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https://www.readbyqxmd.com/read/28918412/prediction-of-early-unplanned-intensive-care-unit-readmission-in-a-uk-tertiary-care-hospital-a-cross-sectional-machine-learning-approach
#1
Thomas Desautels, Ritankar Das, Jacob Calvert, Monica Trivedi, Charlotte Summers, David J Wales, Ari Ercole
OBJECTIVES: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. SETTING: A single academic, tertiary care hospital in the UK. PARTICIPANTS: A set of 3326 ICU episodes collected between October 2014 and August 2016...
September 15, 2017: BMJ Open
https://www.readbyqxmd.com/read/28918390/a-glossary-for-big-data-in-population-and-public-health-discussion-and-commentary-on-terminology-and-research-methods
#2
Daniel Fuller, Richard Buote, Kevin Stanley
The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms...
September 16, 2017: Journal of Epidemiology and Community Health
https://www.readbyqxmd.com/read/28916782/predicting-clinical-outcomes-from-large-scale-cancer-genomic-profiles-with-deep-survival-models
#3
Safoora Yousefi, Fatemeh Amrollahi, Mohamed Amgad, Chengliang Dong, Joshua E Lewis, Congzheng Song, David A Gutman, Sameer H Halani, Jose Enrique Velazquez Vega, Daniel J Brat, Lee A D Cooper
Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes...
September 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28915974/rapid-and-accurate-intraoperative-pathological-diagnosis-by-artificial-intelligence-with-deep-learning-technology
#4
Jing Zhang, Yanlin Song, Fan Xia, Chenjing Zhu, Yingying Zhang, Wenpeng Song, Jianguo Xu, Xuelei Ma
Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm...
September 2017: Medical Hypotheses
https://www.readbyqxmd.com/read/28915930/predicting-activities-of-daily-living-for-cancer-patients-using-an-ontology-guided-machine-learning-methodology
#5
Hua Min, Hedyeh Mobahi, Katherine Irvin, Sanja Avramovic, Janusz Wojtusiak
BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. RESULTS: This retrospective study included 723 cancer patients from the SEER-MHOS dataset...
September 16, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28915659/data-driven-analysis-of-immune-infiltrate-in-a-large-cohort-of-breast-cancer-and-its-association-with-disease-progression-er-activity-and-genomic-complexity
#6
Ruth Dannenfelser, Marianne Nome, Andliena Tahiri, Josie Ursini-Siegel, Hans Kristian Moen Vollan, Vilde D Haakensen, Åslaug Helland, Bjørn Naume, Carlos Caldas, Anne-Lise Børresen-Dale, Vessela N Kristensen, Olga G Troyanskaya
The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry...
August 22, 2017: Oncotarget
https://www.readbyqxmd.com/read/28914640/machine-learning-novel-bioinformatics-approaches-for-combating-antimicrobial-resistance
#7
Nenad Macesic, Fernanda Polubriaginof, Nicholas P Tatonetti
PURPOSE OF REVIEW: Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. RECENT FINDINGS: The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible...
September 12, 2017: Current Opinion in Infectious Diseases
https://www.readbyqxmd.com/read/28913654/discriminating-cirrnas-from-other-lncrnas-using-a-hierarchical-extreme-learning-machine-h-elm-algorithm-with-feature-selection
#8
Lei Chen, Yu-Hang Zhang, Guohua Huang, Xiaoyong Pan, ShaoPeng Wang, Tao Huang, Yu-Dong Cai
As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques...
September 14, 2017: Molecular Genetics and Genomics: MGG
https://www.readbyqxmd.com/read/28912801/efficient-multiple-kernel-learning-algorithms-using-low-rank-representation
#9
Wenjia Niu, Kewen Xia, Baokai Zu, Jianchuan Bai
Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912425/predictive-models-of-minimal-hepatic-encephalopathy-for-cirrhotic-patients-based-on-large-scale-brain-intrinsic-connectivity-networks
#10
Yun Jiao, Xun-Heng Wang, Rong Chen, Tian-Yu Tang, Xi-Qi Zhu, Gao-Jun Teng
We aimed to find the most representative connectivity patterns for minimal hepatic encephalopathy (MHE) using large-scale intrinsic connectivity networks (ICNs) and machine learning methods. Resting-state fMRI was administered to 33 cirrhotic patients with MHE and 43 cirrhotic patients without MHE (NMHE). The connectivity maps of 20 ICNs for each participant were obtained by dual regression. A Bayesian machine learning technique, called Graphical Model-based Multivariate Analysis, was applied to determine ICN regions that characterized group differences...
September 14, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28912043/abnormal-segments-of-right-uncinate-fasciculus-and-left-anterior-thalamic-radiation-in-major-and-bipolar-depression
#11
Feng Deng, Ying Wang, Huiyuan Huang, Meiqi Niu, Shuming Zhong, Ling Zhao, Zhangzhang Qi, Xiaoyan Wu, Yao Sun, Chen Niu, Yuan He, Li Huang, Ruiwang Huang
Differential brain structural abnormalities between bipolar disorder (BD) and major depressive disorder (MDD) may reflect different pathological mechanisms underlying these two brain disorders. However, few studies have directly compared the brain structural properties, especially in white matter (WM) tracts, between BD and MDD. Using automated fiber-tract quantification (AFQ), we utilized diffusion tensor images (DTI) from 67 unmedicated depressed patients, including 31 BD and 36 MDD, and 45 healthy controls (HC) to create fractional anisotropy (FA) tract profiles along 20 major WM tracts...
September 11, 2017: Progress in Neuro-psychopharmacology & Biological Psychiatry
https://www.readbyqxmd.com/read/28911905/gaussian-process-classification-of-superparamagnetic-relaxometry-data-phantom-study
#12
Javad Sovizi, Kelsey B Mathieu, Sara L Thrower, Wolfgang Stefan, John D Hazle, David Fuentes
MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty...
July 24, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28911111/combinatorial-ensemble-mirna-target-prediction-of-co-regulation-networks-with-non-prediction-data
#13
Jason A Davis, Sita J Saunders, Martin Mann, Rolf Backofen
MicroRNAs (miRNAs) are key regulators of cell-fate decisions in development and disease with a vast array of target interactions that can be investigated using computational approaches. For this study, we developed metaMIR, a combinatorial approach to identify miRNAs that co-regulate identified subsets of genes from a user-supplied list. We based metaMIR predictions on an improved dataset of human miRNA-target interactions, compiled using a machine-learning-based meta-analysis of established algorithms. Simultaneously, the inverse dataset of negative interactions not likely to occur was extracted to increase classifier performance, as measured using an expansive set of experimentally validated interactions from a variety of sources...
September 6, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28910768/example-based-image-synthesis-via-randomized-patch-matching
#14
Yi Ren, Yaniv Romano, Michael Elad
Image and texture synthesis is a challenging task that has long been drawing attention in the fields of image processing, graphics, and machine learning. This problem consists of modelling the desired type of images, either through training examples or via a parametric modeling, and then generating images that belong to the same statistical origin. This work addresses the image synthesis task, focusing on two specific families of images - handwritten digits and face images. This paper offers two main contributions...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910760/structure-prediction-for-gland-segmentation-with-hand-crafted-and-deep-convolutional-features
#15
Siyamalan Manivannan, Wenqi Li, Jianguo Zhang, Emanuele Trucco, Stephen McKenna
We present a novel method to segment instances of glandular structures from colon histopathology images. We use a structure learning approach which represents local spatial configurations of class labels, capturing structural information normally ignored by sliding-window methods. This allows us to reveal different spatial structures of pixel labels (e.g., locations between adjacent glands, or far from glands), and to identify correctly neighbouring glandular structures as separate instances. Exemplars of label structures are obtained via clustering and used to train support vector machine classifiers...
September 8, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28910655/self-esteem-recognition-based-on-gait-pattern-using-kinect
#16
Bingli Sun, Zhan Zhang, Xingyun Liu, Bin Hu, Tingshao Zhu
BACKGROUND: Self-esteem is an important aspect of individual's mental health. When subjects are not able to complete self-report questionnaire, behavioral assessment will be a good supplement. In this paper, we propose to use gait data collected by Kinect as an indicator to recognize self-esteem. METHODS: 178 graduate students without disabilities participate in our study. Firstly, all participants complete the 10-item Rosenberg Self-Esteem Scale (RSS) to acquire self-esteem score...
September 8, 2017: Gait & Posture
https://www.readbyqxmd.com/read/28910352/deep-learning-approach-to-bacterial-colony-classification
#17
Bartosz Zieliński, Anna Plichta, Krzysztof Misztal, Przemysław Spurek, Monika Brzychczy-Włoch, Dorota Ochońska
In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria...
2017: PloS One
https://www.readbyqxmd.com/read/28910336/application-of-unsupervised-analysis-techniques-to-lung-cancer-patient-data
#18
Chip M Lynch, Victor H van Berkel, Hermann B Frieboes
This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer patients into groups based on clinically measurable disease-specific variables in order to estimate survival. Variables selected as inputs for machine learning include Number of Primaries, Age, Grade, Tumor Size, Stage, and TNM, which are numeric or can readily be converted to numeric type...
2017: PloS One
https://www.readbyqxmd.com/read/28906450/obstacle-recognition-based-on-machine-learning-for-on-chip-lidar-sensors-in-a-cyber-physical-system
#19
Fernando Castaño, Gerardo Beruvides, Rodolfo E Haber, Antonio Artuñedo
Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink...
September 14, 2017: Sensors
https://www.readbyqxmd.com/read/28906437/developing-fine-grained-actigraphies-for-rheumatoid-arthritis-patients-from-a-single-accelerometer-using-machine-learning
#20
Javier Andreu-Perez, Luis Garcia-Gancedo, Jonathan McKinnell, Anniek Van der Drift, Adam Powell, Valentin Hamy, Thomas Keller, Guang-Zhong Yang
In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients...
September 14, 2017: Sensors
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