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

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

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

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