AIMC Topic: Female

Clear Filters Showing 23711 to 23720 of 29210 articles

Predicting high lymph node positivity risk factors in nasopharyngeal carcinoma patients: A multi-model approach.

Medicine
Identifying patients at high risk of an elevated lymph node ratio (LNR) is critical for optimizing the management of nasopharyngeal carcinoma (NPC), as LNR, defined as the ratio of metastatic to examined lymph nodes, serves as a key prognostic indica...

Personalized prediction of post-SMILE refractive outcomes using a machine-learning nomogram.

Medicine
This study aimed to construct a personalized, machine learning-driven nomogram capable of predicting refractive outcomes following small incision lenticule extraction (SMILE). A total of 1253 eyes from 632 patients who underwent SMILE to correct myop...

Precision identification of endometrial malignancy and precancerous lesions: Development of a machine learning model incorporating multidimensional clinical and imaging parameters.

Medicine
To develop and validate a machine learning (ML) model integrating multidimensional clinical, pathomic, and ultrasound radiomic parameters for precise identification of endometrial malignancy and precancerous lesions, with a focus on addressing the di...

[An objective overview of lifestyle and quality of life in the Spanish population: a quantification of nutri-indices and qualitative health nutritypes].

Nutricion hospitalaria
Introduction: precision nutritional epidemiology studies require the development of initiatives to obtain objective population data that enable the implementation of health strategies. Therefore, the integration of health determinants and risk factor...

T-cell Subset Features and Distributions Evolve across the Colorectal Precancer-Cancer Spectrum.

Cancer immunology research
The immune microenvironment is a crucial component of colorectal carcinoma that has been well characterized, but much less is known about the immune microenvironment of colorectal carcinoma precursors. We hypothesized that T-cell infiltrates might di...

Electronic health record-enhanced signal detection using tree-based scan statistic methods.

American journal of epidemiology
Tree-based scan statistics (TBSS) are data mining methods that screen thousands of hierarchically related health outcomes to detect unsuspected adverse drug effects. TBSS traditionally analyze claims data with outcomes defined via diagnosis codes. TB...

Diagnosing migraine from genome-wide genotype data: a machine learning analysis.

Brain : a journal of neurology
Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the heritability. We aimed to develop machine learning models, capturing non-additive and interactive ef...

Improving accuracy in the estimation of probable dementia in racially and ethnically diverse groups with penalized regression and transfer learning.

American journal of epidemiology
Algorithmic estimations of dementia status are widely used in public health and epidemiologic research, but inadequate algorithm performance across racial/ethnic groups has been a barrier. We present improvements in the accuracy of group-specific "pr...

Heterogeneous cardiovascular effects of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a causal forest and target trial emulation study.

European journal of preventive cardiology
AIMS: Evidence is limited as to who benefit the most from sodium-glucose cotransporter 2 inhibitors (SGLT2i), especially among people without elevated cardiovascular disease (CVD) risk. To address this knowledge gap, we investigated the heterogeneity...

Advanced prediction of heart failure risk in elderly diabetic and hypertensive patients using nine machine learning models and novel composite indices: insights from NHANES 2003-2016.

European journal of preventive cardiology
AIMS: As the global population ages, cardiovascular diseases, particularly heart failure (HF), have become leading causes of mortality and disability among elderly patients. Diabetes and hypertension are major risk factors for cardiovascular diseases...