Identification of findings suspicious for breast cancer based on natural language processing of mammogram reports

By N. L. Jain, Carol Friedman. 1997

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “1 Diagnosis Categorization One of the most important applications of computational phenotyping is diagnosis categorization, which enables the automated and efficient identification of patient cohorts for secondary analysis [15, 27-31].”
EpiDEA: Extracting Structured Epilepsy and Seizure Information from Patient Discharge Summaries for Cohort Identification (2012, methodology): “The MedLEE has been extended to process mammography reports [12] and to additional domains including pathology[13].”
Ad hoc classification of radiology reports. (1999, background): “Jain et al.15,16 studied the experimental identification of suspected tuberculosis from chest radiograms and findings suspicious for breast cancer from mammograms.”
CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines (2017, background): “These systems can extract diverse types of clinical information and have been successfully applied to many information extraction tasks, such as detection of smoking status(6) and identification of respiratory findings(7) and suspicious breast cancer lesions.(8) In addition, many NLP systems have be”
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “Besides being progressively extended to most of the documents present in the EHR [24] and tested for its transferability in another institution [13], it has been used to detect f indings evocative of breast cancer [80], to analyze modif ications to data entry templates [81], and even combined with m”