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The group of discussions showcases enthusiasm and recognition of the study's significance, with phrases like 'innovative approach' and 'personalizing treatment,' reflecting high interest and belief in substantial clinical impact. Comments referencing the integration of routine clinical data and AI models underscore the depth of engagement, emphasizing the potential revolution in cancer treatment and decision-making.
Most discussions acknowledge the value of the research and its potential to improve personalized treatment, showing moderate support.
Posts express high curiosity about the AI models and their clinical applications, indicating strong interest.
Many comments reference specific aspects like EHR data, predictive models, and implications for cancer immunotherapy, demonstrating deep engagement.
Discussions highlight the importance of this research in advancing precision medicine and patient outcomes, reflecting high perceived impact.
Interesting article presenting an AI based model to predict response to and toxicity from immunotherapy for cancer patients developed by Vanderbilt researchers and GE Healthcare https://t.co/1qMPfsVrQX
New #ML models use #EHR data to predict risks & outcomes for patient w/ #cancer on #immunotherapy. Aimed at personalizing treatment by predicting immunotoxicities like hepatitis, colitis & pneumonitis plus survival. ➡️https://t.co/Sfy8bCK6LS @GEHealthCare @VUMChealth https://t.co/etdLmq6CIO
Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data | JCO Clinical Cancer Informatics https://t.co/PwxgSHpztv
Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data | JCO Clinical Cancer Informatics https://t.co/QuF3V2dwFy
Drs Shaalan Beg and Travis Osterman discuss a machine learning model that uses electronic health record data to forecast the effectiveness and toxicity of immune checkpoint inhibitors in cancer treatment. The AI model…
Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data | JCO Clinical Cancer Informatics https://t.co/PwxgSHpztv
New #ML models use #EHR data to predict risks & outcomes for patient w/ #cancer on #immunotherapy. Aimed at personalizing treatment by predicting immunotoxicities like hepatitis, colitis & pneumonitis plus survival. ➡️https://t.co/Sfy8bCK6LS @GEHealthCare @VUMChealth https://t.co/etdLmq6CIO
Interesting article presenting an AI based model to predict response to and toxicity from immunotherapy for cancer patients developed by Vanderbilt researchers and GE Healthcare https://t.co/1qMPfsVrQX
Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data | JCO Clinical Cancer Informatics https://t.co/NlDk769gOW
Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data | JCO Clinical Cancer Informatics https://t.co/QuF3V2dwFy
2024;8 https://doi.org/10.1200/CCI.23.00207. Download RIS. Articles in this issue. Previous slide. Next slide. about 1 year ago. Article. To ...
JCO clinical cancer informatics (2024) doi:10.1200/cci.23.00207; Fabio Dennstädt et al. Exploring the capabilities of Large Language Models ...
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