TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjectiv...
Natural language processing (NLP) algorithms can interpret unstructured text for commonly used terms and phrases. Pancreatic pathologies are diverse and include benign and malignant entities with asso...
Text-based pancreatic anatomic and cytopathologic reports for pancreatic cancer, pancreatic ductal adenocarcinoma, neuroendocrine tumor, intraductal papillary neoplasm, tumor dysplasia, and suspicious...
Over 14,000 reports were obtained from the Mass General Brigham Healthcare System electronic record. Of these, 1252 reports were used for algorithm development. Final accuracy and F1 scores relative t...
Natural language processing algorithms can be used for pancreatic pathologies. Increased training volume, nonoverlapping terminology, and conserved text structure improve NLP algorithm performance....
Chronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough i...
This was a retrospective observational study of enrollees in Optum's Integrated Clinical + Claims Database. Participants were 18-85 years of age with medical and pharmacy health insurance coverage bet...
The positive predictive value and sensitivity of the NLP algorithm were 0.96 and 0.68, respectively, for positive cough contexts, and 0.96 and 0.84, respectively, for negated cough contexts. Among the...
Our EHR-based algorithm integrating NLP methodology with structured fields was able to identify a CC population. Machine learning based approaches can therefore aid in patient selection for future CC ...
Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in ...
Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identify it clinically. Natural language processing (NLP) can potent...
Patients were included in the analysis if they enrolled in the Veterans Aging Cohort Study between 2001 and 2017, had an imaging report inclusive of the liver, and had ≥2 years of observation before t...
NLP achieved 100% sensitivity and 88.5% positive predictive value for the identification of SLD. When applied to 26,706 eligible Veterans Aging Cohort Study patient imaging reports, SLD was identified...
While limited to those undergoing radiologic study, the NLP algorithm accurately identified SLD in people with and without HIV and offers a valuable tool to evaluate the determinants and consequences ...
Standardised medical terminologies are used to ensure accurate and consistent communication of information and to facilitate data exchange. Currently, many terminologies are only available in English,...
Software engineering artifact extraction from natural language requirements without human intervention is a challenging task. Out of these artifacts, the use case plays a prominent role in software de...
Since 2005, female firearm suicide rates increased by 34%, outpacing the rise in male firearm suicide rates over the same period. The objective of this study was to develop and evaluate a natural lang...
Unstructured information from coroner/medical examiner and law enforcement narratives were manually coded for 1,462 randomly selected cases from the National Violent Death Reporting System. Decedents ...
The natural language processing pipeline performed well in identifying recent interpersonal disputes, problems with intimate partners, acute/chronic pain, and intimate partners and immediate family at...
This study developed a natural language processing pipeline to classify 5 female firearm suicide antecedents using narrative reports from the National Violent Death Reporting System, which may improve...
Natural language processing (NLP) is a discipline of machine learning concerned with the analysis of language and text. Although NLP has been applied to various forms of clinical text, the application...
We performed a literature search using the PubMed, Scopus, and Embase databases. Data extraction was performed after appropriate screening. The risk of bias and reporting quality were assessed using t...
A total of 12 full-text articles were included. The most common diseases represented include spondylolisthesis (25%), scoliosis (17%), and lumbar disk herniation (17%). The most common procedures incl...
Although the application of NLP to spine surgery is expanding, current studies face limitations and none are indicated as ready for clinical use. Thus, for future studies we recommend an emphasis on t...
Data extraction from radiology free-text reports is time consuming when performed manually. Recently, more automated extraction methods using natural language processing (NLP) are proposed. A previous...
Free-text reports of CT angiography (CTA) and perfusion (CTP) studies of consecutive patients with acute ischemic stroke admitted to a regional stroke center for endovascular thrombectomy were analyze...
The NLP algorithm's accuracy was > 90% for identifying distal anterior occlusion, posterior circulation occlusion, hemorrhage, and ASPECTS. Accuracy was 85%, 74%, and 79% for proximal anterior circula...
Rule-based NLP has a moderate to good performance for stroke-related data extraction from free-text imaging reports. The algorithm's accuracy was affected by inconsistent report styles and lexicon amo...
