SALTClass: classifying clinical short notes using background knowledge from unlabeled data

Publication date

2019-10-13

Authors

Bagheri, AyoubORCID 0000-0001-6366-2173ISNI 0000000492835784
Oberski, Daniel LeonardORCID 0000-0001-7467-2297ISNI 0000000396652603
Sammani, Arjan
van der Heijden, P.G.M.ISNI 0000000067738801
Asselbergs, Folkert W

Editors

Advisors

Supervisors

Document Type

Working paper
Open Access logo

License

Abstract

Background With the increasing use of unstructured text in electronic health records, extracting useful related information has become a necessity. Text classification can be applied to extract patients’ medical history from clinical notes. However, the sparsity in clinical short notes, that is, excessively small word counts in the text, can lead to large classification errors. Previous studies demonstrated that natural language processing (NLP) can be useful in the text classification of clinical outcomes. We propose incorporating the knowledge from unlabeled data, as this may alleviate the problem of short noisy sparse text. Results The software package SALTClass (short and long text classifier) is a machine learning NLP toolkit. It uses seven clustering algorithms, namely, latent Dirichlet allocation, K-Means, MiniBatchK-Means, BIRCH, MeanShift, DBScan, and GMM. Smoothing methods are applied to the resulting cluster information to enrich the representation of sparse text. For the subsequent prediction step, SALTClass can be used on either the original document-term matrix or in an enrichment pipeline. To this end, ten different supervised classifiers have also been integrated into SALTClass. We demonstrate the effectiveness of the SALTClass NLP toolkit in the identification of patients’ family history in a Dutch clinical cardiovascular text corpus from University Medical Center Utrecht, the Netherlands. Conclusions The considerable amount of unstructured short text in healthcare applications, particularly in clinical cardiovascular notes, has created an urgent need for tools that can parse specific information from text reports. Using machine learning algorithms for enriching short text can improve the representation for further applications. Availability SALTClass can be downloaded as a Python package from Python Package Index (PyPI) website at https://pypi.org/project/saltclass and from GitHub at https://github.com/bagheria/saltclass.

Keywords

Short text classification, Clinical text classification, Clinical cardiovascular notes, Dutch clinical text

Citation

Bagheri, A, Oberski, D L, Sammani, A, van der Heijden, P G M & Asselbergs, F W 2019 'SALTClass: classifying clinical short notes using background knowledge from unlabeled data'. https://doi.org/10.1101/801944