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1. Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V.2.0 randomised clinical trial

Author

Maria Luisa Barrigon, Antonio Artes, Mercedes Perez-Rodriguez, Philippe Courtet, Enrique Baca-Garcia, Sofian Berrouiguet, Jorge Lopez-Castroman, Alejandro Porras-Segovia, María-Mercedes Pérez-Rodríguez, Antonio Artes-Rodríguez, Romain Billot, Juan José Carballo-Belloso, David Delgado-Gomez, Marta Migoya-Borja, Nora Palomar-Ciria, Alba Sedano-Capdevila, Sofía Abascal-Peiró, Susana Amodeo-Escriban, Ana de Granda, Rodrigo Carmona, Elena Gil-Benito, Laura Mata-Iturralde, Laura Muñoz-Lorenzo, Rocío Navarro-Jiménez, Santiago Ovejero, Sergio Sánchez-Alonso, Ezequiel Di Stasio, José Luis Palomo, Pedro Martín-Calvo, Ana José Ortega, Marta Segura-Valverde, María Luisa Martin-Calvo, Raquel Álvarez-García, Pablo Portillo-de Antonio, Pablo Puras-Rico, and Eva María Romero-Gómez

Subjects
Medicine
Abstract

Introduction Suicide is one of the leading public health issues worldwide. Mobile health can help us to combat suicide through monitoring and treatment. The SmartCrisis V.2.0 randomised clinical trial aims to evaluate the effectiveness of a smartphone-based Ecological Momentary Intervention to prevent suicidal thoughts and behaviour.Methods and analysis The SmartCrisis V.2.0 study is a randomised clinical trial with two parallel groups, conducted among patients with a history of suicidal behaviour treated at five sites in France and Spain. The intervention group will be monitored using Ecological Momentary Assessment (EMA) and will receive an Ecological Momentary Intervention called ‘SmartSafe’ in addition to their treatment as usual (TAU). TAU will consist of mental health follow-up of the patient (scheduled appointments with a psychiatrist) in an outpatient Suicide Prevention programme, with predetermined clinical appointments according to the Brief Intervention Contact recommendations (1, 2, 4, 7 and 11 weeks and 4, 6, 9 and 12 months). The control group would receive TAU and be monitored using EMA.Ethics and dissemination This study has been approved by the Ethics Committee of the University Hospital Fundación Jiménez Díaz. It is expected that, in the near future, our mobile health intervention and monitoring system can be implemented in routine clinical practice. Results will be disseminated through peer-reviewed journals and psychiatric congresses. Reference number EC005-21_FJD. Participants gave informed consent to participate in the study before taking part.Trial registration number NCT04775160.

Published
2022
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2. Exact Zoning Optimization Model for Marine Spatial Planning (MSP)

Author

Mohadese Basirati, Romain Billot, Patrick Meyer, and Erwan Bocher

Subjects
marine spatial planning, multi-objective integer linear optimization, buffering, interest, compactness, raster data, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

Marine spatial planning (MSP) has recently attracted more attention as an efficient decision support tool. MSP is a strategic and long-term process gathering multiple competing users of the ocean with the objective to simplify decisions regarding the sustainable use of marine resources. One of the challenges in MSP is to determine an optimal zone to locate a new activity while taking into account the locations of the other existing activities. Most approaches to spatial zoning are formulated as non-linear optimization models involving multiple objectives, which are usually solved using stochastic search algorithms, leading to sub-optimal solutions. In this paper, we propose to model the problem as a Multi-Objective Integer Linear Program. The model is developed for raster data and it aims at maximizing the interest of the area of the zone dedicated to the new activity while maximizing its spatial compactness. We study two resolution methods: first, a weighted-sum of the two objectives, and second, an interactive approach based on an improved augmented version of the ϵ-constraint method, AUGMECON2. To validate and study the model, we perform experiments on artificially generated data. Our experimental study shows that AUGMECON2 represents the most promising approach in terms of relevance and diversity of the solutions, compactness, and computation time.

Published
2021
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3. A Review of Data Cleaning Approaches in a Hydrographic Framework with a Focus on Bathymetric Multibeam Echosounder Datasets

Author

Julian Le Deunf, Nathalie Debese, Thierry Schmitt, and Romain Billot

Subjects
data cleaning algorithms, hydrographic surveying, MBES bathymetric datasets, outlier detection, taxonomy, Geology, QE1-996.5
Abstract

Automatic cleaning of MultiBeam EchoSounder (MBES) bathymetric datasets is a critical issue in data processing especially with the objective of nautical charting. A number of approaches have already been investigated in order to provide solution in views of operationally reaching this still challenging problem. This paper aims at providing a comprehensive and structured overview of existing contributions in the literature. For this purpose, a taxonomy is proposed to categorize the whole set of automatic and semi-automatic methods addressing MBES data cleaning. The non-supervised algorithms that compose the majority of the methods developed in the hydrographic field, are mainly described according to both the features of the bathymetric data and the type of outliers to detect. Based on this detailed review, past and future developments are discussed in light of both implementation and test on datasets and metrics used for performances assessment.

Published
2020
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31. Text mining methods for the characterisation of suicidal thoughts and behaviour

Author

Alba Sedano-Capdevila, Mauricio Toledo-Acosta, María Luisa Barrigon, Eliseo Morales-González, David Torres-Moreno, Bolívar Martínez-Zaldivar, Jorge Hermosillo-Valadez, Enrique Baca-García, Fuensanta Aroca, Antonio Artes-Rodriguez, Sofian Berrouiguet, Romain Billot, Juan Jose Carballo-Belloso, Philippe Courtet, David Delgado Gomez, Jorge Lopez-Castroman, Mercedes Perez Rodriguez, Julia Aznar-Carbone, Fanny Cegla, Pedro Gutiérrez-Recacha, Leire Izaguirre-Gamir, Javier Herrera-Sanchez, Marta Migoya Borja, Nora Palomar-Ciria, Adela Sánchez-Escribano Martínez, Manuel Vasquez, Silvia Vallejo-Oñate, Constanza Vera-Varela, Susana Amodeo-Escribano, Elsa Arrua, Olga Bautista, Maria Luisa Barrigón, Rodrigo Carmona, Irene Caro-Cañizares, Sonia Carollo-Vivian, Jaime Chamorro, Marta González-Granado, Miren Iza, Mónica Jiménez-Giménez, Ana López-Gómez, Laura Mata-Iturralde, Carolina Miguelez, Laura Muñoz-Lorenzo, Rocío Navarro-Jiménez, Santiago Ovejero, María Luz Palacios, Margarita Pérez-Fominaya, Inmaculada Peñuelas-Calvo, Sonia Pérez-Colmenero, Ana Rico-Romano, Alba Rodriguez-Jover, Sergio SánchezAlonso, Juncal Sevilla-Vicente, Carolina Vigil-López, Lucía Villoria-Borrego, Marisa Martin-Calvo, Ana Alcón-Durán, Ezequiel Di Stasio, Juan Manuel García-Vega, Pedro Martín-Calvo, Ana José Ortega, Marta Segura-Valverde, Sara María Bañón-González, Edurne Crespo-Llanos, Rosana Codesal-Julián, Ainara Frade-Ciudad, Elena Hernando Merino, Raquel Álvarez-García, Jose Marcos Coll-Font, Pablo Portillo-de Antonio, Pablo Puras-Rico, and Leticia Serrano-Marugán

Subjects
Psychiatry and Mental health, Biological Psychiatry
Published
2023

33. Une plateforme de crowdfunding et son réseau social

Author

Laurent Brisson, Cécile Bothorel, Romain Billot, and Inna Lyubareva

Subjects
Economics and Econometrics, Strategy and Management, 0502 economics and business, 05 social sciences, 050211 marketing, Business and International Management, 050203 business & management
Abstract

L’analyse empirique de la plateforme de crowdfunding Ulule montre l’existence d’un réseau social transverse aux projets qui présente, à l’échelle de la plateforme, un effet catalyseur sur les campagnes de recherche de fonds. Les résultats mettent en évidence cinq sous-groupes de contributeurs au sein du réseau social de la plateforme–sponsors, suiveurs, précurseurs, spécialistes et spécialistes collaboratifs. Tandis qu’un impact positif global du réseau social sur le taux de succès des projets est mis en lumière, ce résultat est particulièrement significatif en présence de sous-groupes de contributeurs marqués par une spécialisation thématique et une dimension collaborative accrues

Published
2020

34. Developing a Novel Exact Model of Zoning Optimization for Marine Spatial Planning

Author

Mohadese Basirati, Patrick Meyer, Romain Billot, Erwan Bocher, Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Equipe DECIDE (Lab-STICC_DECIDE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Centre National de la Recherche Scientifique (CNRS), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)

Subjects
Optimization, Decision Support Systems, [INFO]Computer Science [cs], Linear Programming
Abstract

International audience; Marine spatial planning (MSP) as an efficient planning tool simplifies decisions on the sustainable use of marine resources. Determining an optimal zone for one marine user, considering the other users' activities, represents one challenge in MSP. We propose modeling the problem as an Exact Multi-Objective Integer Linear Program. We developed the raster data model to maximize the interest of the zone dedicated to a single actor and to maximize its spatial compactness. We are studying two approaches for resolution: first, a weighted sum, and second, an improved augmented version of the ε-constraint method, AUGMECON2. We validate the model by performing experiments on artificially generated data.

Published
2021

35. Seabed prediction from airborne topo-bathymetric lidar point cloud using machine learning approaches

Author

Julian Le Deunf, Rudresh Mishra, Yves Pastol, Romain Billot, Steve Oudot, Service Hydrographique et Océanographique de la Marine (SHOM), Ministère de la Défense, Equipe DECIDE (Lab-STICC_DECIDE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Understanding the Shape of Data (DATASHAPE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
supervised machine learning methods, topo-bathymetric lidar data, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [SDE]Environmental Sciences, [INFO]Computer Science [cs], data processing, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
Abstract

International audience; Predicting the seabed from unfiltered bathymetric lidar data is a very complex task and a critical issue in bathymetric data processing especially with the objective of nautical charting. This is challenging to ensure a high level of quality and security for the needs of a national hydrographic office. This paper proposes a methodology to predict the seabed based on machine learning, which could be useful to automate outlier detection and control the topo-bathymetric lidar point cloud datasets. Several predictive methods have been investigated to predict the seabed from our 2D + 1D data structure. A characteristic dataset of Corsica region was used as a case study for this predictive workflow.

Published
2021

38. Steady-state analysis of second-order traffic models and application to traffic flow control

Author

Julien Monteil, Romain Billot, Jacques Sau, N-E. El Faouzi, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CID_DECIDE, and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
Controller design, 050210 logistics & transportation, 021103 operations research, Steady state, Traffic flow control, Computer science, 05 social sciences, 0211 other engineering and technologies, Transportation, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI]Engineering Sciences [physics], [SPI.GCIV]Engineering Sciences [physics]/Civil Engineering, [MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph], Order (business), Linearization, Control theory, Modeling and Simulation, 0502 economics and business, Computer Science::Networking and Internet Architecture, [INFO]Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS, Software
Abstract

We investigate the problem of controller design after linearization around the steady-state solutions of second-order traffic models. At steady-state the traffic variables follow space-dependent au...

Published
2019

39. Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Author

Romain Billot, Philippe Lenca, Michel Walter, Jordan E. DeVylder, Yannis Haralambous, Taylor C. Ryan, Deok-Hee Kim-Dufor, Jonathan Marsh, Sofian Berrouiguet, Christophe Lemey, Aziliz Le Glaz, Lenca, Philippe, Hopital de Bohars - CHRU Brest (CHU - BREST ), Département Informatique (IMT Atlantique - INFO), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Equipe DECIDE (Lab-STICC_DECIDE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU), Fordham University Graduate School of Social Service, Soins Primaires, Santé Publique, Registre des cancers de Bretagne Occidentale (SPURBO), Université de Brest (UBO)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), and Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
[INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO], Computer science, [SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, PsycINFO, Review, computer.software_genre, Field (computer science), [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Unique identifier, MESH: Santé mentale, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 030212 general & internal medicine, Data Management, education.field_of_study, Medical record, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], artificial intelligence, psychiatry, 3. Good health, Systematic review, machine learning, MESH: Intelligence artificielle, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Public aspects of medicine, RA1-1270, Natural language processing, mental health, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], MESH: Traitement du langage naturel, Population, Computer applications to medicine. Medical informatics, R858-859.7, Health Informatics, MESH: Apprentissage machine, Machine learning, 03 medical and health sciences, Humans, Social media, natural language processing, education, [SDV.IB] Life Sciences [q-bio]/Bioengineering, business.industry, MESH: Data Mining, data mining, Mental health, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], MESH: Service hospitalier de psychiatrie, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

Background Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Published
2021

40. Detecting Dynamic Critical Links within Large Scale Network for Traffic State Prediction

Author

Nour-Eddin El Faouzi, Pierre-Antoine Laharotte, and Romain Billot

Subjects
Computer science, Dimensionality reduction, Feature extraction, Feature selection, Traffic flow, computer.software_genre, Latent Dirichlet allocation, Term (time), symbols.namesake, symbols, Data mining, Predictability, computer, Curse of dimensionality
Abstract

Can we expose the relationship between the physical dynamics of a network and its predictability? To contribute to this point, we propose a dimensionality reduction method for network states prediction based on spatiotemporal data. The method is intended to deal with large scale networks, where only a subset of critical links can be relevant for accurate multidimensional prediction (MIMO) performances. The algorithm is based on Latent Dirichlet Allocation (LDA) to highlight relevant topics in terms of networks dynamics. The feature selection trick relies on the assumption that the most representative links of the most dominant topics are critical links for short term prediction. The method is fully implemented to an original application field: short term road traffic prediction on large scale urban networks based on GPS data. Results highlight significant reductions in dimensionality and execution time, a global improvement of prediction performances as well as a better resilience to non recurrent traffic flow conditions.

Published
2020

41. Reducing Space Search in Combinatorial Optimization Using Machine Learning Tools

Author

Kenneth Sörensen, Romain Billot, Marc Sevaux, Flavien Lucas, Lab-STICC_UBS_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Lab-STICC_IMTA_CID_DECIDE, Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Faculty of Applied Economics, and University of Antwerp (UA)

Subjects
050101 languages & linguistics, business.industry, Computer science, 05 social sciences, Decision tree, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Solver, Space (commercial competition), Machine learning, computer.software_genre, Standard deviation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Vehicle routing problem, 0202 electrical engineering, electronic engineering, information engineering, Combinatorial optimization, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Local search (optimization), Artificial intelligence, business, computer, Metaheuristic, ComputingMilieux_MISCELLANEOUS
Abstract

A new metaheuristic, called Feature-Guided MNS (FG-MNS) is proposed, combining well-known local search with simple machine learning techniques. In this metaheuristic, a solution is represented by features (mean depth of each route, standard deviation of the length of each route, etc.). The solver uses decision trees to define promising areas in the features space. The search is mainly focused on the promising areas, in order to minimize the exploration time, and to improve the quality of the found solutions. Additional neighborhoods, guided by the features are proposed.

Published
2020

42. Prévention du suicide et santé connectée

Author

Alis Le Floch, Valérie Le Moal, Philippe Lenca, Elise Guillodo, Romain Billot, Michel Walter, Sofian Berrouiguet, Lab-STICC_IMTA_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Soins Primaires, Santé Publique, Registre des cancers de Bretagne Occidentale (SPURBO), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), and Hopital de Bohars - CHRU Brest (CHU - BREST )

Subjects
Aide à la décision, Prévention, 020205 medical informatics, Santé connectée, MEDLINE, 02 engineering and technology, Suicide prevention, General Biochemistry, Genetics and Molecular Biology, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0202 electrical engineering, electronic engineering, information engineering, medicine, eHealth, Suicide Risk, Patient discharge, E-health, Data collection, business.industry, Soins personnalisés, General Medicine, Medical decision making, medicine.disease, 3. Good health, Suicide, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Medical emergency, business, 030217 neurology & neurosurgery
Abstract

L’évaluation ponctuelle du risque suicidaire habituellement conduite aux urgences, après un geste suicidaire, ne rend pas compte de son évolution après la sortie des soins, alors même que le risque de récidive reste important plusieurs mois après. Dans ces conditions, les possibilités d’identification, et donc de prise en charge, des patients à risque suicidaire sont limitées. Le développement de la santé connectée (eHealth) donne désormais accès en temps réel à des informations sur l’état de santé d’un patient entre deux séjours en centre de soins. Cette extension de l’évaluation clinique à l’environnement du patient permet de développer des outils d’aide à la décision face à la gestion du risque suicidaire.

Published
2018

43. Machine Learning and Natural Language Processing in Mental Health: Systematic Review (Preprint)

Author

Aziliz Le Glaz, Yannis Haralambous, Deok-Hee Kim-Dufor, Philippe Lenca, Romain Billot, Taylor C Ryan, Jonathan Marsh, Jordan DeVylder, Michel Walter, Sofian Berrouiguet, and Christophe Lemey

Abstract

BACKGROUND Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. OBJECTIVE The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice METHODS This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. RESULTS A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. CONCLUSIONS Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Published
2019

44. Crowdfunding platform and inter-project social network: example of Ulule

Author

Inna Lyubareva, Laurent Brisson, Cécile Bothorel, Romain Billot, Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire d'Economie et de Gestion de l'Ouest (LEGO), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Brestois des Sciences de l'Homme et de la Société (IBSHS), Université de Brest (UBO)-Université de Brest (UBO)-Université de Bretagne Sud (UBS)-Université Bretagne Loire (UBL), Lab-STICC_IMTA_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)

Subjects
[QFIN]Quantitative Finance [q-fin], [SHS.INFO]Humanities and Social Sciences/Library and information sciences, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
Abstract

International audience

Published
2019

45. An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support

Author

Michel Walter, Romain Billot, Philippe Lenca, Philippe Courtet, Enrique Baca-García, Jorge Lopez-Castroman, Mark E. Larsen, Isabelle Jaussent, Sofian Berrouiguet, Lab-STICC_IMTA_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), CHRU Brest - Psychiatrie Adulte (CHU - Brest- Psychiatrie), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Black Dog Institute [Sydney, Australia], University of New South Wales [Sydney] (UNSW), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Montpellier (UM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Department of Psychiatry (Hospital Universitario Fundacion Jimenez Diaz ), This study was funded partially by Instituto de Salud Carlos III (ISCIII PI13/02200, PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), and the American Foundation for Suicide Prevention (LSRG-1-005-16)., UAM. Departamento de Psiquiatría, École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), IMT Atlantique (IMT Atlantique), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), and Lenca, Philippe

Subjects
Computer science, [SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Clinical decision support system, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], computer.software_genre, Suicide prevention, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, prevention, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Electronic health, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], [INFO.INFO-IU] Computer Science [cs]/Ubiquitous Computing, 3. Good health, [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], Suicide, Psychiatry and Mental health, clinical decision support system, Data mining, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Medicina, Automatic identification and data capture, MEDLINE, Sample (statistics), Context (language use), [INFO] Computer Science [cs], [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, 03 medical and health sciences, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, [INFO]Computer Science [cs], suicide attempts, Cluster analysis, suicide, Original Paper, mobile phone, Prevention, data mining, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], 030227 psychiatry, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Mobile phone, Dynamic decision-making, computer, Suicide attempts, 030217 neurology & neurosurgery, electronic health
Abstract

Background: In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health-based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective: The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods: We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results: We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions: Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians., This study received a Hospital Clinical Research Grant (PHRC 2009) from the French Health Ministry. None of the funding sources had any involvement in the study design; collection, analysis, or interpretation of data; writing of the report; or the decision to submit the paper for publication. This study was funded partially by Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), and the American Foundation for Suicide Prevention (LSRG-1-005-16).

Published
2019

46. A comment on 'what makes a VRP solution good? The generation of problem-specific knowledge for heuristics'

Author

Marc Sevaux, Flavien Lucas, Romain Billot, Lab-STICC_UBS_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Lab-STICC_TB_CID_DECIDE, École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), and Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Optimization, 0209 industrial biotechnology, General Computer Science, Computer science, media_common.quotation_subject, 0211 other engineering and technologies, Principal component analysis, 02 engineering and technology, Management Science and Operations Research, Specific knowledge, Machine learning, computer.software_genre, 020901 industrial engineering & automation, Vehicle routing problem, Quality (business), Data mining, ComputingMilieux_MISCELLANEOUS, media_common, Complement (set theory), 021103 operations research, business.industry, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Vehicle routing, Random forest, Modeling and Simulation, Artificial intelligence, Heuristics, business, computer
Abstract

International audience; We propose a comment about the article “What makes a VRP solution good? The generation of problem-specific knowledge for heuristics” (Arnold and Sörensen, 2019) by Florian Arnold and Kenneth Sörensen. In the original contribution, the authors implemented several Machine Learning (ML) algorithms in order to predict good vs. not good solutions. Then, some outcomes of the algorithms were discussed in terms of the predictive power of the solutions features. The purpose was then to use the extracted knowledge to improve existing heuristics. The first contribution of our comment is to validate and complement some of the conclusions of the authors. Then, we argue than most of the extracted knowledge can be retrieved by classical data reduction methods such as Principal Component Analysis (PCA). Hence, instead of ML-based predictions, a factorial analysis provides a powerful and synthetic view of the variables inter-dependencies in the light of solution quality. Thanks to the datasets provided by the authors in the original article, new experimental results are conducted. Finally, the integration of these results into future “boosted” heuristics is discussed.

Published
2019

47. Clinicians’ Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App

Author

Philippe Courtet, Christophe Lemey, Mark E. Larsen, Sofian Berrouiguet, Philippe Lenca, Jordan E. DeVylder, Michel Walter, Enrique Baca-García, Romain Billot, Hopital de Bohars - CHRU Brest (CHU - BREST ), Black Dog Institute, University of New South Wales, Sydney, Fordham University [New York], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Department of Psychiatry (Hospital Universitario Fundacion Jimenez Diaz ), Lab-STICC_IMTA_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), UAM. Departamento de Psiquiatría, and Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD)

Subjects
Decision support system, Prodromal symptoms, 020205 medical informatics, decision support systems, clinical, 02 engineering and technology, clinical, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Feasibility studies, Acceptability, Mobile applications, Outpatients, 0202 electrical engineering, electronic engineering, information engineering, Prospective Studies, Ecological momentary assessment, media_common, Ecology, feasibility studies, Mental Disorders, prodromal symptoms, ecological momentary assessment, Middle Aged, mobile applications, 3. Good health, Feeling, young adult, Mental health, The Internet, Female, Psychology, decision support systems, mental health, Adult, medicine.medical_specialty, Medicina, media_common.quotation_subject, MEDLINE, Health Informatics, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, acceptability, medicine, Web application, Humans, Mobile technology, Psychiatry, Decision support systems, clinical, Internet, Original Paper, business.industry, Readability, outpatients, Young adult, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, internet, business
Abstract

International audience; Background: Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients' environments using ecological momentary assessment techniques to support preventive therapeutic decision making.Objective: The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features.Methods: We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient’s emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies.Results: Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this.Conclusions: We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups.

Published
2019

48. A clustering approach to infer Wikipedia contributors' profile

Author

Nicolas Jullien, Romain Billot, Shubham Krishna, Lab-STICC_IMTA_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Département Logique des Usages, Sciences sociales et Sciences de l'Information (IMT Atlantique - LUSSI), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire d'Economie et de Gestion de l'Ouest (LEGO), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois des Sciences de l'Homme et de la Société (IBSHS), Université de Brest (UBO)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Môle Armoricain de Recherche sur la SOciété de l'information et des usages d'INternet (MARSOUIN), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)-Université de Brest (UBO)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Nicolas Jullien, ACM, ANR-14-CE29-0013,CAPACITY,CAPACITES NUMERIQUES(2014), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), IMT Atlantique (IMT Atlantique), Université de Brest (UBO)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Université de Rennes (UR)-Université de Bretagne Sud (UBS)-Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)-Université de Brest (UBO)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 2 (UR2)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Brestois des Sciences de l'Homme et de la Société (IBSHS), and Université de Brest (UBO)-Université de Brest (UBO)-Université de Bretagne Sud (UBS)-Université Bretagne Loire (UBL)

Subjects
FOS: Computer and information sciences, Casual, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Stability (learning theory), user proole, Computer Science - Human-Computer Interaction, 02 engineering and technology, Statistics - Applications, Human-Computer Interaction (cs.HC), Computer Science - Computers and Society, Online communities, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 020204 information systems, 0502 economics and business, Computers and Society (cs.CY), 0202 electrical engineering, electronic engineering, information engineering, Applications (stat.AP), ACM: C.: Computer Systems Organization, Cluster analysis, User profile, 05 social sciences, Data science, Replication (computing), JEL: J - Labor and Demographic Economics/J.J5 - Labor–Management Relations, Trade Unions, and Collective Bargaining/J.J5.J54 - Producer Cooperatives • Labor Managed Firms • Employee Ownership, [SHS.GESTION]Humanities and Social Sciences/Business administration, 050211 marketing, JEL: L - Industrial Organization/L.L8 - Industry Studies: Services/L.L8.L86 - Information and Internet Services • Computer Software, JEL: O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O31 - Innovation and Invention: Processes and Incentives, clustering, Wikipedia
Abstract

In online communities, recent studies have strongly improved our knowledge about the different types or profiles of contributors, from casual to very involved ones, through focused people. However they do so by using very complex methodologies (qualitative-quantitative mix, with a high workload to manually codify/characterize the edits), making their replication for the practitioners limited. These studies are on the English Wikipedia only. The objective of this paper is to highlight different profiles of contributors with clustering techniques. The originality is to show how using only the edits, and their distribution over time, allows to build these contributors profiles with a good accuracy and stability amongst languages. The methodology is validated with both Romanian and Danish wikis. The highlighted profiles are identifiable early in the history of involvement, suggesting that light monitoring of newcomers may be sufficient to adapt the interaction with them and increase the retention rate.

Published
2018

49. Clinicians’ Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App (Preprint)

Author

Christophe Lemey, Mark Erik Larsen, Jordan Devylder, Philippe Courtet, Romain Billot, Philippe Lenca, Michel Walter, Enrique Baca-García, and Sofian Berrouiguet

Abstract

BACKGROUND Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients’ environments using ecological momentary assessment techniques to support preventive therapeutic decision making. OBJECTIVE The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features. METHODS We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient’s emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies. RESULTS Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this. CONCLUSIONS We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups.

Published
2018

50. An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support (Preprint)

Author

Sofian Berrouiguet, Romain Billot, Mark Erik Larsen, Jorge Lopez-Castroman, Isabelle Jaussent, Michel Walter, Philippe Lenca, Enrique Baca-García, and Philippe Courtet

Abstract

BACKGROUND In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health–based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. OBJECTIVE The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. METHODS We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. RESULTS We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. CONCLUSIONS Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.

Published
2018

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