keyword
MENU ▼
Read by QxMD icon Read
search

machining learning

keyword
https://www.readbyqxmd.com/read/28434153/machine-learning-xgboost-analysis-of-language-networks-to-classify-patients-with-epilepsy
#1
L Torlay, M Perrone-Bertolotti, E Thomas, M Baciu
Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing 'atypical' (compared to 'typical' in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures...
April 22, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28434058/medical-conditions-in-the-first-years-of-life-associated-with-future-diagnosis-of-asd-in-children
#2
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/28433753/computational-image-analysis-for-prognosis-determination-in-dme
#3
Bianca S Gerendas, Hrvoje Bogunovic, Amir Sadeghipour, Thomas Schlegl, Georg Langs, Sebastian M Waldstein, Ursula Schmidt-Erfurth
In this pilot study, we evaluated the potential of computational image analysis of optical coherence tomography (OCT) data to determine the prognosis of patients with diabetic macular edema (DME). Spectral-domain OCT scans with fully automated retinal layer segmentation and segmentation of intraretinal cystoid fluid (IRC) and subretinal fluid of 629 patients receiving anti-vascular endothelial growth factor therapy for DME in a randomized prospective clinical trial were analyzed. The results were used to define 312 potentially predictive features at three timepoints (baseline, weeks 12 and 24) for best-corrected visual acuity (BCVA) at baseline and after one year used in a random forest prediction path...
April 19, 2017: Vision Research
https://www.readbyqxmd.com/read/28433591/-artificial-intelligence-applied-to-radiation-oncology
#4
J-E Bibault, A Burgun, P Giraud
Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the most recent examples is the lack of phase III trials demonstrating the superiority of intensity-modulated radiation therapy in most of its current indications. A new paradigm is developing that consists in the mining of large databases to answer clinical or translational issues. Beyond national databases (such as SEER or NCDB), that often lack the necessary level of details on the population studied or the treatments performed, electronic health records can be used to create detailed phenotypic profiles of any patients...
April 19, 2017: Cancer Radiothérapie: Journal de la Société Française de Radiothérapie Oncologique
https://www.readbyqxmd.com/read/28433431/application-of-structured-support-vector-machine-backpropagation-to-a-convolutional-neural-network-for-human-pose-estimation
#5
Peerajak Witoonchart, Prabhas Chongstitvatana
In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer...
February 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28432182/toward-a-direct-and-scalable-identification-of-reduced-models-for-categorical-processes
#6
Susanne Gerber, Illia Horenko
The applicability of many computational approaches is dwelling on the identification of reduced models defined on a small set of collective variables (colvars). A methodology for scalable probability-preserving identification of reduced models and colvars directly from the data is derived-not relying on the availability of the full relation matrices at any stage of the resulting algorithm, allowing for a robust quantification of reduced model uncertainty and allowing us to impose a priori available physical information...
April 21, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28430949/capturing-non-local-interactions-by-long-short-term-memory-bidirectional-recurrent-neural-networks-for-improving-prediction-of-protein-secondary-structure-backbone-angles-contact-numbers-and-solvent-accessibility
#7
Rhys Heffernan, Yuedong Yang, Kuldip Paliwal, Yaoqi Zhou
Motivation: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some "short to intermediate" non-local interactions...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430318/teaching-medical-students-ultrasound-guided-vascular-access-which-learning-method-is-best
#8
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/28430266/machine-learning-approach-to-oam-beam-demultiplexing-via-convolutional-neural-networks
#9
Timothy Doster, Abbie T Watnik
Orbital angular momentum (OAM) beams allow for increased channel capacity in free-space optical communication. Conventionally, these OAM beams are multiplexed together at a transmitter and then propagated through the atmosphere to a receiver where, due to their orthogonality properties, they are demultiplexed. We propose a technique to demultiplex these OAM-carrying beams by capturing an image of the unique multiplexing intensity pattern and training a convolutional neural network (CNN) as a classifier. This CNN-based demultiplexing method allows for simplicity of operation as alignment is unnecessary, orthogonality constraints are loosened, and costly optical hardware is not required...
April 20, 2017: Applied Optics
https://www.readbyqxmd.com/read/28428140/automated-annotation-and-classification-of-bi-rads-assessment-from-radiology-reports
#10
Sergio M Castro, Eugene Tseytlin, Olga Medvedeva, Kevin Mitchell, Shyam Visweswaran, Tanja Bekhuis, Rebecca S Jacobson
The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach...
April 17, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28428048/multi-center-machine-learning-in-imaging-psychiatry-a-meta-model-approach
#11
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/28427468/learning-from-biomedical-linked-data-to-suggest-valid-pharmacogenes
#12
Kevin Dalleau, Yassine Marzougui, Sébastien Da Silva, Patrice Ringot, Ndeye Coumba Ndiaye, Adrien Coulet
BACKGROUND: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available. METHOD: We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases...
April 20, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28426940/prospective-assessment-of-virtual-screening-heuristics-derived-using-a-novel-fusion-score
#13
Dante A Pertusi, Gregory O'Donnell, Michelle F Homsher, Kelli Solly, Amita Patel, Shannon L Stahler, Daniel Riley, Michael F Finley, Eleftheria N Finger, Gregory C Adam, Juncai Meng, David J Bell, Paul D Zuck, Edward M Hudak, Michael J Weber, Jennifer E Nothstein, Louis Locco, Carissa Quinn, Adam Amoss, Brian Squadroni, Michelle Hartnett, Mee Ra Heo, Tara White, S Alex May, Evelyn Boots, Kenneth Roberts, Patrick Cocchiarella, Alex Wolicki, Anthony Kreamer, Peter S Kutchukian, Anne Mai Wassermann, Victor N Uebele, Meir Glick, Andrew Rusinko, J Christopher Culberson
High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches...
April 1, 2017: SLAS Discovery
https://www.readbyqxmd.com/read/28426817/evaluation-of-machine-learning-algorithms-and-structural-features-for-optimal-mri-based-diagnostic-prediction-in-psychosis
#14
Raymond Salvador, Joaquim Radua, Erick J Canales-Rodríguez, Aleix Solanes, Salvador Sarró, José M Goikolea, Alicia Valiente, Gemma C Monté, María Del Carmen Natividad, Amalia Guerrero-Pedraza, Noemí Moro, Paloma Fernández-Corcuera, Benedikt L Amann, Teresa Maristany, Eduard Vieta, Peter J McKenna, Edith Pomarol-Clotet
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps...
2017: PloS One
https://www.readbyqxmd.com/read/28426306/machine-learning-for-social-services-a-study-of-prenatal-case-management-in-illinois
#15
Ian Pan, Laura B Nolan, Rashida R Brown, Romana Khan, Paul van der Boor, Daniel G Harris, Rayid Ghani
OBJECTIVES: To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. METHODS: We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection...
April 20, 2017: American Journal of Public Health
https://www.readbyqxmd.com/read/28426134/metastasis-detection-from-whole-slide-images-using-local-features-and-random-forests
#16
Mira Valkonen, Kimmo Kartasalo, Kaisa Liimatainen, Matti Nykter, Leena Latonen, Pekka Ruusuvuori
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model...
April 20, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28426133/quantitative-phase-microscopy-spatial-signatures-of-cancer-cells
#17
Darina Roitshtain, Lauren Wolbromsky, Evgeny Bal, Hayit Greenspan, Lisa L Satterwhite, Natan T Shaked
We present cytometric classification of live healthy and cancerous cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cells and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow...
April 20, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28423792/acronym-disambiguation-in-spanish-electronic-health-narratives-using-machine-learning-techniques
#18
Ignacio Rubio-López, Roberto Costumero, Héctor Ambit, Consuelo Gonzalo-Martín, Ernestina Menasalvas, Alejandro Rodríguez González
Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches. Current approaches to process the unstructured texts in EHRs are based in applying text mining or natural language processing (NLP) techniques over the data. In particular Named Entity Recognition (NER) is of paramount importance to retrieve specific biomedical concepts from the text providing the semantic type of the concept retrieved...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423786/prevalence-estimation-of-protected-health-information-in-swedish-clinical-text
#19
Aron Henriksson, Maria Kvist, Hercules Dalianis
Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner. Although automatic de-identification of clinical text using machine learning holds much promise, little is known about the relative prevalence of PHI in different types of clinical text and whether there is a need for domain adaptation when learning predictive models from one particular domain and applying it to another. In this study, we address these questions by training a predictive model and using it to estimate the prevalence of PHI in clinical text written (1) in different clinical specialties, (2) in different types of notes (i...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423765/evaluation-of-machine-learning-methods-to-predict-coronary-artery-disease-using-metabolomic-data
#20
Henrietta Forssen, Riyaz Patel, Natalie Fitzpatrick, Aroon Hingorani, Adam Timmis, Harry Hemingway, Spiros Denaxas
Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a priori selected input features and thus might suffer from poorer accuracy. Supervised machine learning methods can potentially be used in order to fully exploit the dimensionality and richness of the data. In this paper, we systematically implement and evaluate a set of supervised learning methods (L1 regression, random forest classifier) and compare them to traditional regression-based approaches for disease prediction using metabolomic data...
2017: Studies in Health Technology and Informatics
keyword
keyword
79875
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"