Prediction and early identification of stroke is crucial to prevent emergency department (ED) revisits and initiate treatment, reducing morbidity and mortality.

This project focuses on the analysis of non-contrast brain CT (NCCT) data to predict early ED revisits for patients coming back with a stroke diagnosis. The first objective will be gathering open-source NCCT data as well as NCCT data from the Integrated Health and Social Services Center from Chaudiere-Appalaches (CISSS-CA) to classify the presence/absence of stroke using an existing model. The second objective will be to develop and test a machine learning model with weights from the previous model and other relevant clinical data to classify short-term revisits to the ED as an outcome.

From a clinical perspective, the development of such a tool may help support neuroradiologists in image interpretation and clinical decision making in the ED. 

Delirium is a condition that, when left unmanaged, is associated with increased mortality and longer hospitalization of patients in intensive care; therefore, its detection should be an integral part of care. It is characterized by confusion, anxiety and reduced alertness. It is estimated that 75% of delirium cases are not detected on admission to hospital. Detecting such an acute condition requires frequent monitoring of participants, which is labor intensive and requires expertise. However, the participants' vital signs, which can be collected continuously throughout their stay in intensive care, could contain information indicative of the present state of consciousness, and possibly predictive of the future state.
Our goal is to build an automatic machine learning classifier based on vital sign data to (a) identify times when the patient was delirious, and (b) predict delirium incipience. As a primary measure, we will use a clinically validated tool, the Confusion Assessment Method for Intensive Care Unit (CAM-ICU). This assessment was performed twice a day, once in the morning and once in the afternoon, in our study population at the CISSS de Chaudière-Appalache (Hotel Dieu de Lévis). The learning algorithm will be trained on the participants' vital signs before, after, and during the delirium episodes in order to (a) extract the vital sign characteristics related to a delirium state; (b) the probability that the patient is delirious or not, based on these characteristics; and (c) the probability that the patient will develop a delirium state within a reasonable time window (e.g. 1 hour).
Even if the machine learning model does not reach the accuracy and precision of a validated questionnaire, its use in healthcare facilities would optimize care, mainly by drawing attention to any suspicious drift (high sensitivity). Considering that patients who remain with untreated delirium are associated with higher mortality rates and longer ICU stays, a clinical indicator such as this model can help the care team manage this otherwise unnoticed symptom.
 

The risk associated with the stochastic effects of neutron radiation is known to be strongly energy dependent. Over the past decade, several studies have used Monte Carlo simulations to estimate the relative biological effectiveness (RBE) of neutrons for various types of DNA damage in order to understand its energy dependence at the fundamental level. However, none of these studies implemented DNA repair simulations in their pipeline.

In this project, we investigated the effects of adding repair mechanisms to Monte Carlo-based RBE estimates of DNA damage by neutrons. Our group had previously carried out condensed history (CH) simulations to profile the energy spectrum and relative dose contribution of the secondary particles produced by neutron interactions in tissue. In this project, we use the results of our CH simulations to simulate the irradiation of TOPAS-nBio’s DNA model by a flat spectrum of neutrons ranging from 1 eV to 10 MeV, as well as reference X-rays at 250 keV. Induced DNA damage are recorded using the standard DNA damage data (SDD) format abd DNA repair are simulated using the DNA Mechanistic Repair Simulator (DaMaRiS) framework.

Patient portal is an emerging healthcare technology that has shown promising effects in enhancing patient care experience and promoting patient health outcomes. Opal, a digital patient-centred portal, is currently available to patients at the Cedars Cancer Center at McGill University Health Centre (MUHC), providing real-time access to personal health information (upcoming appointments, clinical notes, lab results, etc.) in conjunction with the disease- and treatment-specific education materials. This project aims to quantify the initial impact of the Opal patient portal on operational and health-economic outcomes at the Cedars Cancer Centre. We leverage patient-level data from the Opal patient portal and MUHC. The primary method is to use propensity score matching to construct a matched cohort that compares operational outcomes and resource utilization between Opal users to non-users.

The planned outcomes of interest include missed appointments, emergency room visits, hospitalization, medical record requests, and fertility clinic appointments

Cancer patients go through a lot during treatment. Many patients need support from other patients who know what they are going through because family and friends, no matter how supportive they try to be, don’t always understand their struggles.
The Opal Health Informatics Group is developing two programs for supporting cancer patients in the portal Opal: OncoBuddy and OncoConseil. OncoBuddy is a support system that matches cancer patients with volunteer patients (we call them buddies) based on a matching algorithm that considers criteria selected by the patients themselves. OncoConseil, on the other hand, matches patients with threads of information that might interest them, such as tips and tricks to get a smoother experience during the treatment.
This project focuses on finding selection criteria for building the matching algorithm. Using semi-structured interviews with cancer patients, we will extract a panel of selection criteria that are the most valued by patients, such as type of cancer, age and gender of the buddy, stage of cancer, etc. 
 

Cancer patient peer support is a beneficial tool for current patients and previous patients who can share lived experiences. However, current peer support is inefficient as it is conducted manually and relies on a coordinator to match patients based on a few known factors.

Therefore, this research study will examine ways to develop AI-powered matching algorithms that will more efficiently and effectively match cancer patients according to a wider and more complex set of factors than can be done manually. In this research project, we are designing and developing an AI-matching algorithm for the OncoBuddy/OncoConseil project and evaluating its effectiveness to ensure recommended matches will result in appropriate peer support. We are comparing multiple existing AI models, (i.e. the Deferred Acceptance Algorithm and the Genetic Algorithm) and testing the models on synthetic patient data that we have generated with statistical inferences from the existing Opal database and Statistics Canada. A fitness function derived from previous research will determine the efficacy of the matching algorithms.

As a result, we have generated a synthetic dataset of 1770 patients to be used for training and testing purposes, implemented multiple AI algorithms, and deployed a live dashboard prototype.

The cancer experience and the uncertainty surrounding it is anxiety provoking. One way in which the non-clinical uncertainty of the cancer experience can be reduced is through peer support. The Opal Health Informatics Group seeks to evaluate the efficacy of an artificial intelligence-based peer support matching algorithm in the pre-existing patient portal Opal in the hopes of facilitating peer support programs (for cancer patients and their caregivers) in Quebec.

Our work focused on the architectural design, development, and demonstration of a proof of concept stand-alone web application used to demonstrate the results of several varieties of AI-powered matching algorithms on test patient data.