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AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We investigate the ...

Machine learning assessment of dental age classification based on cone-beam CT images: a different approach.

Dento maxillo facial radiology
OBJECTIVES: Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual's dental age or defining an age classification. This study aims to use ...

Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.

Journal of the National Cancer Institute
Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition algorithms are proliferating. Some initial reports claim outstanding accuracy followed by disappointing lack of confirmation, including our own early work on...

Simultaneous Determination of Estradiol Cypionate and Medroxyprogesterone Acetate Hormones in Injectable Suspension by UV Spectrophotometry Based on Least-Squares Support Vector Machine and Fuzzy Inference System: Comparison with HPLC.

Journal of AOAC International
BACKGROUND: The combination of estradiol cypionate (ECA) and medroxyprogesterone acetate (MPA) is used to prevent pregnancy in women. The analysis of the ECA and MPA combination reveals a challenge due to the strong overlap of the spectra of these co...

Using deep learning to accurately detect sow vulva size in a group pen with a single camera.

Journal of animal science
This paper presents a non-contact method for the detection of changes in sow vulva size in a group pen. The traditional approach to estrus detection is manually pressing down on the back of the sow to elicit standing responses; however, this method c...

Stratum corneum nanotexture feature detection using deep learning and spatial analysis: a noninvasive tool for skin barrier assessment.

GigaScience
BACKGROUND: Corneocyte surface nanoscale topography (nanotexture) has recently emerged as a potential biomarker for inflammatory skin diseases, such as atopic dermatitis (AD). This assessment method involves quantifying circular nano-size objects (CN...

Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly prescribed to treat depression, anxiety, and other psychiatric disorders, is increasing. Previous in vit...

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined mach...

Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program.

AMIA ... Annual Symposium proceedings. AMIA Symposium
While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) models in a retrospective cohort study with a large sample size. We analyzed the association between depr...

Does Cohort Selection Affect Machine Learning from Clinical Data?

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study investigates cohort selection and its effects on the quality of machine learning (ML) models trained on clinical data, focusing on measurements taken within the first 48 hours of hospital admission. It discusses the potential repercussions...