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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Comparison of automatic summarisation methods for clinical free text notes.

Hans Moen¹, Laura-Maria Peltonen², Juho Heimonen³

¹Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelands vei 9, 7491 Trondheim, Norway; Department of Information Technology, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland; Department of Nursing Science, University of Turku, Lemminkäisenkatu 1, 20520 Turku, Finland.

Artificial Intelligence in Medicine

|February 23, 2016

Summary

Automated text summarisation of clinical notes is feasible, with specific methods outperforming others. Automated evaluations correlate highly with human assessments, enabling efficient development of summarisation tools.

Keywords:

Automatic text summarisation Clinical text processing Distributional semantics Electronic health records Summarisation evaluation Word space models

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Area of Science:

Natural Language Processing
Clinical Informatics
Machine Learning

Background:

Electronic Health Records (EHRs) contain vast amounts of clinical free text, posing a challenge for clinicians.
Efficiently managing and summarizing this information is crucial for patient care and documentation.

Purpose of the Study:

To identify optimal automated text summarisation methods for clinical notes.
To assess the reliability and efficiency of automated evaluation methods for summarisation quality.

Main Methods:

Development of novel extractive text summarisation methods using distributional semantic modelling and word space models.
Evaluation of summarisation effectiveness using ROUGE measures and a manual meta-evaluation scheme.

Main Results:

Three methods ('Composite', 'Case-Based', 'Translate') significantly outperformed others.
High correlation (> 0.90 Spearman's rho) between manual and automated evaluations.
Good agreement (ICC=0.74) among human evaluators validates the manual evaluation method.

Conclusions:

Automated summarisation of clinical notes is a feasible approach.
Automated evaluations can serve as a reliable proxy for labor-intensive manual evaluations in method development.