AIMC Topic: Female

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Development and Internal Validation of a Multivariable Prediction Model for Mortality After Hip Fracture with Machine Learning Techniques.

Calcified tissue international
In order to estimate the likelihood of 1, 3, 6 and 12 month mortality in patients with hip fractures, we applied a variety of machine learning methods using readily available, preoperative data. We used prospectively collected data from a single univ...

Exploring the potential of machine learning in gynecological care: a review.

Archives of gynecology and obstetrics
Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecolog...

The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery.

Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study.

Computers in biology and medicine
Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-bi...

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling.

Computers in biology and medicine
Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunoth...

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.

Nature medicine
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we develope...

Mapping Blood Lead Levels in China during 1980-2040 with Machine Learning.

Environmental science & technology
Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county-specific blood lead levels in China from 1980 to 2040 using a machine learning model. Blood lead da...

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Genome medicine
Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interpre...

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

BMC medical imaging
BACKGROUND: The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced...

Machine learning for predicting Chagas disease infection in rural areas of Brazil.

PLoS neglected tropical diseases
INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening comp...