To our knowledge, this analysis is the largest EHR-based study for identifying drug repurposing candidates for ALS. We identified several drugs that warrant further assessment as therapeutic options ...

Explore how proteomics in biomarker discovery accelerates diagnostic assay development and improves clinical validation.

Abstract: Hypertension is a critical global health concern, necessitating accurate prediction models and effective prescription decisions to mitigate its risks. This study proposes a hybrid machine ...

heart-disease-logistic-regression/ ├── data/ # dataset (heart_disease_uci.csv) ├── src/ # source code │ ├── heart_disease_logistic_regression.py │ └── plot_results.py ├── figures/ # generated plots ...

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...

A burst of experimentation followed ChatGPT's release to the public in late 2022. Now many people are integrating the newest models and custom systems into what they do all day: their work. Chefs are ...

Powerful and practical machine learning tools for machine vision applications are already available to everyone, even if you’re not a data scientist. It might come as a bit of a surprise, but machine ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...