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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 attributes detailed in the SCORE specification from unstructured, natural-language EEG reports. Specifically, our system, which jointly utilizes deep learning- and rule-based methods, identifies (1) the type of seizure observed in the recording, per physician impression; (2) whether the patient was diagnosed with epilepsy or not; (3) whether the EEG recording was normal or abnormal according to physician impression. We performed an evaluation of our system using the publicly available Temple University EEG corpus and report F1 scores of 0.93, 0.82, and 0.97 for the respective tasks.
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Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space
Qualitative analysis of verbal data is of central importance in the learning sciences. It is labor-intensive and time-consuming, however, which limits the amount of data researchers can include in studies. This work is a step towards building a statistical machine learning (ML) method for achieving an automated support for qualitative analyses of students' writing, here specifically in score laboratory reports in introductory biology for sophistication of argumentation and reasoning. We start with a set of lab reports from an undergraduate biology course, scored by a four-level scheme that considers the complexity of argument structure, the scope of evidence, and the care and nuance of conclusions. Using this set of labeled data, we show that a popular natural language modeling processing pipeline, namely vector representation of words, a.k.a word embeddings, followed by Long Short Term Memory (LSTM) model for capturing language generation as a state-space model, is able to quantitatively capture the scoring, with a high Quadratic Weighted Kappa (QWK) prediction score, when trained in via a novel contrastive learning set-up. We show that the ML algorithm approached the inter-rater reliability of human analysis. Ultimately, we conclude, that machine learning (ML) for natural language processing (NLP) holds promise for assisting learning sciences researchers in conducting qualitative studies at much larger scales than is currently possible.
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- PAR ID:
- 10288825
- Date Published:
- Journal Name:
- ArXivorg
- ISSN:
- 2331-8422
- Page Range / eLocation ID:
- https://arxiv.org/abs/2011.13384
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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