# Machine Learning Approach to Identify Candidates for Epilepsy Surgery

> **NIH NIH F31** · CINCINNATI CHILDRENS HOSP MED CTR · 2021 · $18,497

## Abstract

Project Summary/Abstract
Despite the widespread adoption of new drug classes to treat epilepsy, approximately one third of patients do
not respond to anti-epileptic medication. Patients with drug-resistant epilepsy (DRE) suffer permanent memory
and cognitive impairment from uncontrolled seizures. Only 8-10% of patients with DRE obtain long-term seizure
freedom. About half of patients with DRE are eligible for surgical treatment, which results in seizure freedom in
58-73% of patients. However, it is difficult to determine when patients with DRE need surgery. DRE has a
complex disease course that fluctuates according to weekly and monthly patterns. Further, neurologists report
that they lack the resources (i.e. time) needed to analyze and interpret large amounts of electronic health record
(EHR) data to determine surgical candidacy. This results in substantial delays in treatment - 6 years in pediatrics
and 20 years in adults, on average - and contributes to avoidable morbidity and mortality.
Automated systems are capable of assisting clinicians in identifying candidates for epilepsy surgery two years
earlier in the disease course. One such system uses natural language processing to analyze free-text neurology
notes in a real-time clinical setting. This system was able to increase the rate of surgical candidate identification
by 46%, but it could still be improved in two important ways. First, the system is not able to incorporate results
from EEG and MRIs - the most influential factors on surgical candidacy - into its recommendations. Second, it
does not utilize rich information hidden in structured EHR data that captures epilepsy disease burden. A system
that fuses information from all three data sources (neurology notes, EEG and MRI reports, and structured data)
would drastically improve the accuracy and impact of the model.
In the proposed research, I will first develop a deep learning (DL) approach for fusing multi-modal EHR data.
Specifically, I will use recent advances in deep representation learning to produce richer features from both free-
text and structured data. I will represent free-text in neurology notes and EEG and MRI reports with word
embedding vectors, and medication, procedure, and visit codes with medical concept vectors. I will combine
disparate data sources with a deep neural network to produce high-level representations of surgical candidacy.
This will enable me to estimate patients' risk of future epilepsy surgery. Second, I will establish the generalizability
of this approach using a neighboring hospital's EHR data and validate the system in a clinical setting. This
contribution will be significant because it will increase the number of surgical candidates identified earlier in the
disease course, thereby reducing epilepsy disease sequelae. This proposal will lay the groundwork for
nationwide expansion of the DL system and generate the only automated DL system designed to improve the
timeliness of surgical referral rates ...

## Key facts

- **NIH application ID:** 10224663
- **Project number:** 5F31NS115447-02
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** Benjamin David Wissel
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $18,497
- **Award type:** 5
- **Project period:** 2020-06-09 → 2021-06-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224663

## Citation

> US National Institutes of Health, RePORTER application 10224663, Machine Learning Approach to Identify Candidates for Epilepsy Surgery (5F31NS115447-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10224663. Licensed CC0.

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