Automatic extraction of medication information from medical discharge summaries

Objective

This article describes a system developed for the 2009 i2b2 Medication Extraction Challenge. The purpose of this challenge is to extract medication information from hospital discharge summaries.

Design

The system explored several linguistic natural language processing techniques (eg, term-based and token-based rule matching) to identify medication-related information in the narrative text. A number of lexical resources was constructed to profile lexical or morphological features for different categories of medication constituents.

Measurements

Performance was evaluated in terms of the micro-averaged F-measure at the horizontal system level.

Results

The automated system performed well, and achieved an F-micro of 80% for the term-level results and 81% for the token-level results, placing it sixth in exact matches and fourth in inexact matches in the i2b2 competition.

Conclusion

The overall results show that this relatively simple rule-based approach is capable of tackling multiple entity identification tasks such as medication extraction under situations in which few training documents are annotated for machine learning approaches, and the entity information can be characterized with a set of feature tokens.     

Integrating existing natural language processing tools for medication extraction from discharge summaries

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.     

Comprehensive temporal information detection from clinical text: medical events,time, and TLINK identification

Background

Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge.

Objective

To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text.

Materials and methods

The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework.

Results

Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537.

Conclusions

Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance.     

High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge

Objective

Medication information comprises a most valuable source of data in clinical records. This paper describes use of a cascade of machine learners that automatically extract medication information from clinical records.

Design

Authors developed a novel supervised learning model that incorporates two machine learning algorithms and several rule-based engines.

Measurements

Evaluation of each step included precision, recall and F-measure metrics. The final outputs of the system were scored using the i2b2 workshop evaluation metrics, including strict and relaxed matching with a gold standard.

Results

Evaluation results showed greater than 90% accuracy on five out of seven entities in the name entity recognition task, and an F-measure greater than 95% on the relationship classification task. The strict micro averaged F-measure for the system output achieved best submitted performance of the competition, at 85.65%.

Limitations

Clinical staff will only use practical processing systems if they have confidence in their reliability. Authors estimate that an acceptable accuracy for a such a working system should be approximately 95%. This leaves a significant performance gap of 5 to 10% from the current processing capabilities.

Conclusion

A multistage method with mixed computational strategies using a combination of rule-based classifiers and statistical classifiers seems to provide a near-optimal strategy for automated extraction of medication information from clinical records.Many of the potential benefits of the electronic medical record (EMR) rely significantly on our ability to automatically process the free-text content in the EMR. To understand the limitations and difficulties of exploiting the EMR we have designed an information extraction engine to identify medication events within patient discharge summaries, as specified by the i2b2 medication extraction shared task.     

Medication information extraction with linguistic pattern matching and semantic rules

Objective

This study presents a system developed for the 2009 i2b2 Challenge in Natural Language Processing for Clinical Data, whose aim was to automatically extract certain information about medications used by a patient from his/her medical report. The aim was to extract the following information for each medication: name, dosage, mode/route, frequency, duration and reason.

Design

The system implements a rule-based methodology, which exploits typical morphological, lexical, syntactic and semantic features of the targeted information. These features were acquired from the training dataset and public resources such as the UMLS and relevant web pages. Information extracted by pattern matching was combined together using context-sensitive heuristic rules.

Measurements

The system was applied to a set of 547 previously unseen discharge summaries, and the extracted information was evaluated against a manually prepared gold standard consisting of 251 documents. The overall ranking of the participating teams was obtained using the micro-averaged F-measure as the primary evaluation metric.

Results

The implemented method achieved the micro-averaged F-measure of 81% (with 86% precision and 77% recall), which ranked this system third in the challenge. The significance tests revealed the system''s performance to be not significantly different from that of the second ranked system. Relative to other systems, this system achieved the best F-measure for the extraction of duration (53%) and reason (46%).

Conclusion

Based on the F-measure, the performance achieved (81%) was in line with the initial agreement between human annotators (82%), indicating that such a system may greatly facilitate the process of extracting relevant information from medical records by providing a solid basis for a manual review process.The 2009 i2b2 medication extraction challenge¹ focused on the extraction of medication-related information including: medication name (m), dosage (do), mode (mo), frequency (f), duration (du) and reason (r) from discharge summaries. In other words, free-text medical records needed to be converted into a structured form by filling a template (a data structure with the predefined slots)² with the relevant information extracted (slot fillers). For example, the following sentence:“In the past two months, she had been taking Ativan of 3–4 mg q.d. for anxiety.”should be converted automatically into a structured form as follows:m=“ativan” ‖ do=“3–4 mg” ‖ mo=“nm” ‖ f=“q.d.” ‖ du=“two months” ‖ r=“for anxiety”Note that only explicitly mentioned information was to be extracted with no attempt to map it to standardized terminology or to interpret it semantically.     

Extracting medical information from narrative patient records: the case of medication-related information

Objective

While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as well as different strategies to improve the extraction.

Design

Our approach relies on a semantic lexicon and extraction rules as a two-phase strategy: first, drug names are recognized and, then, the context of these names is explored to extract drug-related information (mode, dosage, etc) according to rules capturing the document structure and the syntax of each kind of information. Different configurations are tested to improve this baseline system along several dimensions, particularly drug name recognition—this step being a determining factor to extract drug-related information. Changes were tested at the level of the lexicons and of the extraction rules.

Results

The initial system participating in i2b2 achieved good results (global F-measure of 77%). Further testing of different configurations substantially improved the system (global F-measure of 81%), performing well for all types of information (eg, 84% for drug names and 88% for modes), except for durations and reasons, which remain problematic.

Conclusion

This study demonstrates that a simple rule-based system can achieve good performance on the medication extraction task. We also showed that controlled modifications (lexicon filtering and rule refinement) were the improvements that best raised the performance.     

Automated identification of drug and food allergies entered using non-standard terminology

Objective

An accurate computable representation of food and drug allergy is essential for safe healthcare. Our goal was to develop a high-performance, easily maintained algorithm to identify medication and food allergies and sensitivities from unstructured allergy entries in electronic health record (EHR) systems.

Materials and methods

An algorithm was developed in Transact-SQL to identify ingredients to which patients had allergies in a perioperative information management system. The algorithm used RxNorm and natural language processing techniques developed on a training set of 24 599 entries from 9445 records. Accuracy, specificity, precision, recall, and F-measure were determined for the training dataset and repeated for the testing dataset (24 857 entries from 9430 records).

Results

Accuracy, precision, recall, and F-measure for medication allergy matches were all above 98% in the training dataset and above 97% in the testing dataset for all allergy entries. Corresponding values for food allergy matches were above 97% and above 93%, respectively. Specificities of the algorithm were 90.3% and 85.0% for drug matches and 100% and 88.9% for food matches in the training and testing datasets, respectively.

Discussion

The algorithm had high performance for identification of medication and food allergies. Maintenance is practical, as updates are managed through upload of new RxNorm versions and additions to companion database tables. However, direct entry of codified allergy information by providers (through autocompleters or drop lists) is still preferred to post-hoc encoding of the data. Data tables used in the algorithm are available for download.

Conclusions

A high performing, easily maintained algorithm can successfully identify medication and food allergies from free text entries in EHR systems.     

Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries

Objective

A system that translates narrative text in the medical domain into structured representation is in great demand. The system performs three sub-tasks: concept extraction, assertion classification, and relation identification.

Design

The overall system consists of five steps: (1) pre-processing sentences, (2) marking noun phrases (NPs) and adjective phrases (APs), (3) extracting concepts that use a dosage-unit dictionary to dynamically switch two models based on Conditional Random Fields (CRF), (4) classifying assertions based on voting of five classifiers, and (5) identifying relations using normalized sentences with a set of effective discriminating features.

Measurements

Macro-averaged and micro-averaged precision, recall and F-measure were used to evaluate results.

Results

The performance is competitive with the state-of-the-art systems with micro-averaged F-measure of 0.8489 for concept extraction, 0.9392 for assertion classification and 0.7326 for relation identification.

Conclusions

The system exploits an array of common features and achieves state-of-the-art performance. Prudent feature engineering sets the foundation of our systems. In concept extraction, we demonstrated that switching models, one of which is especially designed for telegraphic sentences, improved extraction of the treatment concept significantly. In assertion classification, a set of features derived from a rule-based classifier were proven to be effective for the classes such as conditional and possible. These classes would suffer from data scarcity in conventional machine-learning methods. In relation identification, we use two-staged architecture, the second of which applies pairwise classifiers to possible candidate classes. This architecture significantly improves performance.     

Extracting Rx information from clinical narrative

Objective

The authors used the i2b2 Medication Extraction Challenge to evaluate their entity extraction methods, contribute to the generation of a publicly available collection of annotated clinical notes, and start developing methods for ontology-based reasoning using structured information generated from the unstructured clinical narrative.

Design

Extraction of salient features of medication orders from the text of de-identified hospital discharge summaries was addressed with a knowledge-based approach using simple rules and lookup lists. The entity recognition tool, MetaMap, was combined with dose, frequency, and duration modules specifically developed for the Challenge as well as a prototype module for reason identification.

Measurements

Evaluation metrics and corresponding results were provided by the Challenge organizers.

Results

The results indicate that robust rule-based tools achieve satisfactory results in extraction of simple elements of medication orders, but more sophisticated methods are needed for identification of reasons for the orders and durations.

Limitations

Owing to the time constraints and nature of the Challenge, some obvious follow-on analysis has not been completed yet.

Conclusions

The authors plan to integrate the new modules with MetaMap to enhance its accuracy. This integration effort will provide guidance in retargeting existing tools for better processing of clinical text.     

Effects of personal identifier resynthesis on clinical text de-identification

Objective

De-identified medical records are critical to biomedical research. Text de-identification software exists, including “resynthesis” components that replace real identifiers with synthetic identifiers. The goal of this research is to evaluate the effectiveness and examine possible bias introduced by resynthesis on de-identification software.

Design

We evaluated the open-source MITRE Identification Scrubber Toolkit, which includes a resynthesis capability, with clinical text from Vanderbilt University Medical Center patient records. We investigated four record classes from over 500 patients'' files, including laboratory reports, medication orders, discharge summaries and clinical notes. We trained and tested the de-identification tool on real and resynthesized records.

Measurements

We measured performance in terms of precision, recall, F-measure and accuracy for the detection of protected health identifiers as designated by the HIPAA Safe Harbor Rule.

Results

The de-identification tool was trained and tested on a collection of real and resynthesized Vanderbilt records. Results for training and testing on the real records were 0.990 accuracy and 0.960 F-measure. The results improved when trained and tested on resynthesized records with 0.998 accuracy and 0.980 F-measure but deteriorated moderately when trained on real records and tested on resynthesized records with 0.989 accuracy 0.862 F-measure. Moreover, the results declined significantly when trained on resynthesized records and tested on real records with 0.942 accuracy and 0.728 F-measure.

Conclusion

The de-identification tool achieves high accuracy when training and test sets are homogeneous (ie, both real or resynthesized records). The resynthesis component regularizes the data to make them less “realistic,” resulting in loss of performance particularly when training on resynthesized data and testing on real data.     

Automated concept-level information extraction to reduce the need for custom software and rules development

Objective

Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval.

Materials and methods

A ‘learn by example’ approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge''s concept extraction task provided the data sets and metrics used to evaluate performance.

Results

Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks.

Discussion

With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation.

Conclusion

Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.     

Lancet: a high precision medication event extraction system for clinical text

Objective

This paper presents Lancet, a supervised machine-learning system that automatically extracts medication events consisting of medication names and information pertaining to their prescribed use (dosage, mode, frequency, duration and reason) from lists or narrative text in medical discharge summaries.

Design

Lancet incorporates three supervised machine-learning models: a conditional random fields model for tagging individual medication names and associated fields, an AdaBoost model with decision stump algorithm for determining which medication names and fields belong to a single medication event, and a support vector machines disambiguation model for identifying the context style (narrative or list).

Measurements

The authors, from the University of Wisconsin-Milwaukee, participated in the third i2b2 shared-task for challenges in natural language processing for clinical data: medication extraction challenge. With the performance metrics provided by the i2b2 challenge, the micro F1 (precision/recall) scores are reported for both the horizontal and vertical level.

Results

Among the top 10 teams, Lancet achieved the highest precision at 90.4% with an overall F1 score of 76.4% (horizontal system level with exact match), a gain of 11.2% and 12%, respectively, compared with the rule-based baseline system jMerki. By combining the two systems, the hybrid system further increased the F1 score by 3.4% from 76.4% to 79.0%.

Conclusions

Supervised machine-learning systems with minimal external knowledge resources can achieve a high precision with a competitive overall F1 score.Lancet based on this learning framework does not rely on expensive manually curated rules. The system is available online at http://code.google.com/p/lancet/.Pharmacotherapy is an important part of a patient''s medical treatment, and nearly all patient records incorporate a significant amount of medication information. The administration of medication at a specific time point during the patient''s medical diagnosis, treatment, or prevention of disease is referred to as a medication event,^1–3 and the written representation of these events typically comprises the name of the medication and any of its associated fields, including but not limited to dosage, mode, frequency, etc.⁴ Accurately capturing medication events from patient records is an important step toward large-scale data mining and knowledge discovery,⁵ medication surveillance and clinical decision support⁶ and medication reconciliation.^7–10In addition to its importance, medication event information (eg, treatment outcomes, medication reactions and allergy information) is often difficult to extract, as clinical records exhibit a range of different styles and grammatical structures for recording such information.⁴ Therefore, Informatics for Integrating Biology & the Bedside (i2b2) recognized automatic medication event extraction with natural language processing (NLP) approaches as one of the great challenges in medical informatics. As one of 20 groups that participated in the i2b2 medication extraction challenge, we report in this study on Lancet, which we developed for medication event extraction.     

Vaccine adverse event text mining system for extracting features from vaccine safety reports

Objective

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports.

Design

Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool.

Measurements

The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches.

Results

VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively.

Conclusion

Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.     

A classification approach to coreference in discharge summaries: 2011 i2b2 challenge

Objective

To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test.

Design

An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic extractors and world knowledge, and Pronoun system based on a multi-class support vector machine classifier. The three Person attributes are patient, relative and hospital personnel. Contextual semantic extractors include anatomy, position, medication, indicator, temporal, spatial, section, modifier, equipment, operation, and assertion. The world knowledge is extracted from external resources such as Wikipedia.

Measurements

Micro-averaged precision, recall and F-measure in MUC, BCubed and CEAF were used to evaluate results.

Results

The system achieved an overall micro-averaged precision, recall and F-measure of 0.906, 0.925, and 0.915, respectively, on test data (from four hospitals) released by the challenge organizers. It achieved a precision, recall and F-measure of 0.905, 0.920 and 0.913, respectively, on test data without Pittsburgh data. We ranked the first out of 20 competing teams. Among the four sub-tasks on Person, Problem, Treatment, and Test, the highest F-measure was seen for Person coreference.

Conclusions

This system achieved encouraging results. The Person system can determine whether personal pronouns and proper names are coreferent or not. The Problem/Treatment/Test system benefits from both world knowledge in evaluating the similarity of two mentions and contextual semantic extractors in identifying semantic clues. The Pronoun system can automatically detect whether a Pronoun mention is coreferent to that of the other four types. This study demonstrates that it is feasible to accomplish the coreference task in discharge summaries.     

BoB,a best-of-breed automated text de-identification system for VHA clinical documents

Objective

De-identification allows faster and more collaborative clinical research while protecting patient confidentiality. Clinical narrative de-identification is a tedious process that can be alleviated by automated natural language processing methods. The goal of this research is the development of an automated text de-identification system for Veterans Health Administration (VHA) clinical documents.

Materials and methods

We devised a novel stepwise hybrid approach designed to improve the current strategies used for text de-identification. The proposed system is based on a previous study on the best de-identification methods for VHA documents. This best-of-breed automated clinical text de-identification system (aka BoB) tackles the problem as two separate tasks: (1) maximize patient confidentiality by redacting as much protected health information (PHI) as possible; and (2) leave de-identified documents in a usable state preserving as much clinical information as possible.

Results

We evaluated BoB with a manually annotated corpus of a variety of VHA clinical notes, as well as with the 2006 i2b2 de-identification challenge corpus. We present evaluations at the instance- and token-level, with detailed results for BoB''s main components. Moreover, an existing text de-identification system was also included in our evaluation.

Discussion

BoB''s design efficiently takes advantage of the methods implemented in its pipeline, resulting in high sensitivity values (especially for sensitive PHI categories) and a limited number of false positives.

Conclusions

Our system successfully addressed VHA clinical document de-identification, and its hybrid stepwise design demonstrates robustness and efficiency, prioritizing patient confidentiality while leaving most clinical information intact.     

Computerized clinical decision support for medication prescribing and utilization in pediatrics

Background and objective

Accurate and informed prescribing is essential to ensure the safe and effective use of medications in pediatric patients. Computerized clinical decision support (CCDS) functionalities have been embedded into computerized physician order entry systems with the aim of ensuring accurate and informed medication prescribing. Owing to a lack of comprehensive analysis of the existing literature, this review was undertaken to analyze the effect of CCDS implementation on medication prescribing and use in pediatrics.

Materials and methods

A literature search was performed using keywords in PubMed to identify research studies with outcomes related to the implementation of medication-related CCDS functionalities.

Results and discussion

Various CCDS functionalities have been implemented in pediatric patients leading to different results. Medication dosing calculators have decreased calculation errors. Alert-based CCDS functionalities, such as duplicate therapy and medication allergy checking, may generate excessive alerts. Medication interaction CCDS has been minimally studied in pediatrics. Medication dosing support has decreased adverse drug events, but has also been associated with high override rates. Use of medication order sets have improved guideline adherence. Guideline-based treatment recommendations generated by CCDS functionalities have had variable influence on appropriate medication use, with few studies available demonstrating improved patient outcomes due to CCDS use.

Conclusion

Although certain medication-related CCDS functionalities have shown benefit in medication prescribing for pediatric patients, others have resulted in high override rates and inconsistent or unknown impact on patient care. Further studies analyzing the effect of individual CCDS functionalities on safe and effective prescribing and medication use are required.     

A flexible framework for deriving assertions from electronic medical records

Objective

This paper describes natural-language-processing techniques for two tasks: identification of medical concepts in clinical text, and classification of assertions, which indicate the existence, absence, or uncertainty of a medical problem. Because so many resources are available for processing clinical texts, there is interest in developing a framework in which features derived from these resources can be optimally selected for the two tasks of interest.

Materials and methods

The authors used two machine-learning (ML) classifiers: support vector machines (SVMs) and conditional random fields (CRFs). Because SVMs and CRFs can operate on a large set of features extracted from both clinical texts and external resources, the authors address the following research question: Which features need to be selected for obtaining optimal results? To this end, the authors devise feature-selection techniques which greatly reduce the amount of manual experimentation and improve performance.

Results

The authors evaluated their approaches on the 2010 i2b2/VA challenge data. Concept extraction achieves 79.59 micro F-measure. Assertion classification achieves 93.94 micro F-measure.

Discussion

Approaching medical concept extraction and assertion classification through ML-based techniques has the advantage of easily adapting to new data sets and new medical informatics tasks. However, ML-based techniques perform best when optimal features are selected. By devising promising feature-selection techniques, the authors obtain results that outperform the current state of the art.

Conclusion

This paper presents two ML-based approaches for processing language in the clinical texts evaluated in the 2010 i2b2/VA challenge. By using novel feature-selection methods, the techniques presented in this paper are unique among the i2b2 participants.     

Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection

Objective

The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.

Design

We selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N_pos=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations.

Measurements

Classifiers'' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman''s test; misclassification error rate analysis was also performed.

Results

Rule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively).

Conclusion

Our validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.     

Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010

Objective

As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge.

Design

The three systems perform three key steps in clinical information extraction: (1) extraction of medical problems, tests, and treatments, from discharge summaries and progress notes; (2) classification of assertions made on the medical problems; (3) classification of relations between medical concepts. Machine learning systems performed these tasks using large-dimensional bags of features, as derived from both the text itself and from external sources: UMLS, cTAKES, and Medline.

Measurements

Performance was measured per subtask, using micro-averaged F-scores, as calculated by comparing system annotations with ground-truth annotations on a test set.

Results

The systems ranked high among all submitted systems in the competition, with the following F-scores: concept extraction 0.8523 (ranked first); assertion detection 0.9362 (ranked first); relationship detection 0.7313 (ranked second).

Conclusion

For all tasks, we found that the introduction of a wide range of features was crucial to success. Importantly, our choice of machine learning algorithms allowed us to be versatile in our feature design, and to introduce a large number of features without overfitting and without encountering computing-resource bottlenecks.     

An Electronic Health Record Based on Structured Narrative

Objective

To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse.

Design

We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry.

Validation

The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules.

Discussion

The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale.

Conclusion

Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.