The effect of a lung cancer care coordination program on timeliness of care.

BACKGROUND: Timeliness of care improves patient satisfaction and might improve outcomes. The CCCP was established in November 2007 to improve timeliness of care of NSCLC at the Veterans Affairs Connecticut Healthcare System (VACHS). PATIENTS AND METHODS: We performed a retrospective cohort analysis of patients diagnosed with NSCLC at VACHS between 2005 and 2010. We compared timeliness of care and stage at diagnosis before and after the implementation of the CCCP. RESULTS: Data from 352 patients were analyzed: 163 with initial abnormal imaging between January 1, 2005 and October 31, 2007, and 189 with imaging conducted between November 1, 2007 and December 31, 2010. Variables associated with a longer interval between the initial abnormal image and the initiation of therapy were: (1) earlier stage (mean of 130 days for stages I/II vs. 87 days for stages III/IV; P < .0001); (2) lack of cancer-related symptoms (145 vs. 60 days; P < .0001); (3) presence of more than 1 medical comorbidity (123 vs. 82; P = .0002); and (4) depression (126 vs. 98 days; P = .029). The percent of patients diagnosed at stages I/II increased from 32% to 48% (P = .006) after establishment of the CCCP. In a multivariate model adjusting for stage, histology, reason for imaging, and presence of primary care provider, implementation of the CCCP resulted in a mean reduction of 25 days between first abnormal image and the initiation of treatment (126 to 101 days; P = .015). CONCLUSION: A centralized, multidisciplinary, hospital-based CCCP can improve timeliness of NSCLC care, and help ensure that early stage lung cancers are diagnosed and treated.

Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification.

BACKGROUND: Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification. METHODS: We evaluated our system on biomedical WSD datasets and determined the contribution of our WSD system to clinical document classification on the 2007 Computational Medicine Challenge corpus. RESULTS: Our system compared favorably with other knowledge-based methods. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts. CONCLUSIONS: We developed a WSD system that achieves high disambiguation accuracy on standard biomedical WSD datasets and showed that our WSD system improves clinical document classification. DATA SHARING: We integrated our WSD system with MetaMap and the clinical Text Analysis and Knowledge Extraction System, two popular biomedical natural language processing systems. All codes required to reproduce our results and all tools developed as part of this study are released as open source, available under http://code.google.com/p/ytex.

Semantic similarity in the biomedical domain: an evaluation across knowledge sources.

BACKGROUND: Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods. Knowledge based approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and include path finding measures and intrinsic information content (IC) measures. Distributional measures utilize, in addition to a knowledge source, the distribution of concepts within a corpus to compute similarity; these include corpus IC and context vector methods. Prior evaluations of these measures in the biomedical domain showed that distributional measures outperform knowledge based path finding methods; but more recent studies suggested that intrinsic IC based measures exceed the accuracy of distributional approaches. Limitations of previous evaluations of similarity measures in the biomedical domain include their focus on the SNOMED CT ontology, and their reliance on small benchmarks not powered to detect significant differences between measure accuracy. There have been few evaluations of the relative performance of these measures on other biomedical knowledge sources such as the UMLS, and on larger, recently developed semantic similarity benchmarks. RESULTS: We evaluated knowledge based and corpus IC based semantic similarity measures derived from SNOMED CT, MeSH, and the UMLS on recently developed semantic similarity benchmarks. Semantic similarity measures based on the UMLS, which contains SNOMED CT and MeSH, significantly outperformed those based solely on SNOMED CT or MeSH across evaluations. Intrinsic IC based measures significantly outperformed path-based and distributional measures. We released all code required to reproduce our results and all tools developed as part of this study as open source, available under http://code.google.com/p/ytex. We provide a publicly-accessible web service to compute semantic similarity, available under http://informatics.med.yale.edu/ytex.web/. CONCLUSIONS: Knowledge based semantic similarity measures are more practical to compute than distributional measures, as they do not require an external corpus. Furthermore, knowledge based measures significantly and meaningfully outperformed distributional measures on large semantic similarity benchmarks, suggesting that they are a practical alternative to distributional measures. Future evaluations of semantic similarity measures should utilize benchmarks powered to detect significant differences in measure accuracy.

Ontology-guided feature engineering for clinical text classification.

In this study we present novel feature engineering techniques that leverage the biomedical domain knowledge encoded in the Unified Medical Language System (UMLS) to improve machine-learning based clinical text classification. Critical steps in clinical text classification include identification of features and passages relevant to the classification task, and representation of clinical text to enable discrimination between documents of different classes. We developed novel information-theoretic techniques that utilize the taxonomical structure of the Unified Medical Language System (UMLS) to improve feature ranking, and we developed a semantic similarity measure that projects clinical text into a feature space that improves classification. We evaluated these methods on the 2008 Integrating Informatics with Biology and the Bedside (I2B2) obesity challenge. The methods we developed improve upon the results of this challenge's top machine-learning based system, and may improve the performance of other machine-learning based clinical text classification systems. We have released all tools developed as part of this study as open source, available at http://code.google.com/p/ytex.