Electronic Medical Record Analysis To Determine Medical Diagnosis In Chapter Icd 10 Category Using Machine Learning

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Zulius Akbar Amin
Widya Cholil
M. Izman Herdiansyah
Edi Surya Negara

Abstract

Based on observations of the business process flow at the Siti Fatimah Hospital, the background for this study was the medical record document and ICD-10 code which was carried out manual diagnosis, making it difficult for the medical record section in the proper and fast CHAPTER arrangement of the ICD-10 code. The International Statistical Classification of Diseases and Related Health Problems (ICD) can be used to calculate or record a valid patient history of hospitalization. The Cross-Industry Standard Process For Data Mining (CRISP-DM) method is used in this study to become a strategy to describe the problem in general from the domain or research unit. While the machine learning algorithm for multiclass classification uses the Naïve Bayes algorithm, Support Vector Machine, Logistic Regression to create a diagnostic model for medical action. This study predicts ICD-10 chapter categories from medical action records from electronic medical records. With this research, it is hoped that machine learning can facilitate the medical record section in predicting the ICD-10 chapter category by analyzing electronic medical record data using the Chapter ICD-10 Decision Support System information system

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References

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