Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electro...
Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarmin...
This study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platf...
First, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed ...
We divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each valid...
The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, aut...
Patients with post-COVID-19 syndrome benefit from health promotion programs. Their rapid identification is important for the cost-effective use of these programs. Traditional identification techniques...
Fear of falling (FoF) is a major concern among older adults and is associated with negative outcomes, such as decreased quality of life and increased risk of falls. Despite several systematic reviews ...
Patients increasingly use physician rating websites to evaluate and choose potential healthcare providers. A sentiment analysis and machine learning approach can uniquely analyse written prose to quan...
Online written reviews and star scores were analysed from Healthgrades.com using a natural language processing sentiment analysis package. Demographics of otolaryngologists were compared and a multiva...
This study analysed 18 546 online reviews of 1240 otolaryngologists across the USA. Younger otolaryngologists (aged less than 40 years) had higher sentiment and star scores compared with older otolary...
Positive bedside manner was strongly reflected in better reviews, and younger age and male gender of the otolaryngologist were associated with better sentiment and star scores....
The 21st Century Cures Act mandates the immediate, electronic release of health information to patients. However, in the case of adolescents, special consideration is required to ensure that confident...
This study aimed to determine if a natural language processing (NLP) algorithm can identify confidential content in adolescent clinical progress notes....
A total of 1,200 outpatient adolescent progress notes written between 2016 and 2019 were manually annotated to identify confidential content. Labeled sentences from this corpus were featurized and use...
The prevalence of notes containing confidential content was 21% (255/1,200) and 22% (53/240) in the train/test and validation cohorts, respectively. The ensemble logistic regression model achieved an ...
An NLP algorithm can identify confidential content in progress notes with high accuracy. Its human-in-the-loop deployment in clinical operations augmented an ongoing operational effort to identify con...
Case reports that externalize expert diagnostic reasoning are utilized for clinical reasoning instruction but are difficult to search based on symptoms, final diagnosis, or differential diagnosis cons...
To develop a "reasoning-encoded" case database for advanced clinical reasoning instruction by applying natural language processing (NLP), a sub-field of artificial intelligence, to a large case report...
We collected 2525 cases from the New England Journal of Medicine (NEJM) Clinical Pathological Conference (CPC) from 1965 to 2020 and used NLP to analyze the medical terminology in each case to derive ...
Our NLP algorithms identified clinically relevant categories that reflected the relationships between medical terms (which included symptoms, signs, test results, pathophysiology, and diagnoses). NLP ...
Applying NLP to curated instances of diagnostic reasoning can provide insight into how expert clinicians correlate and coordinate disease categories and processes when creating a differential diagnosi...
Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a m...
This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic he...
A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1...
The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data c...
The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows....
PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386....
Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (...
Artificial intelligence (AI) has been making a significant impact on cardiovascular imaging, transforming everything from data capture to report generation. In the field of echocardiography, AI offers...
