CASE 1: Artificial Intelligence in Primary Care: Implementing New Technology into Existing Systems

The Digital Health Bureau has received funding from the province to develop projects focused on improving telemedicine. The Department of Health Analytics has been instructed by the Digital Health Bureau to use the funding to improve the use of electronic medical records in response to the COVID-19 pandemic. Noor Grewal, a public health liaison officer, has been tasked with determining the best option for electronic medical record integration to address key public health needs in primary care. Currently, the Department of Health Analytics is focused on advocating for the use of artificial intelligence in health care and wants to use this funding opportunity to integrate an artificial intelligence-enabled tool into the province’s certified electronic medical record systems. Noor has narrowed down the top concerns in primary care and searched for artificial intelligence tools that have the potential to solve the identified problems. She has a meeting to provide her recommendations to Damon Miller, the Director of Strategy and Planning, in one week. This case highlights the importance of setting decision making criteria and critically evaluating all evidence before making a decision that has the potential to impact the health of the entire population of the province

Case 14 : Development of an Electronic Health Record Strategy at the Glenburn Public Health Unit

Medical or electronic health records (EHR) are electronic databases that capture an individual’s health and care history throughout their life. EHRs are often used as a single repository of patient information that is shared among multiple health care providers (such as hospitals, laboratories, and family physicians). The Ontario Ministry of Health and Long-Term Care requires all EHR systems in public health units be provincially certified; however, their budget does not provide units with the necessary funding for EHR implementation. The Glenburn Public Health Unit (GPHU) is conducting a review of their recordkeeping practices and has identified a need to streamline their methods for client documentation. There are currently inconsistencies across the unit’s many health teams that result in communication, logistical, and technical issues with respect to document storage and delivery. To address these issues, GPHU must develop an EHR strategy that seeks to improve current recordkeeping practices and, as a result, improves client service delivery

Generative Multiple-Instance Learning Models For Quantitative Electromyography

We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task of facilitating the diagnosis of neuromuscular disorders using sets of motor unit potential trains (MUPTs) detected within a muscle which can be cast as a MIL problem. Our approach leads to a state-of-the-art solution to the problem of muscle classification. By introducing and analyzing generative models for MIL in a general framework and examining a variety of model structures and components, our work also serves as a methodological guide to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets and on the MUSK1 dataset, one of the most widely used benchmarks for MIL.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Development of an EMG-based muscle health model for elbow trauma patients

Wearable robotic braces have the potential to improve rehabilitative therapies for patients suffering from musculoskeletal (MSK) conditions. Ideally, a quantitative assessment of health would be incorporated into rehabilitative devices to monitor patient recovery. The purpose of this work is to develop a model to distinguish between the healthy and injured arms of elbow trauma patients based on electromyography (EMG) data. Surface EMG recordings were collected from the healthy and injured limbs of 30 elbow trauma patients while performing 10 upper-limb motions. Forty-two features and five feature sets were extracted from the data. Feature selection was performed to improve the class separation and to reduce the computational complexity of the feature sets. The following classifiers were tested: linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF). The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Maximum fractal length (MFL), myopulse percentage rate (MYOP), power spectrum ratio (PSR) and spike shape analysis features were identified as the best features for classifying elbow muscle health. A majority vote of the LDA classification models provided a cross-validation accuracy of 82.1%. The work described in this paper indicates that it is possible to discern between healthy and injured limbs of patients with MSK elbow injuries. Further assessment and optimization could improve the consistency and accuracy of the classification models. This work is the first of its kind to identify EMG metrics for muscle health assessment by wearable rehabilitative devices

A mobile app to identify lifestyle indicators related to undergraduate mental health (smart healthy campus): Observational app-based ecological momentary assessment

Background: Undergraduate studies are challenging, and mental health issues can frequently occur in undergraduate students,straining campus resources that are already in demand for somatic problems. Cost-effective measures with ubiquitous devices,such as smartphones, offer the potential to deliver targeted interventions to monitor and affect lifestyle, which may result inimprovements to student mental health. However, the avenues by which this can be done are not particularly well understood,especially in the Canadian context.Objective: The aim of this study is to deploy an initial version of the Smart Healthy Campus app at Western University, Canada,and to analyze corresponding data for associations between psychosocial factors (measured by a questionnaire) and behaviorsassociated with lifestyle (measured by smartphone sensors).Methods: This preliminary study was conducted as an observational app-based ecological momentary assessment. Undergraduatestudents were recruited over email, and sampling using a custom 7-item questionnaire occurred on a weekly basis.Results: First, the 7-item Smart Healthy Campus questionnaire, derived from fully validated questionnaires-such as the BriefResilience Scale; General Anxiety Disorder-7; and Depression, Anxiety, and Stress Scale-21-was shown to significantly correlatewith the mental health domains of these validated questionnaires, illustrating that it is a viable tool for a momentary assessmentof an overview of undergraduate mental health. Second, data collected through the app were analyzed. There were 312 weeklyresponses and 813 sensor samples from 139 participants from March 2019 to March 2020; data collection concluded whenCOVID-19 was declared a pandemic. Demographic information was not collected in this preliminary study because of technicallimitations. Approximately 69.8% (97/139) of participants only completed one survey, possibly because of the absence of anyincentive. Given the limited amount of data, analysis was not conducted with respect to time, so all data were analyzed as a singlecollection. On the basis of mean rank, students showing more positive mental health through higher questionnaire scores tendedto spend more time completing questionnaires, showed more signs of physical activity based on pedometers, and had their devicesrunning less and plugged in charging less when sampled. In addition, based on mean rank, students on campus tended to reportmore positive mental health through higher questionnaire scores compared with those who were sampled off campus. Some datafrom students found in or near residences were also briefly examined.Conclusions: Given these limited data, participants tended to report a more positive overview of mental health when on campusand when showing signs of higher levels of physical activity. These early findings suggest that device sensors related to physical activity and location are useful for monitoring undergraduate students and designing interventions. However, much more sensordata are needed going forward, especially given the sweeping changes in undergraduate studies due to COVID-19

Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. We then use Fitted Q Iteration to train an agent that learns optimal treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that both methods can help improve the treatment decisions of physiotherapists, but the approach based on domain knowledge offers better performance. Our findings provide a "proof of concept" that RL can be used to help improve the treatment of those with an SCI and indicates that continued efforts to gather data and apply RL to this domain are worthwhile.Comment: 31 pages, 7 figure