Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator

Nafiul Nipu; L. V. van Dijk; Guadalupe Canahuate; C. David Fuller; G. Elisabeta Marai

doi:10.1109/VAHC69430.2025.00009

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Conference proceeding

Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator

Nafiul Nipu, L. V. van Dijk, Guadalupe Canahuate, C. David Fuller and G. Elisabeta Marai

IEEE Workshop on Visual Analytics in Healthcare (Online), pp.29-35

11/02/2025

DOI: 10.1109/VAHC69430.2025.00009

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Abstract

In the precision medicine paradigm, oncological treatment leverages complex ensemble datasets of similar patients to estimate the outcomes for a current patient. A key challenge is developing and deploying easy-to-understand AI predictive models for the outcomes of a specific patient, based on patient data from multiple institutions. We describe the lessons learned from the development and deployment of an interactive dashboard to support the analysis of individual head and neck cancer patient outcomes based on cohort data. As required by the project, the dashboard design aims to handle a large client base. The dashboard combines an AI solution with a multi-view interface featuring domain-specific plots to facilitate the visual analysis of patient outcomes and to quickly stratify new patients into risk groups. A year after the successful public deployment of the dashboard, we evaluate it with clinician domain experts. We report the feedback and we reflect on the lessons learned through this experience.

Data Mining

Artificial intelligence

Dashboard

Data visualization

Decision making

Head

Human-centered AI for health decision-making

Medical services

Neck

Precision medicine

Predictive models

Risk Stratification

VA-machine intelligence for healthcare data visualization

Visual analytics

Details

Title: Subtitle: Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator
Creators: Nafiul Nipu - University of Illinois Chicago
L. V. van Dijk - University Medical Center Groningen
Guadalupe Canahuate - University of Iowa
C. David Fuller - The University of Texas MD Anderson Cancer Center
G. Elisabeta Marai - University of Illinois Chicago
Resource Type: Conference proceeding
Publication Details: IEEE Workshop on Visual Analytics in Healthcare (Online), pp.29-35
DOI: 10.1109/VAHC69430.2025.00009
eISSN: 2771-6538
Publisher: IEEE
Grant note: Health (10.13039/100018696)
Language: English
Date published: 11/02/2025
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9985116911602771

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