Lifeboat Foundation

Year 2022 😗

Testing multiple treatments for heterogeneous (varying) effectiveness with respect to many underlying risk factors requires many pairwise tests; we would like to instead automatically discover and visualize patient archetypes and predictors of treatment effectiveness using multitask machine learning. In this paper, we present a method to estimate these heterogeneous treatment effects with an interpretable hierarchical framework that uses additive models to visualize expected treatment benefits as a function of patient factors (identifying personalized treatment benefits) and concurrent treatments (identifying combinatorial treatment benefits). This method achieves state-of-the-art predictive power for COVID-19 in-hospital mortality and interpretable identification of heterogeneous treatment benefits. We first validate this method on the large public MIMIC-IV dataset of ICU patients to test recovery of heterogeneous treatment effects. Next we apply this method to a proprietary dataset of over 3,000 patients hospitalized for COVID-19, and find evidence of heterogeneous treatment effectiveness predicted largely by indicators of inflammation and thrombosis risk: patients with few indicators of thrombosis risk benefit most from treatments against inflammation, while patients with few indicators of inflammation risk benefit most from treatments against thrombosis. This approach provides an automated methodology to discover heterogeneous and individualized effectiveness of treatments.

Year 2022 😗

Automation is not just for high-throughput screening anymore. New devices and greater flexibility are transforming what’s possible throughout drug discovery and development. This article was written by Thomas Albanetti, AstraZeneca; Ryan Bernhardt, Biosero; Andrew Smith, AstraZeneca and Kevin Stewart, AstraZeneca for a 28-page DDW eBook, sponsored by Bio-Rad. Download the full eBook here.

A utomation has been a part of the drug discovery industry for decades. The earliest iterations of these systems were used in large pharmaceutical companies for high-throughput screening (HTS) experiments. HTS enabled the testing of libraries of small molecule compounds by a single or a series of multiple experimental conditions to i dentify the potential of those compounds as a treatment for a target disease. HTS has evolved to enable screening libraries of millions of compounds, but the high cost of equipment has largely resulted in automation occurring primarily in large pharmaceutical companies. Today, though, new types of robots paired with sophisticated software tools have helped to democratise access to automation, making it possible for pharma and biotechnology companies of almost any size to deploy these solutions in their labs.

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A study using 30 years of data links good hydration to a lower risk of developing some chronic illnesses and premature aging.

These global heat maps, published by NASA, illustrate just how much hotter the world is today than it was in the previous decades.

Earthquakes are almost impossible to predict. Luckily, engineers have come up with some amazing ways to protect people the next time one might strike.

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Researchers have found what may be the first-ever “virovore” or an organism which eats viruses. The study was published last week, in the PNAS journal by scientists at the University of Nebraska-Lincoln in the United States, which found two plankton organisms named Halteria and Paramecium, can not only feed on viruses but also thrive by consuming them.

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Irina Rish is a world-renowned professor of computer science and operations research at the Université de Montréal and a core member of the prestigious Mila organisation. She is a Canada CIFAR AI Chair and the Canadian Excellence Research Chair in Autonomous AI. Irina holds an MSc and PhD in AI from the University of California, Irvine as well as an MSc in Applied Mathematics from the Moscow Gubkin Institute. Her research focuses on machine learning, neural data analysis, and neuroscience-inspired AI. In particular, she is exploring continual lifelong learning, optimization algorithms for deep neural networks, sparse modelling and probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis. Prof. Rish holds 64 patents, has published over 80 research papers, several book chapters, three edited books, and a monograph on Sparse Modelling. She has served as a Senior Area Chair for NeurIPS and ICML. Irina’s research is focussed on taking us closer to the holy grail of Artificial General Intelligence. She continues to push the boundaries of machine learning, continually striving to make advancements in neuroscience-inspired AI.

Continue reading “Prof. IRINA RISH — AGI, Complex Systems, Transhumanism #NeurIPS” »

Feat : hessid · zetno creato.

This is generated using Stable diffusion’s deforun model.

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