Data mining applications in healthcare.

By Hian Chye Koh, G. Tan. Journal of healthcare information management : JHIM, 2005

769 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/270fc91b1f552be417ac63d3e1ec40ff4ba75059

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jimeng Sun
2. Yu Cheng
3. Zhengping Che
4. Edward Choi
5. Walter F. Stewart
6. Yan Liu
7. Shuangfei Zhai
8. Joyce Ho

Citations in the corpus

• Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records (2017, background): “Properly-designed deep neural networks have the prospect of handling these issues if equipped with massive data, but the amount of clinical data, especially with accurate labels and for rare diseases and conditions, is somewhat limited and far from most models’ requirements [17].”
• Fraud detection system: A survey (2016, background): “More complex FDSs integrating a wide range of data mining methods are required and are being developed for effective fraud detection (Akhilomen, 2013; Koh and Tan, 2005; Guo and Li, 2008; Ogwueleka, 2011; Desai and Deshmukh, 2013; Table 2 Fraud detection system survey and review articles.”
• Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, background): “The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management.”
• Process mining in healthcare: A literature review (2016, background): “We have identified some reviews of the application of data mining in several medical fields [17–23].”
• Artificial Intelligence in Health Care: Bibliometric Analysis (2020, background): “Medical AI technologies provide algorithms and programs that analyze symbolic models of diseases and their relationships to patient signs and symptoms [4-6].”
• OlloBot - Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results (2019, background): “Overall, AI has the potential to improve outcomes by 30 to 40 percent while cutting treatment costs by as much as 50 percent (Koh et al., 2011).”
• Data Mining for the Internet of Things: Literature Review and Challenges (2015, background): “patients [119], and applied to mass of data generated by millions of prescriptions, operations and treatment courses to identify unusual patterns and uncover fraud [120; 121].”
• Rethinking Construction Cost Overruns: Cognition, Learning and Estimation (2014, methodology): “This has been extensively used in other industries including finance (Kovalerchuk and Vityaev 2000), medicine (Bellazzi and Zupan 2008, Koh and Tan 2011) and business (Apte et al. 2002), but is yet to see widespread adoption in the construction industry.”