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Skip search results from other journals and go to results- 2 JMIR Medical Informatics
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Nevertheless, transforming raw data into meaningful analysis and insights presents numerous challenges, making standardized workflows for data preprocessing essential.
Data preprocessing involves a series of steps designed to clean and refine data to ensure its reliability and suitability for analysis using artificial intelligence and machine learning (AI/ML) techniques.
JMIR Mhealth Uhealth 2024;12:e59587
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Such a method is intended to ease the preprocessing of real data for data analysts or hospital managers who seek a clean database with unbiased medical pathways.
The scientific contributions of this paper are as follows:
This study provides a new framework to model patient pathways considering hospital management constraints (eg, bed occupancy and resource availability). This framework is used to assess patient pathways.
JMIR Med Inform 2024;12:e58978
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A Wolf in Sheep’s Clothing: Reuse of Routinely Obtained Laboratory Data in Research
Researchers reusing routine laboratory data, such as epidemiologists and data scientists, who are oblivious to this “world behind the numbers” may either apply or omit (un)necessary preprocessing steps important for the creation of a clinically meaningful data set that may lead to false conclusions.
J Med Internet Res 2022;24(11):e40516
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The Importance of Nonlinear Transformations Use in Medical Data Analysis
We demonstrate that transformation-based preprocessing is an enhancer tool important for even simple questions using simple tools. Incorporating transformations into the preprocessing stage is essential in order to enable less-sophisticated data analysts to obtain informative and relevant results by simple means.
With the ever-increasing numbers of variables and subjects, there is a notion in the general public that simply the amount of data will reveal all there is to understand from it.
JMIR Med Inform 2018;6(2):e27
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