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Theses

Analyse des médias sociaux de santé pour évaluer la qualité de vie des patientes atteintes d’un cancer du sein

Abstract : In 2015, the number of new cases of breast cancer in France is 54,000.The survival rate after 5 years of cancer diagnosis is 89%.If the modern treatments allow to save lives, some are difficult to bear. Many clinical research projects have therefore focused on quality of life (QoL), which refers to the perception that patients have on their diseases and their treatments.QoL is an evaluation method of alternative clinical criterion for assessing the advantages and disadvantages of treatments for the patient and the health system. In this thesis, we will focus on the patients stories in social media dealing with their health. The aim is to better understand their perception of QoL. This new mode of communication is very popular among patients because it is associated with a great freedom of speech, induced by the anonymity provided by these websites.The originality of this thesis is to use and extend social media mining methods for the French language. The main contributions of this work are: (1) construction of a patient/doctor vocabulary; (2) detection of topics discussed by patients; (3) analysis of the feelings of messages posted by patients and (4) combinaison of the different contributions to quantify patients discourse.Firstly, we used the patient's texts to construct a patient/doctor vocabulary, specific to the field of breast cancer, by collecting various types of non-experts' expressions related to the disease, linking them to the biomedical terms used by health care professionals. We combined several methods of the literature based on linguistic and statistical approaches. To evaluate the relationships, we used automatic and manual validations. Then, we transformed the constructed resource into human-readable format and machine-readable format by creating a SKOS ontology, which is integrated into the BioPortal platform.Secondly, we used and extended literature methods to detect the different topics discussed by patients in social media and to relate them to the functional and symptomatic dimensions of the QoL questionnaires (EORTC QLQ-C30 and EORTC QLQ-BR23). In order to detect the topics discussed by patients, we applied the unsupervised learning LDA model with relevant preprocessing. Then, we applied a customized Jaccard coefficient to automatically compute the similarity distance between the topics detected with LDA and the items in the auto-questionnaires. Thus, we detected new emerging topics from social media that could be used to complete actual QoL questionnaires. This work confirms that social media can be an important source of information for the study of the QoL in the field of cancer.Thirdly, we focused on the extraction of sentiments (polarity and emotions). For this, we evaluated different methods and resources for the classification of feelings in French.These experiments aim to determine useful characteristics in the classification of feelings for different types of texts, including texts from health forums.Finally, we used the different methods proposed in this thesis to quantify the topics and feelings identified in the health social media.In general, this work has opened promising perspectives on various tasks of social media analysis for the French language and in particular the study of the QoL of patients from the health forums.
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Submitted on : Monday, November 12, 2018 - 4:45:05 PM
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Mike Donald Tapi Nzali. Analyse des médias sociaux de santé pour évaluer la qualité de vie des patientes atteintes d’un cancer du sein. Autres [stat.ML]. Université Montpellier, 2017. Français. ⟨NNT : 2017MONTS039⟩. ⟨tel-01919773⟩

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