Automated Natural Language Processing-Based Supplier Discovery for Financial Services.

Public procurement is viewed as a major market force that can be used to promote innovation and drive small and medium-sized enterprises growth. In such cases, procurement system design relies on intermediates that provide vertical linkages between suppliers and providers of innovative services and products. In this work we propose an innovative methodology for decision support in the process of supplier discovery, which precedes the final supplier selection. We focus on data gathered from community-based sources such as Reddit and Wikidata and avoid any use of historical open procurement datasets to identify small and medium sized suppliers of innovative products and services that own very little market shares. We look into a real-world procurement case study from the financial sector focusing on the Financial and Market Data offering and develop an interactive web-based support tool to address certain requirements of the Italian central bank. We demonstrate how a suitable selection of natural language processing models, such as a part-of-speech tagger and a word-embedding model, in combination with a novel named-entity-disambiguation algorithm, can efficiently analyze huge quantity of textual data, increasing the probability of a full coverage of the market.

XGDAG: explainable gene-disease associations via graph neural networks.

MOTIVATION: Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene-disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability. RESULTS: We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determining the ranking of candidate genes and understanding the model's output. Our approach is based on a positive-unlabeled learning strategy, which outperforms existing gene discovery methods by exploiting GNNs in a non-black-box fashion. Our methodology is effective even in scenarios where a large number of associated genes need to be retrieved, in which gene prioritization methods often tend to lose their reliability. AVAILABILITY AND IMPLEMENTATION: The source code of XGDAG is available on GitHub at: https://github.com/GiDeCarlo/XGDAG. The data underlying this article are available at: https://www.disgenet.org/, https://thebiogrid.org/, https://doi.org/10.1371/journal.pcbi.1004120.s003, and https://doi.org/10.1371/journal.pcbi.1004120.s004.

A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study.

CONTEXT: The risk stratification of patients with differentiated thyroid cancer (DTC) is crucial in clinical decision making. The most widely accepted method to assess risk of recurrent/persistent disease is described in the 2015 American Thyroid Association (ATA) guidelines. However, recent research has focused on the inclusion of novel features or questioned the relevance of currently included features. OBJECTIVE: To develop a comprehensive data-driven model to predict persistent/recurrent disease that can capture all available features and determine the weight of predictors. METHODS: In a prospective cohort study, using the Italian Thyroid Cancer Observatory (ITCO) database (NCT04031339), we selected consecutive cases with DTC and at least early follow-up data (n = 4773; median follow-up 26 months; interquartile range, 12-46 months) at 40 Italian clinical centers. A decision tree was built to assign a risk index to each patient. The model allowed us to investigate the impact of different variables in risk prediction. RESULTS: By ATA risk estimation, 2492 patients (52.2%) were classified as low, 1873 (39.2%) as intermediate, and 408 as high risk. The decision tree model outperformed the ATA risk stratification system: the sensitivity of high-risk classification for structural disease increased from 37% to 49%, and the negative predictive value for low-risk patients increased by 3%. Feature importance was estimated. Several variables not included in the ATA system significantly impacted the prediction of disease persistence/recurrence: age, body mass index, tumor size, sex, family history of thyroid cancer, surgical approach, presurgical cytology, and circumstances of the diagnosis. CONCLUSION: Current risk stratification systems may be complemented by the inclusion of other variables in order to improve the prediction of treatment response. A complete dataset allows for more precise patient clustering.

Epidemiological characteristics and outcomes from 187 patients with COVID-19 admitted to 6 reference centers in Greece: an observational study during the first wave of the COVID-19 pandemic.

INTRODUCTION: Epidemiological data from patients with COVID-19 has been recently published in several countries. Nationwide data of hospitalized patients with COVID-19 in Greece remain scarce. MATERIAL AND METHODS: This was an observational, retrospective study from 6 reference centers between February 26 and May 15, 2020. RESULTS: The patients were mostly males (65.7%) and never smokers (57.2%) of median age 60 (95% CI: 57.6-64) years. The majority of the subjects (98%) were treated with the standard-of-care therapeutic regimen at that time, including hydroxychlo-roquine and azithromycin. Median time of hospitalization was 10 days (95% CI: 10-12). Twenty-five (13.3%) individuals were intubated and 8 died (4.2%). The patients with high neutrophil-to-lymphocyte ratio (NLR) ( > 3.58) exhibited more severe disease as indicated by significantly increased World Health Organization (WHO) R&D ordinal scale (4; 95% CI: 4-4 vs 3; 95% CI: 3-4, p = 0.0001) and MaxFiO2% (50; 95% CI: 38.2-50 vs 29.5; 95% CI: 21-31, p < 0.0001). The patients with increased lactate dehydrogenase (LDH) levels ( > 270 IU/ml) also exhibited more advanced disease compared to the low LDH group ( < 270 IU/ml) as indicated by both WHO R&D ordinal scale (4; 95% CI: 4-4 vs 4; 95% CI: 3-4, p = 0.0001) and MaxFiO2% (50; 95% CI: 35-60 vs 28; 95% CI: 21-31, p < 0.0001). CONCLUSION: We present the first epidemiological report from a low-incidence and mortality COVID-19 country. NLR and LDH may represent reliable disease prognosticators leading to timely treatment decisions.

Identification of Social-Media Platform of Videos through the Use of Shared Features.

Videos have become a powerful tool for spreading illegal content such as military propaganda, revenge porn, or bullying through social networks. To counter these illegal activities, it has become essential to try new methods to verify the origin of videos from these platforms. However, collecting datasets large enough to train neural networks for this task has become difficult because of the privacy regulations that have been enacted in recent years. To mitigate this limitation, in this work we propose two different solutions based on transfer learning and multitask learning to determine whether a video has been uploaded from or downloaded to a specific social platform through the use of shared features with images trained on the same task. By transferring features from the shallowest to the deepest levels of the network from the image task to videos, we measure the amount of information shared between these two tasks. Then, we introduce a model based on multitask learning, which learns from both tasks simultaneously. The promising experimental results show, in particular, the effectiveness of the multitask approach. According to our knowledge, this is the first work that addresses the problem of social media platform identification of videos through the use of shared features.

Safety and efficacy of nintedanib in idiopathic pulmonary fibrosis: A real-life observational study in Greece.

BACKGROUND: Nintedanib represents an antifibrotic compound able to slow down disease progression of patients with idiopathic pulmonary fibrosis (IPF). OBJECTIVE: To investigate the safety and efficacy of nintedanib in patients with IPF in a real-life setting. METHODS: This was a multicentre, retrospective, observational, real-life study for patients with IPF receiving nintedanib between October 2014 and October 2016. RESULTS: We identified 94 patients with IPF receiving nintedanib (72 males, mean age±SD: 73.8 ±â€¯7.5, mean%FVC±SD = 68.1 ±â€¯18.3, mean%DLCo±SD = 44.4 ±â€¯14.5). Diarrhea (n = 52, 55.3%) was the most commonly reported adverse event. Twenty patients (21.2%) had to permanently discontinue nintedanib due to severe adverse events. In the 6-months follow-up, median decline in %FVC predicted and %DLCO predicted were 1.36 (95%Cl: 0 to 2.97) and 4.00 (95%Cl: 2.01 to 6.20), respectively, when deaths were censored and excluded from the analysis. At 12 months, mean%FVC±SD and mean%DLCo±SD were 64.5 ±â€¯19.1 and 43.7 ±â€¯15.4, respectively. With regards to mortality, 17 patients (18.1%) died over a study period of 730 days. CONCLUSION: Nintedanib demonstrated an acceptable safety and efficacy profile in our real-world observational study. Prospective observational studies in the context of registries that collect well-defined supporting data over time are sorely needed to answer residual questions on drug's performance.

Longitudinal "Real-World" Outcomes of Pirfenidone in Idiopathic Pulmonary Fibrosis in Greece.

BACKGROUND: Pirfenidone is an antifibrotic compound able to slow down disease progression in patients with idiopathic pulmonary fibrosis (IPF). OBJECTIVE: To investigate the safety and efficacy of pirfenidone in patients with IPF in a real-life setting. METHODS: This was a multicenter, retrospective, real-life, observational study for patients with IPF receiving pirfenidone. RESULTS: We identified 92 patients with IPF receiving pirfenidone. Eighty patients (70 males and 10 females, mean age ± SD: 68.1 + 7.5, mean %FVC ± SD = 74.9 ± 17.2, mean %DLCO ± SD = 48.1 ± 16.9) were included in the analysis. Skin-related (25%) and gastrointestinal (17.5%) adverse events were the most common and led to drug discontinuation in 22.5% of cases. The majority (87%) of patients experienced side effects during the first 6 months of treatment. At 36 months, changes in %FVC and %DLCO were -9.25 ± 16.34 and -9.26 ± 15.26, respectively. At 6, 12, and 24 months after treatment initiation (n = 80, 60, and 26), 18, 15, and 5 patients (22.5, 25, and 19.2%) experienced significant (>10%) and 11, 3, and 3 patients (13.8, 5, and 11.5%) experienced marginal (5-10%) %FVC improvement; and 13, 6, and 1 patient (16.2, 10, and 3.9%) experienced marginal (-5 to -10%) and 20, 21, and 8 patients (25, 35, and 30.8%) experienced significant decline (<-10%) in %FVCpred. Median survival was 851 days, and 41 patients died during the study period. CONCLUSION: Pirfenidone demonstrated an acceptable safety and therapeutic profile in patients with IPF on a longitudinal basis. Prospective observational registries are urgently needed to provide a real-world view of outcomes of pirfenidone in clinical practice.

Corrigendum: Longitudinal "Real-World" Outcomes of Pirfenidone in Idiopathic Pulmonary Fibrosis in Greece.

[This corrects the article on p. 213 in vol. 4, PMID: 29238708.].