Natural Language Processing for Classification of Acute, Communicable Findings on Unstructured Head CT Reports: Comparison of Neural Network and Non-Neural Machine Learning Techniques

Fh, Chokshi; Shin, Bonggun; T, Lee; Lemmon, A; Necessary, S; Jd, Choi

doi:10.1101/173310

Cited by 4 publications

(3 citation statements)

References 19 publications

(22 reference statements)

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“…In the included articles, named entity recognition (NER) primarily focuses on identifying entities such as protected health information (PHI) to deidentify clinical documents [206,207], as well as various clinical concepts. These concepts encompass diseases [20,25,40,41,45,47,49]; findings and symptoms [49,52,[116][117][118][119]121]; and medication names [49,52,[93][94][95]99,100,102,106,107,112,113,115], along with their associated details such as dose, frequency, and duration [52,[93][94][95]112,113,115] as well as potential adverse events [96][97][98]100,101,[106][107][108][109][110]114]. These medical concepts can be mapped to terminologies or ontol...…”

Section: Information Extractionmentioning

confidence: 99%

“…Deep learning approaches such as bidirectional long short-term memory-CRF (BiLSTM-CRF) [93,113,115] and recurrent neural network grammars [93] performed medical entity extraction in French clinical texts. Chokshi et al [119] compared a bag-of-words model with support vector machine (SVM) and 2 neural network models: a convolutional neural network (CNN) and a neural attention model, both with Word2Vec embedding as input. The accuracies of the CNN and neural attention model models were relatively equal, but they were higher than the accuracy of the SVM model.…”

Section: Information Extractionmentioning

confidence: 99%

“…(40); ML: 11(44); DL c : 4 (16) S a : 14 (74); ML b : 5(26) Medical concepts (n=37) S: 22 (56); ML: 12 (31); DL: 5 (13) S: 4 (67); ML: 2 (33) Specific characteristics (n=40)[49,52, S: 8 (57); ML: 1 (7); DL: 5 (36) S: 10 (77); ML: 3(23) Drugs and adverse events (n=26)[49,52,[116][117][118][119][120][121] S: 2 (25); ML: 2 (25); DL: 4 (50) S: 1 (50); ML: 1 (50) Findings and symptoms (n=8)…”

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confidence: 99%

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Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Background In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

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