71 research outputs found
Towards Incremental Parsing of Natural Language using Recursive Neural Networks
In this paper we develop novel algorithmic ideas for building a natural language
parser grounded upon the hypothesis of incrementality. Although widely accepted
and experimentally supported under a cognitive perspective as a model of the human
parser, the incrementality assumption has never been exploited for building automatic
parsers of unconstrained real texts. The essentials of the hypothesis are that words are
processed in a left-to-right fashion, and the syntactic structure is kept totally connected
at each step.
Our proposal relies on a machine learning technique for predicting the correctness of
partial syntactic structures that are built during the parsing process. A recursive neural
network architecture is employed for computing predictions after a training phase on
examples drawn from a corpus of parsed sentences, the Penn Treebank. Our results
indicate the viability of the approach andlay out the premises for a novel generation of
algorithms for natural language processing which more closely model human parsing.
These algorithms may prove very useful in the development of eÆcient parsers
Agminated Blue Nevus: Two Case Reports and a Mini-review of the Literature
Agminated blue nevus (ABN) is a melanocytic nevus rarely mentioned in the literature and not well known. The term agminated is used when many blue nevi are clustered together in a sharply demarcated area ≤10 cm. Specific dermatoscopic features have not currently been clearly defined. We describe two cases of ABN and provide a review of the literature, reporting the main points in order to facilitate the diagnosis of this rare entity.  </p
MiRNAs as Potential Prognostic Biomarkers for Metastasis in Thin and Thick Primary Cutaneous Melanomas.
Background/Aim: The identification of novel
prognostic biomarkers for melanoma metastasis is essential
to improve patient outcomes. To this aim, we characterized
miRNA expression profiles in relation to metastasis in
melanoma and correlated miRNAs expression with clinicalpathological factors. Materials and Methods: MiR-145-5p,
miR-150-5p, miR-182-5p, miR-203-3p, miR-205-5p and miR211-5p expression levels were analyzed in primary cutaneous
melanomas, including thin and thick melanomas, and in
melanoma metastases by quantitative Real-Time PCR.
Results: A significantly lower miR-205-5p expression was
found in metastases compared to primary melanomas.
Furthermore, a progressive down-regulation of miR-205-5p
expression was observed from loco-regional to distant
metastasis. Significantly lower miR-145-5p and miR-203-3p
expression levels were found in cases with Breslow thickness
>1 mm, high Clark level, ulceration and mitotic rate
≥1/mm2. Conclusion: Our findings point to miR-205-5p as
potential biomarker of distant metastases and to miR-145-5p
and miR-203-3p as markers of aggressiveness in melanoma
AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study
Recent epidemiological data report that worldwide more than 53 million people
have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease
has been spreading very rapidly and few months after the identification of the
first infected, shortage of hospital resources quickly became a problem. In
this work we investigate whether chest X-ray (CXR) can be used as a possible
tool for the early identification of patients at risk of severe outcome, like
intensive care or death. CXR is a radiological technique that compared to
computed tomography (CT) it is simpler, faster, more widespread and it induces
lower radiation dose. We present a dataset including data collected from 820
patients by six Italian hospitals in spring 2020 during the first COVID-19
emergency. The dataset includes CXR images, several clinical attributes and
clinical outcomes. We investigate the potential of artificial intelligence to
predict the prognosis of such patients, distinguishing between severe and mild
cases, thus offering a baseline reference for other researchers and
practitioners. To this goal, we present three approaches that use features
extracted from CXR images, either handcrafted or automatically by convolutional
neuronal networks, which are then integrated with the clinical data. Exhaustive
evaluation shows promising performance both in 10-fold and leave-one-centre-out
cross-validation, implying that clinical data and images have the potential to
provide useful information for the management of patients and hospital
resources
The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2
Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701
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