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https://www.readbyqxmd.com/read/28333183/machine-learning-algorithms-for-objective-remission-and-clinical-outcomes-with-thiopurines
#1
Akbar K Waljee, Kay Sauder, Anand Patel, Sandeep Segar, Boang Liu, Yiwei Zhang, Ji Zhu, Ryan W Stidham, Ulysses Balis, Peter D R Higgins
Background and Aims: Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease [IBD] has proved difficult. Current methods using 6-thioguanine nucleotide [6-TGN] metabolites have failed in randomized controlled trials [RCTs], and have not been used to predict objective remission [OR]. Our aims were to: 1) develop machine learning algorithms [MLA] using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted objective remission resulted in fewer clinical events per year...
March 14, 2017: Journal of Crohn's & Colitis
https://www.readbyqxmd.com/read/28332099/integrated-multisystem-analysis-in-a-mental-health-and-criminal-justice-ecosystem
#2
Erin Falconer, Tal El-Hay, Dimitris Alevras, John P Docherty, Chen Yanover, Alan Kalton, Yaara Goldschmidt, Michal Rosen-Zvi
BACKGROUND: Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system...
December 2017: Health & Justice
https://www.readbyqxmd.com/read/28328520/a-natural-language-processing-framework-for-assessing-hospital-readmissions-for-patients-with-copd
#3
Ankur Agarwal, Christopher Baechle, Ravi Behara, Xingquan Zhu
With the passage of recent federal legislation many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and Chronic Obstructive Pulmonary Disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have been efforts to statistically predict those most in danger of readmission, few have focused primarily on unstructured clinical notes...
March 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#4
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28318505/machine-learning-in-the-prediction-of-cardiac-epicardial-and-mediastinal-fat-volumes
#5
É O Rodrigues, V H A Pinheiro, P Liatsis, A Conci
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segmenting just one of them suffices, while the volume of the other may be predicted fairly precisely. The correlation coefficient obtained by the Rotation Forest algorithm using MLP Regressor for predicting the mediastinal fat based on the epicardial fat was 0...
February 24, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28315069/toolkits-and-libraries-for-deep-learning
#6
REVIEW
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick
Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data...
March 17, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28301734/deep-learning-in-medical-image-analysis
#7
Dinggang Shen, Guorong Wu, Heung-Il Suk
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications...
March 9, 2017: Annual Review of Biomedical Engineering
https://www.readbyqxmd.com/read/28298265/supervised-machine-learning-algorithms-can-classify-open-text-feedback-of-doctor-performance-with-human-level-accuracy
#8
Chris Gibbons, Suzanne Richards, Jose Maria Valderas, John Campbell
BACKGROUND: Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. OBJECTIVE: The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom...
March 15, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28295412/machine-learning-will-transform-radiology-significantly-within-the-next-five-years
#9
Ge Wang, Mannudeep Kalra, Colin G Orton
With impressive progress in machine learning, there has been increasingly more interest in its relevance to medical physics, which involves both medical imaging and radiation treatment planning. However, because it is still generally unclear how to identify unique niches, utilize big data, and optimize neural networks for machine learning, machine learning is yet to have a major impact on medical physics practice. Nevertheless, there are optimistic opinions that machine learning will have a major impact on medical physics and radiology within the next five years...
March 11, 2017: Medical Physics
https://www.readbyqxmd.com/read/28293473/separating-generalized-anxiety-disorder-from-major-depression-using-clinical-hormonal-and-structural-mri-data-a-multimodal-machine-learning-study
#10
Kevin Hilbert, Ulrike Lueken, Markus Muehlhan, Katja Beesdo-Baum
BACKGROUND: Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD, MD and healthy subjects to differentiate subjects with a disorder from healthy subjects (case-classification) and to differentiate GAD from MD (disorder-classification). METHODS: Subjects with GAD (n = 19), MD without GAD (n = 14), and healthy comparison subjects (n = 24) were included...
March 2017: Brain and Behavior
https://www.readbyqxmd.com/read/28287963/a-dataset-and-a-technique-for-generalized-nuclear-segmentation-for-computational-pathology
#11
Neeraj Kumar, Ruchika Verma, Sanuj Sharma, Surabhi Bhargava, Abhishek Vahadane, Amit Sethi
Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques such as Otsu thresholding and watershed segmentation do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms require datasets of images in which a vast number of nuclei have been annotated...
March 6, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28282591/a-novel-computer-aided-diagnosis-system-for-breast-mri-based-on-feature-selection-and-ensemble-learning
#12
Wei Lu, Zhe Li, Jinghui Chu
Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers...
March 6, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28269817/features-extraction-and-multi-classification-of-semg-using-a-gpu-accelerated-ga-mlp-hybrid-algorithm
#13
Weizhen Luo, Zhongnan Zhang, Tingxi Wen, Chunfeng Li, Ziheng Luo
BACKGROUND: Surface electromyography (sEMG) signal is the combined effect of superficial muscle EMG and neural electrical activity. In recent years, researchers did large amount of human-machine system studies by using the physiological signals as control signals. OBJECTIVE: To develop and test a new multi-classification method to improve performance of analyzing sEMG signals based on public sEMG dataset. METHODS: First, ten features were selected as candidate features...
March 3, 2017: Journal of X-ray Science and Technology
https://www.readbyqxmd.com/read/28269002/classification-of-large-scale-fundus-image-data-sets-a-cloud-computing-framework
#14
Sohini Roychowdhury
Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268825/identification-of-promising-research-directions-using-machine-learning-aided-medical-literature-analysis
#15
Victor Andrei, Ognjen Arandjelovic
The rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora, and of tracking complex temporal changes within it...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268823/prediction-using-patient-comparison-vs-modeling-a-case-study-for-mortality-prediction
#16
Mark Hoogendoorn, Ali El Hassouni, Kwongyen Mok, Marzyeh Ghassemi, Peter Szolovits
Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268580/a-deep-bag-of-features-model-for-the-classification-of-melanomas-in-dermoscopy-images
#17
S Sabbaghi, M Aldeen, R Garnavi
Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level representations using machine learning techniques. Such interest has been growing steadily in the field of medical image diagnosis, particularly in melanoma classification. In this paper, a novel application of deep learning (stacked sparse auto-encoders) is presented for skin lesion classification task. The stacked sparse auto-encoder discovers latent information features in input images (pixel intensities)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28263940/early-detection-of-heart-failure-using-electronic-health-records-practical-implications-for-time-before-diagnosis-data-diversity-data-quantity-and-data-density
#18
Kenney Ng, Steven R Steinhubl, Christopher deFilippi, Sanjoy Dey, Walter F Stewart
BACKGROUND: Using electronic health records data to predict events and onset of diseases is increasingly common. Relatively little is known, although, about the tradeoffs between data requirements and model utility. METHODS AND RESULTS: We examined the performance of machine learning models trained to detect prediagnostic heart failure in primary care patients using longitudinal electronic health records data. Model performance was assessed in relation to data requirements defined by the prediction window length (time before clinical diagnosis), the observation window length (duration of observation before prediction window), the number of different data domains (data diversity), the number of patient records in the training data set (data quantity), and the density of patient encounters (data density)...
November 2016: Circulation. Cardiovascular Quality and Outcomes
https://www.readbyqxmd.com/read/28263925/image-quality-transfer-and-applications-in-diffusion-mri
#19
Daniel C Alexander, Darko Zikic, Aurobrata Ghosh, Ryutaro Tanno, Viktor Wottschel, Jiaying Zhang, Enrico Kaden, Tim B Dyrby, Stamatios N Sotiropoulos, Hui Zhang, Antonio Criminisi
This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content...
March 3, 2017: NeuroImage
https://www.readbyqxmd.com/read/28247975/integrating-ultrasound-into-modern-medical-curricula
#20
REVIEW
Shilpan G Patel, Brion Benninger, S Ali Mirjalili
INTRODUCTION: Ultrasonography is widely practiced in many disciplines. It is becoming increasingly important to design well-structured curricula to introduce imaging to students during medical school. This review aims to analyze the literature for evidence of how ultrasonography has been incorporated into anatomy education in medical school curricula worldwide. MATERIALS AND METHODS: A literature search was conducted using multiple databases with the keywords: "Ultrasound OR Ultrasonographic examination*" AND "Medical student* OR Undergraduate teaching* OR Medical education*" AND "Anatomy* OR Living anatomy* OR Real-time anatomy*"...
March 1, 2017: Clinical Anatomy
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