
doi: 10.5281/zenodo.18153475 , 10.5281/zenodo.18153569 , 10.5281/zenodo.18153431 , 10.5281/zenodo.15600614 , 10.5281/zenodo.14731458 , 10.5281/zenodo.18356065 , 10.5281/zenodo.18340611 , 10.5281/zenodo.15857397 , 10.5281/zenodo.17901647 , 10.5281/zenodo.14731457 , 10.5281/zenodo.17891777 , 10.5281/zenodo.18152676 , 10.5281/zenodo.18164486 , 10.5281/zenodo.14733694 , 10.5281/zenodo.17918536 , 10.5281/zenodo.17913059 , 10.5281/zenodo.14731775
doi: 10.5281/zenodo.18153475 , 10.5281/zenodo.18153569 , 10.5281/zenodo.18153431 , 10.5281/zenodo.15600614 , 10.5281/zenodo.14731458 , 10.5281/zenodo.18356065 , 10.5281/zenodo.18340611 , 10.5281/zenodo.15857397 , 10.5281/zenodo.17901647 , 10.5281/zenodo.14731457 , 10.5281/zenodo.17891777 , 10.5281/zenodo.18152676 , 10.5281/zenodo.18164486 , 10.5281/zenodo.14733694 , 10.5281/zenodo.17918536 , 10.5281/zenodo.17913059 , 10.5281/zenodo.14731775
A Rust-powered Python framework for modern healthcare research. MedModels provides tools for data handling, data preprocessing, model training and evaluation with a focus on real-world evidence and electronic health records.
Machine Learning, Medical Outcomes, Data Science, Electronic Health Records, Real World Evidence
Machine Learning, Medical Outcomes, Data Science, Electronic Health Records, Real World Evidence
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| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
