Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports
By Marcelo Fiszman, Wendy W. Chapman, Dominik Aronsky, R. Evans, P. Haug. 2000
255 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Family Practice, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yuan Luo
- Ozlem Uzuner
- Peter Szolovits
- Tristan Naumann
- Zexian Zeng
- Dina Demner-Fushman
- Xiaoyu Li
- Yu Deng
In Family Practice
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “[75] Self-defined rules SymText Chest x-ray reports Set of augmented transition network grammars and a lexicon derived from the specialist Lexicon Garla et al.”
- Artificial intelligence in healthcare: past, present and future (2017, methodology): “For example, Fiszman et al showed that introducing NLP for reading the chest X-ray reports would assist the antibiotic assistant system to alert physicians for the possible need for anti-infective therapy.(43) Miller et al used NLP to automatically monitor the laboratory-based adverse effects.”
- The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text (2003, background): “SymText has been evaluated for clinical applications [6,30,31].”
- EpiDEA: Extracting Structured Epilepsy and Seizure Information from Patient Discharge Summaries for Cohort Identification (2012, methodology): “Similarly, the SymText NLP system has been used to detect acute bacterial pneumonia from chest x-rays [10].”
- Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy (2013, background): “Over the last 20 years, various combinations of approaches, including natural language processing [28] [29] [30] [31] , expert rules [32, 33] , Bayesian [32, 34] or neural networks [35] and machine-learning [33] , have held their own compared to physicians for their ability to find pneumonia-related”
- Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “They have been used to extract interpretations of lung scans [86], to detect pneumonia [87], and to detect mentions of central venous catheters [88].”
- Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “Developed systems can automatically code and classify diagnoses from Electronic Medical Records (EMRs) [38–42].”
- Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts (2013, background): “Examples of BM-NER systems include extracting clinical information from radiology reports [1–3], identifying diseases and drug names in discharge summaries [4–6], detecting gene and protein mentions in biomedical paper abstracts [7–9].”