A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries

By Wendy W. Chapman, Will Bridewell, P. Hanbury, Gregory F. Cooper, Bruce G. Buchanan. Journal of Biomedical Informatics, 2001

1,213 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3142d6d2c60634b714e40207fe3da7b9cadecf1d

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

In Molecular Biology

Citations in the corpus

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “Regarding assertion classification, many authors [70, 75, 79, 81, 84] have used NegEx [71] to detect negation on clinical findings and diseases in order to filter spurious ADEs.”
NegAIT: A new parser for medical text simplification using morphological, sentential and double negation (2017, methodology): “Different tools exist that can detect some types of negation in text, such as NegEx [34], DEEPEN [35], MedLEE [36], ConText [37] and ontology-based-approaches [38].”
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].”
A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases (2015, methodology): “Current versions of MetaMap perform built-in negation extraction following Chapman's (2001) NegEx algorithm.”
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts (2011, methodology): “The negation module is similar to the approach of the NegEX method [46,47].”
Extracting information from the text of electronic medical records to improve case detection: a systematic review (2016, methodology): “Context modifiers and negation were assessed by several add-on algorithms, notably NegEx (5 studies), 46 and ConText (2 studies).”
Learning a Health Knowledge Graph from Electronic Medical Records (2017, methodology): “A modified version of NegEx was used to find negation scopes in the clinical text 27, 28 .”
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text (2011, neutral): “Assertion classification was run on reports annotated with the reference standard concepts. Its goal was to classify the assertions made on given medical concepts as being present, absent, or possible in the patient, conditionally present in the patient under certain circumstances, hypothetically pr”
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “More recently, a negation detection algorithm called NegEx was developed using regular expressions [52] and achieved 94.”
What can Natural Language Processing do for Clinical Decision Support? (2009, methodology): “negation identification [50–53].”
BMC Medical Informatics and Decision Making (2006, neutral): “Accurate identification of negation and uncertainty modifiers: HITEx has a negation finder module that uses the Chapman algorithm [18], the error rate of which is between 5% to 10%.”
An overview of MetaMap: historical perspective and recent advances (2010, methodology): “47 MetaMap now implements an extension of the complete NegEx algorithm.”
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (2017, background): “Although many text processing systems (such as [6]) can handle the negation/uncertainty detection problem, most of them exploit regular expressions on the text directly.”
Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections (2010, methodology): “To determine if concepts identified by the text strings were affirmed or negated, for example whether a patient was ‘‘coughing’’ or denied ‘‘coughing’’, we used the publicly available NegEx version 2 algorithm [55].”