Developing clinical natural language systems based on machine learning and deep learning is dependent on the availability of large-scale annotated clinical text datasets, most of which are time-consum...
Computed tomography (CT) imaging is routinely obtained for diagnostics, especially in trauma and emergency rooms, often identifying incidental findings. We utilized a natural language processing (NLP)...
We utilized the electronic medical record to perform a retrospective chart review of adult patients admitted for trauma to a level 1 tertiary care center between 2010 and 2020 who underwent abdominal ...
The algorithm identified pancreatic lesions in 27 of 28 patients who underwent pancreatic surgery and excluded 1 patient who had a pure ampullary mass. The study cohort consisted of 18,769 patients wh...
In this study, we propose a novel use of NLP software to identify potentially malignant pancreatic lesions annotated in CT imaging performed for other purposes. This methodology can significantly incr...
Epilepsy surgery is an underutilised, efficacious management strategy for selected individuals with drug-resistant epilepsy. Natural language processing (NLP) may aid in the identification of patients...
In accordance with the PRISMA guidelines, a systematic review of the databases PubMed, EMBASE and Cochrane library was performed. This systematic review was prospectively registered on PROSPERO....
6 studies fulfilled inclusion criteria. The majority of included studies reported on datasets from only a single centre, with one study utilising data from two centres and one study six centres. The m...
NLP is a promising approach to aid in the identification of patients that may be suitable to undergo epilepsy surgery evaluation. Further studies are required examining diverse datasets with additiona...
Rates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to imp...
To examine whether using natural language processing (NLP) in outpatient chart notes can identify cases of CM not documented by ICD diagnosis code, the overlap between the coding of child maltreatment...
Outpatient chart notes of children age 0-18 years old within Kaiser Permanente Washington (KPWA) 2018-2020 were used to examine a selected set of maltreatment-related terms categorized into concept un...
The NLP results indicated a crude rate of 1.55 % to 2.36 % (2018-2020) of notes with reference to CM. The rate of CM identified by ICD code was 3.32 per 1000 children, whereas the rate identified by N...
Use of NLP substantially increased the estimated number of children who have been impacted by CM. Accurately capturing this population will improve identification of vulnerable youth at high risk for ...
Text analyses of social media posts are a promising source of mental health information. This study used natural language processing to explore distinct language patterns on Twitter related to self-re...
A total of 233.000 tweets made by 605 users (300 reporting anxiety diagnosis and 305 not) over six months were comparatively analysed, considering user behavior, Linguistic Inquiry Word Count (LIWC), ...
Supervised machine learning models showed a high prediction accuracy (Naïve Bayes 81.1 %, Random Forests 79.8 %, and LASSO-regression 79.4 %) in identifying Twitter users' self-disclosed diagnosis of ...
The digital footprint of self-disclosed anxiety on Twitter posts presented a high frequency of words conveying either negative sentiment, a low frequency of positive sentiment, a reduced frequency of ...
Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clini...
Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal...
We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of co...
Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but d...
This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we acc...
Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a la...
Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also gen...
Natural language processing (NLP) tools are increasingly used to quantify semantic anomalies in schizophrenia. Automatic speech recognition (ASR) technology, if robust enough, could significantly spee...
