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Natural language processing

Aida Kamišalić, David Riaño, Tatjana Welzer
BACKGROUND: In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions...
May 2018: Computer Methods and Programs in Biomedicine
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
Daniela Katharina Ahlberg, Heike Bischoff, Jessica Vanessa Strozyk, Doreen Bryant, Barbara Kaup
While much support is found for embodied language processing in a first language (L1), evidence for embodiment in second language (L2) processing is rather sparse. In a recent study, we found support for L2 embodiment, but also an influence of L1 on L2 processing in adult learners. In the present study, we compared bilingual schoolchildren who speak German as one of their languages with monolingual German schoolchildren. We presented the German prepositions auf (on), über (above), and unter (under) in a Stroop-like task...
2018: PloS One
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
H Branch Coslett, Myrna F Schwartz
Although the parietal lobe was considered by many of the earliest investigators of disordered language to be a major component of the neural systems instantiating language, most views of the anatomic substrate of language emphasize the role of temporal and frontal lobes in language processing. We review evidence from lesion studies as well as functional neuroimaging, demonstrating that the left parietal lobe is also crucial for several aspects of language. First, we argue that the parietal lobe plays a major role in semantic processing, particularly for "thematic" relationships in which information from multiple sensory and motor domains is integrated...
2018: Handbook of Clinical Neurology
Michelle O'Reilly, Nikki Kiyimba, Jessica N Lester
The field of couple and family therapy has benefitted from evidence generated from qualitative approaches. Evidence developed from approaches relying on language and social interaction using naturally occurring recordings of real-world practice has the benefit of facilitating practice-based recommendations and informing practice. The aim of this article is to provide an overview of one approach to discourse analysis, Discursive Psychology (DP), demonstrating how a social constructionist framework and focus on discourse can provide an important contribution to the field of therapy...
March 8, 2018: Journal of Marital and Family Therapy
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
Stephanie Buck
Astrology was a lifelong interest for C.G. Jung and an important aid in his formulation of psyche and psychic process. Archetypally configured, astrology provided Jung an objective means to a fuller understanding of the analysand's true nature and unique individuation journey. Jung credits astrology with helping to unlock the mystery of alchemy and in so doing providing the symbol language necessary for deciphering the historically remote cosmology of Gnosticism. Astrology also aided Jung's work on synchronicity...
April 2018: Journal of Analytical Psychology
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
Fernando Aparicio, María Luz Morales-Botello, Margarita Rubio, Asunción Hernando, Rafael Muñoz, Hugo López-Fernández, Daniel Glez-Peña, Florentino Fdez-Riverola, Manuel de la Villa, Manuel Maña, Diego Gachet, Manuel de Buenaga
BACKGROUND: Student participation and the use of active methodologies in classroom learning are being increasingly emphasized. The use of intelligent systems can be of great help when designing and developing these types of activities. Recently, emerging disciplines such as 'educational data mining' and 'learning analytics and knowledge' have provided clear examples of the importance of the use of artificial intelligence techniques in education. OBJECTIVE: The main objective of this study was to gather expert opinions regarding the benefits of using complementary methods that are supported by intelligent systems, specifically, by intelligent information access systems, when processing texts written in natural language and the benefits of using these methods as companion tools to the learning activities that are employed by biomedical and health sciences teachers...
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
Jessica Dick, Jennifer Fredrick, Grace Man, Jessica E Huber, Jiyeon Lee
While growing evidence reports changes in language use in non-demented individuals with Parkinson's disease (PD), the presence and nature of the deficits remain largely unclear. Researchers have proposed that dysfunctioning fronto-basal ganglia circuit results in impaired grammatical processes, predicting qualitatively similar language impairments between individuals with PD and agrammatic Broca's aphasia, whereas others suggest that PD is not associated with language-specific grammatical impairment. In addition, there is a paucity of research examining syntactic production in PD at the sentence-level...
March 1, 2018: Clinical Linguistics & Phonetics
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
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