A machine learning approach for identifying anatomical locations of actionable findings in radiology reports
Recognizing the anatomical location of actionable findings in radiology reports is an important part of the communication of critical test results between caregivers. One of the difficulties of identifying anatomical locations of actionable findings stems from the fact that anatomical locations are not always stated in a simple, easy to identify manner. Natural language processing techniques are capable of recognizing the relevant anatomical location by processing a diverse set of lexical and syntactic contexts that correspond to the various ways that radiologists represent spatial relations. We report a precision of 86.2%, recall of 85.9%, and F(1)-measure of 86.0 for extracting the anatomical site of an actionable finding. Additionally, we report a precision of 73.8%, recall of 69.8%, and F(1)-measure of 71.8 for extracting an additional anatomical site that grounds underspecified locations. This demonstrates promising results for identifying locations, while error analysis reveals challenges under certain contexts. Future work will focus on incorporating new forms of medical language processing to improve performance and transitioning our method to new types of clinical data.
Available from: Dmitriy Dligach
[Show abstract] [Hide abstract]
ABSTRACT: To research computational methods for discovering body site and severity modifiers in clinical texts.
We cast the task of discovering body site and severity modifiers as a relation extraction problem in the context of a supervised machine learning framework. We utilize rich linguistic features to represent the pairs of relation arguments and delegate the decision about the nature of the relationship between them to a support vector machine model. We evaluate our models using two corpora that annotate body site and severity modifiers. We also compare the model performance to a number of rule-based baselines. We conduct cross-domain portability experiments. In addition, we carry out feature ablation experiments to determine the contribution of various feature groups. Finally, we perform error analysis and report the sources of errors.
The performance of our method for discovering body site modifiers achieves F1 of 0.740-0.908 and our method for discovering severity modifiers achieves F1 of 0.905-0.929.
Results indicate that both methods perform well on both in-domain and out-domain data, approaching the performance of human annotators. The most salient features are token and named entity features, although syntactic dependency features also contribute to the overall performance. The dominant sources of errors are infrequent patterns in the data and inability of the system to discern deeper semantic structures.
We investigated computational methods for discovering body site and severity modifiers in clinical texts. Our best system is released open source as part of the clinical Text Analysis and Knowledge Extraction System (cTAKES).
Available from: Licong Cui
[Show abstract] [Hide abstract]
ABSTRACT: Epilepsy is a common serious neurological disorder with a complex set of possible phenotypes ranging from pathologic abnormalities to variations in electroencephalogram. This paper presents a system called Phenotype Exaction in Epilepsy (PEEP) for extracting complex epilepsy phenotypes and their correlated anatomical locations from clinical discharge summaries, a primary data source for this purpose. PEEP generates candidate phenotype and anatomical location pairs by embedding a named entity recognition method, based on the Epilepsy and Seizure Ontology, into the National Library of Medicine's MetaMap program. Such candidate pairs are further processed using a correlation algorithm. The derived phenotypes and correlated locations have been used for cohort identification with an integrated ontology-driven visual query interface. To evaluate the performance of PEEP, 400 de-identified discharge summaries were used for development and an additional 262 were used as test data. PEEP achieved a micro-averaged precision of 0.924, recall of 0.931, and F1-measure of 0.927 for extracting epilepsy phenotypes. The performance on the extraction of correlated phenotypes and anatomical locations shows a micro-averaged F1-measure of 0.856 (Precision: 0.852, Recall: 0.859). The evaluation demonstrates that PEEP is an effective approach to extracting complex epilepsy phenotypes for cohort identification.
Available from: Francesco Isgrò
[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes and discusses the use of text mining techniques for the extraction of information from clinical records written in Italian. However, as it is very difficult and expensive to obtain annotated material for languages different from English, we only consider unsupervised approaches, where no annotated training set is necessary. We therefore propose a complete system that is structured in two steps. In the first one domain entities are extracted from the clinical records by means of a metathesaurus and standard natural language processing tools. The second step attempts to discover relations between the entity pairs extracted from the whole set of clinical records. For this last step we investigate the performance of unsupervised methods such as clustering in the space of entity pairs, represented by an ad hoc feature vector. The resulting clusters are then automatically labeled by using the most significant features. The system has been tested on a fairly large data set of clinical records in Italian, investigating the variation in the performance adopting different similarity measures in the feature space. The results of our experiments show that the unsupervised approach proposed is promising and well suited for a semi-automatic labeling of the extracted relations.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.