This study sought to explore the use of novel natural language processing (NLP) methods for classifying unstructured, qualitative textual data from interviews of patients with cancer to identify patie...
We tested the ability of 4 NLP models to accurately classify text from interview transcripts as "symptom," "quality of life impact," and "other." Interview data sets from patients with hepatocellular ...
NLP models accurately classified multiclass text from patient interviews. The Bidirectional Encoder Representations from Transformers model generally outperformed all other models at paragraph and sen...
NLP models were accurate in predicting multiclass classification of text from interviews of patients with cancer, with most surpassing 0.9 ROC AUC at paragraph level. NLP may be a useful tool for scal...
The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis...
We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use....
We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to J...
In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance ...
This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential o...
The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which ...
A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 ...
More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, resp...
Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machi...
Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively...
Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recent...
We applied our previously validated NLP algorithms to extract seizure freedom, seizure frequency, and date of most recent seizure from outpatient visits at our epilepsy center from 2010 to 2022. We ex...
Performance of our algorithms on classifying seizure freedom was comparable to that of human reviewers (algorithm F...
Our findings demonstrate that epilepsy outcome measures can be extracted accurately from unstructured clinical note text using NLP. At our tertiary center, the disease course often followed a remittin...
International Classification of Diseases-coded hospital discharge data do not accurately reflect whether firearm injuries were caused by assault, unintentional injury, self-harm, legal intervention, o...
To assess the accuracy with which an ML model identified firearm injury intent....
A cross-sectional retrospective EHR review was conducted at 3 level I trauma centers, 2 from health care institutions in Boston, Massachusetts, and 1 from Seattle, Washington, between January 1, 2000,...
Classification of firearm injury intent....
Intent classification accuracy by the NLP model was compared with ICD codes assigned by medical record coders in discharge data. The NLP model extracted intent-relevant features from narrative text th...
The NLP model was evaluated in 381 patients presenting with firearm injury at the model development site (mean [SD] age, 39.2 [13.0] years; 348 [91.3%] men) and 304 patients at the external developmen...
The findings of this study suggest that NLP ML can be used to improve the accuracy of firearm injury intent classification compared with ICD-coded discharge data, particularly for cases of accident an...
Information pertaining to mechanisms, management and treatment of disease-causing pathogens including viruses and bacteria is readily available from research publications indexed in MEDLINE. However, ...
In this work, we lay the foundations for the development of automatic methods for characterising mentions of pathogens in scientific literature, focusing on the task of identifying research that invol...
We developed a pathogen mention characterisation literature data set -READBiomed-Pathogens- automatically using NCBI resources, which we make available. Resources such as the NCBI Taxonomy, MeSH and G...
We show that our data set READBiomed-Pathogens can be used to explore natural language processing configurations for experimental pathogen mention characterisation. READBiomed-Pathogens includes citat...
We studied the characterisation of experimentally studied pathogens in scientific literature, developing several natural language processing methods supported by an automatically developed data set. A...
N/A....
Sound recognition is effortless for humans but poses a significant challenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently...
Alcohol cravings are considered a major factor in relapse among individuals with alcohol use disorder (AUD). This study aims to investigate the frequency and triggers of cravings in the daily lives of...
For the analysis, posts from the online forum "stopdrinking" on the Reddit platform were used as the dataset from April 2017 to April 2022. The posts were filtered for craving content and processed us...
Approximately 16% of the forum posts discuss cravings. The number of craving-related posts decreases exponentially with the number of days since the author's last alcoholic drink. The topic model conf...
This exploratory approach is the first to analyze alcohol cravings in the daily lives of over 24,000 individuals, providing a foundation for further AI-based craving analyses. The analysis confirms co...
Mpox has been declared a Public Health Emergency of International Concern by the World Health Organization on July 23, 2022. Since early May 2022, Mpox has been continuously reported in several endemi...
This was a detailed qualitative study using natural language processing on the user-generated comments from social media....
A detailed analysis using topic modeling and sentiment analysis on Reddit comments (n = 289,073) that were posted between June 1 and August 5, 2022, was conducted. While the topic modeling was used to...
The results revealed several interesting and useful themes, such as Mpox symptoms, Mpox transmission, international travel, government interventions, and homophobia from the user-generated contents. T...
Analyzing public discourse and sentiments toward health emergencies and disease outbreaks is highly important. The insights that could be leveraged from the user-generated comments from public forums ...
Artificial intelligence (AI) can interpret abnormal signs in chest radiography (CXR) and generate captions, but a prospective study is needed to examine its practical value....
To prospectively compare natural language processing (NLP)-generated CXR captions and the diagnostic findings of radiologists....
A multicenter diagnostic study was conducted. The training data set included CXR images and reports retrospectively collected from February 1, 2014, to February 28, 2018. The retrospective test data s...
A bidirectional encoder representation from a transformers model was used to extract language entities and relationships from unstructured CXR reports to establish 23 labels of abnormal signs to train...
Time to write reports based on different caption generation models....
The training data set consisted of 74 082 cases (39 254 [53.0%] women; mean [SD] age, 50.0 [17.1] years). In the retrospective (n = 8126; 4345 [53.5%] women; mean [SD] age, 47.9 [15.9] years) and pros...
In this diagnostic study of NLP-generated CXR captions, prior information provided by NLP was associated with greater efficiency in the reporting process, while maintaining good consistency with the f...
