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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation.

Gaurav Singh¹, Iain J Marshall², James Thomas¹

¹University College London London, UK.

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management

|January 9, 2018

Summary

This summary is machine-generated.

This study introduces a new neural model for automatically annotating clinical trial texts with medical concepts. The model effectively identifies patient populations, interventions, and outcomes (PICO elements) from trial descriptions.

Keywords:

biomedical informatics deep learning text mining

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Area of Science:

Medical Informatics
Computational Linguistics
Bioinformatics

Background:

Automated annotation of clinical trial texts is crucial for information extraction.
Existing methods struggle with large vocabularies, sparse data, and concept correlations.

Purpose of the Study:

To develop a novel neural model for accurate PICO element annotation in clinical trial descriptions.
To address challenges of vast output spaces, data sparsity, and inter-concept correlations.

Main Methods:

Proposed a Candidate-Selector neural architecture to infer PICO concepts.
Utilized a 'candidate set' generator and a conditional discriminative model for joint concept selection.
Evaluated performance against strong baselines and conducted expert qualitative assessment.

Main Results:

The novel neural model demonstrated superior predictive performance compared to existing baselines.
The Candidate-Selector architecture effectively handles large concept spaces and sparse data.
Domain expert evaluation confirmed the quality of the generated annotations.

Conclusions:

The developed neural model offers an effective solution for automated PICO element annotation in clinical trials.
This approach has the potential to improve clinical trial data accessibility and analysis.
Exploiting concept correlations within a conditional discriminative framework is key to success.