Abstract: Semantic-based sublanguage grammars have been shown to be an efficient method for medical language processing. However, given the complexity of the medical domain, parsers using such grammars inevitably encounter ambiguous sentences, which could be interpreted by different groups of production rules and consequently result in two or more parse trees. One possible solution, which has not been extensively explored previously, is to augment productions in medical sublanguage grammars with probabilities to resolve the ambiguity. In this study, we associated probabilities with production rules in a semantic-based grammar for medication findings and evaluated its performance on reducing parsing ambiguity. Using the existing data set from 2009 i2b2 NLP (Natural Language Processing) challenge for medication extraction, we developed a semantic-based CFG (Context Free Grammar) for parsing medication sentences and manually created a Treebank of 4564 medication sentences from discharge summaries. Using the Treebank, we derived a semantic-based PCFG (Probabilistic Context Free Grammar) for parsing medication sentences. Our evaluation using a 10-fold cross validation showed that the PCFG parser dramatically improved parsing performance when compared to the CFG parser.
Abstract: Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.
Abstract: OBJECTIVE: To develop an automated system to extract medications and related information from discharge summaries as part of the 2009 i2b2 natural language processing (NLP) challenge. This task required accurate recognition of medication name, dosage, mode, frequency, duration, and reason for drug administration. DESIGN: We developed an integrated system using several existing NLP components developed at Vanderbilt University Medical Center, which included MedEx (to extract medication information), SecTag (a section identification system for clinical notes), a sentence splitter, and a spell checker for drug names. Our goal was to achieve good performance with minimal to no specific training for this document corpus; thus, evaluating the portability of those NLP tools beyond their home institution. The integrated system was developed using 17 notes that were annotated by the organizers and evaluated using 251 notes that were annotated by participating teams. MEASUREMENTS: The i2b2 challenge used standard measures, including precision, recall, and F-measure, to evaluate the performance of participating systems. There were two ways to determine whether an extracted textual finding is correct or not: exact matching or inexact matching. The overall performance for all six types of medication-related findings across 251 annotated notes was considered as the primary metric in the challenge. RESULTS: Our system achieved an overall F-measure of 0.821 for exact matching (0.839 precision; 0.803 recall) and 0.822 for inexact matching (0.866 precision; 0.782 recall). The system ranked second out of 20 participating teams on overall performance at extracting medications and related information. CONCLUSIONS: The results show that the existing MedEx system, together with other NLP components, can extract medication information in clinical text from institutions other than the site of algorithm development with reasonable performance.
Abstract: Clinical research often requires extracting detailed drug information, such as medication names and dosages, from Electronic Health Records (EHR). Since medication information is often recorded as both structured and unstructured formats in the EHR, extracting all the relevant drug mentions and determining the daily dose of a medication for a selected patient at a given date can be a challenging and time-consuming task. In this paper, we present an automated approach using natural language processing to calculate daily doses of medications mentioned in clinical text, using tacrolimus as a test case. We evaluated this method using data sets from four different types of unstructured clinical data. Our results showed that the system achieved precisions of 0.90-1.00 and recalls of 0.81-1.00.
Abstract: This paper explores the role of named entities (NEs) in the classification of disease outbreak report. In the annotation schema of BioCaster, a text mining system for public health protection, important concepts that reflect information about infectious diseases were conceptually analyzed with a formal ontological methodology and classified into types and roles. Types are specified as NE classes and roles are integrated into NEs as attributes such as a chemical and whether it is being used as a therapy for some infectious disease. We focus on the roles of NEs and explore different ways to extract, combine and use them as features in a text classifier. In addition, we investigate the combination of roles with semantic categories of disease-related nouns and verbs. Experimental results using naïve Bayes and Support Vector Machine (SVM) algorithms show that: (1) roles in combination with NEs improve performance in text classification, (2) roles in combination with semantic categories of noun and verb features contribute substantially to the improvement of text classification. Both these results were statistically significant compared to the baseline "raw text" representation. We discuss in detail the effects of roles on each NE and on semantic categories of noun and verb features in terms of accuracy, precision/recall and F-score measures for the text classification task.
Abstract: INTRODUCTION: This paper explores the benefits of using n-grams and semantic features for the classification of disease outbreak reports, in the context of the BioCaster disease outbreak report text mining system. A novel feature of this work is the use of a general purpose semantic tagger - the USAS tagger - to generate features. BACKGROUND: We outline the application context for this work (the BioCaster epidemiological text mining system), before going on to describe the experimental data used in our classification experiments (the 1000 document BioCaster corpus). FEATURE SETS: Three broad groups of features are used in this work: Named Entity based features, n-gram features, and features derived from the USAS semantic tagger. METHODOLOGY: Three standard machine learning algorithms - Naïve Bayes, the Support Vector Machine algorithm, and the C4.5 decision tree algorithm - were used for classifying experimental data (that is, the BioCaster corpus). Feature selection was performed using the chi(2) feature selection algorithm. Standard text classification performance metrics - Accuracy, Precision, Recall, Specificity and F-score - are reported. RESULTS: A feature representation composed of unigrams, bigrams, trigrams and features derived from a semantic tagger, in conjunction with the Naïve Bayes algorithm and feature selection yielded the highest classification accuracy (and F-score). This result was statistically significant compared to a baseline unigram representation and to previous work on the same task. However, it was feature selection rather than semantic tagging that contributed most to the improved performance. CONCLUSION: This study has shown that for the classification of disease outbreak reports, a combination of bag-of-words, n-grams and semantic features, in conjunction with feature selection, increases classification accuracy at a statistically significant level compared to previous work in this domain.
Abstract: SUMMARY: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. AVAILABILITY: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org.
Abstract: In this paper we present a summary of the BioCaster system architecture for Web rumour surveillance, the rationale for the choices made in the system design and an empirical evaluation of topic classification accuracy for a gold-standard of English and Vietnamese news.
Abstract: Text categorization involves assigning a natural language document to one or more predefined classes. One of the most interesting issues is feature selection. We propose an approach using multicriteria ranking of eatures, a new procedure for feature selection, and apply these to text categorization. Experimental results dealing with Reuters-21578 and 20Newsgroups benchmark data and the naive Bayes algorithm show that our proposal outperforms conventional feature selection in text categorization performance.
Abstract: We present the Global Health Monitor, an online Web-based system for detecting and mapping infectious disease outbreaks that appear in news stories. The system analyzes English news stories from news feed providers, classifies them for topical relevance and plots them onto a Google map using geo-coding information, helping public health workers to monitor the spread of diseases in a geo-temporal context. The
background knowledge for the system is contained in the BioCaster ontology (BCO) (Collier et al., 2007a)
which includes both information on infectious diseases as well as geographical locations with their latitudes/
longitudes. The system consists of four main stages: topic classification, named entity recognition (NER),
disease/location detection and visualization. Evaluation of the system shows that it achieved high accuracy on a gold standard corpus. The system is now in practical use. Running on a clustercomputer, it monitors more than 1500 news feeds 24/7, updating the map every hour.
Abstract: This paper explores the benefits of using n-grams and semantic features for the classification of disease outbreak reports, in the context of a text mining system â BioCaster â that identifies and tracks emerging infectious disease outbreaks from online news. We show that a combination of bag-of-words features, n-grams and semantic features, in conjunction with feature selection, improves classification accuracy at a statistically significant level when compared to previous work. A novel feature of the work reported in this paper is the use of
a semantic taggerâthe USAS taggerâto generate features.
Abstract: This paper investigates the roles of named entities (NEâs) in annotated biomedical text classification. In the annotation schema of BioCaster, a text mining system for public health protection, important concepts that reflect information about infectious diseases were conceptually analyzed with a formal ontological methodology. Concepts were classified as Types, while others were identified as being Roles. Types are specified as NE classes and Roles are integrated into NEs as attributes. We focus on the Roles of NEs by extracting and using them in different ways as features in the classifier. Experimental results show that: 1) Roles for each NE greatly helped improve performance of the system, 2) combining information about NE classes with their Roles contribute significantly to the improvement of performance. We discuss in detail the effect of each Role on the accuracy of text classification.
Abstract: In this paper we develop the general framework for text representation based on fuzzy set theory. This work is extended from our original ideas [5],[4], in which a document is represented by a set of fuzzy concepts. The importance degree of these fuzzy concepts characterize the semantics of documents and can be calculated by a specified aggregation function of index terms. Based on this representation, a general framework is proposed and applied to text categorization problem. An algorithm is given in detail for choosing fuzzy concepts. Experiments on the real-world data set show that the proposed method is superior to the conventional method for text representation in text categorization.
Abstract: Document representation is one of the most important tasks in text processing, especially in text categorization. This task has many applications that include document management, information retrieval, text routing, etc. In this paper, the author proposes a novel scheme for text representation based on fuzzy set theory. A new algorithm for choosing a term set that characterizes a document in the corpus is given under the view of fuzzy set. Experimental results applied to text categorization problem using the relevance feedback technique show that our proposed method reduced the number of dimensions and achieves higher performances compared to other baseline methods. In addition, it also produces results that compare favorably to the result achieved with the all vocabulary method.
Abstract: Text categorization is a problem of assigning a document into one or more predefined classes. One of the most interesting issues in text categorization is feature selection. This paper proposes a novel approach in feature selection based on multi-criteria ranking of features. Based on a threshold value for each criterion, a new procedure for feature selection is proposed and applied to a text categorization. Experiments dealing with the Reuters-21578 benchmark data and the naive Bayes algorithm show that the proposed approach outperforms performances in compare to conventional feature selection methods.
