A UMLS-based spell checker for natural language processing in vaccine safety

BACKGROUND: The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI) from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS) concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP) pipeline for AEFI reports. METHODS: We developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1) error detection, (2) word list generation, (3) word list disambiguation and (4) error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction. RESULTS: We used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV) for the spell checker were 74% (95% CI: 74–75), 100% (95% CI: 100–100), and 47% (95% CI: 46%–48%), respectively. CONCLUSION: We created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available tools, but the specificity was much superior. The slow processing speed may be improved by trimming it down to the most useful component algorithms. Other investigators may find the methods we developed useful for cleaning text using lexicons specific to their area of interest

Subacute Sclerosing Panencephalitis: Results of the Canadian Paediatric Surveillance Program and review of the literature

BACKGROUND: Subacute Sclerosing Panencephalitis (SSPE) is so rare in developed countries with measles immunization programs that national active surveillance is now needed to capture sufficient number of cases for meaningful analysis of data. Through the Canadian Paediatric Surveillance Program (CPSP), the SSPE study was able to document a national incidence and determine the epidemiology of affected Canadian children. METHODS: Between 1997 and 2000, the CPSP surveyed monthly 1978 to 2294 Canadian pediatricians and sub-specialists for SSPE cases. The response rate varied from 82–86% over those years. RESULTS: Altogether, four SSPE cases were reported to the CPSP: one case before, two during and one after the study period. The incidence of SSPE in Canadian children was 0.06/million children/year. Of the four cases, diagnosed between ages four and 17 years, three children had measles infection in infancy. All children showed a progressive course of dementia, loss of motor skills and epilepsy. Two children were treated with isoprinosine and intraventricular interferon but died in less than three years from disease onset. One child did not have any treatment and died after seven years of illness. One child received intraventricular ribavirin and remains alive, but markedly impaired, nine years following diagnosis. CONCLUSION: The CPSP has demonstrated that Canadian paediatricians and paediatric neurologists may encounter cases of SSPE. This report highlights the clinical course of affected Canadian children and provides a review of the disease and its management

Persistent crying in infants and children as an adverse event following immunization: case definition and guidelines for data collection, analysis, and presentation

Jan Bonhoeffer, Patricia Vermeer, Scott Halperin, Anne Kempe, Stanley Music, Judy Shindman, Wikke Walop, The Brighton Collaboration Persistent Crying Working Grou

Concept Negation in Free Text Components of Vaccine Safety Reports

Large amounts of information are locked up in free text components of clinical reports. Surveillance systems that monitor adverse events following immunizations (AEFI) can utilize these components after concept extraction using natural language processing (NLP). Specifically, our method for the identification and filtering of negated concepts using the Unified Medical Language System (UMLS) potentially improves the quality of AEFI surveillance systems

BMC Medical Informatics and Decision Making 2007, 7:3 doi:10.1186/1472-6947-7-3

Research article A UMLS-based spell checker for natural language processing in vaccine safet