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Optimizing sepsis treatment timing with a machine learning model: Approach designed to support physicians' decision making

Optimizing sepsis treatment timing with a machine learning model: Approach designed to support physicians' decision making
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AI-Based Approach Crunches Real-World Data To Emulate Clinical Trials

February 4, 2021
By Deborah Borfitz
February 4, 2021 | A computer scientist at The Ohio State University (OSU) recently demonstrated the potential of artificial intelligence to crunch real-world data and emulate randomized clinical trials, speeding the pace of drug repurposing. The initial use case was focused on preventing heart failure and stroke in patients with coronary artery disease, but the model could be applied to any disease with a definable outcome, says Ping Zhang, who leads the Artificial Intelligence in Medicine Lab at OSU.
Zhang is senior author of a recently published study in
Nature Machine Intelligence (DOI: 10.1038/s42256-020-00276-w) where a deep learning algorithm ingested insurance claims on nearly 1.2 million deidentified patients to identify existing medications with a heretofore unknown therapeutic effect on coronary artery disease (CAD; e.g., diabetes drug metformin and antidepressant escitalopram, both of which were alrea ....

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Novel AI tool takes a DeepCE dive into potential drugs for COVID-19

Novel AI tool takes a DeepCE dive into potential drugs for COVID-19
5
A new phenotype-based compound screening technology, called DeepCE, identified 10 compounds that could be repurposed for COVID-19.
DeepCE (pronounced ‘Deep Sea’), a novel deep-learning computer model that can predict how gene expression will change in response to medicines, has been used to identify 10 compounds that could be repurposed as treatments for COVID-19.
Two of the drugs – cyclosporin, an immunosuppressant used to prevent transplant organ rejection, and anidulafungin, an antifungal agent – are currently approved for clinical use. The others are investigational and being evaluated in a wide range of indications, from hepatitis C and fungal disease to cancer and heart disease. According to the team, some of these candidates have already been evaluated in COVID-19 patients. ....

United States , Ohio State University , Ping Zhang , National Institutes Of Health , Artificial Intelligence In Medicine Lab , Translational Data Analytics Institute At Ohio , Deep Sea , National Institutes , Artificial Intelligence , Medicine Lab , Translational Data Analytics Institute , Nature Machine , ஒன்றுபட்டது மாநிலங்களில் , ஓஹியோ நிலை பல்கலைக்கழகம் , பிங் ஜாங் , தேசிய நிறுவனங்கள் ஆஃப் ஆரோக்கியம் , செயற்கை உளவுத்துறை இல் மருந்து ஆய்வகம் , மொழிபெயர்ப்பு தகவல்கள் பகுப்பாய்வு நிறுவனம் இல் ஓஹியோ , ஆழமான கடல் , தேசிய நிறுவனங்கள் , செயற்கை உளவுத்துறை , மருந்து ஆய்வகம் , மொழிபெயர்ப்பு தகவல்கள் பகுப்பாய்வு நிறுவனம் , இயற்கை இயந்திரம் ,

Computer model makes strides in search for COVID-19 treatments

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COLUMBUS, Ohio - A new deep-learning model that can predict how human genes and medicines will interact has identified at least 10 compounds that may hold promise as treatments for COVID-19.
All but two of the drugs are still considered investigational and are being tested for effectiveness against hepatitis C, fungal disease, cancer and heart disease. The list also includes the approved drugs cyclosporine, an immunosuppressant that prevents transplant organ rejection, and anidulafungin, an antifungal agent.
The discovery was made by computer scientists, meaning much more work needs to be done before any of these medications would be confirmed as safe and effective treatments for people infected with SARS-CoV-2. But by using artificial intelligence to arrive at these options, the scientists have saved pharmaceutical and clinical researchers the time and money it would take to search for potential COVID-19 drugs on a piecemeal basis. ....

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