributor to the amount of arsenic present in the body. An estimated 40% of households in Nova Scotia (Canada) use water from private wells, and there is a concern that exposure to arsenic may be linked to/associated with cancer.

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.

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study.

Background: Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation.

Staged reflexive artificial intelligence driven testing algorithms for early diagnosis of pituitary disorders.

Background: Sellar masses (SM) frequently present with insidious hormonal dysfunction. We previously showed that, by utilizing a combined reflex/reflecting approach involving a laboratory clinician (LC) on common endocrine test results requested by non-specialists, and subsequently adding further warranted tests, previously undiagnosed pituitary disorders can be identified. However, manually employing these strategies by an LC is not feasible for wider screening of pituitary disorders.

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).

Using Knowledge Graphs to Plausibly Infer Missing Associations in EMR Data.

Electronic Medical Records (EMRs) are increasingly being deployed at primary points of care and clinics for digital record keeping, increasing productivity and improving communication. In practice, however, there still exists an often incomplete picture of patient profiles, not only because of disconnected EMR systems but also due to incomplete EMR data entry - often caused by clinician time constraints and lack of data entry restrictions. To complete a patient's partial EMR data, we plausibly infer missing causal associations between medical EMR concepts, such as diagnoses and treatments, for situations that lack sufficient raw data to enable machine learning methods.

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.

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.

Analyzing Association Rules for Graft Failure Following Deceased and Live Donor Kidney Transplantation.

This paper investigates the clinical attributes that contribute to kidney graft failure following live and deceased donor transplantation using an association rule mining approach. The generated rules are used to analyze the distinctive co-occurrence of attributes for those with or without all-cause graft failure. Analysis of a kidney transplantation dataset acquired from the Scientific Registry of Transplant Recipients that included over 95000 deceased and live donor recipients over 5-years was performed.

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.

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.