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https://www.readbyqxmd.com/read/28440912/performance-of-in-silico-tools-for-the-evaluation-of-p16ink4a-cdkn2a-variants-in-cagi
#1
Marco Carraro, Giovanni Minervini, Manuel Giollo, Yana Bromberg, Emidio Capriotti, Rita Casadio, Roland Dunbrack, Lisa Elefanti, Pietro Fariselli, Carlo Ferrari, Julian Gough, Panagiotis Katsonis, Emanuela Leonardi, Olivier Lichtarge, Chiara Menin, Pier Luigi Martelli, Abhishek Niroula, Lipika R Pal, Susanna Repo, Maria Chiara Scaini, Mauno Vihinen, Qiong Wei, Qifang Xu, Yuedong Yang, Yizhou Yin, Jan Zaucha, Huiying Zhao, Yaoqi Zhou, Steven E Brenner, John Moult, Silvio C E Tosatto
Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of ten variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene...
April 25, 2017: Human Mutation
https://www.readbyqxmd.com/read/28439010/cyclops-reveals-human-transcriptional-rhythms-in-health-and-disease
#2
Ron C Anafi, Lauren J Francey, John B Hogenesch, Junhyong Kim
Circadian rhythms modulate many aspects of physiology. Knowledge of the molecular basis of these rhythms has exploded in the last 20 years. However, most of these data are from model organisms, and translation to clinical practice has been limited. Here, we present an approach to identify molecular rhythms in humans from thousands of unordered expression measurements. Our algorithm, cyclic ordering by periodic structure (CYCLOPS), uses evolutionary conservation and machine learning to identify elliptical structure in high-dimensional data...
April 24, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28435015/using-classification-models-for-the-generation-of-disease-specific-medications-from-biomedical-literature-and-clinical-data-repository
#3
Liqin Wang, Peter J Haug, Guilherme Del Fiol
OBJECTIVE: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations...
April 20, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28434058/medical-conditions-in-the-first-years-of-life-associated-with-future-diagnosis-of-asd-in-children
#4
Stacey E Alexeeff, Vincent Yau, Yinge Qian, Meghan Davignon, Frances Lynch, Phillip Crawford, Robert Davis, Lisa A Croen
This study examines medical conditions diagnosed prior to the diagnosis of autism spectrum disorder (ASD). Using a matched case control design with 3911 ASD cases and 38,609 controls, we found that 38 out of 79 medical conditions were associated with increased ASD risk. Developmental delay, mental health, and neurology conditions had the strongest associations (ORs 2.0-23.3). Moderately strong associations were observed for nutrition, genetic, ear nose and throat, and sleep conditions (ORs 2.1-3.2). Using machine learning methods, we clustered children based on their medical conditions prior to ASD diagnosis and demonstrated ASD risk stratification...
April 22, 2017: Journal of Autism and Developmental Disorders
https://www.readbyqxmd.com/read/28430318/teaching-medical-students-ultrasound-guided-vascular-access-which-learning-method-is-best
#5
Alwin Lian, James C R Rippey, Peter J Carr
INTRODUCTION: Ultrasound is recommended to guide insertion of peripheral intravenous vascular cannulae (PIVC) where difficulty is experienced. Ultrasound machines are now common-place and junior doctors are often expected to be able to use them. The educational standards for this skill are highly varied, ranging from no education, to self-guided internet-based education, to formal, face-to-face traditional education. In an attempt to decide which educational technique our institution should introduce, a small pilot trial comparing educational techniques was designed...
April 20, 2017: Journal of Vascular Access
https://www.readbyqxmd.com/read/28428048/multi-center-machine-learning-in-imaging-psychiatry-a-meta-model-approach
#6
Petr Dluhoš, Daniel Schwarz, Wiepke Cahn, Neeltje van Haren, René Kahn, Filip Španiel, Jiří Horáček, Tomáš Kašpárek, Hugo Schnack
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues...
April 17, 2017: NeuroImage
https://www.readbyqxmd.com/read/28416144/feasibility-of-spirography-features-for-objective-assessment-of-motor-function-in-parkinson-s-disease
#7
Aleksander Sadikov, Vida Groznik, Martin Možina, Jure Žabkar, Dag Nyholm, Mevludin Memedi, Dejan Georgiev
OBJECTIVE: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations...
March 31, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28410487/structural-brain-changes-in-medically-refractory-focal-epilepsy-resemble-premature-brain-aging
#8
Heath R Pardoe, James H Cole, Karen Blackmon, Thomas Thesen, Ruben Kuzniecky
OBJECTIVE: We used whole brain T1-weighted MRI to estimate the age of individuals with medically refractory focal epilepsy, and compared with individuals with newly diagnosed focal epilepsy and healthy controls. The difference between neuroanatomical age and chronological age was compared between the three groups. METHODS: Neuroanatomical age was estimated using a machine learning-based method that was trained using structural MRI scans from a large independent healthy control sample (N=2001)...
April 3, 2017: Epilepsy Research
https://www.readbyqxmd.com/read/28407777/automatic-migraine-classification-via-feature-selection-committee-and-machine-learning-techniques-over-imaging-and-questionnaire-data
#9
Yolanda Garcia-Chimeno, Begonya Garcia-Zapirain, Marian Gomez-Beldarrain, Begonya Fernandez-Ruanova, Juan Carlos Garcia-Monco
BACKGROUND: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions...
April 13, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28405948/a-de-identification-pipeline-for-ultrasound-medical-images-in-dicom-format
#10
Eriksson Monteiro, Carlos Costa, José Luís Oliveira
Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images...
May 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28397956/a-project-course-sequence-in-innovation-and-commercialization-of-medical-devices
#11
Alan W Eberhardt, Shea Tillman, Brandon Kirkland, Brandon Sherrod
There exists a need for educational processes in which students gain experience with design and commercialization of medical devices. This manuscript describes the implementation of, and assessment results from, the first year offering of a "project course" sequence in a Master of Engineering (MEng) in Design and Commercialization at our institution. The three-semester course sequence focused on developing and applying hands-on skills that contribute to product development to address medical device needs found within our university hospital and local community...
April 11, 2017: Journal of Biomechanical Engineering
https://www.readbyqxmd.com/read/28386181/analytical-fuzzy-approach-to-biological-data-analysis
#12
Weiping Zhang, Jingzhi Yang, Yanling Fang, Huanyu Chen, Yihua Mao, Mohit Kumar
The assessment of the physiological state of an individual requires an objective evaluation of biological data while taking into account both measurement noise and uncertainties arising from individual factors. We suggest to represent multi-dimensional medical data by means of an optimal fuzzy membership function. A carefully designed data model is introduced in a completely deterministic framework where uncertain variables are characterized by fuzzy membership functions. The study derives the analytical expressions of fuzzy membership functions on variables of the multivariate data model by maximizing the over-uncertainties-averaged-log-membership values of data samples around an initial guess...
March 2017: Saudi Journal of Biological Sciences
https://www.readbyqxmd.com/read/28384801/the-world-health-organization-adult-attention-deficit-hyperactivity-disorder-self-report-screening-scale-for-dsm-5
#13
Berk Ustun, Lenard A Adler, Cynthia Rudin, Stephen V Faraone, Thomas J Spencer, Patricia Berglund, Michael J Gruber, Ronald C Kessler
Importance: Recognition that adult attention-deficit/hyperactivity disorder (ADHD) is common, seriously impairing, and usually undiagnosed has led to the development of adult ADHD screening scales for use in community, workplace, and primary care settings. However, these scales are all calibrated to DSM-IV criteria, which are narrower than the recently developed DSM-5 criteria. Objectives: To update for DSM-5 criteria and improve the operating characteristics of the widely used World Health Organization Adult ADHD Self-Report Scale (ASRS) for screening...
April 5, 2017: JAMA Psychiatry
https://www.readbyqxmd.com/read/28380048/a-study-of-the-transferability-of-influenza-case-detection-systems-between-two-large-healthcare-systems
#14
Ye Ye, Michael M Wagner, Gregory F Cooper, Jeffrey P Ferraro, Howard Su, Per H Gesteland, Peter J Haug, Nicholas E Millett, John M Aronis, Andrew J Nowalk, Victor M Ruiz, Arturo López Pineda, Lingyun Shi, Rudy Van Bree, Thomas Ginter, Fuchiang Tsui
OBJECTIVES: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. METHODS: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH)...
2017: PloS One
https://www.readbyqxmd.com/read/28379377/eliie-an-open-source-information-extraction-system-for-clinical-trial-eligibility-criteria
#15
Tian Kang, Shaodian Zhang, Youlan Tang, Gregory W Hruby, Alexander Rusanov, Noémie Elhadad, Chunhua Weng
Objective: To develop an open-source information extraction system called Eli gibility Criteria I nformation E xtraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0. Materials and Methods: EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring...
April 1, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/28371826/development-of-an-automated-assessment-tool-for-medwatch-reports-in-the-fda-adverse-event-reporting-system
#16
Lichy Han, Robert Ball, Carol A Pamer, Russ B Altman, Scott Proestel
Objective: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports. Methods: FDA safety evaluators assessed 326 reports for medication-related causality...
March 21, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/28365546/joint-multiple-fully-connected-convolutional-neural-network-with-extreme-learning-machine-for-hepatocellular-carcinoma-nuclei-grading
#17
Siqi Li, Huiyan Jiang, Wenbo Pang
Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists...
March 22, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28365240/using-electronic-health-records-to-build-an-ophthalmological-data-warehouse-and-visualize-patients-data
#18
Karsten U Kortüm, Michael Müller, Christoph Kern, Alexander Babenko, Wolfgang J Mayer, Anselm Kampik, Thomas C Kreutzer, Siegfried Priglinger, Christoph Hirneiss
PURPOSE: To develop a near real-time data warehouse (DW) in an academic ophthalmological center to gain scientific use of increasing digital data from electronic medical records (EMR) and diagnostic devices. Design; Database development METHODS: Specific macular clinic user interfaces within the institutional hospital information system were created. Orders for imaging modalities were sent by an EMR -linked picture-archiving and communications system to the respective devices. All data of 325,767 patients since 2002 were gathered in a DW running on a SQL database...
March 29, 2017: American Journal of Ophthalmology
https://www.readbyqxmd.com/read/28363456/handling-limited-datasets-with-neural-networks-in-medical-applications-a-small-data-approach
#19
Torgyn Shaikhina, Natalia A Khovanova
MOTIVATION: Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. METHODS: In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work...
January 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28357398/machine-learning-in-multimodal-medical-imaging
#20
EDITORIAL
Yong Xia, Zexuan Ji, Andrey Krylov, Hang Chang, Weidong Cai
No abstract text is available yet for this article.
2017: BioMed Research International
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