A Deep Learning Framework for Automated ICD-10 Coding

Autor: Abdelahad, Chraibi, David, Delerue, Julien, Taillard, Ismat, Chaib Draa, Régis, Beuscart, Arnaud, Hansske

Publikováno v: Studies in health technology and informatics. 281

The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a pati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::507897f7f099c70c2f373421b785cad6
https://pubmed.ncbi.nlm.nih.gov/34042763

A Biclustering Method for Heterogeneous and Temporal Medical Data

Autor: Julien Taillard, Maxence Vandromme, Laetitia Jourdan, Clarisse Dhaenens, Julie Jacques

Publikováno v: IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, In press, pp.1-1. ⟨10.1109/TKDE.2020.2983692⟩
IEEE Transactions on Knowledge and Data Engineering, 2022, 34 (2), pp.506-518. ⟨10.1109/TKDE.2020.2983692⟩

We address the problem of biclustering on heterogeneous data, that is, data of various types (binary, numeric, symbolic, temporal). We propose a new method, HBC-t (Heterogeneous BiClustering for temporal data), designed to extract biclusters from het

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6710e3c1a7b64ef24e5a0ab87d0aa5ec
https://hal.archives-ouvertes.fr/hal-02927063

The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based 'white-box' classification algorithm for medical data

Autor: Justine Lemtiri-Florek, Julien Taillard, Laetitia Jourdan, Hélène Martin-Huyghe, Clarisse Dhaenens, Valérie Leclercq, David Delerue, Arnaud Hansske, Julie Jacques

Publikováno v: International Journal of Medical Informatics
International Journal of Medical Informatics, Elsevier, In press, October 2020, 142, ⟨10.1016/j.ijmedinf.2020.104242⟩
International Journal of Medical Informatics, 2020, October 2020, 142, ⟨10.1016/j.ijmedinf.2020.104242⟩

Background Multi-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized pati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de72300e98d488b85bd83d32d3282873
https://doi.org/10.1016/j.ijmedinf.2020.104242

Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets

Autor: Clarisse Dhaenens, Julien Taillard, Julie Jacques, David Delerue, Laetitia Jourdan

Publikováno v: Applied Soft Computing
Applied Soft Computing, Elsevier, 2015, 34, pp.705--720. ⟨10.1016/j.asoc.2015.06.002⟩
Applied Soft Computing, 2015, 34, pp.705--720. ⟨10.1016/j.asoc.2015.06.002⟩

Graphical abstractDisplay Omitted HighlightsFormulation of the classification rule mining problem as a multi-objective problem.Proposal of MOCA-I that deals both with uncertainty, class imbalance and volumetry.Comparison of different MOCA-I based DML

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a610347b0ac3bc69b19e7f4fc99545df
https://doi.org/10.1016/j.asoc.2015.06.002

A scalable biclustering method for heterogeneous medical data

Autor: Laetitia Jourdan, Clarisse Dhaenens, Maxence Vandromme, Julie Jacques, Julien Taillard

Publikováno v: International Workshop on Machine Learning, Optimization and Big Data
International Workshop on Machine Learning, Optimization and Big Data, Aug 2016, Volterra, Italy. pp.12
Lecture Notes in Computer Science ISBN: 9783319514680
MOD
HAL

International audience; We define the problem of biclustering on heterogeneous data,that is, data of various types (binary, numeric, etc.). This problem hasnot yet been investigated in the biclustering literature.We propose a newmethod, HBC (Heteroge

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de53569228dc5a4408017630e0791cba
https://inria.hal.science/hal-01420947