First machine-learning (ML) platform developer Unlearn.AI has reported that its machine learning (ML) platform is capable of providing digital patient records designed to supplement actual control patients in clinical trials of Alzheimer’s disease.

Through its ML-based model, which incorporates Digital Twins and Intelligent Control Arms to reduce clinical trial timelines, the company intends to address patient recruitment in Alzheimer’s disease clinical trials.

Unlearn.AI founder and CEO Charles Fisher said: “We are excited by the results we presented today as they further validate our platform and its potential to significantly decrease the time spent running clinical trials in an area of significant patient need like Alzheimer’s disease.

“Drug development in Alzheimer’s disease is increasingly expensive and time-consuming. We believe that our platform can help alleviate these burdens and accelerate clinical trials to help get new medicines to the patients who need them.”

According to the company, a Digital Twin is a comprehensive and computationally generated clinical record describing the outcomes if a specific patient had received a placebo.

The DiGenesis process of the platform explores historical clinical trial datasets from a number of patients, disease-specific ML models and severe statistical analysis to create digital records. These records match patients who are part of the investigational-treatment arm of trials.

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To gather a diverse sample of control data for its Alzheimer’s disease model, the company used records through its membership with Critical Path for Alzheimer’s Disease (CPAD) from around 5,000 people with early to moderate Alzheimer’s disease.

These people were selected from the control arms of 16 historical clinical trials.