Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Thoug...
We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multip...
We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term "dry january" or "dryjanuary" posted from December 15 to February 15 across three separate...
We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content ov...
The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemp...
Efficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published ...
We fine-tuned the 'SAPBERT', which was pretrained on biomedical ontologies, to perform question/answering (QA) and name entity recognition (NER) tasks for medical corpora. The model was fine-tuned wit...
For the QA task, the model showed an F1 score of 0.79. For the NER task, the model showed F1 scores of 0.90 and 0.76 for drug and gene name recognition, respectively. When asked what the molecular tar...
Using NLP deep learning models, we could explore potential targets and treatments based on medical research and clinical trial corpora. The knowledge found by the models may be used for treating recur...
Natural language processing (NLP) may be a tool for automating trauma teamwork assessment in simulated scenarios....
Using the Trauma Nontechnical Skills Assessment (T-NOTECHS), raters assessed video recordings of trauma teams in simulated pre-debrief (Sim1) and post-debrief (Sim2) trauma resuscitations. We develope...
Automatically coded behaviors revealed significant post-debrief increases in teams' simulation discourse: Verbalizing Findings, Acknowledging Communication, Directed Communication, Directing Assessmen...
Our results suggest NLP can capture changes in trauma team discourse. These findings have implications for the expedition of team assessment and innovations in real-time feedback when paired with spee...
Suicide is a major public-health problem that exists in virtually every part of the world. Hundreds of thousands of people commit suicide every year. The early detection of suicidal ideation is critic...
The current education evaluation is limited not only to the mode of simplification, indexing, and datafication, but also to the scientific nature of college teaching evaluation. This work firstly cond...
Medical texts present significant domain-specific challenges, and manually curating these texts is a time-consuming and labor-intensive process. To address this, natural language processing (NLP) algo...
This study aims to describe the development and preliminary evaluation of Ascle. Ascle is tailored for biomedical researchers and clinical staff with an easy-to-use, all-in-one solution that requires ...
We fine-tuned 32 domain-specific language models and evaluated them thoroughly on 27 established benchmarks. In addition, for the question-answering task, we developed a retrieval-augmented generation...
The fine-tuned models and RAG framework consistently enhanced text generation tasks. For example, the fine-tuned models improved the machine translation task by 20.27 in terms of BLEU score. In the qu...
This study introduces the development and evaluation of Ascle, a user-friendly NLP toolkit designed for medical text generation. All code is publicly available through the Ascle GitHub repository. All...
Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, succ...
In this work, we survey relevant literature to identify and classify approaches which exploit NLP in the clinical context. Our contribution involves a systematic mapping of related research onto a pro...
While a patient's hospital journey produces a significant amount of structured and unstructured documents, certain steps and documents receive more research attention than others. Diagnosis, Admission...
Our findings suggest that there is a significant opportunity to leverage NLP approaches to advance clinical decision-making systems, as there remains a considerable understudied potential for the anal...
This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery....
Natural language processing models were derived and validated to predict discharge within the next 48 hours and 7 days and readmission within 30 days (based on daily ward round notes and discharge sum...
For discharge prediction analyses, 14,690 admissions were included. For readmission prediction analyses, 12,457 patients were included. For prediction of discharge within 48 hours, derivation and vali...
Modern natural language processing models, particularly Bidirectional Encoder Representations from Transformers, can effectively and accurately identify general surgery patients who will be discharged...
Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the ability to predict who will die by suicide is still no better than 50%. Traditional screening instr...
The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, AD...
