Predictive analytics is making strides in healthcare by predicting and optimizing healthcare outcomes. Machine Learning Times brought this interesting information to us in their article, “Wise Practitioner – Predictive Analytics Interview Series: David Talby, PhD of John Snow Labs.”

Predictive analytics has been applied to accelerating clinical trials, predicting disease progression, providing clinical decision support, analyzing real-world evidence, and detecting and preventing adverse drug events – all providing a more comprehensive view of each patient’s journey.

As emerging technologies mature, there are cases in which algorithms are bypassing human accuracy. Developments in artificial intelligence (AI) and human enhancement technologies have the potential to remake society in the coming decades.

A recent Pew Research Center survey finds that Americans see promise in the ways these technologies could improve daily life and human abilities. Yet public views are also defined by the context of how these technologies would be used, what constraints would be in place, and who would stand to benefit – or lose – if these advances become widespread.

Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact, and its potential biases.

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Melody K. Smith

Sponsored by Data Harmony, harmonizing knowledge for a better search experience.