Oncology symptom scientist using AI to predict patient-reported outcomes in lung cancer patients

Joosun Shin, PhD, RN, AGACNP-BC, an assistant professor at the UCLA Joe C. Wen School of Nursing, has received a seed grant from the UCLA Health Jonnson Comprehensive Cancer Center to build a predictive tool that forecasts shortness of breath in patients with lung cancer. 

Each year, approximately 60,000 Americans with early-stage lung cancer have surgery to remove their tumor. While most are cured through this process, roughly 70% experience lasting shortness of breath, dramatically impacting their quality of life for years to come. By predicting shortness of breath, clinicians can make tailored surgical decisions, enable earlier rehabilitation, and improve recovery protocols for each patient. 

Through this new grant, Shin will utilize artificial intelligence and machine learning (AI/ML) models to integrate patients’ medical history, pulmonary function tests, and chest CT scans to build a tool that predicts, before surgery, who is most likely to experience postoperative shortness of breath. Shin says this is the first effort in thoracic oncology to apply AI/ML methods to predict persistent or worsening postoperative shortness of breath.

Shin, who along with her UCLA Nursing role is a member of the UCLA Health Jonsson Comprehensive Cancer Center Cancer Control and Survivorship Program, focuses much of her research on improving patient-reported outcomes, especially shortness of breath, for lung cancer patients. In 2026, she also received an early career award from the International Association for the Study of Lung Cancer, as well as an NIH R21 grant focused on personalized assessment and management of shortness of breath.