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This data was blind to analysis during algorithm development and used to evaluate algorithm performance.
This is likely a reflection of the emphasis on identifying high-risk patients during clinical decision-making 20; for population uses, however, several authors have emphasised the importance of examining calibration in important target populations, particularly when these populations and exposures were not included during algorithm development.
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supervised the algorithm development.
contributed to algorithm development.
Algorithm development is more than just programming.
Computer laboratory and algorithm development.
For algorithm development, data visualization and analysis.
And they're entirely separate from the algorithm development teams.
>>>> None really, our focus has been on algorithm development.
Typical uses include: Math & computation and algorithm development.
Having someone in this role even pays dividends when it comes to algorithm development.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com