Weidan Cao
Ohio State University, OH·Medicine & Clinical Research
Treatments for breast cancer, such as anthracyclines, biologic drugs and immunotherapy, greatly improve survival. But up to 20% of patients who receive them develop serious heart disease, such as heart failure, heart inflammation (myocarditis) or irregular heartbeat.
- Fields
Medicine & Clinical Research
Computer Science
Biomedical Engineering
- Topics
breast cancer
cardiotoxicity
cardio-oncology
- Grants
- 1 known grant, through May 2030
What they work on
- Design and develop, with stakeholders, a human-centered smart system that pairs wearable sensors with a conversational large language model chatbot to collect health data at home with little patient effort.
- Recruit breast cancer patients in active treatment to collect heart rate variability from wearable devices and record possible cardiotoxicity symptoms through the LLM chatbot at home.
- Build a deidentified dataset of electronic health records, chemotherapy and cardiotoxicity outcomes. Train a cardiotoxicity prediction model on large-scale EHR data and fine-tune it with small-scale, detailed home data.
- Add a changing cardiotoxicity risk score to a physician-facing dashboard and have physicians evaluate the updated home-based system for clinical decision making.
More about the research
Heart disease is now the leading cause of death other than cancer itself during or after breast cancer treatment. If doctors could predict this heart damage (cardiotoxicity) early, they could change treatment or add protective care. Today that prediction is hard. Cardiotoxicity is unpredictable, guidelines are lacking, and tests often miss it.
Why it matters
Breast cancer patients face a real risk of heart failure, myocarditis or arrhythmia from their treatment, and current tools detect it poorly. A risk score that combines medical records with home wearable data and reported symptoms could help oncologists catch rising risk early. They could then adjust therapy or start heart-protective care, which may reduce deaths after breast cancer treatment.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Oguz Akbilgic
Wake Forest University, NC·Medicine & Clinical Research
Develop an AI model that predicts future heart failure with preserved ejection fraction (HFpEF) from ECG data, using carefully verified data from NIH-funded studies.
Shamim Nemati
University of California-San Diego, CA·Biomedical Engineering
Compare a large language model–supported differential diagnosis system against the binary sepsis model COMPOSER for accuracy, with expert clinician review of the diagnosis lists, using retrospective hospital data.
Lindsay Edwards
Duke University, NC·Medicine & Clinical Research·Early-career
Optimize a machine learning pipeline that predicts cardiomyopathy in pediatric cancer survivors from routinely collected echocardiographic and clinical data.
The school
Ohio State University: 71 labs on PI Pages, $1.6B a year on research, 882 research doctorates a year.
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