# Deep Neural Networks To Treat Atrial Fibrillation

> **NIH NIH K23** · STANFORD UNIVERSITY · 2020 · $193,536

## Abstract

PROJECT SUMMARY
Atrial fibrillation (AF) is a major health problem affecting over 5 million people in the US leading to significant
morbidity and even mortality. Therapy for this epidemic is suboptimal, with success of 30-70% at 1 year for
most therapies. Despite great advances in understanding potential AF mechanisms, these insights have not
yet translated into better AF therapy.
The scientific focus of the project centers on the issue of identifying novel phenotypes for the heterogeneous
conditions that currently fall under the rubric of AF. Machine learning is an approach well-suited to identify
novel classifications from large diverse data sets that are traditionally difficult to separate. I will use machine
learning and computational methods to analyze detailed clinical, structural, cardiac electrophysiological and
biochemical features in patients with AF, to better predict responders and non-responders to various therapies.
This may enable prospective guidance to tailor personalized therapy. In performing this project, I will grow as a
physician-scientist focused on patient-oriented research in atrial fibrillation.
The specific aims of the scientific project are as follows: First, I will create a novel disease taxonomy for AF
that classifies patients successfully treated by risk factor modification, antiarrhythmic drug therapy, or diverse
approaches to ablation, using computational methods and supervised learning on large training data from my
collaborators. I will assess the predictive efficacy of these disease partitions in a testing cohort of patients
referred for treatment of AF. Second, I will use advanced techniques in machine learning and patient-level
analyses to explain why a certain strategy may fail or succeed in an individual, paving the way for clinical use.
Third, in a pilot prospective clinical study, I will assess the feasibility and accuracy of these machine learning
models.
The findings from these experiments may provide an immediate clinical impact by delivering AF therapy
options in a patient-specific manner that optimizes benefit while reducing risk. In addition, under the balanced
and expert mentorship provided by this award, I will gain the necessary computational modelling, clinical
research design and biostatistical methodology experience to design comprehensive studies and be
competitive for independent funding.

## Key facts

- **NIH application ID:** 9995010
- **Project number:** 5K23HL145017-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Tina Baykaner
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $193,536
- **Award type:** 5
- **Project period:** 2019-08-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995010

## Citation

> US National Institutes of Health, RePORTER application 9995010, Deep Neural Networks To Treat Atrial Fibrillation (5K23HL145017-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9995010. Licensed CC0.

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