AIMC Topic: Natural Language Processing
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Predicting the Utility of Scientific Articles for Emerging Pandemics Using Their Titles and Natural Language Processing.
Disaster medicine and public health preparedness
May 10, 2024
OBJECTIVE: Not all scientific publications are equally useful to policy-makers tasked with mitigating the spread and impact of diseases, especially at the start of novel epidemics and pandemics. The urgent need for actionable, evidence-based informat...
Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness.
Journal of orthopaedic surgery and research
May 10, 2024
BACKGROUND: The Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries. This study focuses on total hip, knee, and shoulder arthroplasty procedures as they account for mo...
Harnessing Generative Artificial Intelligence for Medical Education.
Academic medicine : journal of the Association of American Medical Colleges
May 9, 2024
Generative artificial intelligence (AI) tools powered by large language models (LLMs) are poised to transform medical education. Generative AI's broad knowledge base and powerful natural language processing allow educators to use it for numerous task...
A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.
International journal of nursing studies
May 9, 2024
BACKGROUND: ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their ...
Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.
PloS one
May 9, 2024
OBJECTIVE: To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging.
Getting to Know Named Entity Recognition: Better Information Retrieval.
Medical reference services quarterly
May 9, 2024
Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant ...
What do you think caused your ALS? An analysis of the CDC national amyotrophic lateral sclerosis patient registry qualitative risk factor data using artificial intelligence and qualitative methodology.
Amyotrophic lateral sclerosis & frontotemporal degeneration
May 8, 2024
OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is an incurable, progressive neurodegenerative disease with a significant health burden and poorly understood etiology. This analysis assessed the narrative responses from 3,061 participants in the Cente...
Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies.
The American journal of Chinese medicine
May 8, 2024
Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the abse...
An artificial intelligence-based dental semantic search engine as a reliable tool for dental students and educators.
Journal of dental education
May 7, 2024
PURPOSE/OBJECTIVES: This study proposes the utilization of a Natural Language Processing tool to create a semantic search engine for dental education while addressing the increasing concerns of accuracy, bias, and hallucination in outputs generated b...
Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study.
Anaesthesia, critical care & pain medicine
May 6, 2024
BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the...
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