AIMC Topic: Humans

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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...

Advocating for population health: The role of public health practitioners in the age of artificial intelligence.

Canadian journal of public health = Revue canadienne de sante publique
Over the past decade, artificial intelligence (AI) has begun to transform Canadian organizations, driven by the promise of improved efficiency, better decision-making, and enhanced client experience. While AI holds great opportunities, there are also...

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...

Sample self-selection using dual teacher networks for pathological image classification with noisy labels.

Computers in biology and medicine
Deep neural networks (DNNs) involve advanced image processing but depend on large quantities of high-quality labeled data. The presence of noisy data significantly degrades the DNN model performance. In the medical field, where model accuracy is cruc...

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...

Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis.

Analytical chemistry
Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes de...