1,720,956 research outputs found
Explainable Classification of Medical Documents Through a Text-to-Text Transformer
No full textDeath certificates are important medical records which are collected for the purpose of public healthcare and statistics by multiple organizations around the globe. Due to their importance, those certificates are compiled by experienced medical practitioner according to a standard defined by the World Health Organization including rules to select an underlying cause of death (UCOD). For this reason, the coding of death certificates is a slow and costly process. To overcome these issues, the scientific community proposed deep learning approaches to perform such a task. Despite those systems achieve high accuracy scores (close to 1), their complexity makes the obscure to the final user, making it unfeasible the adoption as a decision support system. In this paper, we propose a model based on text-to-text transformers which is able to provide a UCOD as well as to generate a human-readable explanation for its classification. We compare the proposed approach to state-of-the-art interpretable rule-based systems
Automatic Assignment of ICD-10 Codes to Diagnostic Texts using Transformers Based Techniques
No full textA fundamental task for epidemiology, statistics, and health informatics is to associate some standardized meaning to textual expressions, to enable their retrieval, aggregation and interpretation. Among the relevant expressions, those mentioning health conditions and diagnoses are of paramount importance and can be found in almost any clinical document, including death certificates. These expressions are usually coded with the International Classification of Diseases. In this paper we employ both classical Machine Learning and BERT based models to perform the automatic classification of diagnostic texts extracted from death certificates. We show the effectiveness of our proposed approach over a set of experiments, where we experiment with multiple set of features and variant of the algorithms. Our results show that BERT based models, and in particular the ones pre-trained on the specific domain outperform classical ML algorithms, reaching Accuracy and F1-Score of respectively 0.952 and 0.943
Few-Shot Learning of Medical Coding Systems: A Case Study on Death Certificates with BERT and Mistral
Full text linkIdentifying the Underlying Cause of Death accurately is crucial for effective healthcare policy and planning. The World Health Organization recommends using the ICD-10 system to standardize death certificate coding, a task often supported by semi-automated systems. This paper assesses the effectiveness of BERT and Mistral language models in automating this process, focusing particularly on their handling of varied instance densities per ICD code, ranging from 1 (simulating the introduction of a new code) to 100 (representing well-established codes). Through extensive comparative experiments, we find that the finetuned Mistral model substantially outperforms BERT, especially in scenarios with limited data. Mistral's higher effectiveness, even with limited data from less commonly used codes, highlight its potential to significantly enhance automated coding systems
Logical Rules and a Preliminary Prototype for Translating Mortality Coding Rules from ICD-10 to ICD-11
Full text linkIris is a system for coding multiple causes of death in ICD-10 and for the selection of the underlying cause of death, based on a knowledge base composed by a large number of rules. With the adoption of ICD-11, those rules need translation to ICD-11. A pre-project has been carried out to evaluate feasibility of transition to ICD-11, which included the analysis of the logical meta-rules needed for rule translation and development of a prototype support system for the expert that will translate the coding rules
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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