Estimation and Conformity Evaluation of Multi-Class Counterfactual Explanations for Chronic Disease Prevention.

Recent advances in Artificial Intelligence (AI) in healthcare are driving research into solutions that can provide personalized guidance. For these solutions to be used as clinical decision support tools, the results provided must be interpretable and consistent with medical knowledge. To this end, this study explores the use of explainable AI to characterize the risk of developing cardiovascular disease in patients diagnosed with chronic obstructive pulmonary disease.

Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records.

This study leverages data from a Canadian database of primary care Electronic Medical Records to develop machine learning models predicting type 2 diabetes mellitus (T2D), prediabetes, or normoglycemia. These models are used as a basis for extracting counterfactual explanations and derive personalized changes in biomarkers to prevent T2D onset, particularly in the still reversible prediabetic state. The models achieve satisfactory performance.

Validation of a Design Architecture to Deliver Health Management and Behavior Change Evidence at Scale.

Forty-four percent of Canadians over the age of 20 have a non-communicable disease (NCD). Millions of Canadians are at risk of developing the complications of NCDs; millions have already experienced those complications. Fortunately, the evidence base for NCD prevention and behavior change is large and growing and digital technologies can deliver them at scale and with high fidelity.

Towards a Regulatory Framework for Electronic Medical Record Interoperability in Canada.

All complex systems are potentially predisposed to failure. Healthcare systems are complex systems that are prone to many errors that can result in dire consequences for patients and healthcare providers. The healthcare system in Canada is under unprecedented strain due to shortages of healthcare providers, provider burnout, inefficient workflows, and a lack of appropriate digital infrastructure.

Towards a Regulatory Framework for Workflow Improvement in Electronic Medical Records.

Physicians have to complete several time-consuming and burnout-inducing tasks in their EMRs for everyday care of patients. Poor workflow design generates increased effort for physicians. In this study, we measure time doctors take to retrieve and review information in the patient chart at the beginning of a visit; one of approximately 12 tasks a doctor must do in the EMR during the visit.

Measuring and Managing Healthcare Supply and Demand in Real-Time.

Measuring the supply and demand for access to and wait-times for healthcare is key to managing healthcare services and allocating resources appropriately. Yet, few jurisdictions in distributed, socialized medicine settings have any way to do so. In this paper, we propose the requirements for a jurisdictional patient scheduling system that can measure key metrics, such as supply of and demand for regulated health care professional care, access to and wait times for care, real-time health system utilization and provide the data to compute patient journeys.

A Framework for Implementing Disease Prevention and Behavior Change Evidence at Scale.

The current corpus of evidence-based information for chronic disease prevention and treatment is vast and growing rapidly. Behavior change theories are increasingly more powerful but difficult to operationalize in the current healthcare system. Millions of Canadians are unable to access personalized preventive and behavior change care because our in-person model of care is running at full capacity and is not set up for mass education and behavior change programs.

Artificial intelligence with temporal features outperforms machine learning in predicting diabetes.

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction.

Multi-Input Multi-Output Dynamic Modelling of Type 2 Diabetes Progression.

Type 2 Diabetes Mellitus (T2D) is a chronic health condition that affects millions of people globally. Early identification of risk can support preventive intervention and therefore slow down disease progression. Risk characterization is also necessary to monitor the mechanisms behind the pathology through the analysis of the interrelationships between the predictors and their time course.

Characterization of Inclination Analysis for Predicting Onset of Heart Failure from Primary Care Electronic Medical Records.

The aim of this study is to characterize the performance of an inclination analysis for predicting the onset of heart failure (HF) from routinely collected clinical biomarkers extracted from primary care electronic medical records. A balanced dataset of 698 patients (with/without HF), including a minimum of five longitudinal measures of nine biomarkers (body mass index, diastolic and systolic blood pressure, fasting glucose, glycated hemoglobin, low-density and high-density lipoproteins, total cholesterol, and triglycerides) is used. The proposed algorithm achieves an accuracy of 0.

A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations.

Despite the growing availability of artificial intelligence models for predicting type 2 diabetes, there is still a lack of personalized approaches to quantify minimum viable changes in biomarkers that may help reduce the individual risk of developing disease. The aim of this article is to develop a new method, based on counterfactual explanations, to generate personalized recommendations to reduce the one-year risk of type 2 diabetes. Ten routinely collected biomarkers extracted from Electronic Medical Records of 2791 patients at low risk and 2791 patients at high risk of type 2 diabetes were analyzed.

High cardiovascular disease risk-associated with the incidence of Type 2 diabetes among prediabetics.

Background: Prediabetes is a risk factor for developing Type 2 diabetes mellitus (T2D). We report on the first cohort study of the association between high cardiovascular diseases (CVD) risk with the incidence of T2D in prediabetics. First, estimate the direct effect of developing T2D on patients with prediabetes who have high CVDs risk; and 2) assess the potential increased risk of developing T2D mediated by statins.

Improving Shared Decision-Making Using Cognitive Effort-Optimization.

Diabetes Prevention Programs (DPPs) can prevent or delay type 2 diabetes (T2D). However, the participation rates in DPPs have been limited. Many individuals at risk of developing diabetes have difficulties making healthy choices because of the cognitive effort required to understand the risks, the role of biomarkers, the consequences of inaction and the actions required to delay or avoid development of T2D.

Design for a Virtual Peer-to-Peer Knowledge to Action Platform for Type 2 Diabetes.

Many patients with Type 2 Diabetes (T2D) have difficulty in controlling their disease despite wide-spread availability of high-quality guidelines, T2D education programs and primary care follow-up programs. Current diabetes education and treatment programs translate knowledge from bench to bedside well, but underperform on the 'last-mile' of converting that knowledge into action (KTA). Two innovations to the last-mile problem in management of patients with T2D are introduced.

Behavioral Segmentation for Enhanced Peer-to-Peer Patient Education.

The aim of this study was to develop a peer-to-peer virtual intervention for patients with type 2 diabetes from different segments: patients who take several medications (medication group), patients who do not take diabetes medications (lifestyle group), and a mixed group. Preliminary results showed that patients in the lifestyle group were interested in preventive strategies, reporting better learning experience and higher motivation than those in the medication group. Future research is needed to design approaches tailored to patients in the medication group.

Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI.

Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, along with a healthy lifestyle can help prevent the onset of disease. This preliminary study proposes the use of explainable artificial intelligence techniques with the aim of (i) characterizing diabetic patients through a set of easily interpretable rules and (ii) providing individualized recommendations for the prevention of the onset of the disease through the generation of counterfactual explanations, based on minimal variations of biomarkers routinely collected in primary care.

Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach.

Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number.

FIUS: Fixed partitioning undersampling method.

Background And Objective: In the medical field, data techniques for prediction and finding patterns of prevalent diseases are of increasing interest. Classification is one of the methods used to provide insight into predicting the future onset of type 2 diabetes of those at high risk of progression from pre-diabetes to diabetes. When applying classification techniques to real-world datasets, imbalanced class distribution has been one of the most significant limitations that leads to patients' misclassification.

A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression.

Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex.

Predictive models for diabetes mellitus using machine learning techniques.

Background: Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities.

Methods: Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques.

Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique.

Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing diabetes in an individual effectively? To our knowledge, no study has attempted use of HMM to validate the performance of FDRSM.

Design for a Canadian Digital Health Policy & Practices Observatory.

Canada has struggled to make digital health a reality. We identified 6 key issues that appear to impede progress: 1) an inability to coordinate the actions of a rapidly evolving set of stakeholders, 2) patients who lack the ability and resources to play a meaningful role in health system decision-making, 3) world-class innovation that doesn't reach the market, 4) an inability to kick-start interoperability projects that can catalyze system transformation, 5) an inability to procure early-stage innovative technologies at scale, and 6) an inability to share data seamlessly across organizational silos for patient coordination and care, health system management and research. We propose a set of policies and practices that can help Canada assess, monitor and provide feedback to stakeholders and citizens on how well they are progressing toward seamless digital health.

Patient Empowerment: The Role of Technology.

Patient empowerment is a buzzword that has gained much currency in recent years. It is defined as a process that helps people gain control over their own lives and increases their capacity to act on issues that they themselves define as important. This paper outlines the problems faced by the current medical model of patient empowerment and proposes a unique framework for patient empowerment that provides guidance on how health technology supports or detracts from empowering patients and families.

Review of cognitive behavioural therapy mobile apps using a reference architecture embedded in the patient-provider relationship.

Background: Mobile health apps (mHealth apps) are increasing in popularity and utility for the management of many chronic diseases. Although the current reimbursement structure for mHealth apps is lagging behind the rapidly improving functionality, more clinicians will begin to recommend these apps as they prove their clinical worth. Payors such as the government or private insurance companies will start to reimburse for the use of these technologies, especially if they add value to patients by providing timely support, a more streamlined patient experience, and greater patient convenience.