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1. Privacy-preserving mimic models for clinical named entity recognition in French

Author

Nesrine Bannour, Perceval Wajsbürt, Bastien Rance, Xavier Tannier, Aurélie Névéol, Information, Langue Ecrite et Signée (ILES), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), This work was supported by ITMO Cancer Aviesan. The funding organization had no role in the conceptualization, design, data collection and analysis, preparation of the paper, or the decision to publish it., Tannier, Xavier, École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE)

Subjects
Computer, Narration, Mimic learning, Privacy, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Natural language processing, Confidentiality Datasets as topic, Humans, Electronic health records, Health Informatics, Neural networks, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science Applications
Abstract

International audience; A vast amount of crucial information about patients resides solely in unstructured clinical narrative notes. There has been a growing interest in clinical Named Entity Recognition (NER) task using deep learning models. Such approaches require sufficient annotated data. However, there is little publicly available annotated corpora in the medical field due to the sensitive nature of the clinical text. In this paper, we tackle this problem by building privacy-preserving shareable models for French clinical Named Entity Recognition using the mimic learning approach to enable the knowledge transfer through a teacher model trained on a private corpus to a student model. This student model could be publicly shared without any access to the original sensitive data. We evaluated three privacy-preserving models using three medical corpora and compared the performance of our models to those of baseline models such as dictionary-based models. An overall macro F-measure of 70.6% could be achieved by a student model trained using silver annotations produced by the teacher model, compared to 85.7% for the original private teacher model. Our results revealed that these privacy-preserving mimic learning models offer a good compromise between performance and data privacy preservation.

Published
2022

2. RNN Language Model Estimation for Out-of-Vocabulary Words

Author

Illina, Irina, Fohr, Dominique, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), This work is funded by the ContNomina project supported by the French National Research Agency (ANR) under contract ANR-12-BS02-0009. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria, CNRS, RENATER and other Universities and organizations (https://www.grid5000.fr)., and ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012)

Subjects
Speech Recognition, Neural Networks, Vocabulary Extension, Proper Names, Out-Of-Vocabulary Words, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]
Abstract

International audience; One important issue of speech recognition systems is Out-of Vocabulary words (OOV). These words, often proper nouns or new words, are essential for documents to be transcribed correctly. Thus, they must be integrated in the language model (LM) and the lexicon of the speech recognition system. This article proposes new approaches to OOV proper noun probability estimation using Recurrent Neural Network Language Model (RNNLM). The proposed approaches are based on the notion of closest in-vocabulary (IV) words (list of brothers) to a given OOV proper noun. The probabilities of these words are used to estimate the probabilities of OOV proper nouns thanks to RNNLM. Three methods for retrieving the relevant list of brothers are studied. The main advantages of the proposed approaches are that the RNNLM is not retrained and the architecture of the RNNLM is kept intact. Experiments on real text data from the website of the Euronews channel show relative perplexity reductions of about 14% compared to baseline RNNLM.

Published
2020

3. Disentangling the attention network test: behavioral, event related potentials, and neural source analyses

Author

Guillermo Izquierdo, Manuel Vázquez-Marrufo, Alejandro Galvao-Carmona, Dolores Páramo, María Luisa Benítez, Javier J. Gonzalez-Rosa, Antonio R. Hidalgo-Muñoz, [Galvao-Carmona,A, Hidalgo-Muñoz,AR, Vázquez-Marrufo,M] Psychophysiology Unit (Lab B508), Department of Experimental Psychology, Faculty of Psychology, University of Seville, Seville, Spain. [González-Rosa,JJ] Laboratory for Clinical Neuroscience, Centre of Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain. [Páramo,D, Izquierdo,G] Multiple Sclerosis Unit, Virgen Macarena University Hospital, Seville, Spain. [Benítez,ML] Department of Physiotherapy, Faculty of Nursing, University of Seville, Seville, Spain., This study was supported by Plan Nacional I+D+i (Ministerio de Economía y Competitividad, Government of Spain, PSI2010- 16825), Asociación Neuroinvest and Asociación Sanitaria Virgen Macarena. which has greatly improved this paper, Universidad de Sevilla. Departamento de Psicología Experimental, Universidad de Sevilla. Departamento de Fisioterapia, and Ministerio de Economía y Competitividad (MINECO). España

Subjects
CNV, Neurociencias, Sensory system, source analysis, lcsh:RC321-571, Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings], Behavioral Neuroscience, Psychiatry and Psychology::Psychological Phenomena and Processes::Mental Processes::Learning::Cues [Medical Subject Headings], Psychiatry and Psychology::Psychological Phenomena and Processes::Mental Processes [Medical Subject Headings], Disciplines and Occupations::Natural Science Disciplines::Biological Science Disciplines::Neurosciences [Medical Subject Headings], Event-related potential, Attention network, Ssource analysis, Psychiatry and Psychology::Psychological Phenomena and Processes::Psychophysiology::Arousal::Attention [Medical Subject Headings], Orienting, Phenomena and Processes::Physiological Phenomena::Electrophysiological Phenomena::Evoked Potentials [Medical Subject Headings], Attention, Original Research Article, P300, Source analysis, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biological Psychiatry, Expectancy theory, Artificial neural network, orienting, Information processing, Psychiatry and Psychology::Behavior and Behavior Mechanisms::Adaptation, Psychological::Orientation [Medical Subject Headings], neural networks, attention, Psychiatry and Mental health, Neuropsychology and Physiological Psychology, Neurology, Potenciales evocados, alerting, Procesos mentales, Basic level, ANT, Alerting, Psychology, Neuroscience, Neural networks
Abstract

Background: The study of the attentional system remains a challenge for current neuroscience. The “Attention Network Test” (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. Results: This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. Conclusions: The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects. Ministerio de Economía y Competitividad PSI2010- 16825

Published
2014