Search results
6 sty 2022 · Big Data Analytics in medicine and healthcare refers to the integration and analysis of a large amount of complex heterogeneous data, such as various omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenetics, deasomics), biomedical data, talemedicine data (sensors, medical equipment data) and ...
- Basic Introduction to Statistics in Medicine, Part 1: Describing Data
In this article, we provide examples of how to describe and...
- Basic Introduction to Statistics in Medicine, Part 1: Describing Data
14 lip 2021 · In this article, we provide examples of how to describe and visualize data using a nationally representative database, the Nationwide Inpatient Sample, to demonstrate a robust and thorough description of the methods and data used, while also highlighting specific pitfalls.
18 lut 2021 · Here’s a look at what data analytics is, examples of how it applies to health care, common pitfalls to avoid, and how to build your data skills as a health care professional.
22 cze 2022 · The positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management.
31 sie 2020 · Receiving patient or operation information, the healthcare industry's next big step is data analytics applied to various processes in patient treatment, equipment maintenance, and diagnostics. Collecting data and making sense of it to predict health conditions of individuals is a primary task of healthcare analytics.
26 maj 2021 · Big data analytics has the potential to transform health care in the coming years. Medium to high levels of accuracy have been recorded in the analysis of large data sets aiming to diagnose and predict clinical outcomes and complications associated with several chronic diseases.
29 mar 2024 · Data Collection: Healthcare data sources include electronic health records (EHRs), medical imaging files, laboratory test results, billing and claims data, administrative data, patient-generated data from wearable devices and mobile apps, and public health datasets.
