1,720,989 research outputs found
Alpha-fetoprotein for Diagnosis, Prognosis, and Transplant Selection
No full textReliable biomarkers are of great clinical value in predicting cancer occurrence/recurrence, anticipating its detection at an asymptomatic stage, supporting the radiological diagnosis, stratifying patients for prognosis and proper therapy, and measuring the response to treatment. Despite the plethora of biomarkers proposed for hepatocellular carcinoma (HCC), the first one identified, α-fetoprotein (AFP), remains the most utilized. This article reviews the lights and shadows of AFP as a surveillance test for patients at risk of HCC, and as a diagnostic test for those with chronic liver disease and a suspected hepatic mass. Moreover, the article scrutinizes the large body of evidence supporting the prognostic relevance of AFP in patients undergoing both curative and palliative treatment of HCC and the growing importance attributed to this biomarker (as a static or a dynamic variable) in the selection of potential candidates for liver transplantation. In fact, the inclusion of AFP among transplant criteria would improve the ability of identifying poor candidates due to an unacceptable risk of HCC recurrence regardless of tumor burden, and of adopting flexible morphological selection criteria
One transformer for all time series: representing and training with time-dependent heterogeneous tabular data
No full textThere is a recent growing interest in applying Deep Learning techniques to tabular data in order to replicate the success of other Artificial Intelligence areas in this structured domain. Particularly interesting is the case in which tabular data have a time dependence, such as, for instance, financial transactions. However, the heterogeneity of the tabular values, in which categorical elements are mixed with numerical features, makes this adaptation difficult. In this paper we propose UniTTab, a Transformer based architecture whose goal is to uniformly represent heterogeneous time-dependent tabular data, in which both numerical and categorical features are described using continuous embedding vectors. Moreover, differently from common approaches, which use a combination of different loss functions for training with both numerical and categorical targets, UniTTab is uniformly trained with a unique Masked Token pretext task. Finally, UniTTab can also represent time series in which the individual row components have a variable internal structure with a variable number of fields, which is a common situation in many application domains, such as in real world transactional data. Using extensive experiments with five datasets of variable size and complexity, we empirically show that UniTTab consistently and significantly improves the prediction accuracy over several downstream tasks and with respect to both Deep Learning and more standard Machine Learning approaches. Our code and our models are available at: https://github.com/fabriziogaruti/UniTTab
De novo hepatocellular carcinoma of liver allograft: a neglected issue. Cancer Lett in corso di stampa
No full textDe novo hepatocellular carcinoma (HCC) is a rare neoplasm, ensuing after liver transplantation. Its definitive identification requires sophisticated molecular analyses. Hence, some cases, particularly those ensuing in patients who have been transplanted with HCC, are probably misclassified as recurrences of the primary tumor. Nevertheless, a tumor recurrence cannot be excluded in patients transplanted without apparent malignancy, because of an occult HCC. The main risk factor for de novo HCC is the recurrence of hepatitis/cirrhosis in the allograft. All the described de novo HCCs occurred at least 2 years after OLT, whereas most recurrent HCCs develop within 2 years from surgery. The treatment of this tumor can follow the recommendations of guidelines for primary HCC and, unlike recurrent HCC, re-transplant can be considered a therapeutic option for these patients. Prevention of this tumor relies on the prevention/cure of recurrent liver disease in the allograft and on judicious post-transplant immunosuppression.
The present review analyzes this topic by addressing seven key questions. An algorithm based on clinical factors – regarding primary and secondary tumors – to trigger the suspicion of de novo origin of a post-transplant HCC is proposed
Large-Scale Transformer models for Transactional Data
No full textFollowing the spread of digital channels for everyday activities and electronic payments, huge collections of online transactions are available from financial institutions. These transactions are usually organized as time series, i.e., a time-dependent sequence of tabular data, where each element of the series is a collection of heterogeneous fields (e.g., dates, amounts, categories, etc.). Transactions are usually evaluated by automated or semi-automated procedures to address financial tasks and gain insights into customers’ behavior. In the last years, many Trees-based Machine Learning methods (e.g., RandomForest, XGBoost) have been proposed for financial tasks, but they do not fully exploit in an end-to-end pipeline all the information richness of individual transactions, neither they fully model the underling temporal patterns. Instead, Deep Learning approaches have proven to be very effective in modeling complex data by representing them in a semantic latent space. In this paper, inspired by the multi-modal Deep Learning approaches used in Computer Vision and NLP, we propose UniTTab, an end-to-end Deep Learning Transformer model for transactional time series which can uniformly represent heterogeneous time-dependent data in a single embedding. Given the availability of large sets of tabular transactions, UniTTab defines a pre-training self-supervised phase to learn useful representations which can be employed to solve financial tasks such as churn prediction and loan default prediction. A strength of UniTTab is its flexibility since it can be adopted to represent time series of arbitrary length and composed of different data types in the fields. The flexibility of our model in solving different types of tasks (e.g., detection, classification, regression) and the possibility of varying the length of the input time series, from a few to hundreds of transactions, makes UniTTab a general-purpose Transformer architecture for bank transactions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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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