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Benjamin L Cook, Ana M Progovac, Pei Chen, Brian Mullin, Sherry Hou, Enrique Baca-Garcia
Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, "how do you feel today?" We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data...
2016: Computational and Mathematical Methods in Medicine
Anthony P Nunes, Jing Yang, Larry Radican, Samuel S Engel, Karen Kurtyka, Kaan Tunceli, Shengsheng Yu, Kristy Iglay, Michael C Doherty, David D Dore
AIMS: Accurate measures of hypoglycemia within electronic health records (EHR) can facilitate clinical population management and research. We quantify the occurrence of serious and mild-to-moderate hypoglycemia in a large EHR database in the US, comparing estimates based only on structured data to those from structured data and natural language processing (NLP) of clinical notes. METHODS: This cohort study included patients with type 2 diabetes identified from January 2009 through March 2014...
September 21, 2016: Diabetes Research and Clinical Practice
Robert Eugene Hoyt, Dallas Snider, Carla Thompson, Sarita Mantravadi
BACKGROUND: We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS)...
October 11, 2016: JMIR Public Health and Surveillance
Erin Holve, Samantha Weiss
In September 2015 the EDM Forum hosted AcademyHealth's newest national conference, Concordium. The 11 papers featured in the eGEMs "Concordium 2015" special issue successfully reflect the major themes and issues discussed at the meeting. Many of the papers address informatics or methodological approaches to natural language processing (NLP) or text analysis, which is indicative of the importance of analyzing text data to gain insights into care coordination and patient-centered outcomes. Perspectives on the tools and infrastructure requirements that are needed to build learning health systems were also recurrent themes...
2016: EGEMS
Guy Divita, Marjorie E Carter, Le-Thuy Tran, Doug Redd, Qing T Zeng, Scott Duvall, Matthew H Samore, Adi V Gundlapalli
INTRODUCTION: Substantial amounts of clinically significant information are contained only within the narrative of the clinical notes in electronic medical records. The v3NLP Framework is a set of "best-of-breed" functionalities developed to transform this information into structured data for use in quality improvement, research, population health surveillance, and decision support. BACKGROUND: MetaMap, cTAKES and similar well-known natural language processing (NLP) tools do not have sufficient scalability out of the box...
2016: EGEMS
Donald A Szlosek, Jonathan Ferrett
INTRODUCTION: As the number of clinical decision support systems (CDSSs) incorporated into electronic medical records (EMRs) increases, so does the need to evaluate their effectiveness. The use of medical record review and similar manual methods for evaluating decision rules is laborious and inefficient. The authors use machine learning and Natural Language Processing (NLP) algorithms to accurately evaluate a clinical decision support rule through an EMR system, and they compare it against manual evaluation...
2016: EGEMS
Mei-Perng Lim, Mohd Firdaus-Raih, Sheila Nathan
Burkholderia pseudomallei, the causative agent of melioidosis, is among a growing number of bacterial pathogens that are increasingly antibiotic resistant. Antimicrobial peptides (AMPs) have been investigated as an alternative approach to treat microbial infections, as generally, there is a lower likelihood that a pathogen will develop resistance to AMPs. In this study, 36 candidate Caenorhabditis elegans genes that encode secreted peptides of <150 amino acids and previously shown to be overexpressed during infection by B...
2016: Frontiers in Microbiology
Farhood Farjah, Scott Halgrim, Diana S M Buist, Michael K Gould, Steven B Zeliadt, Elizabeth T Loggers, David S Carrell
INTRODUCTION: The incidence of incidentally detected lung nodules is rapidly rising, but little is known about their management or associated patient outcomes. One barrier to studying lung nodule care is the inability to efficiently and reliably identify the cohort of interest (i.e. cases). Investigators at Kaiser Permanente Southern California (KPSC) recently developed an automated method to identify individuals with an incidentally discovered lung nodule, but the feasibility of implementing this method across other health systems is unknown...
2016: EGEMS
Laurence Yang, Ding Ma, Ali Ebrahim, Colton J Lloyd, Michael A Saunders, Bernhard O Palsson
BACKGROUND: Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. RESULTS: Here, we address these computational challenges...
2016: BMC Bioinformatics
Neelakshi Bhagat, Roger Turbin, Paul Langer, N G Soni, A M Bauza, J H Son, David Chu, Mohammad Dastjerdi, Marco Zarbin
Loss of light perception (LP) after open globe injury (OGI) does not necessarily mean the patient will have permanent complete visual loss. Findings that seem to be associated reliably with permanent profound vision loss after OGI include optic nerve avulsion, optic nerve transection, and profound loss of intraocular contents, which can be identified with CT/MRI imaging albeit with varying degrees of confidence. Eyes with NLP after OGI that undergo successful primary repair with intact optic nerves may be considered for additional surgery, particularly if there is: (1) recovery of LP on the first day after primary repair; (2) treatable pathology underlying NLP status (e...
July 2016: Journal of Ophthalmic & Vision Research
Debasis Sahoo, Cyril Robbe, Caroline Deck, Frank Meyer, Alexandre Papy, Remy Willinger
The main objective of this study is to develop a methodology to assess this risk based on experimental tests versus numerical predictive head injury simulations. A total of 16 non-lethal projectiles (NLP) impacts were conducted with rigid force plate at three different ranges of impact velocity (120, 72 and 55m/s) and the force/deformation-time data were used for the validation of finite element (FE) NLP. A good accordance between experimental and simulation data were obtained during validation of FE NLP with high correlation value (>0...
September 3, 2016: Injury
Yu-Qi Huang, Zi-Ang Hu, Hu Cui, Zhi-Chao Luo, Ai-Ping Luo, Wen-Cheng Xu
We report the coexistence of high-order harmonic soliton molecules and rectangular noise-like pulses (NLP) in a figure-eight fiber laser mode-locked by a nonlinear amplifying loop mirror. The harmonic soliton molecule has a repetition rate of 936.6 MHz, corresponding to the 466th harmonics of the fundamental cavity repetition rate, with soliton separation of 16.5 ps. Meanwhile, the rectangular NLP operates at the fundamental repetition rate. In addition, these two types of pulses could be generated independently by manipulating the polarization controllers...
September 1, 2016: Optics Letters
Grzegorz Sobon, Jaroslaw Sotor, Aleksandra Przewolka, Iwona Pasternak, Wlodek Strupinski, Krzysztof Abramski
We report on the generation of noise-like pulse (NLP) trains in a Tm-doped fiber laser mode-locked by multilayer graphene saturable absorber. The spectral bandwidth obtained directly from the oscillator exceeds 60 nm, centered at 1950 nm, with 23.5 MHz repetition rate. The pulses were also amplified in a fully fiberized amplifier based on a double-cladding Tm-doped fiber. The system was capable of delivering 1.21 W of average power, which corresponds to 51.5 nJ energy stored in the noise-like bundle. We believe that the presented source might serve as a pump for supercontinuum generation in highly nonlinear fibers...
September 5, 2016: Optics Express
Ying Qi, Yan F Zhang, Yu Zhu, Ming G Wan, Shan S Du, Zhen Z Yue
Purpose. To investigate the prognostic factors for visual outcome in traumatic cataract patients. Methods. The demographic features of traumatic cataract patients in Central China were studied. The factors that might influence the visual outcome were analyzed. The sensitivity and specificity of OTS (ocular trauma score) in predicting VA were calculated. Results. The study enrolled 480 cases. 65.5% of patients achieved VA at >20/60. The factors associated with the final VA were initial VA, injury type, wound location, the way of cataract removal, and IOL implantation...
2016: Journal of Ophthalmology
Xiong Zhang, Chunhe Hu, Kunlun Yu, Jiangbo Bai, Dehu Tian, Yi Xu, Bing Zhang
PURPOSE: This study aims to evaluate whether volar locking plate was superior over non-locking plate in the treatment of die-punch fractures of the distal radius. METHOD: s: A total of 57 patients with closed die-punch fractures of the distal radius were included and analyzed. Of them, 32 were treated by non-locking plate (NLP) and the remaining 25 were treated by volar locking plate (VLP). Preoperative radiographs, computer tomographs and three-dimensional reconstruction, radiographs taken at immediate postoperation and at last follow-up were extracted and evaluated...
September 1, 2016: International Journal of Surgery
Yanshan Wang, Stephen Wu, Dingcheng Li, Saeed Mehrabi, Hongfang Liu
In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users' search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic health records (EHRs) and searching literature for topics of interest are some IR use cases. Meanwhile, natural language processing (NLP), such as tokenization or Part-Of-Speech (POS) tagging, has been developed for processing clinical documents or biomedical literature...
September 1, 2016: Journal of Biomedical Informatics
Bertalan Gyenes, André E X Brown
High-throughput analysis of animal behavior is increasingly common following the advances of recording technology, leading to large high-dimensional data sets. This dimensionality can sometimes be reduced while still retaining relevant information. In the case of the nematode worm Caenorhabditis elegans, more than 90% of the shape variance can be captured using just four principal components. However, it remains unclear if other methods can achieve a more compact representation or contribute further biological insight to worm locomotion...
2016: Frontiers in Behavioral Neuroscience
Jean-Baptiste Lamy, Adrien Ugon, Hélène Berthelot
BACKGROUND: Potential adverse effects (AEs) of drugs are described in their summary of product characteristics (SPCs), a textual document. Automatic extraction of AEs from SPCs is useful for detecting AEs and for building drug databases. However, this task is difficult because each AE is associated with a frequency that must be extracted and the presentation of AEs in SPCs is heterogeneous, consisting of plain text and tables in many different formats. METHODS: We propose a taxonomy for the presentation of AEs in SPCs...
2016: Studies in Health Technology and Informatics
Tejal A Patel, Mamta Puppala, Richard O Ogunti, Joe E Ensor, Tiancheng He, Jitesh B Shewale, Donna P Ankerst, Virginia G Kaklamani, Angel A Rodriguez, Stephen T C Wong, Jenny C Chang
BACKGROUND: A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. METHODS: The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report...
August 29, 2016: Cancer
Wen-Wai Yim, Tyler Denman, Sharon W Kwan, Meliha Yetisgen
Hepatocellular carcinoma (HCC) is a deadly disease affecting the liver for which there are many available therapies. Targeting treatments towards specific patient groups necessitates defining patients by stage of disease. Criteria for such stagings include information on tumor number, size, and anatomic location, typically only found in narrative clinical text in the electronic medical record (EMR). Natural language processing (NLP) offers an automatic and scale-able means to extract this information, which can further evidence-based research...
2016: AMIA Summits on Translational Science Proceedings
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