ACTA: A Tool for Argumentative Clinical Trial Analysis

International audienceArgumentative analysis of textual documents of various nature (e.g., persuasive essays, online discussion blogs, scientific articles) allows to detect the main argumentative components (i.e., premises and claims) present in the text and to predict whether these components are connected to each other by argumentative relations (e.g., support and attack), leading to the identification of (possibly complex) argumentative structures. Given the importance of argument-based decision making in medicine, in this demo paper we introduce ACTA, a tool for automating the argumentative analysis of clinical trials. The tool is designed to support doctors and clinicians in identifying the document(s) of interest about a certain disease, and in analyzing the main argumentative content and PICO elements

ACTA: A Tool for Argumentative Clinical Trial Analysis

International audienceArgumentative analysis of textual documents of various nature (e.g., persuasive essays, online discussion blogs, scientific articles) allows to detect the main argumentative components (i.e., premises and claims) present in the text and to predict whether these components are connected to each other by argumentative relations (e.g., support and attack), leading to the identification of (possibly complex) argumentative structures. Given the importance of argument-based decision making in medicine, in this demo paper we introduce ACTA, a tool for automating the argumentative analysis of clinical trials. The tool is designed to support doctors and clinicians in identifying the document(s) of interest about a certain disease, and in analyzing the main argumentative content and PICO elements

Knowledge graphs for covid-19: An exploratory review of the current landscape

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research

ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials

International audienceEvidence-based medicine aims at making decisions about the care of individual patients based on the explicit use of the best available evidence in the patient clinical history and the medical literature results. Argumentation represents a natural way of addressing this task by (i) identifying evidence and claims in text, and (ii) reasoning upon the extracted arguments and their relations to make a decision. ACTA 2.0 is an automated tool which relies on Argument Mining methods to analyse the abstracts of clinical trials to extract argument components and relations to support evidence-based clinical decision making. ACTA 2.0 allows also for the identification of PICO (Patient, Intervention, Comparison, Outcome) elements, and the analysis of the effects of an intervention on the outcomes of the study. A REST API is also provided to exploit the tool's functionalities

Covid-on-the-Web: Knowledge Graph and Services to Advance COVID-19 Research

International audienceScientists are harnessing their multidisciplinary expertise and resources to fight the COVID-19 pandemic. Aligned with this mind-set, the Covid-on-the-Web project aims to allow biomedical researchers to access, query and make sense of COVID-19 related literature. To do so, it adapts, combines and extends tools to process, analyze and enrich the "COVID-19 Open Research Dataset" (CORD-19) that gathers 50,000+ full-text scientific articles related to the coronaviruses. We report on the RDF dataset and software resources produced in this project by leveraging skills in knowledge representation, text, data and argument mining, as well as data visualization and exploration. The dataset comprises two main knowledge graphs describing (1) named entities mentioned in the CORD-19 corpus and linked to DBpedia, Wikidata and other BioPortal vocabularies, and (2) arguments extracted using ACTA, a tool automating the extraction and visualization of argumentative graphs, meant to help clinicians analyze clinical trials and make decisions. On top of this dataset, we provide several visualization and exploration tools based on the Corese Semantic Web platform, MGExplorer visualization library, as well as the Jupyter Notebook technology. All along this initiative, we have been engaged in discussions with healthcare and medical research institutes to align our approach with the actual needs of the biomedical community, and we have paid particular attention to comply with the open and reproducible science goals, and the FAIR principles

Correlation between Serum Uric Acid Level and Severity of Coronary Artery Stenosis in Patients with Acute Coronary Syndrome

Acute coronary syndrome (ACS) is a life-threatening disease which remains one of the causes of high morbidity and mortality despite the current advances in treatment. The relationship between the serum uric acid  (SUA) level and ischemic heart disease continues to be controversial and still has not been established as a cardiovascular risk factor. The cooperative interaction between the two factors has not yet fully understood. Prior epidemiological evidence of the causal relationship between the too is still argumentative. Various studies have been done using the same methods; yet, the outcomes were different. This study aimeds to conduct a meta-analysis to synthesize the results of recent studies in order to obtain accurate quantitative data. This systematic study followed the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guideline and studies published in the period of  January 2010 to May 2020 were screened using the Cochrane Library, Ebsco, Medline/PubMed, ProQuest and Science Direct as the sources. Meta-analysis was conducted to synthesize the association between the SUA level and severity of coronary artery stenosis using random effect model to account for possible study heterogeneity. Heterogeneity was assessed using I2 and the meta-analysis was performed using Comprehensive Meta Analysis Version 3 (CMA3) software. Five studies (n = 601 subjects) identified a correlation between serum uric acid level and Gensini score (r = 0.548; p <0.001) in ACS patients. Heterogeneity bias was found in the analysis, whereas publication bias was not found. Thus, the severity of coronary artery stenosis in patients with ACS is positively correlated with serum uric acid level

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Transformer-based Argument Mining for Healthcare Applications

International audienceArgument(ation) Mining (AM) typically aims at identifying argumentative components in text and predicting the relations among them. Evidence-based decision making in the health-care domain targets at supporting clinicians in their deliberation process to establish the best course of action for the case under evaluation. Although the reasoning stage of this kind of frameworks received considerable attention, little effort has been devoted to the mining stage. We extended an existing dataset by annotating 500 abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database, leading to a dataset of 4198 argument components and 2601 argument relations on different diseases (i.e., neoplasm, glau-coma, hepatitis, diabetes, hypertension). We propose a complete argument mining pipeline for RCTs, classifying argument components as evidence and claims, and predicting the relation, i.e., attack or support , holding between those argument components. We experiment with deep bidirectional transformers in combination with different neural architectures (i.e., LSTM, GRU and CRF) and obtain a macro F1-score of .87 for component detection and .68 for relation prediction , outperforming current state-of-the-art end-to-end AM systems

Validity of the Shahin Mixed Depression Scale: A Self-Rated Instrument Designed to Measure the Non-DSM Mixed Features in Depression

Background: The DSM5-defined mixed features in depression do not include psychomotor agitation, irritability or distractibility because they are considered overlapping symptoms. A growing number of modern psychiatrists have expressed dissatisfaction with this and proposed alternative sets of mixed symptoms that are much more common and clinically relevant. Among such alternative criteria were those proposed by Koukopoulos. He utilized the research diagnostic criteria of agitated depression (RDC-A) as a mixed depression subtype, and validated another form of mixed depression, the Koukopoulos criteria for mixed depression (K-DMX). Purpose: This study provides psychometric validation for the first self-rated scale designed to measure the most common mixed symptoms in depression as proposed by Koukopoulos. Patients and methods: We conducted a multicenter cross-sectional study of 170 patients with unipolar depression. They completed the Shahin Mixed Depression Scale (SMDS) and underwent expert interviews as a gold standard reference. SMDS' psychometric properties were assessed, including Cronbach's alpha, factor analysis, sensitivity, specificity, predictive value and accuracy. Results: We found significant association and agreement between mixity according to SMDS and the gold standard (K-DMX and RDC-A according to expert interview) with good internal consistency (Cronbach's alpha=0.87), high sensitivity (=91.4%), specificity (=98.0%), positive predictive value (=96.9%), negative predictive value (= 94.2%) and accuracy (=95.2%). Factor analysis identified one factor for psychomotor agitation and another for mixity without psychomotor agitation. Conclusion: SMDS was a reliable and valid instrument for assessing the frequently encountered and clinically relevant mixed features in depression