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Key Points Question Can machine learning deployed in electronic health records be used to improve readmission risk estimation for patients following acute myocardial infarction? Findings In this cohort study examining externally validated machine learning risk models for 30-day readmission of 10 187 patients following hospitalization for acute myocardial infarction, good discrimination performance was noted at the development site, but the best discrimination did not result in the best calibration. External validation yielded significant declines in discrimination and calibration. Meaning The findings of this study highlight that robust calibration assessments are a necessary complement to discrimination when machine learning models are used to predict post–acute myocardial infarction readmission; challenges with data availability across sites, even in the presence of a common data model, limit external validation performance.

Concurrent regional and global environmental changes are affecting freshwater ecosystems. Decadal-scale data on lake ecosystems that can describe processes affected by these changes are important as multiple stressors often interact to alter the trajectory of key ecological phenomena in complex ways. Due to the practical challenges associated with long-term data collections, the majority of existing long-term data sets focus on only a small number of lakes or few response variables. Here we present physical, chemical, and biological data from 28 lakes in the Adirondack Mountains of northern New York State. These data span the period from 1994–2012 and harmonize multiple open and as-yet unpublished data sources. The dataset creation is reproducible and transparent; R code and all original files used to create the dataset are provided in an appendix. This dataset will be useful for examining ecological change in lakes undergoing multiple stressors.

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