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, Note that even for qualitative outcomes "disease burden") measurement tool may not be defined

, outcome measure + measurement tool used: "depression measured by the BDI-II, the QALY based on the EQ-5D

, claim duration (in days) during 12 months follow-up" 4. outcome measure + analysis metric

, outcome measure + analysis metric + time points: "change in HOMA index, from week 0 (pre-treatment) to week 6

, outcome measure + aggregation metric: "the proportion of women reporting a live birth defined as the delivery of one or more living infants, >20 weeks gestation or 400 g or more birth weight

, outcome measure + analysis metric + aggregation metric: "mean IMT-CCA change" 8. outcome measure + analysis metric + time points + aggregation metric, The mean decrease in HAM-D score from baseline

, for the per protocol (PP) population using a "worse eye" analysis". An outcome description may state that the outcome is a surrogate measure: "a surrogate marker, Ang-2", -and may also refer to the substituted measure: "the active local radiation dose leading to metastasis infiltrating T cells as a surrogate parameter for antitumor activity". Apart from aspects describing what was measured and how, an outcome may explicitly state the comparison between groups that was performed, IOP from baseline to week 4 at 8 a.m. and 4 p.m

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, Nous avons présenté l'état de l'art et nos premières expériences pour ces tâches. Pour chaque tâche, nous avons suggéré quelques directions possibles pour les travaux futurs. Pour évaluer nos algorithmes à base de règles décrits dans les articles ci-dessus et pour entrainer des algorithmes d'apprentissage automatique, un corpus annoté avec les informations pertinentes est nécessaire. Le chapitre 2 décrit nos efforts pour collecter un corpus d'articles biomédicaux et les annoter pour le spin et les informations d'appoint. Le papier a présenté un schéma d'annotation pour le spin et l'information liée, nos guidelines d'annotation et les difficultés que nous avons rencontré, Le chapitre 1 a présenté nos premières pas dans le développement d'algorithmes de TAL pour la détection automatique du spin dans des articles biomédicaux. Nous avons proposé un schéma pour un algorithme d'extraction automatique d'affirmations importants dans les résumés d'articles biomédicaux et des informations d'appoint possibles

, L'extraction des résultats déclarés (primaires) et rapportés est une tâche principale pour la détection de spin. Dans cet article, nous avons examiné l'état de l'art pour l'extraction des résultats. Nous avons présenté notre corpus annoté manuellement de 2 000 phrases avec résultats déclarés (primaires) et 1 940 phrases avec résultats rapportés, qui est disponible gratuitement. Nous avons comparé deux approches d'apprentissage profond: une approche d'adaptation (" fine-tuning ") simple et une approche utilisant des champs aléatoires conditionnels (CRF) et des réseaux récurrents bi-directionnels avec mémoire à long terme (Bi-LSTM), en conjonction avec des plongements lexicaux ("embeddings") de caractères et des plongements lexicaux dérivées de modèles linguistiques pré-appris, Le chapitre 3 a décrit nos expériences d'utilisation de l'apprentissage profond pour extraire les résultats d'un essai -variables surveillées au cours d'essais cliniques

, Les meilleurs résultats obtenus ont été la F-mesure au niveau des tokens de 88, p.52

, pour les résultats primaires (fine-tuned BioBERT) et de 79.42% pour les résultats rapportés

, Le chapitre 4 a décrit le développement d'un algorithme d'évaluation de la similarité sé-203

, Sur la base du corpus annoté pour les résultats primaires et rapportés (décrit dans le chapitre précédent), nous avons annoté des paires de résultats primaires et rapportés pour la similarité sémantique (sur une échelle binaire). Le corpus est disponible gratuitement. Nous avons créé un corpus étendu en ajoutant les variantes permettant de faire référence à un résultat (par exemple, l'utilisation d'un nom d'outil de mesure à la place de l'expression

, Pour l'approche de base, nous avons utilisé un nombre de mesures de similarité sémantique, basées sur des charactères, des tokens et des lemmes, des distances entre des expressions dans le réseau sémantique WordNet et des représentations vectorielles des expressions

, Nous avons entraîné et testé différents classificateurs d'apprentissage automatique en utilisant une combinaison de mesures de similarité en tant que traits. Enfin, nous avons utilisé une approche d'apprentissage profond consistant à adpater ("fine tuning") les représentations linguistiques profondes pré-apprises sur le corpus des paires de résultats. Nous avons testé plusieurs modèles de langue: BERT ( entraîné sur des textes de domaine général, BioBERT et SciBERT (entraînés respectivement sur le domaines biomédical et le domaine scientifique général

, Le meilleur résultat sur le corpus original a été montré par le modèle BioBERT, avec une F-mesure de 89.75%

, Le chapitre 5 a décrit les expériences d'utilisation des algorithmes d'extraction de résultats et d'évaluation de la similarité sémantique (décrits dans les deux chapitres précédents) pour développer un algorithme de détection de la substitution de résultat -en anglais "outcome switching" -un type de spin consistant en un changement injustifié (en omettant ou en ajoutant) des résultats définis d'un essai. Nous nous sommes concentrés sur la substitution de résultat primaire. Nous avons annoté un corpus avec les informations nécessaires à la détection de substitution de résultat : 2 000 phrases avec 1 694 résultats primaires; 1 940 phrases avec 2 251 résultats rapportés

, Nous avons utilisé une combinaison d'extraction d'informations, d'analyse de données structurées et de méthodes d'évaluation de la similarité sémantique pour identifier la substitution du résultat primaire. Les algorithmes d'évaluation de la similarité sémantique ont été évalués sur le corpus d'origine et sur un corpus étendu avec des variantes de référence à un résultat

, La meilleure performance obtenue était la F-mesure de 88.42% pour l'extraction des résultats PhD Portfolio Name PhD student: Anna Koroleva PhD period, 2016.

, Name PhD supervisor: Patrick Paroubek, Patrick Bossuyt PhD training Year Workload (Hours) in the 21st century. The editor-in-chief, vol.3, p.5

, From Government to Bench: How a funding agency spends government's science budget

, Parcours de l'après-thèse / what about post-thesis?, p.5, 2019.

, Peer-review -answering to reviewers and editors 2019 2 Devising your career plan: an alliance between your mind, your heart and your guts, p.1, 2019.

, Preparing job applications outside academia: optimizing your written and oral communication

, A review of basic statistical concepts: variability, uncertainty, confidence intervals 2016 1,5 A review of basic statistical concepts: p values, replicability 2016, vol.1, p.5

, Using causal diagrams to understand problems of confounding and selection bias, p.1, 2016.

, Effect measures, Effect modification and non-collapsibility. Adjustment for confounding, 20161.

, Identify causal effect parameters which our research is targeting / What assumptions are reasonable, how might we approach it? 2016 2 13th EUROLAN School on Natural Language Processing, vol.3, p.25, 2019.

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, Value of Qualitative Research; Introduction to Qualitative Research

, Collecting Data; Writing an Interview Guide, Qualitative Research Methods, p.33, 20171.

, Conducting an Interview; Qualitative Analysis; Analysing Transcribed interview Data, 2008.

, From quantitative to qualitative, vol.1, p.5

. Séminaire,

, Webinar on Entrepreneurship, p.1, 2018.

. Webinar, The current research climate: changing culture and the incentive systems

, Presentations Vers la détection automatique des affirmations inappropriées dans les articles scientifiques, 18e REncontres jeunes Chercheurs en Informatique pour le TAL, p.2017

, Automatic detection of inadequate claims in biomedical articles: first steps, 2017.

, On the contribution of specific entity detection and comparative construction to automatic spin detection in biomedical scientific publications. The Second Workshop on Processing Emotions, Decisions and Opinions, The 8th Language and Technology Conference, p.2017, 2017.

, Annotating Spin in Biomedical Scientific Publications: the case of Randomized Controlled, Trials (RCTs), 2018.

, Scientific rigour versus power to convince: an NLP approach to detecting distorted conclusions in biomedical, 2018.

, Demonstrating ConstruKT, a text annotation toolkit for generalized linguistic constructions applied to communication spin, LTC, vol.2019, 2019.

, Extracting relations between outcome and significance level in Randomized Controlled Trials (RCTs) publications. ACL BioNLP workshop, 2019.

, Analysing clinical trial outcomes in trial registries, 2019.

, A machine learning algorithm and tools for automatic detection of spin (distorted presentation of results) in articles reporting randomized controlled trials, ICTMC, pp.26-30, 2017.

, Workshop on Curative Power of MEdical Data, pp.12-13, 2017.

, The Second Workshop on Processing Emotions, Decisions and Opinions, 2017.

, months Secondment at the UK EQUATOR Network, 2018.

A. Koroleva, Towards automatic detection of inadequate claims in scientific articles, Vers la détection automatique des affirmations inappropriées dans les articles scientifiques, p.18

, REncontres jeunes Chercheurs en Informatique pour le TAL, pp.135-148, 2017.

A. Koroleva and P. Paroubek, Automatic detection of inadequate claims in biomedical articles: first steps, Proceedings of Workshop on Curative Power of MEdical Data, Constanta, 2017.

A. Koroleva and P. Paroubek, Annotating Spin in Biomedical Scientific Publications: the case of Random Controlled Trials (RCTs), 2018.

A. Koroleva and P. Paroubek, Demonstrating ConstruKT, a text annotation toolkit for generalized linguistic constructions applied to communication spin, LTC 2019: Demo Session Anna Koroleva, Patrick Paroubek. Extracting relations between outcome and significance level in Randomized Controlled Trials (RCTs) publications. Proceedings of ACL BioNLP workshop, 2019.

A. Koroleva, S. Kamath, and P. Paroubek, Extracting primary and reported outcomes from articles reporting randomized controlled trials using pre-trained deep language representations

A. Koroleva and P. Paroubek, Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations, Journal of Biomedical Informatics -X, 2019.

A. Koroleva, P. Paroubek-;-sanjay, . Kamath, M. M. Patrick, P. Bossuyt et al., Can computers be taught to peer review and detect spin? Issues and challenges of a novel application of Natural Language Processing: a case study. Under revision Anna Koroleva

A. Koroleva, C. O. Parra, and P. Paroubek, On improving the implementation of automatic updating of systematic reviews, JAMIA Open, p.44, 2019.

A. Other, C. Koroleva, P. Masson, and . Paroubek, Analysing clinical trial outcomes in trial registries, 2019.

A. Koroleva and P. Paroubek, A machine learning algorithm and tools for automatic detection of spin (distorted presentation of results) in articles reporting randomized controlled trials, 2019.

A. Koroleva and P. Paroubek, Automating the detection of communication spin in scientific articles reporting Randomized Controlled Trials, preparation Contributing authors ? Patrick Paroubek (PP)

C. Limsi and U. Paris-saclay,

?. Patrick and M. M. Bossuyt, PMMB), co-supervisor Academic Medical Center

P. Sideview and . Risborough, PhD student LIMSI, Croatia ? Sanjay Kamath (SK)