Nunez Lab for Computational Mental Health & Cancer Care

Navigation Assistant for Patients

Wed, 01 Oct 2025 00:00:00 +0000

Cancer patients in British Columbia face significant challenges in navigating care resources scattered across multiple providers and locations. Delays in accessing these resources can impact not only quality of life but even survival, for example when a patient cannot reach their cancer centre for chemotherapy or is unaware of a transportation assistance program. To address this gap, we have developed an AI-powered chatbot utilizing RAG technology, which generates reliable answers by drawing from a curated database of verified cancer care resources.

Example Talk

Sat, 01 Jun 2030 13:00:00 +0000

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes.

Further event details, including page elements such as image galleries, can be added to the body of this page.

Lab day at office

Mon, 23 Mar 2026 00:00:00 +0000

Lab day at office

Lab day at office (2)

AI-Supported Physical Activity Behaviour Change for People with Colon Cancer

Sun, 01 Mar 2026 00:00:00 +0000

Cooperating Researchers:

Dr. Kelcey Bland
Dr. Lauren Capozzi
Dr. Kristin Campbell
Kaela Cranston
Myles Chan

This project explores how an AI-powered chatbot can support people with colon cancer to become more physically active, building on the CHALLENGE trial (June 2025), which showed that structured exercise can improve survival. Because delivering individualized exercise counselling is resource-intensive, we are adapting an existing BC Cancer large language model chatbot to support exercise professionals to provide personalized, evidence-based behaviour change support.

Through co-design workshops with patients, clinicians, and exercise professionals, we will identify the features, safety needs, and delivery approaches required for a trustworthy and useful tool. A small pilot study will then assess the chatbot’s feasibility, usability, and acceptability. Findings will guide refinement and help determine whether the chatbot is ready for larger-scale evaluation.

Long-term tolerance to ADHD stimulants in youth

Sun, 01 Mar 2026 00:00:00 +0000

Cooperating Researcher: John Ferreira

Credentials: MSc in Applied Neuroscience (student - co-supervised)

Interests: ADHD, Pharmacology

Education:

University of Western Ontario
University of Oxford
King’s College London

This project uses British Columbia’s province-wide PharmaNet prescription database (2005 to present) to study stimulant treatment for ADHD in children and adolescents. We retrospectively analyze three-year periods of continuous stimulant prescribing within each individual, looking for dosing patterns that may be consistent with pharmacological tolerance and how these patterns relate to age, sex, stimulant type, and cumulative exposure. Findings could inform safer, more effective long-term prescribing strategies for ADHD in young people.

Navigating Supportive Cancer Care: A Network-Based Analysis of Tumour Site Specific Pathways

Sun, 01 Mar 2026 00:00:00 +0000

Developing network-based models to map how cancer patients access supportive care services across a complex healthcare system. Built dynamic visualization tools in Python to represent referral pathways and quantify transitions between providers. The project aims to uncover hidden patterns in care navigation, for example missed referral opportunities and delays in accessing mental health services, with the goal of informing system-level improvements in supportive care delivery.

Cooperating researcher:

Dr. Robert Grmek, MD (Resident Research Associate)

Dr. Robert Grmek, MD is currently a senior psychiatry resident at the University of British Columbia. His work focuses on the intersection of health care, technology, and health systems innovation. With a unique background in software development and medicine, he has developed clinical tools, including a psychiatric drug formulary app, and he has recently been focusing on using computational tools to better understand and optimize care systems as well as service delivery.

Interests:

Artificial intelligence
Digital innovation in healthcare delivery
Psychiatry

Education:

Bachelor in Computer Information Sciences, University of British Columbia Okanagan

Dr. Robert Grmek, MD

Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents

Thu, 01 Jan 2026 00:00:00 +0000

This study investigates whether fine-tuning open-weight large language models (LLMs) improves cancer survival prediction from oncology consultation notes compared to zero-shot approaches. Using Meta’s Llama models on 59,800 patient records from BC Cancer, fine-tuning consistently improved performance over zero-shot inference, but did not outperform smaller task-specific NLP models. The findings suggest both approaches merit continued investigation, with the best choice depending on clinical context and practical constraints such as hardware, privacy, and deployment feasibility.

Lab dinner at Xiang Chuan Mansion restaurant

Fri, 19 Dec 2025 00:00:00 +0000

Lab dinner at Xiang Chuan restaurant 👋👋👋👋👋👋

A Chatbot for the Management of Bipolar Disorder: Using Retrieval-Augmented Generation with an Open-Weight Large Language Model to Answer Clinical Questions Based on the CANMAT and ISBD 2018 Guidelines

Tue, 02 Dec 2025 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Create your slides in Markdown - click the Slides button to check out the example.

Supplementary notes can be added here, including code, math, and images.

Bipolar Disorder Treatment Guideline Chatbot

Wed, 01 Oct 2025 00:00:00 +0000

Chatbot for Bipolar Disorder

Wed, 01 Oct 2025 00:00:00 +0000

A chatbot integrated into a mental health app, designed to help those living with bipolar disorder.

Depression Treatment Guideline Chatbot

Wed, 01 Oct 2025 00:00:00 +0000

Equity in Cancer Precision Medicine

Wed, 01 Oct 2025 00:00:00 +0000

Using machine learning to explore whether personalized cancer treatment approaches work equally well for all patients, regardless of their background.

How Patients and Clinicians Want to Use AI in Cancer Care

Wed, 01 Oct 2025 00:00:00 +0000

Identifying Patients Who May Benefit from Psychiatric Support

Wed, 01 Oct 2025 00:00:00 +0000

Using NLP applied to clinical notes to identify cancer patients who would benefit from psychiatric referral but may not yet have been flagged.

Navigation Assistant for Clinical Staff

Wed, 01 Oct 2025 00:00:00 +0000

An AI tool to help clinical teams assess incoming referrals, identify gaps in information, and route patients to the most appropriate services more efficiently.

Patient Attitudes Toward AI in Cancer Care

Wed, 01 Oct 2025 00:00:00 +0000

Surveying and interviewing patients to understand their comfort with and concerns about artificial intelligence being used in their care.

Patient Perspectives on Supportive Care Referral

Wed, 01 Oct 2025 00:00:00 +0000

A qualitative research study exploring how patients experience the process of being referred to supportive care services, and how they feel about the idea of AI being involved in that process.

Predicting Functional Recovery in Depression

Wed, 01 Oct 2025 00:00:00 +0000

Using clinical and patient-reported data to predict how well someone with depression will recover their day-to-day functioning, to support better treatment planning.

Sexual Function and Mood Disorders

Wed, 01 Oct 2025 00:00:00 +0000

Examining the relationship between mood disorder treatment and sexual function, an often-overlooked aspect of patient wellbeing.

Social Isolation, Psychological Distress, and Survival

Wed, 01 Oct 2025 00:00:00 +0000

Examining how social isolation and psychological distress relate to outcomes for cancer patients, to better understand the human factors that influence survival.

Survival Prediction for Brain Cancer Patients

Wed, 01 Oct 2025 00:00:00 +0000

Developing tools to better predict outcomes for patients with central nervous system (CNS) cancers, with the goal of improving prognostic conversations and palliative care planning.

Wearable Sensing in Mood Disorders

Wed, 01 Oct 2025 00:00:00 +0000

Exploring the use of wearable technology to better understand and support people living with mood disorders.

Patients' Attitudes toward Artificial Intelligence (AI) in Cancer Care: A Scoping Review Protocol

Wed, 15 Jan 2025 00:00:00 +0000

This scoping review protocol explores how patients with cancer perceive and accept artificial intelligence in their medical care, addressing an important gap in understanding patient perspectives on AI implementation in oncology.

Cancer Care Needs Explainable Artificial Intelligence: Motivations from Potential Users

Wed, 01 Jan 2025 00:00:00 +0000

A Randomized Evaluation of MoodFX, a Patient-Centred e-Health Tool to Support Outcome Measurement for Depression

Mon, 01 Jul 2024 00:00:00 +0000

MoodFX is a patient-centered e-health tool designed to support outcome measurement for depression. This randomized evaluation demonstrates its effectiveness in helping patients track symptoms and improve treatment outcomes.

Predicting Which Patients with Cancer Will See a Psychiatrist or Counsellor from Their Initial Oncology Consultation Document Using Natural Language Processing

Tue, 09 Apr 2024 00:00:00 +0000

This groundbreaking study demonstrates that artificial intelligence can predict psychosocial needs of cancer patients by analyzing initial oncology consultation documents. Different linguistic patterns predict psychiatrist versus counselor referrals, providing clinically relevant insights for patient care.

Factors influencing delays in the diagnosis and treatment of bipolar disorder in adolescents and young adults: A systematic scoping review

Mon, 01 Jan 2024 00:00:00 +0000

Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report

Wed, 01 Nov 2023 00:00:00 +0000

This study demonstrates that machine learning can predict quality of life improvements in depression treatment, supporting the use of early clinical indicators to identify patients likely to benefit from antidepressant therapy in terms of functional outcomes.

Response Trajectories during Escitalopram Treatment of Patients with Major Depressive Disorder

Fri, 01 Sep 2023 00:00:00 +0000

This study reveals distinct response trajectories to escitalopram treatment in depression by applying unsupervised machine learning to clinical data. The findings highlight that subjective mood and anhedonia are central to treatment response, while other symptom domains show more variable patterns.

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Mon, 27 Feb 2023 00:00:00 +0000

This study demonstrates that natural language processing applied to initial oncology consultation documents can effectively predict patient survival outcomes across multiple cancer types. The models perform comparably with or better than previous prediction models while using readily available clinical data.

Supportive Care and Health Care Service Utilization in Older Adults with a New Cancer Diagnosis: A Population Based Review in British Columbia, Canada

Sun, 01 Jan 2023 00:00:00 +0000

This publication examines supportive care and health care service utilization patterns in older adults with new cancer diagnoses in British Columbia.

Using Neural Language Models to Predict the Psychosocial Needs of Cancer Patients

Wed, 20 Apr 2022 00:00:00 +0000

This doctoral dissertation explores the application of neural language models to predict and address psychosocial needs in cancer patients, contributing to the field of AI in oncology and mental health care.

Replication of Machine Learning Methods to Predict Treatment Outcome with Antidepressant Medications in Patients with Major Depressive Disorder from STAR*D and CAN-BIND-1

Tue, 01 Jun 2021 00:00:00 +0000

This replication and external validation study demonstrates that machine learning methods can successfully predict antidepressant treatment outcomes, with prediction of remission performing better than prediction of response. These findings support the clinical utility of machine learning approaches for personalized depression treatment planning.

Slides

Tue, 05 Feb 2019 00:00:00 +0000

Create slides in Markdown with Wowchemy

Wowchemy | Documentation

Features

Efficiently write slides in Markdown
3-in-1: Create, Present, and Publish your slides
Supports speaker notes
Mobile friendly slides

Controls

Next: Right Arrow or Space
Previous: Left Arrow
Start: Home
Finish: End
Overview: Esc
Speaker notes: S
Fullscreen: F
Zoom: Alt + Click
PDF Export: E

Code Highlighting

Inline code: variable

Code block:

porridge = "blueberry"
if porridge == "blueberry":
print("Eating...")

Math

In-line math: $x + y = z$

Block math:

$$ f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}} $$

Fragments

Make content appear incrementally

{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}

Press Space to play!

One Two Three

A fragment can accept two optional parameters:

class: use a custom style (requires definition in custom CSS)
weight: sets the order in which a fragment appears

Speaker Notes

Add speaker notes to your presentation

{{% speaker_note %}}
- Only the speaker can read these notes
- Press `S` key to view
{{% /speaker_note %}}

Press the S key to view the speaker notes!

Themes

black: Black background, white text, blue links (default)
white: White background, black text, blue links
league: Gray background, white text, blue links
beige: Beige background, dark text, brown links
sky: Blue background, thin dark text, blue links

night: Black background, thick white text, orange links
serif: Cappuccino background, gray text, brown links
simple: White background, black text, blue links
solarized: Cream-colored background, dark green text, blue links

Custom Slide

Customize the slide style and background

{{< slide background-image="/media/boards.jpg" >}}
{{< slide background-color="#0000FF" >}}
{{< slide class="my-style" >}}

Custom CSS Example

Let’s make headers navy colored.

Create assets/css/reveal_custom.css with:

.reveal section h1,
.reveal section h2,
.reveal section h3 {
color: navy;
}

Questions?

Ask

Documentation

Mon, 01 Jan 0001 00:00:00 +0000