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Data mining in telemedicine

Data mining in telemedicine

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To date, the field of telemedicine is at a critical standpoint and faces a wide variety of challenges. Voluminous data are generated through the interaction among the telemedicine stakeholders, which are ever increasing. It is well conjectured that the successful implementation of telemedicine largely depends on the effective and efficient knowledge extraction from this available data cloud. However, due to lack of proper integration of the data mining techniques, the stakeholders are not getting the full-fledged benefit from this promising platform. Considering the aforementioned fact, this book chapter provides a contrivance to integrate data mining techniques into telemedicine connecting all the stakeholders into a single podium using data engine. It illustrates the prospects of different data mining techniques and their integration for telemedicine. These techniques combine all the basic classification and clustering method including the state-of-the-art artificial neural network (ANN) and deep learning procedure for disease prediction. Two case studies, heart diseases, and breast cancer prediction have been demonstrated applications of the integrated data mining engine.

Chapter Contents:

  • 6.1 Introduction to data mining
  • 6.2 Data mining in telemedicine
  • 6.2.1 Role of data mining in telemedicine
  • 6.2.1.1 Patient health tracking and predictive analytics
  • 6.2.1.2 Post-discharge observation
  • 6.2.1.3 Convenient and accurate diagnosis
  • 6.2.1.4 Precise medication and observation
  • 6.2.1.5 Specialist outreach
  • 6.2.1.6 Observing infection trends and timely intervention
  • 6.2.1.7 Tackling disaster emergency
  • 6.2.1.8 Detection of fraud and abuse
  • 6.2.2 Big data sources and characterization
  • 6.2.2.1 Qualitative data
  • 6.2.2.2 Quantitative data
  • 6.2.2.3 Multimedia data
  • 6.3 Integration of data mining techniques into telemedicine
  • 6.3.1 Data mining framework
  • 6.3.2 Data mining techniques
  • 6.3.2.1 Association rules mining
  • 6.3.2.2 Artificial neural network
  • 6.3.2.3 Deep learning
  • 6.3.2.4 Clustering algorithms
  • 6.3.2.5 Classification algorithms
  • 6.3.2.6 Bayesian network algorithm
  • 6.3.2.7 Adaptive fuzzy cognitive maps
  • 6.3.2.8 Text mining
  • 6.4 Case study
  • 6.4.1 Heart diseases prediction
  • 6.4.2 Breast cancer prediction
  • 6.5 Challenges of deploying data mining techniques into telemedicine
  • 6.6 Conclusion
  • References

Inspec keywords: learning (artificial intelligence); telemedicine; neural nets; cancer; data mining; diseases

Other keywords: telemedicine stakeholders; available data cloud; efficient knowledge extraction; data engine; integrated data mining engine; different data mining techniques; effective knowledge extraction

Subjects: Biology and medical computing; Neural computing techniques; Biomedical communication; Knowledge engineering techniques; Data handling techniques

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