Publications by authors named "S Havaldar"
Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs for new diseases. The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist. Here we introduce TxGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs.
View Article and Find Full Text PDFAims: To evaluate the ability of logistic regression and machine learning methods to predict active arterial extravasation on computed tomographic angiography (CTA) in patients with acute gastrointestinal hemorrhage using clinical variables obtained prior to image acquisition.
Materials And Methods: CT angiograms performed for the indication of gastrointestinal bleeding at a single institution were labeled retrospectively for the presence of arterial extravasation. Positive and negative cases were matched for age, gender, time period, and site using Propensity Score Matching.
Article Synopsis
- Drug repurposing means using old, already approved medicines for new diseases.
- The TXGNN model can help find new uses for drugs, even for diseases that have no treatments yet, and it's way better at predicting drug uses than other methods.
- TXGNN not only predicts where drugs can be used but also explains its predictions clearly, making it easier for doctors to understand and investigate its suggestions.
Article Synopsis
- Target trial emulation involves using real-world data to replicate randomized trials, with a focus on overcoming confounding issues to accurately assess treatment effects.
- The study examined thousands of medications over more than 10 years, utilizing data from 170 million patients to discover new uses for drugs in treating Alzheimer's disease.
- Various propensity score models were evaluated, revealing that deep learning models did not always perform better than traditional logistic regression, and five medications were identified as having promising effects for Alzheimer's patients.
Article Synopsis
- Interatrial block (IAB) is linked to higher risks of stroke, mortality, and heart failure, particularly in patients without any history of atrial fibrillation (AF) or atrial flutter (AFL).
- A large study analyzed nearly 5 million ECGs from over 1 million patients to explore the association between IAB and adverse outcomes.
- The findings indicate that IAB significantly increases the risk of stroke and other health issues, regardless of the presence of AF/AFL, emphasizing the need for monitoring even in patients without previous arrhythmias.