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[PMID]: 29524784
[Au] Autor:George Y; Aldeen M; Garnavi R
[Ad] Address:Department of Electric and Electronic Engineering, University of Melbourne, VIC, Australia. Electronic address: ygeorge@student.unimelb.edu.au.
[Ti] Title:Psoriasis image representation using patch-based dictionary learning for erythema severity scoring.
[So] Source:Comput Med Imaging Graph;66:44-55, 2018 Feb 23.
[Is] ISSN:1879-0771
[Cp] Country of publication:United States
[La] Language:eng
[Ab] Abstract:Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1803
[Cu] Class update date: 180310
[Lr] Last revision date:180310
[St] Status:Publisher

  2 / 3042 MEDLINE  
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[PMID]: 29506465
[Au] Autor:Badal VD; Kundrotas PJ; Vakser IA
[Ad] Address:Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA.
[Ti] Title:Natural language processing in text mining for structural modeling of protein complexes.
[So] Source:BMC Bioinformatics;19(1):84, 2018 Mar 05.
[Is] ISSN:1471-2105
[Cp] Country of publication:England
[La] Language:eng
[Ab] Abstract: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., PLoS Comput Biol, 2015; 11: e1004630). Still, many extracted residues were not relevant to the docking. RESULTS: We present an extension of the TM tool, which utilizes natural language processing (NLP) for analyzing the context of the residue occurrence. The procedure was tested using generic and specialized dictionaries. The results showed that the keyword dictionaries designed for identification of protein interactions are not adequate for the TM prediction of the binding mode. However, our dictionary designed to distinguish keywords relevant to the protein binding sites led to considerable improvement in the TM performance. We investigated the utility of several methods of context analysis, based on dissection of the sentence parse trees. The machine learning-based NLP filtered the pool of the mined residues significantly more efficiently than the rule-based NLP. Constraints generated by NLP were tested in docking of unbound proteins from the DOCKGROUND X-ray benchmark set 4. The output of the global low-resolution docking scan was post-processed, separately, by constraints from the basic TM, constraints re-ranked by NLP, and the reference constraints. The quality of a match was assessed by the interface root-mean-square deviation. The results showed significant improvement of the docking output when using the constraints generated by the advanced TM with NLP. CONCLUSIONS: The basic TM procedure for extracting protein-protein binding site residues from the PubMed abstracts was significantly advanced by the deep parsing (NLP techniques for contextual analysis) in purging of the initial pool of the extracted residues. Benchmarking showed a substantial increase of the docking success rate based on the constraints generated by the advanced TM with NLP.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1803
[Cu] Class update date: 180311
[Lr] Last revision date:180311
[St] Status:In-Data-Review
[do] DOI:10.1186/s12859-018-2079-4

  3 / 3042 MEDLINE  
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[PMID]: 29381145
[Au] Autor:Demner-Fushman D; Shooshan SE; Rodriguez L; Aronson AR; Lang F; Rogers W; Roberts K; Tonning J
[Ad] Address:U.S. National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.
[Ti] Title:A dataset of 200 structured product labels annotated for adverse drug reactions.
[So] Source:Sci Data;5:180001, 2018 Jan 30.
[Is] ISSN:2052-4463
[Cp] Country of publication:England
[La] Language:eng
[Ab] Abstract:Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). We present the curation process and the structure of the publicly available database SPL-ADR-200db containing 5,098 distinct ADRs. The database is available at https://bionlp.nlm.nih.gov/tac2017adversereactions/; the code for preparing and validating the data is available at https://github.com/lhncbc/fda-ars.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1801
[Cu] Class update date: 180309
[Lr] Last revision date:180309
[St] Status:In-Data-Review
[do] DOI:10.1038/sdata.2018.1

  4 / 3042 MEDLINE  
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[PMID]: 29516539
[Au] Autor:Joy A; Paul JS
[Ad] Address:Medical Image Computing and Signal Processing Laboratory, Indian Institute of Information Technology and Management-Kerala, India.
[Ti] Title:A mixed-order nonlinear diffusion compressed sensing MR image reconstruction.
[So] Source:Magn Reson Med;, 2018 Mar 07.
[Is] ISSN:1522-2594
[Cp] Country of publication:United States
[La] Language:eng
[Ab] Abstract:PURPOSE: Avoid formation of staircase artifacts in nonlinear diffusion-based MR image reconstruction without compromising computational speed. METHODS: Whereas second-order diffusion encourages the evolution of pixel neighborhood with uniform intensities, fourth-order diffusion considers smooth region to be not necessarily a uniform intensity region but also a planar region. Therefore, a controlled application of fourth-order diffusivity function is used to encourage second-order diffusion to reconstruct the smooth regions of the image as a plane rather than a group of blocks, while not being strong enough to introduce the undesirable speckle effect. RESULTS: Proposed method is compared with second- and fourth-order nonlinear diffusion reconstruction, total variation (TV), total generalized variation, and higher degree TV using in vivo data sets for different undersampling levels with application to dictionary learning-based reconstruction. It is observed that the proposed technique preserves sharp boundaries in the image while preventing the formation of staircase artifacts in the regions of smoothly varying pixel intensities. It also shows reduced error measures compared with second-order nonlinear diffusion reconstruction or TV and converges faster than TV-based methods. CONCLUSION: Because nonlinear diffusion is known to be an effective alternative to TV for edge-preserving reconstruction, the crucial aspect of staircase artifact removal is addressed. Reconstruction is found to be stable for the experimentally determined range of fourth-order regularization parameter, and therefore not does not introduce a parameter search. Hence, the computational simplicity of second-order diffusion is retained.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1803
[Cu] Class update date: 180308
[Lr] Last revision date:180308
[St] Status:Publisher
[do] DOI:10.1002/mrm.27162

  5 / 3042 MEDLINE  
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[PMID]: 29272517
[Au] Autor:Putrik P; Ramiro S; Lie E; Michaud K; Kvamme MK; Keszei AP; Kvien TK; Uhlig T; Boonen A
[Ad] Address:Rheumatology, Maastricht University Medical Center and CAPHRI Research Institute, Maastricht, the Netherlands.
[Ti] Title:Deriving common comorbidity indices from the MedDRA classification and exploring their performance on key outcomes in patients with rheumatoid arthritis.
[So] Source:Rheumatology (Oxford);57(3):548-554, 2018 Mar 01.
[Is] ISSN:1462-0332
[Cp] Country of publication:England
[La] Language:eng
[Ab] Abstract:Objective: To develop algorithms for calculating the Rheumatic Diseases Comorbidity Index (RDCI), Charlson-Deyo Index (CDI) and Functional Comorbidity Index (FCI) from the Medical Dictionary for Regulatory Activities (MedDRA), and to assess how these MedDRA-derived indices predict clinical outcomes, utility and health resource utilization (HRU). Methods: Two independent researchers linked the preferred terms of the MedDRA classification into the conditions included in the RDCI, the CDI and the FCI. Next, using data from the Norwegian Register-DMARD study (a register of patients with inflammatory joint diseases treated with DMARDs), the explanatory value of these indices was studied in models adjusted for age, gender and DAS28. Model fit statistics were compared in generalized estimating equation (prediction of outcome over time) models using as outcomes: modified HAQ, HAQ, physical and mental component summary of SF-36, SF6D and non-RA related HRU. Results: Among 4126 patients with RA [72% female, mean (s.d.) age 56 (14) years], median (interquartile range) of RDCI at baseline was 0.0 (1.0) [range 0-6], CDI 0.0 (0.0) [0-7] and FCI 0.0 (1.0) [0-6]. All the comorbidity indices were associated with each outcome, and differences in their performance were moderate. The RDCI and FCI performed better on clinical outcomes: modified HAQ and HAQ, hospitalization, physical and mental component summary, and SF6D. Any non-RA related HRU was best predicted by RDCI followed by CDI. Conclusion: An algorithm is now available to compute three commonly used comorbidity indices from MedDRA classification. Indices performed comparably well in predicting a variety of outcomes, with the CDI performing slightly worse when predicting outcomes reflecting functioning and health.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1712
[Cu] Class update date: 180308
[Lr] Last revision date:180308
[St] Status:In-Data-Review
[do] DOI:10.1093/rheumatology/kex440

  6 / 3042 MEDLINE  
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[PMID]: 29244058
[Au] Autor:Leng Y; Diem SJ; Stone KL; Yaffe K
[Ad] Address:Department of Psychiatry, San Francisco VA Medical Center, University of California, California.
[Ti] Title:Antidepressant Use and Cognitive Outcomes in Very Old Women.
[So] Source:J Gerontol A Biol Sci Med Sci;, 2017 Dec 12.
[Is] ISSN:1758-535X
[Cp] Country of publication:United States
[La] Language:eng
[Ab] Abstract:Background: Antidepressant use is very common in the elderly, but the effects of antidepressants on cognition in the elderly are controversial with some studies suggesting harm and others protection. We aimed to investigate the association between different antidepressant use and change in cognition and risk of mild cognitive impairment (MCI) or dementia in very old women. Methods: We examined 1,234 community-dwelling women (mean age 83.2 years) from the Study of Osteoporotic Fractures. Baseline antidepressant use was reported and verified by medication containers, and medications were coded with computerized dictionary. Cognitive status (normal, MCI, or dementia) was adjudicated by an expert clinical panel 5 years later. Change in a short-form Mini-Mental State Examination and Trails B were evaluated over 5 years. Results: Eleven per cent of the women were taking antidepressants. Users of selective serotonin reuptake inhibitors (SSRIs) had the greatest cognitive decline over 5 years, after adjustment for demographics, medical comorbidities, benzodiazepine use, and baseline cognition. Multivariable logistic regression shows that the users of SSRIs were more than twice (OR = 2.69, 95% CI = 1.64-4.41) and trazodone users more than three times (3.48, 1.12-10.81) as likely to develop MCI or dementia compared with the nonusers. Further adjustment for baseline cognition or depressive symptoms did not appreciably alter the results, and the association remained after excluding women with high depressive symptoms. The use of tricyclic antidepressants or other antidepressants was not significantly associated with cognitive outcomes. Conclusions: The use of antidepressants, especially SSRIs and trazodone, was associated with an increased risk of cognitive impairment 5 years later among the oldest old women.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1712
[Cu] Class update date: 180308
[Lr] Last revision date:180308
[St] Status:Publisher
[do] DOI:10.1093/gerona/glx226

  7 / 3042 MEDLINE  
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[PMID]: 29477308
[Au] Autor:Arana JE; Harrington T; Cano M; Lewis P; Mba-Jonas A; Rongxia L; Stewart B; Markowitz LE; Shimabukuro TT
[Ad] Address:Immunization Safety Office, Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, United States. Electronic address: JArana@cdc.gov.
[Ti] Title:Post-licensure safety monitoring of quadrivalent human papillomavirus vaccine in the Vaccine Adverse Event Reporting System (VAERS), 2009-2015.
[So] Source:Vaccine;36(13):1781-1788, 2018 Mar 20.
[Is] ISSN:1873-2518
[Cp] Country of publication:Netherlands
[La] Language:eng
[Ab] Abstract:BACKGROUND: The Food and Drug Administration (FDA) approved quadrivalent human papillomavirus vaccine (4vHPV) for use in females and males aged 9-26 years, since 2006 and 2009 respectively. We characterized reports to the Vaccine Adverse Event Reporting System (VAERS), a US spontaneous reporting system, in females and males who received 4vHPV vaccination. METHODS: We searched VAERS for US reports of adverse events (AEs) following 4vHPV from January 2009 through December 2015. Signs and symptoms were coded using Medical Dictionary for Regulatory Activities (MedDRA). We calculated reporting rates and conducted empirical Bayesian data mining to identify disproportional reports. Clinicians reviewed available information, including medical records, and reports of selected pre-specified conditions. FINDINGS: VAERS received 19,760 reports following 4vHPV; 60.2% in females, 17.2% in males, and in 22.6% sex was missing. Overall, 94.2% of reports were non-serious; dizziness, syncope and injection site reactions were commonly reported in both males and females. Headache, fatigue and nausea were commonly reported serious AEs. More than 60 million 4vHPV doses were distributed during the study period. Crude AE reporting rates were 327 reports per million 4vHPV doses distributed for all reports, and 19 per million for serious reports. Among 29 verified reports of death, there was no pattern of clustering of deaths by diagnosis, co-morbidities, age, or interval from vaccination to death. INTERPRETATION: No new or unexpected safety concerns or reporting patterns of 4vHPV with clinically important AEs were detected. Safety profile of 4vHPV is consistent with data from pre-licensure trials and postmarketing safety data.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1802
[Cu] Class update date: 180307
[Lr] Last revision date:180307
[St] Status:In-Data-Review

  8 / 3042 MEDLINE  
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[PMID]: 29505403
[Au] Autor:Schretter C; Bundervoet S; Blinder D; Dooms A; D'hooge J; Schelkens P
[Ti] Title:Ultrasound Imaging From Sparse RF Samples Using System Point Spread Functions.
[So] Source:IEEE Trans Ultrason Ferroelectr Freq Control;65(3):316-326, 2018 Mar.
[Is] ISSN:1525-8955
[Cp] Country of publication:United States
[La] Language:eng
[Ab] Abstract:Upcoming phased-array 2-D sensors will soon enable fast high-definition 3-D ultrasound imaging. Currently, the communication of raw radio-frequency (RF) channel data from the probe to the computer for digital beamforming is a bottleneck. For reducing the amount of transferred data samples, this paper investigates the design of an adapted sparse sampling technique for image reconstruction inspired by the compressed sensing framework. Echo responses from isolated points are generated using a physically based simulation of ultrasound wave propagation through tissues. These point spread functions form a dictionary of shift-variant bent waves, which depend on the specific sound excitation and acquisition protocols. Speckled ultrasound images can be approximately decomposed in this dictionary where sparsity is enforced at the system matrix design. The Moore-Penrose pseudoinverse is precomputed and used at the reconstruction stage for fast minimum-norm recovery from nonuniform pseudorandom sampled raw RF data. Results on simulated and acquired phantoms demonstrate the benefits of an optimized basis function design for high-quality B-mode image recovery from few RF channel data samples.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1803
[Cu] Class update date: 180305
[Lr] Last revision date:180305
[St] Status:In-Data-Review
[do] DOI:10.1109/TUFFC.2017.2772916

  9 / 3042 MEDLINE  
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[PMID]: 28459684
[Au] Autor:Xing J; Niu Z; Huang J; Hu W; Zhou X; Yan S
[Ti] Title:Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment.
[So] Source:IEEE Trans Pattern Anal Mach Intell;40(4):987-1001, 2018 Apr.
[Is] ISSN:1939-3539
[Cp] Country of publication:United States
[La] Language:eng
[Ab] Abstract:Face alignment acts as an important task in computer vision. Regression-based methods currently dominate the approach to solving this problem, which generally employ a series of mapping functions from the face appearance to iteratively update the face shape hypothesis. One keypoint here is thus how to perform the regression procedure. In this work, we formulate this regression procedure as a sparse coding problem. We learn two relational dictionaries, one for the face appearance and the other one for the face shape, with coupled reconstruction coefficient to capture their underlying relationships. To deploy this model for face alignment, we derive the relational dictionaries in a stage-wised manner to perform close-loop refinement of themselves, i.e., the face appearance dictionary is first learned from the face shape dictionary and then used to update the face shape hypothesis, and the updated face shape dictionary from the shape hypothesis is in return used to refine the face appearance dictionary. To improve the model accuracy, we extend this model hierarchically from the whole face shape to face part shapes, thus both the global and local view variations of a face are captured. To locate facial landmarks under occlusions, we further introduce an occlusion dictionary into the face appearance dictionary to recover face shape from partially occluded face appearance. The occlusion dictionary is learned in a data driven manner from background images to represent a set of elemental occlusion patterns, a sparse combination of which models various practical partial face occlusions. By integrating all these technical innovations, we obtain a robust and accurate approach to locate facial landmarks under different face views and possibly severe occlusions for face images in the wild. Extensive experimental analyses and evaluations on different benchmark datasets, as well as two new datasets built by ourselves, have demonstrated the robustness and accuracy of our proposed model, especially for face images with large view variations and/or severe occlusions.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1705
[Cu] Class update date: 180305
[Lr] Last revision date:180305
[St] Status:In-Data-Review
[do] DOI:10.1109/TPAMI.2017.2697958

  10 / 3042 MEDLINE  
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[PMID]: 29329701
[Au] Autor:Gupta A; Banerjee I; Rubin DL
[Ad] Address:Department of Computer Science, Columbia University, New York City, NY, USA. Electronic address: ag3900@columbia.edu.
[Ti] Title:Automatic information extraction from unstructured mammography reports using distributed semantics.
[So] Source:J Biomed Inform;78:78-86, 2018 Feb.
[Is] ISSN:1532-0480
[Cp] Country of publication:United States
[La] Language:eng
[Ab] Abstract:To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F -score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules.
[Pt] Publication type:JOURNAL ARTICLE
[Em] Entry month:1801
[Cu] Class update date: 180303
[Lr] Last revision date:180303
[St] Status:In-Data-Review


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