ascopubs.org

Hybrid ReGex and Natural Language Inference Model as a Zero-Shot Classifier for Extracting Data From Medical Reports

Machine Learning Models of Early Longitudinal Toxicity Trajectories Predict Cetuximab Concentration and Metastatic Colorectal Cancer Survival in the Canadian Cancer Trials Group/AGITG CO.17/20 Trials ...
The Hill

Anthropic offering AI model to all three government branches for $1

Anthropic announced Tuesday it will offer its artificial intelligence (AI) model Claude to all three branches of the federal government for $1, following a similar move by OpenAI last week. Federal ...
Frontiers

Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence

Background: Childhood obesity is a growing problem worldwide, leading to non-alcoholic fatty liver disease (NAFLD), which is the most common liver disease in children. Liver biopsy is the gold ...
GitHub

TreeSHAP-compatible AdaBoost and RUSBoost classifiers for explainable AI on imbalanced datasets.

This package provides custom implementations of AdaBoost and RUSBoost classifiers that are compatible with SHAP's TreeExplainer, enabling fast local explanations for boosting algorithms on imbalanced ...
Frontiers

Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus

1 Department of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, China 2 Panyu Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China Objective: ...
IEEE

The Study of Face Detection Algorithm Based on Improved AdaBoost with Skin Color Model

Abstract: This paper proposes a face detection algorithm combined skin color detection and improved AdaBoost algorithm. First, skin regions are segmented from the detected image, and candidate face ...
Scientific Research Publishing

Sen, P.C., Hajra, M. and Ghosh, M. (2019) Supervised Classification Algorithms in Machine Learning: A Survey and Review. In: Mandal, J.K. and Bhattacharya, D., Eds., Emerging ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...