Dead End Discovery Reinforcement Learning A.I. in Healthcare Policy

Developed by Microsoft, Adobe, MIT, and Vector Institute

While Microsoft powers?innovation behind AI at Scale, Microsoft is also applying A.I. to healthcare. DeD or dead-end-discovery, is using reinforcement learning to identify high-risk states and treatments in healthcare.

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Reinforcement Learning Breakthrough with Healthcare Implications

In this research project, Microsoft built a machine learning (ML) model that works with scenarios where data is limited, such as healthcare. This?model?was developed to recognize treatment protocols that could contribute to negative outcomes and to alert clinicians when a patient’s health could decline to a dangerous level.?(see below).

Microsoft Becoming a Juggernaut of A.I. Research

I like this project because it takes a real-world condition like the pandemic, to improve potential A.I. applications to deal with it and similar situations. Microsoft is showing a commitment to work projects in A.I. where every organization in the world would benefit from the power of these models, which is why Microsoft’s AI at Scale initiative is making these large models – and the systems and infrastructure to enable training and utilization – available as a platform.

Aether, a Microsoft cross-company initiative on AI Ethics and Effects in Engineering and Research, as outreach from their commitment to advancing the practice of human-centered responsible AI is also hard at work to improve?AI Trustworthiness.

Microsoft is also partnering with Nvidia more to work on?large scale generative language models. According to Microsoft, thanks to self-supervised learning, few-shot, zero-shot, and fine-tuning techniques, the size of the language models are growing each passing day significantly, calling for high-performance hardware, software, and algorithms to enable training large models.?

Microsoft and Nvidia recently have claimed to established state-of-the-art results, alongside SOTA accuracies in natural language processing (NLP), by adapting to downstream tasks via few-shot, zero-shot, and fine-tuning techniques.?

While these large-scale pretrained language models have made significant breakthroughs in language understanding, they still struggle with commonsense knowledge gathered in our daily lives.?Microsoft KEAR?achieved this breakthrough in commonsense that surpassed human parity in the CommonsenseQA benchmark in December 2021.

Microsoft’s potential impact and implications of research in Medical, Health and Genomics is?above average.

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What is DeD

Off-policy Reinforcement Learning (RL) separates behavioral policies that generate experience from the target policy that seeks optimality. It also allows for learning several target policies with distinct aims using the same data stream or prior experience.

• In the medical field, RL has been used to determine the best treatment plans based on the outcomes of previous treatments.
• Given a patient’s condition, these policies equate to advising what therapies to deliver.
• RL estimates of optimal policies are usually unreliable in healthcare, and most clinical environments prohibit the investigation of different treatment courses due to legal and ethical concerns.

Source: https://www.microsoft.com/en-us/research/blog/using-reinforcement-learning-to-identify-high-risk-states-and-treatments-in-healthcare/

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