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https://www.readbyqxmd.com/read/28092576/multiple-instance-learning-for-medical-image-and-video-analysis
#1
Gwenole Quellec, Guy Cazuguel, Beatrice Cochener, Mathieu Lamard
Multiple-Instance Learning (MIL) is a recent machine learning paradigm that is particularly well suited to Medical Image and Video Analysis (MIVA) tasks. Based solely on class labels assigned globally to images or videos, MIL algorithms learn to detect relevant patterns locally in images or videos. These patterns are then used for classification at a global level. Because supervision relies on global labels, manual segmentations are not needed to train MIL algorithms, unlike traditional Single-Instance Learning (SIL) algorithms...
January 10, 2017: IEEE Reviews in Biomedical Engineering
https://www.readbyqxmd.com/read/28065773/using-the-electronic-medical-record-to-identify-patients-at-high-risk-for-frequent-ed-visits-and-high-system-costs
#2
David W Frost, Shankar Vembu, Jiayi Wang, Karen Tu, Quaid Morris, Howard B Abrams
BACKGROUND: A small proportion of patients accounts for a very high proportion of healthcare utilization. Accurate pre-emptive identification may facilitate tailored intervention. We sought to determine whether Machine Learning techniques using text from a family practice Electronic Medical Record (EMR) can be used to predict future high Emergency Department (ED) use and total costs by patients who are not yet high ED users or high cost to the healthcare system. METHODS: Text from fields of the Cumulative Patient Profile within an EMR (PS Suite) of 43,111 patients was indexed...
January 5, 2017: American Journal of Medicine
https://www.readbyqxmd.com/read/28065767/derivation-and-internal-validation-of-a-clinical-prediction-tool-for-30-day-mortality-in-lower-gastrointestinal-bleeding
#3
Neil Sengupta, Elliot B Tapper
BACKGROUND AND AIMS: There are limited data to predict which patients with lower gastrointestinal bleeding are at risk for adverse outcomes. We aimed to develop a clinical tool based on admission variables to predict 30-day mortality in lower gastrointestinal bleeding. METHODS: We used a validated machine learning algorithm to identify adult patients hospitalized with lower gastrointestinal bleeding at an academic medical center between 2008 and 2015. The cohort was split randomly into a derivation and validation cohort...
January 5, 2017: American Journal of Medicine
https://www.readbyqxmd.com/read/28060903/a-comparison-of-a-machine-learning-model-with-euroscore-ii-in-predicting-mortality-after-elective-cardiac-surgery-a-decision-curve-analysis
#4
Jérôme Allyn, Nicolas Allou, Pascal Augustin, Ivan Philip, Olivier Martinet, Myriem Belghiti, Sophie Provenchere, Philippe Montravers, Cyril Ferdynus
BACKGROUND: The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. METHODS AND FINDING: We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA...
2017: PloS One
https://www.readbyqxmd.com/read/28059233/cytopathological-image-analysis-using-deep-learning-networks-in-microfluidic-microscopy
#5
G Gopakumar, K Hari Babu, Deepak Mishra, Sai Siva Gorthi, Gorthi R K Sai Subrahmanyam
Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60)...
January 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
https://www.readbyqxmd.com/read/28055930/deep-learning-for-health-informatics
#6
Daniele Ravi, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu Perez, Benny Lo, Guang-Zhong Yang
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data...
December 29, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28048442/su-d-206-02-evaluation-of-partial-storage-of-the-system-matrix-for-cone-beam-computed-tomography-using-a-gpu-platform
#7
D Matenine, G Cote, J Mascolo-Fortin, Y Goussard, P Despres
PURPOSE: Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersections between the photons' trajectories and the object, also called ray-tracing or system matrix computation. This work evaluates different ways to store the system matrix, aiming to reconstruct dense image grids in reasonable time. METHODS: We propose an optimized implementation of the Siddon's algorithm using graphics processing units (GPUs) with a novel data storage scheme...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048384/su-d-207b-06-predicting-breast-cancer-malignancy-on-dce-mri-data-using-pre-trained-convolutional-neural-networks
#8
N Antropova, B Huynh, M Giger
PURPOSE: We investigate deep learning in the task of distinguishing between malignant and benign breast lesions on dynamic contrast-enhanced MR images (DCE-MRIs), eliminating the need for lesion segmentation and extraction of tumor features. We evaluate convolutional neural network (CNN) after transfer learning with ImageNet, a database of thousands of non-medical images. METHODS: Under a HIPAA-compliant IRB protocol, a database of 551 (357 malignant and 194 benign) breast MRI cases was collected...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048357/su-f-r-05-multidimensional-imaging-radiomics-geodesics-a-novel-manifold-learning-based-automatic-feature-extraction-method-for-diagnostic-prediction-in-multiparametric-imaging
#9
V Parekh, M A Jacobs
PURPOSE: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient's pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). METHODS: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048318/we-h-brc-06-a-unified-machine-learning-based-probabilistic-model-for-automated-anomaly-detection-in-the-treatment-plan-data
#10
X Chang, A Kalet, S Liu, D Yang
PURPOSE: The purpose of this work was to investigate the ability of a machine-learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information. METHODS: In total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048281/th-b-brc-00-how-to-identify-and-resolve-potential-clinical-errors-before-they-impact-patients-treatment-lessons-learned
#11
Indra Das
Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient's treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048252/su-f-e-18-training-monthly-qa-of-medical-accelerators-illustrated-instructions-for-self-learning
#12
L Court, D Brown, H Wang, B Maddox, D Aten, H MacGregor, P Chi, A Yock, S Gao, M Aristophanous, P Balter
PURPOSE: To develop and test clear illustrated instructions for training of monthly mechanical QA of medical linear accelerators. METHODS: Illustrated instructions were created for monthly mechanical QA with tolerance tabulated, and underwent several steps of review and refinement. Testers with zero QA experience were then recruited from our radiotherapy department (1 student, 2 computational scientists and 8 dosimetrists). The following parameters were progressively de-calibrated on a Varian C-series linac: Group A = gantry angle, ceiling laser position, X1 jaw position, couch longitudinal position, physical graticule position (5 testers); Group B = Group A + wall laser position, couch lateral and vertical position, collimator angle (3 testers); Group C = Group B + couch angle, wall laser angle, and optical distance indicator (3 testers)...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048166/mo-de-207b-06-computer-aided-diagnosis-of-breast-ultrasound-images-using-transfer-learning-from-deep-convolutional-neural-networks
#13
B Huynh, K Drukker, M Giger
PURPOSE: To assess the performance of using transferred features from pre-trained deep convolutional networks (CNNs) in the task of classifying cancer in breast ultrasound images, and to compare this method of transfer learning with previous methods involving human-designed features. METHODS: A breast ultrasound dataset consisting of 1125 cases and 2393 regions of interest (ROIs) was used. Each ROI was labeled as cystic, benign, or malignant. Features were extracted from each ROI using pre-trained CNNs and used to train support vector machine (SVM) classifiers in the tasks of distinguishing non-malignant (benign+cystic) vs malignant lesions and benign vs malignant lesions...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047670/su-f-p-20-predicting-waiting-times-in-radiation-oncology-using-machine-learning
#14
A Joseph, D Herrera, T Hijal, L Hendren, A Leung, J Wainberg, M Sawaf, M Gorshkov, R Maglieri, M Keshavarz, J Kildea
PURPOSE: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047259/tu-fg-209-12-treatment-site-and-view-recognition-in-x-ray-images-with-hierarchical-multiclass-recognition-models
#15
X Chang, T Mazur, D Yang
PURPOSE: To investigate an approach of automatically recognizing anatomical sites and imaging views (the orientation of the image acquisition) in 2D X-ray images. METHODS: A hierarchical (binary tree) multiclass recognition model was developed to recognize the treatment sites and views in x-ray images. From top to bottom of the tree, the treatment sites are grouped hierarchically from more general to more specific. Each node in the hierarchical model was designed to assign images to one of two categories of anatomical sites...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047176/su-c-bra-05-delineating-high-dose-clinical-target-volumes-for-head-and-neck-tumors-using-machine-learning-algorithms
#16
C Cardenas, A Wong, A Mohamed, J Yang, L Court, A Rao, C Fuller, M Aristophanous
PURPOSE: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. METHODS: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil)...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28046519/su-f-t-462-lessons-learned-from-a-machine-incident-reporting-system
#17
S Sutlief, J Hoisak
PURPOSE: Linear accelerators must operate with minimal downtime. Machine incident logs are a crucial tool to meet this requirement. They providing a history of service and demonstrate whether a fix is working. This study investigates the information content of a large department linear accelerator incident log. METHODS: Our department uses an electronic reporting system to provide immediate information to both key department staff and the field service department...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28042838/a-comparison-study-of-classifier-algorithms-for-cross-person-physical-activity-recognition
#18
Yago Saez, Alejandro Baldominos, Pedro Isasi
Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing...
December 30, 2016: Sensors
https://www.readbyqxmd.com/read/28034788/an-unsupervised-machine-learning-model-for-discovering-latent-infectious-diseases-using-social-media-data
#19
Sunghoon Lim, Conrad S Tucker, Soundar Kumara
INTRODUCTION: The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms...
December 26, 2016: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28034407/causality-patterns-and-machine-learning-for-the-extraction-of-problem-action-relations-in-discharge-summaries
#20
Jae-Wook Seol, Wangjin Yi, Jinwook Choi, Kyung Soon Lee
Clinical narrative text includes information related to a patient's medical history such as chronological progression of medical problems and clinical treatments. A chronological view of a patient's history makes clinical audits easier and improves quality of care. In this paper, we propose a clinical Problem-Action relation extraction method, based on clinical semantic units and event causality patterns, to present a chronological view of a patient's problem and a doctor's action. Based on our observation that a clinical text describes a patient's medical problems and a doctor's treatments in chronological order, a clinical semantic unit is defined as a problem and/or an action relation...
February 2017: International Journal of Medical Informatics
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