One of the developments in machine learning is the technology that has been used for disease prediction in many fields around the world, including the healthcare industry. Analysis has been attempted to classify the most influential heart disease causes and to reliably predict the overall risk ...
including diabetic retinopathy and glaucoma; second, ocular disease prognosis, specifically conversion of contralateral (‘fellow’) eyes to neovascular (‘wet’) AMD in a 1-year time period and, finally, oculomic challenges, specifically the 3-year prediction of cardiovascular diseases (ischaemic stro...
The PAMAM dendrimers can encapsulate or modify various functional elements to form stable nanoplatforms with the cationic surface, active diffusion, and good cell membrane affinity, resulting in facilitating tissue penetration and cellular internalization [35,36]. It is well known that the nanocarriers ...
which physicians can use to evaluate an individual’s overall insulin response and avoid the risk of hyperglycemic or hypoglycemic events. When evaluated using the internal test set, the AI model achieved an area under the curve of 0.830 for the prediction of WTR of ...
There are many AI models in this paper that we consider using due to their high performance on our data and due to their algorithm and structure. Since we are dealing with tabular data such as heart rate and step count, we decided to use two strong tree-based models since they outperform...
we will explore the possibility of AI aided renaissance in the field of medical science. Let's see use cases of deep learning in health care.
Flexible parametric model hip fracture prediction survival Thai Introduction Many studies have proposed that hip fracture increases short- and long-term all-cause mortality compared to nonhip fracture/community control populations (1,2). The highest mortality risks have been variously reported as occurrin...
For image segmentation tasks, the cross-entropy is one of the most popular loss functions. The function compares pixel-wisely the predicted category vector with the real segmentation result vector. For the case of binary segmentation, letand, then the prediction is given by the sigmoid function,...
(Supplementary Fig.3). TxGNN processes different therapeutic tasks, such as indication and contraindication prediction, in a unified manner using drug and disease representations from the unified latent space of the KG (Methods). Given a queried disease, TxGNN ranks drugs based on their predicted ...
Then, in the second round, the prediction results by the DL model along with the corresponding CTA scans were provided to the readers to evaluate whether the diagnostic performances were improved with the model’s assistance (Supplementary Methods). Statistical analysis Categorical data are presented ...