# SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports

arXiv: https://arxiv.org/abs/2109.05694 · HTML: https://mlsanity.com/paper/2109.05694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures](https://mlsanity.com/paper/1712.09771/) (2017)

## Built on by (most cited first, 1 of 1)

- [Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts](https://mlsanity.com/paper/2209.11233/) (2022)

## Abstract (arXiv)

Machine learning (ML)-based analysis of electroencephalograms (EEGs) is playing an important role in advancing neurological care. However, the difficulties in automatically extracting useful metadata from clinical records hinder the development of large-scale EEG-based ML models. EEG reports, which are the primary sources of metadata for EEG studies, suffer from lack of standardization. Here we propose a machine learning-based system that automatically extracts components from the SCORE specification from unstructured, natural-language EEG reports. Specifically, our system identifies (1) the type of seizure that was observed in the recording, per physician impression; (2) whether the session recording was normal or abnormal according to physician impression; (3) whether the patient was diagnosed with epilepsy or not. We performed an evaluation of our system using the publicly available TUH EEG corpus and report F1 scores of 0.92, 0.82, and 0.97 for the respective tasks.