This technical report presents an applied framework for assessing risk in central nervous system (CNS) clinical trials, emphasizing interpretable, structured decision-making using biological, clinical, and operational signals. The platform integrates curated trial metadata, biomarker information, and machine learning to flag potential trial risks before execution. A focused amyotrophic lateral sclerosis (ALS) case study illustrates how trial design decisions such as biomarker use, patient stratification, and endpoint selection are translated into explicit risk factors. The report includes baseline scoring, preliminary machine learning evaluation, decision scenarios, and responsible use considerations.
The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper…
Live Interactive Clinical Interface: https://ventrigelcds.streamlit.app/ Abstract: Phase II cardiovascular trials fail frequently because of patient heterogeneity and high capital costs, with only an estimated 25 percent of cardiovascular drugs successfully transitioning to Phase…
OTC_Advisor is an interactive R Shiny application designed to evaluate and visualize Outdoor Thermal Comfort (OTC) in urban outdoor spaces. The tool allows users to upload meteorological data, apply machine learning models for thermal comfort classification, visualize results on…
This archive contains the analysis code, the predictor dictionary, and the retrained primary model objects underlying the manuscript "Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: developmen…
This dataset contains the data used in the paper "Hybrid Machine Learning Framework for Microstructure-Based Composition Reconstruction and Hardness Prediction of Al–Si Die-Casting Alloys". The dataset (approximately 3GB) is divided into two main parts: OMtoEDS: Contains the data…
This repository contains the R code used in the paper "Bayesian Additive Regression Trees for Circular Data: A Machine Learning Framework" by Talal Kurdi and Saralees Nadarajah. The code implements Bayesian Additive Regression Trees (BART) methods for regression with circular dat…