Search Results - "Filannino, Michele"

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records by Henry, Sam, Buchan, Kevin, Filannino, Michele, Stubbs, Amber, Uzuner, Ozlem

“…Abstract Objective This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges…”
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A Survey of Bioinformatics Database and Software Usage through Mining the Literature by Duck, Geraint, Nenadic, Goran, Filannino, Michele, Brass, Andy, Robertson, David L, Stevens, Robert

“…Computer-based resources are central to much, if not most, biological and medical research. However, while there is an ever expanding choice of bioinformatics…”
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De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1 by Stubbs, Amber, Filannino, Michele, Uzuner, Özlem

“…[Display omitted] •NLP shared task with new set of 1000 de-identified psychiatric records.•“Sight-unseen” task: top F1 of 0.799 using out-of-the-box system on…”
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Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks by Filannino, Michele, Uzuner, Özlem

“…Summary Objectives:  To review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most…”
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Automatic prediction of coronary artery disease from clinical narratives by Buchan, Kevin, Filannino, Michele, Uzuner, Özlem

“…[Display omitted] •We propose a system to predict coronary artery disease from clinical narratives.•We employ an ontology-guided approach to feature…”
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Cohort selection for clinical trials: n2c2 2018 shared task track 1 by Stubbs, Amber, Filannino, Michele, Soysal, Ergin, Henry, Samuel, Uzuner, Özlem

“…Abstract Objective Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused on identifying which patients in a corpus of longitudinal medical…”
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Temporal expression extraction with extensive feature type selection and a posteriori label adjustment by Filannino, Michele, Nenadic, Goran

“…The automatic extraction of temporal information from written texts is pivotal for many Natural Language Processing applications such as question answering,…”
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Prescription extraction using CRFs and word embeddings by Tao, Carson, Filannino, Michele, Uzuner, Özlem

“…[Display omitted] •A high-performing system to extract and organize prescription information.•Evaluating the contribution of real-valued word embeddings in…”
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Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2 by Filannino, Michele, Stubbs, Amber, Uzuner, Özlem

“…[Display omitted] •Results from 110 researchers in 24 teams and 65 submissions.•The best system performs comparably to the least experienced…”
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FABLE: A Semi-Supervised Prescription Information Extraction System by Tao, Carson, Filannino, Michele, Uzuner, Özlem

“…Prescription information is an important component of electronic health records (EHRs). This information contains detailed medication instructions that are…”
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Corrigendum to “Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2” [J Biomed Inform. 2017 Nov;75S:S62–S70] by Filannino, Michele, Stubbs, Amber, Uzuner, Özlem

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Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives by Kovacevic, Aleksandar, Dehghan, Azad, Filannino, Michele, Keane, John A, Nenadic, Goran

“…Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and…”
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A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry by Stubbs, Amber, Filannino, Michele

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Hysteresis-type current–voltage characteristics in Au/eumelanin/ITO/glass structure: Towards melanin based memory devices by Ambrico, Marianna, Cardone, Antonio, Ligonzo, Teresa, Augelli, Vincenzo, Ambrico, Paolo Francesco, Cicco, Stefania, Farinola, Gianluca M., Filannino, Michele, Perna, Giuseppe, Capozzi, Vito

“…Hysteresis behaviour of the current–voltage characteristics collected on spin coated synthetic eumelanin layer embedded in the Au/eumelanin/ITO/glass structure…”
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Data-Driven Temporal Information Extraction with Applications in General and Clinical Domains by Filannino, Michele

“…The automatic extraction of temporal information from written texts is pivotal for many Natural Language Processing applications such as question answering,…”
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Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists by Lanubile, Filippo, Calefato, Fabio, Quaranta, Luigi, Amoruso, Maddalena, Fumarola, Fabio, Filannino, Michele

“…The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report…”
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Temporal expression normalisation in natural language texts by Filannino, Michele

“…Automatic annotation of temporal expressions is a research challenge of great interest in the field of information extraction. In this report, I describe a…”
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Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists by Lanubile, Filippo, Calefato, Fabio, Quaranta, Luigi, Amoruso, Maddalena, Fumarola, Fabio, Filannino, Michele

“…Proc. of 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), pp. 129-132 The transition from AI/ML models to production-ready…”
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Introducing Serendipity in a Content-Based Recommender System by Iaquinta, Leo, de Gemmis, Marco, Lops, Pasquale, Semeraro, Giovanni, Filannino, Michele, Molino, Piero

“…Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely…”
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ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge by Filannino, Michele, Brown, Gavin, Nenadic, Goran

“…This paper describes a temporal expression identification and normalization system, ManTIME, developed for the TempEval-3 challenge. The identification phase…”
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