Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports.
By Navendu Jain, C. Knirsch, Carol Friedman, George Hripcsak. 1996
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- Zexian Zeng
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- Stéphane M. Meystre
- Guergana K. Savova
- Xiaoyu Li
- Licong Cui
- Tristan Naumann
Citations in the corpus
- Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “Awide range of diseases has been investigated in the past, including suspected tuberculosis (TB) [32], [33], colorectal cancer [34], rheu-”
- Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, background): “[127] demonstrated a very impressive F-measure and finding cases of tuberculosis in radiographic reports.”
- Identification of findings suspicious for breast cancer based on natural language processing of mammogram reports (1997, background): “…and demonstrate that natural language processing is feasible; only one study addressed a specific clinical issue.24 Various evaluations conducted on MedLEE have shown that natural language processing is practical and feasible,10"11"16"17,27 and MedILEE has now been put into routine clinical use.”
- EpiDEA: Extracting Structured Epilepsy and Seizure Information from Patient Discharge Summaries for Cohort Identification (2012, methodology): “The Medical Language Extraction and Encoding System (MedLEE) was developed at the Columbia University to extract information from radiological reports for detecting tuberculosis in patients [11].”
- A Comparison of Classification Algorithms to Automatically Identify Chest X-Ray Reports That Support Pneumonia (2001, background): “Hripcsak and co-workers have tested the ability of their NLP system (MedLee) combined with medical logic modules to identify suspected tuberculosis patients [1, 14].”
- 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.”