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"Natural Language Processing"

Dieter Galea, Ivan Laponogov, Kirill Veselkov
Motivation: Recognition of biomedical entities from scientific text is a critical component of natural language processing and automated information extraction platforms. Modern named entity recognition approaches rely heavily on supervised machine learning techniques, which are critically dependent on annotated training corpora. These approaches have been shown to perform well when trained and tested on the same source. However, in such scenario, the performance and evaluation of these models may be optimistic, as such models may not necessarily generalize to independent corpora, resulting in potential non-optimal entity recognition for large-scale tagging of widely diverse articles in databases such as PubMed...
March 10, 2018: Bioinformatics
Mohammed Alsuhaibani, Danushka Bollegala, Takanori Maehara, Ken-Ichi Kawarabayashi
Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words...
2018: PloS One
Luis A Camuñas-Mesa, Yaisel L Domínguez-Cordero, Alejandro Linares-Barranco, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco
Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli...
2018: Frontiers in Neuroscience
Wenhao Zhu, Tengjun Yao, Jianyue Ni, Baogang Wei, Zhiguo Lu
Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representations have been developed, such as the continuous bag of words (CBOW) and skip-gram models, and they have been extensively employed in a variety of NLP tasks...
2018: PloS One
Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang
Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data...
December 2017: Stroke and Vascular Neurology
Varsha D Badal, Petras J Kundrotas, Ilya A Vakser
BACKGROUND: Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al...
March 5, 2018: BMC Bioinformatics
Nicolas Garcelon, Antoine Neuraz, Rémi Salomon, Hassan Faour, Vincent Benoit, Arthur Delapalme, Arnold Munnich, Anita Burgun, Bastien Rance
INTRODUCTION: Clinical data warehouses are often oriented toward integration and exploration of coded data. However narrative reports are of crucial importance for translational research. This paper describes Dr Warehouse®, an open source data warehouse oriented toward clinical narrative reports and designed to support clinicians' day-to-day use. METHOD: Dr Warehouse relies on an original database model to focus on documents in addition to facts. Besides classical querying functionalities, the system provides an advanced search engine and Graphical User Interfaces adapted to the exploration of text...
March 1, 2018: Journal of Biomedical Informatics
Joy T Wu, Franck Dernoncourt, Sebastian Gehrmann, Patrick D Tyler, Edward T Moseley, Eric T Carlson, David W Grant, Yeran Li, Jonathan Welt, Leo Anthony Celi
Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning...
April 2018: International Journal of Medical Informatics
Sheng-Feng Sung, Kuanchin Chen, Darren Philbert Wu, Ling-Chien Hung, Yu-Hsiang Su, Ya-Han Hu
OBJECTIVE: To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients through use of an enhanced task-specific electronic medical record (EMR) interface powered by natural language processing (NLP) techniques. MATERIALS AND METHODS: The information processing algorithm utilized MetaMap to extract medical concepts from IVT eligibility criteria and expanded the concepts using the Unified Medical Language System Metathesaurus...
April 2018: International Journal of Medical Informatics
Thomas H McCoy, Victor M Castro, Kamber L Hart, Amelia M Pellegrini, Sheng Yu, Tianxi Cai, Roy H Perlis
BACKGROUND: Genetic studies of neuropsychiatric disease strongly suggest an overlap in liability. There are growing efforts to characterize these diseases dimensionally rather than categorically, but the extent to which such dimensional models correspond to biology is unknown. METHODS: We applied a newly developed natural language processing method to extract five symptom dimensions based on the National Institute of Mental Health Research Domain Criteria definitions from narrative hospital discharge notes in a large biobank...
February 20, 2018: Biological Psychiatry
Thomas H McCoy, Sheng Yu, Kamber L Hart, Victor M Castro, Hannah E Brown, James N Rosenquist, Alysa E Doyle, Pieter J Vuijk, Tianxi Cai, Roy H Perlis
BACKGROUND: Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes. METHODS: We developed a natural language processing-based approach to extract five symptom dimensions, based on the National Institute of Mental Health Research Domain Criteria definitions, from narrative clinical notes...
February 23, 2018: Biological Psychiatry
Ashkan Zehfroosh, Elena Kokkoni, Herbert G Tanner, Jeffrey Heinz
This paper offers a new approach to learning discrete models for human-robot interaction (HRI) from small data. In the motivating application, HRI is an integral part of a pediatric rehabilitation paradigm that involves a play-based, social environment aiming at improving mobility for infants with mobility impairments. Designing interfaces in this setting is challenging, because in order to harness, and eventually automate, the social interaction between children and robots, a behavioral model capturing the causality between robot actions and child reactions is needed...
July 2017: Mediterranean Conference on Control & Automation: [proceedings]
Bin Liu, Yingming Li, Zenglin Xu
Multi-label learning is a common machine learning problem arising from numerous real-world applications in diverse fields, e.g, natural language processing, bioinformatics, information retrieval and so on. Among various multi-label learning methods, the matrix completion approach has been regarded as a promising approach to transductive multi-label learning. By constructing a joint matrix comprising the feature matrix and the label matrix, the missing labels of test samples are regarded as missing values of the joint matrix...
February 14, 2018: Neural Networks: the Official Journal of the International Neural Network Society
B E Jones, B R South, Y Shao, C C Lu, J Leng, B C Sauer, A V Gundlapalli, M H Samore, Q Zeng
BACKGROUND:  Identifying pneumonia using diagnosis codes alone may be insufficient for research on clinical decision making. Natural language processing (NLP) may enable the inclusion of cases missed by diagnosis codes. OBJECTIVES:  This article (1) develops a NLP tool that identifies the clinical assertion of pneumonia from physician emergency department (ED) notes, and (2) compares classification methods using diagnosis codes versus NLP against a gold standard of manual chart review to identify patients initially treated for pneumonia...
January 2018: Applied Clinical Informatics
Muhammad F Amith, Zhe He, Jiang Bian, Juan Antonio Lossio-Ventura, Cui Tao
With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications...
February 17, 2018: Journal of Biomedical Informatics
Rachel L Graves, Jesse Goldshear, Jeanmarie Perrone, Lyle Ungar, Elissa Klinger, Zachary F Meisel, Raina M Merchant
AIM: To characterize Yelp reviews about pain management and opioids. METHODS: We manually coded and applied natural language processing to 836 Yelp reviews of US hospitals mentioning an opioid medication. RESULTS: Yelp reviews by patients and caregivers describing experiences with pain management and opioids had lower ratings compared with other reviews. Negative descriptions of pain management and opioid-related experiences were more commonly described than positive experiences, and the number of themes they reflected was more diverse...
February 16, 2018: Pain Management
Prashanth Sunkureddi, Dawn Gibson, Stephen Doogan, John Heid, Samir Benosman, Yujin Park
INTRODUCTION: Online communities contain a wealth of information containing unsolicited patient experiences that may go beyond what is captured by guided surveys or patient-reported outcome (PRO) instruments used in clinical settings. This study described patient experiences reported online to better understand the day-to-day disease burden of ankylosing spondylitis (AS). METHODS: Unguided, English-language patient narratives reported between January 2010 and May 2016 were collected from 52 online sources (e...
February 15, 2018: Advances in Therapy
Shira Maguen, Erin Madden, Olga V Patterson, Scott L DuVall, Lizabeth A Goldstein, Kristine Burkman, Brian Shiner
To derive a method of identifying use of evidence-based psychotherapy (EBP) for post-traumatic stress disorder (PTSD), we used clinical note text from national Veterans Health Administration (VHA) medical records. Using natural language processing, we developed machine-learning algorithms to classify note text on a large scale in an observational study of Iraq and Afghanistan veterans with PTSD and one post-deployment psychotherapy visit by 8/5/15 (N = 255,968). PTSD visits were linked to 8.1 million psychotherapy notes...
February 15, 2018: Administration and Policy in Mental Health
Prashanth Sunkureddi, Stephen Doogan, John Heid, Samir Benosman, Alexis Ogdie, Layne Martin, Jacqueline B Palmer
OBJECTIVE: To evaluate the types of experiences and treatment access challenges of patients with psoriatic arthritis (PsA) using self-reported online narratives. METHODS: English-language patient narratives reported between January 2010 and May 2016 were collected from 31 online sources (general health social networking sites, disease-focused patient forums, treatment reviews, general health forums, mainstream social media sites) for analysis of functional impairment and 40 online sources for assessment of barriers to treatment...
February 15, 2018: Journal of Rheumatology
Sebastian Gehrmann, Franck Dernoncourt, Yeran Li, Eric T Carlson, Joy T Wu, Jonathan Welt, John Foote, Edward T Moseley, David W Grant, Patrick D Tyler, Leo A Celi
In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition...
2018: PloS One
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