RESUMO
Pancreatic cancer is one of the deadliest cancers, mostly diagnosed at late stages. Patients with pancreatic cysts are at higher risk of developing cancer and their surveillance can help to diagnose the disease in earlier stages. In this retrospective study we collected a corpus of 1064 records from 44 patients at Indiana University Hospital from 1990 to 2012. A Natural Language Processing (NLP) system was developed and used to identify patients with pancreatic cysts. NegEx algorithm was used initially to identify the negation status of concepts that resulted in precision and recall of 98.9% and 89% respectively. Stanford Dependency parser (SDP) was then used to improve the NegEx performance resulting in precision of 98.9% and recall of 95.7%. Features related to pancreatic cysts were also extracted from patient medical records using regex and NegEx algorithm with 98.5% precision and 97.43% recall. SDP improved the NegEx algorithm by increasing the recall to 98.12%.
Assuntos
Registros Eletrônicos de Saúde , Registros de Saúde Pessoal , Processamento de Linguagem Natural , Cisto Pancreático/classificação , Cisto Pancreático/diagnóstico , Vocabulário Controlado , Algoritmos , Inteligência Artificial , Mineração de Dados/métodos , Sistemas de Apoio a Decisões Clínicas , Humanos , Reconhecimento Automatizado de Padrão/métodos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
We sought to determine the accuracy of two electronic methods of identifying pancreatic cancer in a cohort of pancreatic cyst patients, and to examine the reasons for identification failure. We used the International Classification of Diseases, 9(th) Edition (ICD-9) codes and natural language processing (NLP) technology to identify pancreatic cancer in these patients. We compared both methods to a human-validated gold-standard surgical database. Both ICD-9 codes and NLP technology achieved high sensitivity for identifying pancreatic cancer, but the ICD-9 code method achieved markedly lower specificity and PPV compared to the NLP method. The NLP method required only slightly greater expenditures of time and effort compared to the ICD-9 code method. We identified several variables influencing the accuracy of ICD-9 codes to identify cancer patients including: the identification algorithm, kind of cancer to be identified, presence of other conditions similar to cancer, and presence of conditions that are precancerous.
