Detection of early symptoms in cervical cancer is crucial for early treatment and survival. To find symptoms of cervical cancer in clinical text, Named Entity Recognition is needed. In this paper the Clinical Entity Finder, a machine-learning tool trained on annotated clinical text from a Swedish internal medicine emergency unit, is evaluated on cervical cancer records. The Clinical Entity Finder identifies entities of the types body part, finding and disorder and is extended with negation detection using the rule-based tool NegEx, to distinguish between negated and non-negated entities. To measure the performance of the tools on this new domain, two physicians annotated a set of clinical notes from the health records of cervical cancer patients. The inter-annotator agreement for finding, disorder and body part obtained an average F-score of 0.677 and the Clinical Entity Finder extended with NegEx had an average F-score of 0.667.

Objective: The goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised methods to generate such representations. Methods: The significance of this work stands on its experimental layout. The experiments were carried out under the same conditions for both languages. Several classification approaches were explored: maximum probability, CRF, Perceptron and SVM. The classifiers were enhanced by means of ensembles of semantic spaces and ensembles of Brown trees. In order to mitigate sparsity of data, without a significant increase in the dimension of the decision space, we propose the use of clustered approaches of the hierarchical Brown clustering represented by trees and vector quantization for each semantic space. Results: The results showed that the semi-supervised approaches significantly improved standard supervised techniques for both languages. Moreover, clustering the semantic spaces contributed to the quality of the entity recognition while keeping the dimension of the feature-space two orders of magnitude lower than when directly using the semantic spaces. Conclusions: The contributions of this study are: (a) a set of thorough experiments that enable comparisons regarding the influence of different types of features on different classifiers, exploring two languages other than English; and (b) the use of ensembles of clusters of Brown trees and semantic spaces on EHRs to tackle the problem of scarcity of available annotated data.

In this article we present a system that extracts information from pathology reports. The reports are written in Norwegian and contain free text describing prostate biopsies. Currently, these reports are manually coded for research and statistical purposes by trained experts at the Cancer Registry of Norway where the coders extract values for a set of predefined fields that are specific for prostate cancer. The presented system is rule based and achieves an average F-score of 0.91 for the fields Gleason grade, Gleason score, the number of biopsies that contain tumor tissue, and the orientation of the biopsies. The system also identifies reports that contain ambiguity or other content that should be reviewed by an expert. The system shows potential to encode the reports considerably faster, with less resources, and similar high quality to the manual encoding.

Background

Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets.

Methods

A bi-directional Long Short-Term Memory network is applied to clinical notes in Spanish and Swedish for the task of medical named entity recognition. Several types of embeddings, both generated from in-domain and out-of-domain text corpora, and a number of generation and combination strategies for embeddings have been evaluated in order to investigate different input representations and the influence of domain on the final results.

Results

For Spanish, a micro averaged F1-score of 75.25 was obtained and for Swedish, the corresponding score was 76.04. The best results for both languages were achieved using embeddings generated from in-domain corpora extracted from electronic health records, but embeddings generated from related domains were also found to be beneficial.

Conclusions

A recurrent neural network with in-domain embeddings improved the medical named entity recognition compared to shallow learning methods, showing this combination to be suitable for entity recognition in clinical text for both languages.