AIMC Topic: Humans

Clear Filters Showing 16051 to 16060 of 95995 articles

SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions.

Genome biology
The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for ...

Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in patients with unresectable synchronous liver-only metastatic colorectal cancer (mCRC) treated with bev...

Exploring machine learning algorithms to predict not using modern family planning methods among reproductive age women in East Africa.

BMC health services research
BACKGROUND: The use of the modern family planning method provides chances for women to reach optimal child spacing, increase quality of life, increase economic status, achieve the desired family size, and prevent unsafe abortions and maternal and per...

A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.

BMC medical informatics and decision making
Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing early warning methods struggle to extract crucial information comprehensively from complex multi-source ...

Application of deep learning in wound size measurement using fingernail as the reference.

BMC medical informatics and decision making
OBJECTIVE: Most current wound size measurement devices or applications require manual wound tracing and reference markers. Chronic wound care usually relies on patients or caregivers who might have difficulties using these devices. Considering a more...

Demographic factors, knowledge, attitude and perception and their association with nursing students' intention to use artificial intelligence (AI): a multicentre survey across 10 Arab countries.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is becoming increasingly important in healthcare, with a significant impact on nursing practice. As future healthcare practitioners, nursing students must be prepared to incorporate AI technologies into their ...

Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

BMC medical informatics and decision making
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

BMC medical informatics and decision making
BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this s...

Cost-effectiveness analysis of AI-based image quality control for perinatal ultrasound screening.

BMC medical education
PURPOSE: This study aimed to compare the cost-effectiveness of AI-based approaches with manual approaches in ultrasound image quality control (QC).

Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.

BMC public health
BACKGROUND: Anemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers promising avenues for improved anemia detection and management. This study investigates the potential ...