Collaboration Hub

Researcher: Karim Mekhail seeks skills from another member
Institution: University of Toronto
Website: https://www.mekhaillab.com
Project aims: DNA mobility can contribute to genome stability. Although initially believed to be random, this mobility can exhibit non-random motions consistent with gaining access to repair-conducive factors. The current goal is to predict the motion of DNA and the outcome of repair on a single-cell level.
Desired skills in collaborators: Advanced knowledge of time series prediction models.

Contact: Dr. Fa-Hsuan Lin
Organization: Sunnybrook Health Sciences Centre
Website: https://linbrainlab.org
Funding available: NSERC, MITACS
Project title and aims: Encoding and decoding the human brains under complex naturalistic stimuli
We are interested in measuring neuronal responses under complex and naturalistic stimuli, such as movie clips and musical pieces. The goals include the modeling of brain responses with sensory inputs (brain coding) and estimating the behavioural responses and sensory inputs based on brain activity (brain decoding). We hypothesize that relationships between brain activity, sensory input, and behavioural responses implicate the mental states, which are different between age groups, lifestyles, and disorders. We aim at classifying and predicting their subjective feelings, behaviours, and treatment responses based on neuroimaging measurements.
Research team currently has these skills: Our lab is specialized in neuroimaging data acquisition and analysis. The modality we used includes magnetic resonance imaging (MRI) and electroencephalography (EEG). We are specialized in recording MRI and EEG with high spatiotemporal resolution and sensitivity on healthy adults, schizophrenic patients, and teenagers suffering from attention deficit disorders.
Desired skills in potential collaborators: We are enthusiastic in collaborating with experts in data analysis. We are interested in learning data modeling methods capable of generating robust results with our sample size (tens to hundreds of participants) and analytic strategies that support adaptive models with evolving data samples.
Other project collaborators: We are collaborating with international industrial partners and major medical imaging vendors to provide infrastructure (AI computations) and technical development (parts and information for imaging hardware) supports.

Contact: Adam Kepecs (Tel: 647-812-6678)
Organization: IntelAGENT
Project aims: This project aims to use Machine Learning to parse a clinician's unstructured free-text patient encounter note in their Electronic Medical Record (EMR) system to identify billable OHIP services. This will alleviate administrative overhead and help physicians spend more time in clinical practice.
Desired skills: Expertise in Natural Language Processing models for unstructured clinical notes.
Other collaborators in this project: Prof. Frank Rudzicz

Researcher: Yalini Senathirajah
Institution: University of Toronto
Project description: Interested in collaborations with those with a mature AI recommendation algorithm that could be integrated into the EHR (our research is on user-controlled platforms for EHR redesign)
Project aims: Composable approaches to EHR design may have benefits for human-computer interaction, cognitive support, efficiency, workflow, and other benefits. It also may make it easier to incorporate AI-based recommendations at the point-of-care. We are interested in possibly piloting this approach with those with appropriate tested algorithms seeking to incorporate them into the EHR.
Desired skills in collaborators: Those who have developed AI-based algorithms aimed at clinical care, at an appropriate stage of development/testing.

Researcher: Arun Ghoshal
Institution: University Health Network (UHN)
Project title: AI-related projects in medicine
Project description: • Precision Medicine: ML algorithms for personalized treatment strategies by analyzing genomic data, clinical records, and treatment outcomes. NLP for extraction of relevant information from medical literature and clinical notes, enabling the identification of potential therapeutic targets and the prediction of treatment responses.

• Clinical Decision Support: ML models trained on comprehensive datasets to aid in clinical decision-making, offering insights and recommendations based on patient characteristics, treatment history, and real-time data. NLP techniques can be applied to extract information from clinical guidelines and research papers, facilitating the integration of evidence-based medicine into decision-support systems.

• Patient Support and Education

• Data Integration and Analysis

Desired skills in collaborators: ML/NLP

Notes: Please email if interested and I am happy to discuss.

Researcher: Abigail Ortiz
Institution: (CAMH) Centre for Addiction and Mental Health
Project description: Join the B.R.A.I.N. lab! 
Project aims: Looking to work in a dream team? Stretch your mind a little bit? Come work with us! We are one of the most innovative affective labs in Canada. We want to help people diagnosed with mood disorders get better, faster. For that, we need clinical + AI skills. If you are a mathematician, physicist, bioengineer, computer scientist enthusiast - we want you!
     Specifically, we are using state of the art machine learning to create digital signatures.
Desired skills: • Building mathematical models of physiological phenomena 
• Attention to detail, self motivated
• Good programming skills (e.g., Python)
• Interest in exploring new areas, dynamic environment
• PhDs and postdocs welcome
Notes: • Funding is available for this position.
• We will teach you how to write better papers, how to get funded and how to really make sense of the technology to benefit a lot of people. Come!

Researcher: Dr. Aviv Shachak
Research Assistant: Ms. Lidia Kojic

Project title: Data Science in Context: Understanding the Work of Health Data Scientists

In this SSHRC-funded study, we seek to gain an in-depth insight into the work of data professionals working in the area of health and the contextual factors affecting it. Through this study, we seek to identify challenges and complexities within the work of health data professionals and ways to improve this work through policy changes, better tools, and education. Ethics approval for the study has been obtained from the University of Toronto Research Ethics Board.

We are looking for volunteers to take part in a 30 - 60-minute interview and/or a 1–2-day job shadowing experience. Time commitment and meeting locations are flexible.

Financial compensation is provided for participation.

If you are interested in participating in this study, please complete the form found at this link, providing us with your contact information and availability for an interview and/or job shadowing.

Please do not hesitate to contact us if you have any questions.

The Imagine Institute and Necker Hospital have developed a data warehouse of 1 million pediatric patients with “all for all“ phenotyping based on their raw electronic health record data. Histories of rare disease patients (as well as common disease patients) are extracted from patient electronic health records, and clinical images and photographs (e.g., for facial dysmorphia) are are also available. Clinical data can be linked with the -omic data generated at the Imagine Institute. Omic data are from different types: genomic, exome, transcriptome, single cell, and organ-specific such as those derived from Urine-Derived Renal Epithelial Cells (URECs). In addition, collaborators will have the opportunity to work with the Paris AI Institute (PR[AI]RIE). All interested collaborators are requested to contact

Contact: Dr. Anita Burgun

Researchers: Dr. Sabri Soussi and Dr. Claudia dos Santos
Institution: University of Toronto
Project title: AI in Shock and acute Conditions OutcOmes Platform (Shock CO-OP)

Notes: If interested, please email ASAP. Preferred applicants are those who apply before May 12, 2024, but we will look at applications after that as well. 
sabri.soussi@uhn.ca;  claudia.dossantos@unityhealth.to

Project description: This collaborative research initiative between Université Paris Cité (France) and the University of Toronto represents a groundbreaking effort to redefine the classification of circulatory shock through the application of artificial intelligence (AI) approaches for the integration of high dimensional biomarker data.

The collaborative project, known as the AI in Shock and acute Conditions OutcOmes Platform (Shock CO-OP), seeks to enable a paradigm shift in the understanding of circulatory shock clinical syndrome by identifying distinct subclasses based on physiological/molecular profiles (i.e., subphenotypes).

Our international/multidisciplinary research group already identified distinct subphenotypes in septic shock patients with different outcomes and inflammatory and cardiovascular patterns. By leveraging existing clinical and biomarker data from complementary cohorts/clinical trials with biobanks in Europe and North America, our research group aims to further uncover novel insights into the pathophysiology of circulatory shock independently from its etiology (e.g., infection, myocardial infarction, trauma, major surgery) by identifying distinct biomarker-driven subphenotypes. This will allow the development of biomarker/subphenotype-based therapies to improve short-/long-term patient-centered outcomes (i.e., precision critical care medicine).

Desired skills in collaborators: 
• Seeking Graduate Students for 1-2 Years
• Good programming skills in Python and/or R especially for:
1) high dimensional metagenomic, transcriptomic and proteomic data management and analysis.
2) Longitudinal and functional data analysis and unsupervised machine learning and model-based clustering (i.e., mixture modeling) for subphenotyping purposes in heterogeneous populations.
-Proactive, team player, self-motivated and able to learn new approaches 
-Master and PhD candidates

Contact: Dr. Tavis Apramian
Research organization: University of Toronto
Telelphone number: 647-619-0538
Funding: Pending

Research project: "Democratization of prognosis in serious illness"
Terminal life-limiting illness follows a predictable downward trajectory. The slope of the decline varies but it always end in the same place. Patients have limited access to their own prognosis and it is not clear from research what they might be interested in know if prognosis accuracy was improved. I'm interested in collaborating with other interested in helping patients with serious illness better anticipate what their remaining time entails.

About the researcher: I am a clinician-investigator in palliative care and a PhD trained qualitative social scientist with expertise in sociocultural theory.

Skils sought in collaborators:
I'm looking for data scientists with expertise in building learning AI models and clinicians with expertise in big data.

Which specific data sets are you seeking to obtain:
I anticipate we will need clinical data on approximately 1M patient deaths. I have a potential collaboration pending with a health data firm who may be willing to provide anonymized charts for 2-3M patients deaths (non-Canadian).