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https://www.readbyqxmd.com/read/28730995/decoding-human-mental-states-by-whole-head-eeg-fnirs-during-category-fluency-task-performance
#1
Ahmet Omurtag, Haleh Aghajani, Hasan Onur Keles
Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results...
July 21, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28716018/prediction-of-extubation-readiness-in-extremely-preterm-infants-by-the-automated-analysis-of-cardiorespiratory-behavior-study-protocol
#2
Wissam Shalish, Lara J Kanbar, Smita Rao, Carlos A Robles-Rubio, Lajos Kovacs, Sanjay Chawla, Martin Keszler, Doina Precup, Karen Brown, Robert E Kearney, Guilherme M Sant'Anna
BACKGROUND: Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities...
July 17, 2017: BMC Pediatrics
https://www.readbyqxmd.com/read/28713293/predicting-future-high-cost-schizophrenia-patients-using-high-dimensional-administrative-data
#3
Yajuan Wang, Vijay Iyengar, Jianying Hu, David Kho, Erin Falconer, John P Docherty, Gigi Y Yuen
BACKGROUND: The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions. OBJECTIVES: The study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods...
2017: Frontiers in Psychiatry
https://www.readbyqxmd.com/read/28711679/reproducibility-of-studies-on-text-mining-for-citation-screening-in-systematic-reviews-evaluation-and-checklist
#4
Babatunde Kazeem Olorisade, Pearl Brereton, Peter Andras
CONTEXT: Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields...
July 12, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28706727/pd_manager-an-mhealth-platform-for-parkinson-s-disease-patient-management
#5
Kostas M Tsiouris, Dimitrios Gatsios, George Rigas, Dragana Miljkovic, Barbara Koroušić Seljak, Marko Bohanec, Maria T Arredondo, Angelo Antonini, Spyros Konitsiotis, Dimitrios D Koutsouris, Dimitrios I Fotiadis
PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease...
June 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28699566/entity-recognition-from-clinical-texts-via-recurrent-neural-network
#6
Zengjian Liu, Ming Yang, Xiaolong Wang, Qingcai Chen, Buzhou Tang, Zhe Wang, Hua Xu
BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years...
July 5, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28690054/fuzzy-evidential-network-and-its-application-as-medical-prognosis-and-diagnosis-models
#7
Amin Janghorbani, Mohammad Hassan Moradi
Uncertainty is one of the important facts of the medical knowledge. Medical prognosis and diagnosis, as the essential parts of medical knowledge, is affected by different aspects of uncertainty, which must be managed. In the previous studies, different theories such as Bayesian probability theory, evidence theory, and fuzzy set theory have been developed to represent and manage different aspects of uncertainty. Recently, hybrid frameworks are suggested to deal with various types of uncertainty in a single framework...
July 6, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28689314/overview-of-deep-learning-in-medical-imaging
#8
REVIEW
Kenji Suzuki
The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification...
July 8, 2017: Radiological Physics and Technology
https://www.readbyqxmd.com/read/28684255/prescription-extraction-using-crfs-and-word-embeddings
#9
Carson Tao, Michele Filannino, Özlem Uzuner
In medical practices, doctors detail patients' care plan via discharge summaries written in the form of unstructured free texts, which among the others contain medication names and prescription information. Extracting prescriptions from discharge summaries is challenging due to the way these documents are written. Handwritten rules and medical gazetteers have proven to be useful for this purpose but come with limitations on performance, scalability, and generalizability. We instead present a machine learning approach to extract and organize medication names and prescription information into individual entries...
July 3, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28679415/classification-of-caesarean-section-and-normal-vaginal-deliveries-using-foetal-heart-rate-signals-and-advanced-machine-learning-algorithms
#10
Paul Fergus, Abir Hussain, Dhiya Al-Jumeily, De-Shuang Huang, Nizar Bouguila
BACKGROUND: Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death...
July 6, 2017: Biomedical Engineering Online
https://www.readbyqxmd.com/read/28678808/validation-of-accuracy-of-svm-based-fall-detection-system-using-real-world-fall-and-non-fall-datasets
#11
Omar Aziz, Jochen Klenk, Lars Schwickert, Lorenzo Chiari, Clemens Becker, Edward J Park, Greg Mori, Stephen N Robinovitch
Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings...
2017: PloS One
https://www.readbyqxmd.com/read/28675617/developing-a-practical-suicide-risk-prediction-model-for-targeting-high-risk-patients-in-the-veterans-health-administration
#12
Ronald C Kessler, Irving Hwang, Claire A Hoffmire, John F McCarthy, Maria V Petukhova, Anthony J Rosellini, Nancy A Sampson, Alexandra L Schneider, Paul A Bradley, Ira R Katz, Caitlin Thompson, Robert M Bossarte
OBJECTIVES: The US Veterans Health Administration (VHA) has begun using predictive modeling to identify Veterans at high suicide risk to target care. Initial analyses are reported here. METHODS: A penalized logistic regression model was compared with an earlier proof-of-concept logistic model. Exploratory analyses then considered commonly-used machine learning algorithms. Analyses were based on electronic medical records for all 6,360 individuals classified in the National Death Index as having died by suicide in fiscal years 2009-2011 who used VHA services the year of their death or prior year and a 1% probability sample of time-matched VHA service users alive at the index date (n = 2,112,008)...
July 4, 2017: International Journal of Methods in Psychiatric Research
https://www.readbyqxmd.com/read/28675381/matched-computed-tomography-segmentation-and-demographic-data-for-oropharyngeal-cancer-radiomics-challenges
#13
(no author information available yet)
Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that 'radiomics', or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes...
July 4, 2017: Scientific Data
https://www.readbyqxmd.com/read/28670152/deep-learning-in-medical-imaging-general-overview
#14
REVIEW
June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, Namkug Kim
The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network...
July 2017: Korean Journal of Radiology: Official Journal of the Korean Radiological Society
https://www.readbyqxmd.com/read/28663166/researching-mental-health-disorders-in-the-era-of-social-media-systematic-review
#15
REVIEW
Akkapon Wongkoblap, Miguel A Vadillo, Vasa Curcin
BACKGROUND: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. OBJECTIVE: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research...
June 29, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28663162/developing-and-evaluating-digital-interventions-to-promote-behavior-change-in-health-and-health-care-recommendations-resulting-from-an-international-workshop
#16
Susan Michie, Lucy Yardley, Robert West, Kevin Patrick, Felix Greaves
Devices and programs using digital technology to foster or support behavior change (digital interventions) are increasingly ubiquitous, being adopted for use in patient diagnosis and treatment, self-management of chronic diseases, and in primary prevention. They have been heralded as potentially revolutionizing the ways in which individuals can monitor and improve their health behaviors and health care by improving outcomes, reducing costs, and improving the patient experience. However, we are still mainly in the age of promise rather than delivery...
June 29, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28653122/machine-learning-discovering-the-future-of-medical-imaging
#17
EDITORIAL
Bradley J Erickson
No abstract text is available yet for this article.
June 26, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28651852/differences-in-repolarization-heterogeneity-among-heart-failure-with-preserved-ejection-fraction-phenotypic-subgroups
#18
Suzanne K Oskouie, Stuart B Prenner, Sanjiv J Shah, Andrew J Sauer
Heart failure with preserved ejection fraction (HFpEF) is a highly heterogeneous syndrome associated with multiple medical comorbidities and pathophysiologic pathways or phenotypes. We recently developed a phenomapping method combining deep phenotyping with machine learning analysis to classify HFpEF patients into 3 clinically distinct phenotypic subgroups (phenogroups) with different clinical outcomes. Phenogroup #1 was younger with lower B-type natriuretic peptide levels, phenogroup #2 had the highest prevalence of obesity and diabetes mellitus, and phenogroup #3 was the oldest with the most factors for chronic kidney disease, the most dysfunctional myocardial mechanics, and the highest adverse outcomes...
May 30, 2017: American Journal of Cardiology
https://www.readbyqxmd.com/read/28648568/application-of-machine-learning-classification-for-structural-brain-mri-in-mood-disorders-critical-review-from-a-clinical-perspective
#19
REVIEW
Yong-Ku Kim, Kyoung-Sae Na
Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder...
June 22, 2017: Progress in Neuro-psychopharmacology & Biological Psychiatry
https://www.readbyqxmd.com/read/28644378/an-energy-efficient-multi-tier-architecture-for-fall-detection-using-smartphones
#20
M Amac Guvensan, A Oguz Kansiz, N Cihan Camgoz, H Irem Turkmen, A Gokhan Yavuz, M Elif Karsligil
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms...
June 23, 2017: Sensors
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