Julie Jacques

Julie Jacques
Lille Catholic University · FGES - Faculty of Economics, Management and Sciences

PhD

About

29
Publications
3,126
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110
Citations

Publications

Publications (29)
Chapter
Electronic health records (EHRs) involve heterogeneous data types such as binary, numeric and categorical attributes. As traditional clustering approaches require the definition of a single proximity measure, different data types are typically transformed into a common format or amalgamated through a single distance function. Unfortunately, this ea...
Preprint
Full-text available
Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as bioinformatics, text mining, and pattern recognition. However, finding significant biclusters is an NP-hard problem t...
Conference Paper
MOCA-I is a multi-objective local search algorithm, based on the Pittsburgh representation, that has been formerly designed to solve partial classification problems with imbalanced data. Recently, multi-objective automatic algorithm configuration (MO-AAC) has proven effective in boosting the performance of multi-objective local search algorithms fo...
Chapter
MOCA-I is a multi-objective local search algorithm, based on the Pittsburgh representation, that has been formerly designed to solve partial classification problems with imbalanced data. Recently, multi-objective automatic algorithm configuration (MO-AAC) has proven effective in boosting the performance of multi-objective local search algorithms fo...
Article
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 patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resis...
Conference Paper
Classification problems can be modeled as multi-objective optimization problems. MOCA-I is a multi-objective local search designed to solve these problems, particularly when the data are imbalanced. However, this algorithm has been tuned by hand in order to be efficient on particular datasets. In this paper, we propose a methodology to automaticall...
Chapter
Volatile organic compounds (VOCs) are continuous medical data regularly studied to perform non-invasive diagnosis of diseases using machine learning tasks for example. The project PATHACOV aims to use VOCs in order to predict invasive diseases such as lung cancer. In this context, we propose to use a multi-objective modeling for the partial supervi...
Article
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 patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resis...
Conference Paper
Volatile organic compounds (VOCs) are continuous medical data regularly studied to perform non-invasive diagnosis of diseases using machine learning tasks for example. The project PATHACOV aims to use VOCs in order to predict invasive diseases such as lung cancer. In this context, we propose to use a multi-objective modeling for the partial supervi...
Article
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 heterogeneous, temporal, large-scale, sparse data matrices. HBC-t is based on HBC, using similar mechan...
Article
Full-text available
Context: A better understanding of "patient pathway" thanks to data analysis can lead to better treatments for patients. The ClinMine project, supported by the French National Research Agency (ANR), aims at proposing, from various case studies, algorithmic and statistical models able to handle this type of pathway data, focusing primarily on hospit...
Article
This study focuses on the problem of supervised classification on heterogeneous temporal data featuring a mixture of attribute types (numeric, binary, symbolic, temporal). We present a model for classification rules designed to use both non-temporal attributes and sequences of temporal events as predicates. We also propose an efficient local search...
Conference Paper
We define the problem of biclustering on heterogeneous data, that is, data of various types (binary, numeric, etc.). This problem has not yet been investigated in the biclustering literature. We propose a new method, HBC (Heterogeneous BiClustering), designed to extract biclusters from heterogeneous, large-scale, sparse data matrices. The goal of t...
Article
The number of patients that benefit from remote monitoring of cardiac implantable electronic devices, such as pacemakers and defibrillators, is growing rapidly. Consequently, the huge number of alerts that are generated and transmitted to the physicians represents a challenge to handle. We have developed a system based on a formal ontology that int...
Article
Full-text available
Aims Remote monitoring of cardiac implantable electronic devices is a growing standard; yet, remote follow-up and management of alerts represents a time-consuming task for physicians or trained staff. This study evaluates an automatic mechanism based on artificial intelligence tools to filter atrial fibrillation (AF) alerts based on their medical s...
Article
Full-text available
Classification on medical data raises several problems such as class imbalance, double meaning of missing data, volumetry or need of highly interpretable results. In this paper a new algorithm is proposed: MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data), a multi-objective local search algorithm that is conceived to deal with t...
Article
Biomedical research progresses rapidly, in particular in the area of genomic and postgenomic research. Hence many challenges appear for bio-statistics and bioinformatics to deal with the large amount of data generated. After presenting some of these challenges, this chapter aims at presenting evolutionary combinatorial optimization approaches propo...
Article
Full-text available
Medical data suffer from uncertainty and a lack of uniformisation, making them hard to use in medical software, especially for patient screening in clinical trials. In this PhD work, we propose to deal with these problems using supervised classification methods. We will focus on 3 properties of these data : imbalance, uncertainty and volumetry. We...
Conference Paper
A large number of rule interestingness measures have been used as objectives in multi-objective classification rule mining algorithms. Aggregation or Pareto dominance are commonly used to deal with these multiple objectives. This paper compares these approaches on a partial classification problem over discrete and imbalanced data. After performing...
Conference Paper
Full-text available
This paper focuses on the modeling and the implementation as a multi-objective optimization problem of a Pittsburgh classification rule mining algorithm adapted to large and imbalanced datasets, as encountered in hospital data. We associate to this algorithm an original post-processing method based on ROC curve to help the decision maker to choose...
Conference Paper
Full-text available
This abstract presents a modeling of the classification rule mining problem as a dominance-based multi-objective local search, with Pittsburgh solution encoding, using accuracy and the number of terms as objectives. This solution is then compared to results from literature of 22 rule mining classification algorithms.
Article
Full-text available
Implantable cardioverter defibrillators can generate numerous alerts. Automatically classifying these alerts according to their severity hinges on the CHA2DS2VASc score. It requires some reasoning capabilities for interpreting the patient's data. We compared two approaches for implementing the reasoning module. One is based on the Drools engine, an...
Article
Implantable cardiac defibrillators along with telecardiology services provide improvements in health care delivery and clinical outcomes in the field of heart failure. This implies a shift from strictly device-centered follow-up to perspectives centered on the patient. In the AKENATON project, we have designed a formal ontology that supports integr...
Conference Paper
Full-text available
Cette communication expose la problématique du recrutement (inclusion) dans les essais cliniques. Après une brève présentation des données disponibles dans le système hospitalier, un scénario avec un critère d'inclusion sera présenté. L'apport du datamining et de l'optimisation combinatoire dans ce cas sera ensuite présenté, ainsi qu'une partie de...

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