Abstract
Diagnosis-Related Groups (DRG) billing for hospital stays is based on the collection of coded and standardized information constituting the Hospital Discharge Abstract (HDA). The HDA describes the pathological state of the patient and the care provided during his stay. This work aims to design and implement a coding support system for diagnoses and acts expressed in ICD-10 (ICD-10: International Classification of Disease, \(10^{th}\) version) and GNPA (GNPA: General Nomenclature of Professional Acts), respectively. The proposed solution takes a medical report as input and provides a list of recommended diagnoses and acts. It is based on the combination of two approaches, namely, NLP (NLP: Natural Language Processing) and machine learning. Firstly, the medical reports are pre-processed, via the NLP algorithms, in order to better understand the extent of the codes concerned. Secondly, the use of machine learning approaches offers the means of making the choice of codes as relevant as possible. The experiments carried out showed very satisfactory results, which are confirmed by hospital practitioners.
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Amarouche, I.A., Ahmed Zaid, D., Kenaza, T. (2019). Implementation of a Medical Coding Support System by Combining Approaches: NLP and Machine Learning. In: Gadepally, V., Mattson, T., Stonebraker, M., Wang, F., Luo, G., Teodoro, G. (eds) Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2018 2018. Lecture Notes in Computer Science(), vol 11470. Springer, Cham. https://doi.org/10.1007/978-3-030-14177-6_11
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