AUTONOMOUS CT REPLACEMENT METHOD FOR THE SKULL PROSTHESIS MODELLING

The geometric modeling of prosthesis is a complex task from medical and engineering viewpoint. A method based on CT replacement is proposed in order to circumvent the related problems with the missing information to modeling. The method is based on digital image processing and swarm intelligence algorithm. In this approach, a missing region on the defective skull is represented by curvature descriptors. The main function of the descriptors is to simplify the skull’s contour geometry; and they are defined from the Cubic Bezier Curves using a meta-heuristic process for parameter’s estimation. The Artificial Bee Colony (ABC) optimization technique is applied in order to evaluate the best solution. The descriptors from a defective CT slice image are the searching parameters in medical image databases, and a similar image, i.e. with similar descriptors, can be retrieval and used to replace the defective slice. Thus, a prosthesis piece is automatically modeled with information extracted from distinct skulls with similar anatomical characteristics

A CAD-BASED CONCEPTUAL METHOD FOR SKULL PROSTHESIS MODELLING

The geometric modeling of a personalized part of the tissue built according to individual morphology is an essential requirement in anatomic prosthesis. A 3D model to fill the missing areas in the skull bone requires a set of information sometimes unavailable. The unknown information can be estimated through a set of rules referenced to a similar yet known set of parameters of the similar CT image. The proposed method is based on the Cubic Bezier Curves descriptors generated by the de Casteljou algorithm in order to generate a control polygon. This control polygon can be compared to a similar CT slice in an image database. The level of similarity is evaluated by a meta-heuristic fitness function. The research shows that it is possible to reduce the amount of points in the analysis from the original edge to an equivalent Bezier curve defined by a minimum set of descriptors. A study case shows the feasibility of method through the interoperability between the prosthesis descriptors and the CAD environment

Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

International audienceAutomatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented

SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks

The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multi-purpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. In this study, we developed a semantically annotated corpus using clinical texts from multiple medical specialties, document types, and institutions. We present the following: (1) a survey listing common aspects and lessons learned from previous research, (2) a fine-grained annotation schema which could be replicated and guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. The result of this work is the SemClinBr, a corpus that has 1,000 clinical notes, labeled with 65,117 entities and 11,263 relations, and can support a variety of clinical NLP tasks and boost the EHR's secondary use for the Portuguese language

Temporal Relation Extraction in Clinical Texts

International audienceUnstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages

A Multilabel Approach to Portuguese Clinical Named Entity Recognition

Objectives: Clinical Named Entity Recognition is a critical Natural Language Processing task, as it could support biomedical research and healthcare systems. While most extracted clinical entities are based on single-label concepts, it is very common in the clinical domain entities with more than one semantic category simultaneously. This work proposes BERT-based models to support multilabel clinical named entity recognition in the Portuguese language. Methods: For the experiment, we used the Label Powerset method applied to the multilabel corpus SemClinBr. Results: We compare our results with a Conditional Random Fields baseline, reaching +2.1 in precision, +11.2 in recall, and +7.4 in F1 with a clinical-biomedical BERT model (BioBERTpt). Conclusion: We achieved higher results for both exact and partial metrics, contributing to the multilabel semantic processing of clinical narratives in Portuguese

BioBERTpt ::a Portuguese neural language model for clinical named entity recognition

With the growing number of electronic health record data, clinical NLP tasks have be-come increasingly relevant to unlock valu-able information from unstructured clinical text. Although the performance of down-stream NLP tasks, such as named-entity recog-nition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to sup-port clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narra-tives and compared the results with existing BERT models. Our in-domain model out-performed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process en-hanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model