Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial Intelligence-Based Medical Data Mining
45
Zitationen
6
Autoren
2022
Jahr
Abstract
Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.879 Zit.
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
2005 · 10.574 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 9.011 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.666 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.220 Zit.