Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding

@article{Rios2013SupervisedEO,
  title={Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding},
  author={Anthony Rios and Ramakanth Kavuluru},
  journal={2013 IEEE International Conference on Healthcare Informatics},
  year={2013},
  pages={66-73}
}
  • Anthony Rios, Ramakanth Kavuluru
  • Published in
    IEEE International Conference…
    2013
  • Computer Science, Medicine
  • Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 29 REFERENCES

    Automatic construction of rule-based ICD-9-CM coding systems

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Preparing for the icd-10 code set

    • American Medical Association
    • http://www.ama-assn.org/ama1/pub/upload/mm/399/icd10-icd9differences-fact-sheet.pdf, 2010.
    • 2010
    VIEW 1 EXCERPT