Ingenuity Labs Invited Lecture Presents

Using novel natural language processing algorithms for advanced mental health evaluation and evidenced-based decision making

Dr. Mohsen Omrani
December 15, 2021
Mitchell Hall Room 395

A major challenge in addressing mental health problems is the absence of rigorous and objective characterization of symptoms, based on which evidence based decision making algorithms could be developed. Speech is the main medium of diagnosis and intervention in today’s psychiatry and we believe the advent of new natural language processing (NLP) algorithms can help with developing such objective measurements which could be used in evidence-based decision making processes. In this presentation, I will discuss:

  • Role of NLP algorithms in objective symptom evaluation, triage and diagnosis of mental health problems
  • How textual data analysis could provide clues to the outcome of care in individual patients and form individualized care plans
  • How textual analysis and text generation can help increase the capacity and quality of mental healthcare


Parvin Mousavi

Prof. Comp/ECE/Pathology, member of the Royal Soc of Canada, College of New Scholars

As the world’s population grows to 10B by 2050, technology-enabled innovation is the critical to ensuring well-being for all. While AI and informatics show great promise in rapid analysis of next-generation large scale data, future workers will require highly specialized training fit to the unique nature of health data. We aim to solidify Canada’s competitive advantage in the global space through concerted efforts to train computer scientists with specialized multi-disciplinary experience in medical informatics and digital health, and engage diverse groups and lived experiences in our training.


Integration, federation, and retrieval of large-scale data repositories in Canada

We collaborate with the Canadian Institute of Health Information (CIHI) and the Ontario Health Data Platform (OHDP) to tackle novel challenges in data management and devise evaluation frameworks that center on security, performance and fairness.

Next generation of actionable prescriptive analysis using multi-resolution, multi-modality data

We build computation models of disease from multi-omics data using machine learning, deep learning and evolutionary algorithms, focusing on innovations that address the unique nature of health data and the unavailability and vagueness of gold-standard labels (e.g., pathology labels).

Democratization of AI, software and data for healthcare

We're international leaders in developing and disseminating open source software using low-cost point-of-care imaging. Our positive impact on the global computer–assisted medical interventions community has is constantly expanding. We'll meet new challenges for discovery, refinement of methods and innovative AI-powered solutions using large open source data, hence democratizing access to care and positive outcomes of AI.