AIMC Topic: Probability

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An improved multiply robust estimator for the average treatment effect.

BMC medical research methodology
BACKGROUND: In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). How...

Drug Intelligence Science (DIS®): Pioneering a high-resolution translational platform to enhance the probability of success for drug discovery and development.

Drug discovery today
Translational research has a crucial role in bridging the gap between basic biology discoveries and their clinical applications. Deep scientific understanding and advanced technology platforms are both crucial for translational research. Here, I desc...

Insight Extraction From E-Health Bookings by Means of Hypergraph and Machine Learning.

IEEE journal of biomedical and health informatics
New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional gover...

Human-machine cooperation meta-model for clinical diagnosis by adaptation to human expert's diagnostic characteristics.

Scientific reports
Artificial intelligence (AI) using deep learning approaches the capabilities of human experts in medical image diagnosis. However, due to liability issues in medical decisions, AI is often relegated to an assistant role. Based on this responsibility ...

Mean-field neural networks: Learning mappings on Wasserstein space.

Neural networks : the official journal of the International Neural Network Society
We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e.g. in mean-field games/control problems. Two classes of neural networks based on bin density a...

Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks have become increasingly significant in our daily lives due to their remarkable performance. The issue of adversarial examples, which are responsible for the vulnerability problem of deep neural networks, has attracted the attent...

Oral Cancer Prediction Using a Probability Neural Network (PNN).

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: In India, usually, oral cancer is mostly identified at a progressive stage of malignancy. Hence, we are motivated to identify oral cancer in its early stages, which helps to increase the lifetime of the patient, but this early detection is...

Metaparametric Neural Networks for Survival Analysis.

IEEE transactions on neural networks and learning systems
Survival analysis is a critical tool for the modeling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex machinery. However, current neural network models provide an imperfect solutio...

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.

ACS synthetic biology
Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete inform...

External validation of machine learning algorithm predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients using a Taiwanese cohort.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND/PURPOSE: Identifying patients at risk of prolonged opioid use after surgery prompts appropriate prescription and personalized treatment plans. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was developed to pred...