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Increasing secondary diagnosis encoding quality using data mining techniques

Chahbandarian, Ghazar and Souf, Nathalie and Bastide, Rémi and Steinbach, Jean-Christophe Increasing secondary diagnosis encoding quality using data mining techniques. (2016) In: 10th IEEE International Conference on Research Challenges in Information Science (RCIS 2016), 1 June 2016 - 3 June 2016 (Grenoble, France).

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Official URL: https://doi.org/10.1109/RCIS.2016.7549339

Abstract

In order to measure the medical activity, hospitals are required to manually encode information concerning an inpatient episode using International Classification of Disease (ICD-10). This task is time consuming and requires substantial training for the staff. We propose to help by speeding up and facilitating the tedious task of coding patient information, specially while coding some secondary diagnoses that are not well described in the medical resources such as discharge letter and medical records. Our approach leverages data mining techniques in order to explore medical databases of previously encoded secondary diagnoses and use the stored structured information (age, gender, diagnoses count, medical procedures...) to build a decision tree that assigns the proper secondary diagnosis code into the corresponding inpatient episode or indicates the impatient episodes that contains implausible secondary diagnoses. The results suggest that better performance could be achieved by using low level of diagnoses granularity along with adding some filters to balance the repartition of the negative and positive examples in the training set. The obtained results show that there is big variation in the evaluation scores of the studied diagnoses, the highest score is 75% using F1 measurement and the lowest 25% using F1 measurement which indicates further enhancements are needed to achieve better performance regardless of the encoded diagnosis. However, the average accuracy of all the studied secondary diagnoses is around 80% which indicates better negative predictions therefore it could be useful in the prevention or the detection of wrong coding assignments of secondary diagnoses in the inpatient stay.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org This papers appears in Proceedings to RCIS 2016. Electronic ISBN: 978-1-4799-8710-8 Electronic ISSN: 2151-1357 The original PDF of the article can be found at: https://ieeexplore.ieee.org/document/7549339/ Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-01809380
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Other partners > Centre Hospitalier InterCommunal Castres-Mazamet - CHIC (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Ecole nationale supérieure des Mines d'Albi-Carmaux - IMT Mines Albi (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Université de Toulouse > Institut National Universitaire Champollion - INU (FRANCE)
Laboratory name:
Funders:
Midi-Pyrénées region - Paul Sabatier University - INU Champollion - Castres-Mazamet Technopole
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Deposited By: IRIT IRIT
Deposited On:17 May 2018 09:53

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