Data-Mining-in-Healthcare: diferenças entre revisões
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* Improves operational efficiencies in healthcare systems; | * Improves operational efficiencies in healthcare systems; | ||
* Supports public health research and epidemiological studies. | * Supports public health research and epidemiological studies. | ||
The image above is presented in Gordan et al., 2022 <ref name="dm-soa">Gordan M, Sabbagh-Yazdi SR, Ismail Z, Ghaedi K, Carroll P, McCrum D, et al. State-of-the-art review on advancements of data mining in structural health monitoring. Measurement. 2022 Apr 1;193:110939.</ref>, and illustrates a historical timeline of advancements in Structural Health Monitoring (SHM) and its integration with Artificial Intelligence (AI) and Data Mining technologies. | The image above is presented in Gordan et al., 2022 <ref name="dm-soa">Gordan M, Sabbagh-Yazdi SR, Ismail Z, Ghaedi K, Carroll P, McCrum D, et al. State-of-the-art review on advancements of data mining in structural health monitoring. Measurement. 2022 Apr 1;193:110939.</ref>, and illustrates a historical timeline of advancements in Structural Health Monitoring (SHM) and its integration with Artificial Intelligence (AI) and Data Mining technologies. | ||
(image-evolution-dm) | |||
The timeline traces significant developments across multiple decades, emphasizing the evolution of data mining techniques in SHM and highlighting their growing relevance from the 1990s onwards. Key milestones in this context include real-time data monitoring in 1992, operational modal analysis in 2001. | |||
The recent focus, as seen in the "Data mining-based SHM" from 2014 to the present, underscores the role of data mining and AI in optimizing decision-making, predictive analysis, and enhancing the overall reliability of critical infrastructure. | |||
In the context of healthcare, this timeline provides an analogous framework for understanding how data mining and AI can revolutionize decision-making, diagnostics, and operational efficiency by leveraging advancements in real-time monitoring, predictive analytics, and data integration. | |||
= Key Data Mining Techniques in Healthcare = | = Key Data Mining Techniques in Healthcare = | ||
Revisão das 14h50min de 26 de dezembro de 2024
Authors (HEADS-INFORM 2025):
- Henrique Pereira
- Olívia Oliveira
Introduction
Data mining refers to the process of discovering meaningful patterns and extracting actionable knowledge from vast amounts of data, often with the aid of computational tools.
The healthcare sector generates massive amounts of data, including electronic health records (EHRs), genomic sequences, medical imaging, wearable device outputs, patient-generated data, and clinical trial data. These data sources provide a fertile ground for applying data mining to improve care delivery, patient outcomes, and operational efficiency (Islam et al., 2018 [1]).
Importance:
- Enhances clinical decision-making;
- Facilitates the early detection and prediction of diseases;
- Improves operational efficiencies in healthcare systems;
- Supports public health research and epidemiological studies.
The image above is presented in Gordan et al., 2022 [2], and illustrates a historical timeline of advancements in Structural Health Monitoring (SHM) and its integration with Artificial Intelligence (AI) and Data Mining technologies.
(image-evolution-dm)
The timeline traces significant developments across multiple decades, emphasizing the evolution of data mining techniques in SHM and highlighting their growing relevance from the 1990s onwards. Key milestones in this context include real-time data monitoring in 1992, operational modal analysis in 2001.
The recent focus, as seen in the "Data mining-based SHM" from 2014 to the present, underscores the role of data mining and AI in optimizing decision-making, predictive analysis, and enhancing the overall reliability of critical infrastructure.
In the context of healthcare, this timeline provides an analogous framework for understanding how data mining and AI can revolutionize decision-making, diagnostics, and operational efficiency by leveraging advancements in real-time monitoring, predictive analytics, and data integration.
Key Data Mining Techniques in Healthcare
Classification
Regression
Clustering
Anomaly Detection
Association Rules
Neural Networks and Deep Learning
Applications of Data Mining in Healthcare
Challenges in Healthcare Data Mining
Future Directions of Data Mining in Healthcare
References
- ↑ Islam MS, Hasan MM, Wang X, Germack HD, Noor-E-Alam M. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare. 2018 May 23;6(2):54.
- ↑ Gordan M, Sabbagh-Yazdi SR, Ismail Z, Ghaedi K, Carroll P, McCrum D, et al. State-of-the-art review on advancements of data mining in structural health monitoring. Measurement. 2022 Apr 1;193:110939.
