Ad hoc classification of radiology reports.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “But these methods are still lacking in computational phenotyping [192, 193].”
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “NLP systems such as the LSP [11] or MedLEE [12] include negation analysis in their processing, but research focused explicitly on negation detection started only a few years ago with NegExpander [51], a program detecting negation terms and then expanding (NegExpanding) the related concepts.”
Identification of Patients with Congestive Heart Failure using a Binary Classifier: A Case Study. (2003, methodology): “As one of the future steps, we are planning to implement a negation detector such as the NegExpander used by Aronow et al.[ 7 ].”
ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports (2009, background): “…of NegEx as well as several other algorithms for negation detection have been integrated in NLP applications that process a variety of different clinical report types (e.g., mammography reports [27], pathology reports [23], clinical practice guidelines [28], and discharge summaries [1]).”