QEbu: an advanced graphical editor for the EBU Core metadata set

The creation and management of metadata documents can be quite a difficult task to accomplish manually. To address this issue in the context of the EBUCore v1.3 metadata set, we propose a GUI-based metadata editor, called QEbu, developed during the Multimedia Archival Techniques course, held at the Polytechnic University of Turin in collaboration with RAI. QEbu was developed with the aim of providing a user-friendly graphical editor to create and manage XML documents, relieving the user from the burden of worrying about the structure of the data and letting him focus on the actual content. The editor is usable by both experienced users and novices in the field. QEbu has been developed in C++ using the cross-platform and open source library Qt 4.8; this framework was chosen in order to exploit its natural features for developing interface-centred applications, running locally on the machine of the user under a desktop environment

QEbu: an advanced graphical editor for the EBU Core metadata set

The creation and management of metadata documents can be quite a difficult task to accomplish manually. To address this issue in the context of the EBUCore v1.3 metadata set, we propose a GUI-based metadata editor, called QEbu, developed during the Multimedia Archival Techniques course, held at the Polytechnic University of Turin in collaboration with RAI. QEbu was developed with the aim of providing a user-friendly graphical editor to create and manage XML documents, relieving the user from the burden of worrying about the structure of the data and letting him focus on the actual content. The editor is usable by both experienced users and novices in the field. QEbu has been developed in C++ using the cross-platform and open source library Qt 4.8; this framework was chosen in order to exploit its natural features for developing interface-centred applications, running locally on the machine of the user under a desktop environmen

Bone Morphogenic Proteins and Their Antagonists in the Lower Airways of Stable COPD Patients

Background: Bone morphogenic proteins (BMPs) and their antagonists are involved in the tissue development and homeostasis of various organs. Objective: To determine transcriptomic and protein expression of BMPs and their antagonists in stable COPD. Methods: We measured the expression and localization of BMPs and some relevant antagonists in bronchial biopsies of stable mild/moderate COPD (MCOPD) (n = 18), severe/very severe COPD (SCOPD) (n = 16), control smokers (CS) (n = 13), and control non-smokers (CNS) (n = 11), and in lung parenchyma of MCOPD (n = 9), CS (n = 11), and CNS (n = 9) using immunohistochemistry and transcriptome analysis, in vitro after the stimulation of the 16HBE cells. Results: In bronchial biopsies, BMP4 antagonists CRIM1 and chordin were increased in the bronchial epithelium and lamina propria of COPD patients. BMP4 expression was decreased in the bronchial epithelium of SCOPD and MCOPD compared to CNS. Lung transcriptomic data showed non-significant changes between groups. CRIM1 and chordin were significantly decreased in the alveolar macrophages and alveolar septa in COPD patients. External 16HBE treatment with BMP4 protein reduced the bronchial epithelial cell proliferation. Conclusions: These data show an imbalance between BMP proteins and their antagonists in the lungs of stable COPD. This imbalance may play a role in the remodeling of the airways, altering the regenerative-reparative responses of the diseased bronchioles and lung parenchyma

Artificial Intelligence Algorithms and Natural Language Processing for the Recognition of Syncope Patients on Emergency Department Medical Records

Background: Enrollment of large cohorts of syncope patients from administrative data is crucial for proper risk stratification but is limited by the enormous amount of time required for manual revision of medical records. Aim: To develop a Natural Language Processing (NLP) algorithm to automatically identify syncope from Emergency Department (ED) electronic medical records (EMRs). Methods: De-identified EMRs of all consecutive patients evaluated at Humanitas Research Hospital ED from 1 December 2013 to 31 March 2014 and from 1 December 2015 to 31 March 2016 were manually annotated to identify syncope. Records were combined in a single dataset and classified. The performance of combined multiple NLP feature selectors and classifiers was tested. Primary Outcomes: NLP algorithms’ accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F3 score. Results: 15,098 and 15,222 records from 2013 and 2015 datasets were analyzed. Syncope was present in 571 records. Normalized Gini Index feature selector combined with Support Vector Machines classifier obtained the best F3 value (84.0%), with 92.2% sensitivity and 47.4% positive predictive value. A 96% analysis time reduction was computed, compared with EMRs manual review. Conclusions: This artificial intelligence algorithm enabled the automatic identification of a large population of syncope patients using EMRs

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Abstract Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process

Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer

The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63–0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis