Weekly AI Meeting

Weekly AI Meeting
An introduction — who I am, where I come from, and my research area
April 2026 · Manzoor Zarlish
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About Me
Zarlish Manzoor
I'm a new PhD student from Lahore, Pakistan, joining the Department of Medical Informatics at Kyoto University as a D1 student. 
DPT Degree
University of Sargodha, 2019–2024
Clinical Background
Stroke rehabilitation, Gulab Devi Hospital
Research Interest
AI + Medical Informatics
My Journey
From Physical Therapy to Medical Informatics
1
2019
Enrolled in DPT program at University of Sargodha
2
2020–21
Ranked 1st in First Professional Year; began exploring AI tools independently
3
2022–24
Clinical rotations at Gulab Devi Hospital; six months in stroke rehab post-graduation
4
2025
Explored research opportunities; connected with Prof. Kurado's lab 
5
April 2026
Joined Kyoto University, Department of Medical Informatics
My Research Area
Utilization of Artificial Intelligence in Medicine
Affiliated with the Graduate School of Medicine 
My Research Area
Research Background
A substantial subgroup of stroke rehabilitation patients shows minimal functional gains despite intensive therapy, and the variability in outcomes remains poorly understood.
Limited recovery
Some patients make only minimal progress even with intensive rehabilitation.
Short treatment window
The high-intensity rehabilitation period is only 2–4 weeks.
Early identification gap
Clinicians currently lack implementating of the tools due to trust
Research question: Can we identify limited responders within the first few days of admission?
Existing Literature
More than 50 ML models for stroke rehabilitation outcomes have been published in the past two decades. 
A recent systematic review (Zu et al., 2023) included 10 studies comprising 32 prediction models, with reported AUC from 0.63 to 0.91. All were rated as having high or unclear risk of bias.
Almost none of these models are used in routine clinical practice. Two reasons recur in the literature: 
Most are black-box models. Clinicians do not act on a probability score they cannot interpret.
Most predict outcomes directly using arbitrary cut-offs, rather than recognizing the patient subgroups that actually exist in the data.
The four phases
1. Discover — K-means++ clustering on FIM gain and FIM efficiency to surface patient phenotypes from the data
2. Characterise — Profile each phenotype using clinical, demographic, and downstream outcome variables
3. Predict — Multinomial logistic regression with LASSO regularisation to assign new patients to a phenotype within Day 1–3
4.Explore—Compare therapy utilisation patterns across phenotypes; generate hypotheses for future intervention trials
Key design choices
•Data-driven phenotypes rather than arbitrary thresholds
•Interpretability treated as a primary design constraint, not a post-hoc add-on
•SHAP for individual-level explanations at the bedside
•TRIPOD+AI reporting standard throughout
•Designed specifically for the Kaifukuki context, not retrofitted from a Western dataset
•Approximately 300 to 350 historical stroke cases from collaborating Kaifukuki hospitals in the Kyoto region
•Sample size derived using the Riley et al. (2020) prediction-model methodology rather than the legacy events-per-variable rule
•Internal validation: bootstrap .632+
•External validation: temporal if data spans multiple years, otherwise geographic across collaborating hospitals
•Missing data: Multiple Imputation by Chained Equations (MICE), with sensitivity analyses
•Implementation in Python; code will be version-controlled and prepared for open release alongside publication
•Methodological details. The choices in this proposal are my starting point. I expect to refine algorithm parameters, predictor sets, and validation specifics with the lab during protocol development.
•Japanese clinical research culture. I want to learn the institutional norms of clinical informatics work in Japan, including how to engage with Kaifukuki hospitals and clinicians directly.
•Japanese language. I am studying Japanese for both bedside engagement and lab life.
•Publication and conference strategy. I would value the lab's view on which venues are the right fit for this kind of work, both in Japan and internationally.
And one open question I would like to put to the room:
•Within the lab's existing collaborations, which Kaifukuki hospitals would be the most natural starting point for the data access conversations?
I am grateful for the chance to begin this work here, and I am looking forward to learning from this group.
Discussion welcome.
In particular, feedback on:
•Methodological suggestions or concerns
•Hospital and data partnership ideas
•Conferences and journals the lab respects in this area
•Anything I should be aware of as a new student in this lab and in Japan
Goals
What I Want to Learn · What I Hope to Contribute
What I Want to Learn
Rigorous research methodology in medical informatics
Interpretable machine learning techniques for clinical data
How to design studies that clinicians actually trust and use
Japanese healthcare system and Kaifukuki data structures
What I Hope to Contribute
A clinical perspective grounded in hands-on rehabilitation experience
Research that addresses real problems in stroke recovery prediction
Enthusiasm for cross-cultural, interdisciplinary collaboration
Work that could meaningfully support rehabilitation teams in Japan
Research Direction
Three Pillars of My Research
My research aims to use interpretable machine learning to identify stroke patients who are likely to struggle in rehabilitation.
This direction emerged from conversations with senior doctors during my clinical work, where the challenge of "poor responders" came up repeatedly. More detail will follow as the work develops.
Thank You
I'm genuinely grateful for the opportunity to join this lab and to learn from all of you. This is just the beginning — I'm looking forward to growing here, contributing where I can, and hopefully doing work that matters for patients and clinicians alike.
Questions, thoughts, or suggestions are very welcome. I'd love to hear your perspective — especially from those who have worked with rehabilitation data or clinical ML before.
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