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https://www.readbyqxmd.com/read/29771663/applications-of-deep-learning-and-reinforcement-learning-to-biological-data
#1
Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence...
June 2018: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/29765940/a-comparative-survey-of-methods-for-remote-heart-rate-detection-from-frontal-face-videos
#2
Chen Wang, Thierry Pun, Guillaume Chanel
Remotely measuring physiological activity can provide substantial benefits for both the medical and the affective computing applications. Recent research has proposed different methodologies for the unobtrusive detection of heart rate (HR) using human face recordings. These methods are based on subtle color changes or motions of the face due to cardiovascular activities, which are invisible to human eyes but can be captured by digital cameras. Several approaches have been proposed such as signal processing and machine learning...
2018: Frontiers in Bioengineering and Biotechnology
https://www.readbyqxmd.com/read/29763764/machine-learning-based-brain-tumour-segmentation-on-limited-data-using-local-texture-and-abnormality
#3
Stijn Bonte, Ingeborg Goethals, Roel Van Holen
Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four different MRI-sequences, but since this is often not available, there is need for a method able to delineate the different tumour tissues based on a minimal amount of data. We present a novel approach using a Random Forests model combining voxelwise texture and abnormality features on a contrast-enhanced T1 and FLAIR MRI...
May 7, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29763706/exploiting-semantic-patterns-over-biomedical-knowledge-graphs-for-predicting-treatment-and-causative-relations
#4
Gokhan Bakal, Preetham Talari, Elijah V Kakani, Ramakanth Kavuluru
BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach...
May 12, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29760666/candyflipping-and-other-combinations-identifying-drug-drug-combinations-from-an-online-forum
#5
Michael Chary, David Yi, Alex F Manini
Novel psychoactive substances (NPS) refer to synthetic compounds or derivatives of more widely known substances of abuse that have emerged over the last two decades. Case reports suggest that users combine substances to achieve desired psychotropic experiences while reducing dysphoria and unpleasant somatic effects. However, the pattern of combining NPS has not been studied on a large scale. Here, we show that posts discussing NPS describe combining nootropics with sedative-hypnotics and stimulants with plant hallucinogens or psychiatric medications...
2018: Frontiers in Psychiatry
https://www.readbyqxmd.com/read/29754799/extracting-cancer-mortality-statistics-from-death-certificates-a-hybrid-machine-learning-and-rule-based-approach-for-common-and-rare-cancers
#6
Bevan Koopman, Guido Zuccon, Anthony Nguyen, Anton Bergheim, Narelle Grayson
OBJECTIVE: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality of certificates written in natural language. This paper proposes an automatic classification system for identifying all cancer related causes of death from death certificates. METHODS: Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates...
May 10, 2018: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29753874/simulation-of-patient-flow-in-multiple-healthcare-units-using-process-and-data-mining-techniques-for-model-identification
#7
Sergey V Kovalchuk, Anastasia A Funkner, Oleg G Metsker, Aleksey N Yakovlev
INTRODUCTION: An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study. METHODS: A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed...
May 10, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29744763/accuracy-of-ultra-wide-field-fundus-ophthalmoscopy-assisted-deep-learning-a-machine-learning-technology-for-detecting-age-related-macular-degeneration
#8
Shinji Matsuba, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Enno, Naofumi Ishitobi, Hiroki Masumoto, Yoshiaki Kiuchi
PURPOSE: To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system. METHODS: First, to evaluate the diagnostic accuracy of DCNN, 364 photographic images (AMD: 137) were amplified and the area under the curve (AUC), sensitivity and specificity were examined. Furthermore, in order to compare the diagnostic abilities between DCNN and six ophthalmologists, we prepared yield 84 sheets comprising 50% of normal and wet-AMD data each, and calculated the correct answer rate, specificity, sensitivity, and response times...
May 9, 2018: International Ophthalmology
https://www.readbyqxmd.com/read/29743531/identifying-suicide-ideation-and-suicidal-attempts-in-a-psychiatric-clinical-research-database-using-natural-language-processing
#9
Andrea C Fernandes, Rina Dutta, Sumithra Velupillai, Jyoti Sanyal, Robert Stewart, David Chandran
Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information extraction from free text notes as well as structured fields concerning suicidality and this allows access to much larger cohorts than previously possible. This paper presents two novel NLP approaches - a rule-based approach to classify the presence of suicide ideation and a hybrid machine learning and rule-based approach to identify suicide attempts in a psychiatric clinical database...
May 9, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29740974/a-machine-learning-approach-as-a-surrogate-of-finite-element-analysis-based-inverse-method-to-estimate-the-zero-pressure-geometry-of-human-thoracic-aorta
#10
Liang Liang, Minliang Liu, Caitlin Martin, Wei Sun
Advances in structural finite element analysis (FEA) and medical imaging have made it possible to investigate the in vivo biomechanics of human organs such as blood vessels, for which organ geometries at the zero-pressure level need to be recovered. Although FEA-based inverse methods are available for zero-pressure geometry estimation, these methods typically require iterative computation, which are time-consuming and may be not suitable for time-sensitive clinical applications. In this study, by using machine learning (ML) techniques, we developed a ML-model to estimate the zero-pressure geometry of human thoracic aorta by given two pressurized geometries of the same patient at two different blood pressure levels...
May 9, 2018: International Journal for Numerical Methods in Biomedical Engineering
https://www.readbyqxmd.com/read/29740058/model-based-and-model-free-machine-learning-techniques-for-diagnostic-prediction-and-classification-of-clinical-outcomes-in-parkinson-s-disease
#11
Chao Gao, Hanbo Sun, Tuo Wang, Ming Tang, Nicolaas I Bohnen, Martijn L T M Müller, Talia Herman, Nir Giladi, Alexandr Kalinin, Cathie Spino, William Dauer, Jeffrey M Hausdorff, Ivo D Dinov
In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements...
May 8, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29736781/combination-of-rs-fmri-and-smri-data-to-discriminate-autism-spectrum-disorders-in-young-children-using-deep-belief-network
#12
Maryam Akhavan Aghdam, Arash Sharifi, Mir Mohsen Pedram
In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets...
May 7, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29734666/cardiac-arrhythmia-classification-by-multi-layer-perceptron-and-convolution-neural-networks
#13
Shalin Savalia, Vahid Emamian
The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN)...
May 4, 2018: Bioengineering
https://www.readbyqxmd.com/read/29734508/using-machine-learning-to-identify-patterns-of-lifetime-health-problems-in-decedents-with-autism-spectrum-disorder
#14
Lauren Bishop-Fitzpatrick, Arezoo Movaghar, Jan S Greenberg, David Page, Leann S DaWalt, Murray H Brilliant, Marsha R Mailick
Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a machine learning algorithm to characterize diagnostic patterns in decedents with ASD and matched decedent community controls. Participants were 91 decedents with ASD and 6,186 sex and birth year matched decedent community controls who had died since 1979, the majority of whom were middle aged or older adults at the time of their death...
May 7, 2018: Autism Research: Official Journal of the International Society for Autism Research
https://www.readbyqxmd.com/read/29728780/root-exploit-detection-and-features-optimization-mobile-device-and-blockchain-based-medical-data-management
#15
Ahmad Firdaus, Nor Badrul Anuar, Mohd Faizal Ab Razak, Ibrahim Abaker Targio Hashem, Syafiq Bachok, Arun Kumar Sangaiah
The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims' private keys linked to the blockchain...
May 4, 2018: Journal of Medical Systems
https://www.readbyqxmd.com/read/29728245/relative-location-prediction-in-ct-scan-images-using-convolutional-neural-networks
#16
Jiajia Guo, Hongwei Du, Jianyue Zhu, Ting Yan, Bensheng Qiu
BACKGROUND AND OBJECTIVE: Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely...
July 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29728226/radiomics-in-radiooncology-challenging-the-medical-physicist
#17
REVIEW
Jan C Peeken, Michael Bernhofer, Benedikt Wiestler, Tatyana Goldberg, Daniel Cremers, Burkhard Rost, Jan J Wilkens, Stephanie E Combs, Fridtjof Nüsslin
PURPOSE: Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. METHODS: Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described...
April 2018: Physica Medica: PM
https://www.readbyqxmd.com/read/29727354/applying-machine-learning-to-pediatric-critical-care-data
#18
Jon B Williams, Debjit Ghosh, Randall C Wetzel
OBJECTIVES: To explore whether machine learning applied to pediatric critical care data could discover medically pertinent information, we analyzed clinically collected electronic medical record data, after data extraction and preparation, using k-means clustering. DESIGN: Retrospective analysis of electronic medical record ICU data. SETTING: Tertiary Children's Hospital PICU. PATIENTS: Anonymized electronic medical record data from PICU admissions over 10 years...
April 30, 2018: Pediatric Critical Care Medicine
https://www.readbyqxmd.com/read/29725961/hello-world-deep-learning-in-medical-imaging
#19
Paras Lakhani, Daniel L Gray, Carl R Pett, Paul Nagy, George Shih
There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects...
May 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29723481/the-potential-for-machine-learning-algorithms-to-improve-and-reduce-the-cost-of-3-dimensional-printing-for-surgical-planning
#20
Trevor J Huff, Parker E Ludwig, Jorge M Zuniga
3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition...
May 3, 2018: Expert Review of Medical Devices
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