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https://www.readbyqxmd.com/read/28213145/how-are-you-feeling-a-personalized-methodology-for-predicting-mental-states-from-temporally-observable-physical-and-behavioral-information
#1
Suppawong Tuarob, Conrad S Tucker, Soundar Kumara, C Lee Giles, Aaron L Pincus, David E Conroy, Nilam Ram
It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population...
February 14, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28212054/machine-learning-for-medical-imaging
#2
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L Kline
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region...
February 17, 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/28203249/autism-spectrum-disorder-detection-from-semi-structured-and-unstructured-medical-data
#3
Jianbo Yuan, Chester Holtz, Tristram Smith, Jiebo Luo
Autism spectrum disorder (ASD) is a developmental disorder that significantly impairs patients' ability to perform normal social interaction and communication. Moreover, the diagnosis procedure of ASD is highly time-consuming, labor-intensive, and requires extensive expertise. Although there exists no known cure for ASD, there is consensus among clinicians regarding the importance of early intervention for the recovery of ASD patients. Therefore, to benefit autism patients by enhancing their access to treatments such as early intervention, we aim to develop a robust machine learning-based system for autism detection by using Natural Language Processing techniques based on information extracted from medical forms of potential ASD patients...
December 2017: EURASIP Journal on Bioinformatics & Systems Biology
https://www.readbyqxmd.com/read/28196139/using-data-from-the-microsoft-kinect-2-to-determine-postural-stability-in-healthy-subjects-a-feasibility-trial
#4
Behdad Dehbandi, Alexandre Barachant, Anna H Smeragliuolo, John Davis Long, Silverio Joseph Bumanlag, Victor He, Anna Lampe, David Putrino
The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: "both feet on the ground" (1), "One foot off the ground" (2), and "both feet off the ground" (3)...
2017: PloS One
https://www.readbyqxmd.com/read/28167394/deep-ensemble-learning-of-sparse-regression-models-for-brain-disease-diagnosis
#5
Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications...
January 24, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28166774/highly-wearable-cuff-less-blood-pressure-and-heart-rate-monitoring-with-single-arm-electrocardiogram-and-photoplethysmogram-signals
#6
Qingxue Zhang, Dian Zhou, Xuan Zeng
BACKGROUND: Long-term continuous systolic blood pressure (SBP) and heart rate (HR) monitors are of tremendous value to medical (cardiovascular, circulatory and cerebrovascular management), wellness (emotional and stress tracking) and fitness (performance monitoring) applications, but face several major impediments, such as poor wearability, lack of widely accepted robust SBP models and insufficient proofing of the generalization ability of calibrated models. METHODS: This paper proposes a wearable cuff-less electrocardiography (ECG) and photoplethysmogram (PPG)-based SBP and HR monitoring system and many efforts are made focusing on above challenges...
February 6, 2017: Biomedical Engineering Online
https://www.readbyqxmd.com/read/28126242/artificial-intelligence-in-medicine
#7
Pavel Hamet, Johanne Tremblay
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation...
January 11, 2017: Metabolism: Clinical and Experimental
https://www.readbyqxmd.com/read/28124477/a-predictive-model-for-obstructive-sleep-apnea-and-down-syndrome
#8
Brian G Skotko, Eric A Macklin, Marco Muselli, Lauren Voelz, Mary Ellen McDonough, Emily Davidson, Veerasathpurush Allareddy, Yasas S N Jayaratne, Richard Bruun, Nicholas Ching, Gil Weintraub, David Gozal, Dennis Rosen
Obstructive sleep apnea (OSA) occurs frequently in people with Down syndrome (DS) with reported prevalences ranging between 55% and 97%, compared to 1-4% in the neurotypical pediatric population. Sleep studies are often uncomfortable, costly, and poorly tolerated by individuals with DS. The objective of this study was to construct a tool to identify individuals with DS unlikely to have moderate or severe sleep OSA and in whom sleep studies might offer little benefit. An observational, prospective cohort study was performed in an outpatient clinic and overnight sleep study center with 130 DS patients, ages 3-24 years...
January 26, 2017: American Journal of Medical Genetics. Part A
https://www.readbyqxmd.com/read/28116551/tensor-factorization-for-precision-medicine-in-heart-failure-with-preserved-ejection-fraction
#9
Yuan Luo, Faraz S Ahmad, Sanjiv J Shah
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. Tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies...
January 23, 2017: Journal of Cardiovascular Translational Research
https://www.readbyqxmd.com/read/28113828/efficient-and-privacy-preserving-online-medical-pre-diagnosis-framework-using-nonlinear-svm
#10
Hui Zhu, Xiaoxia Liu, Rongxing Lu, Hui Li
With the advances of machine learning algorithms and the pervasiveness of network terminals, online medical prediagnosis system, which can provide the diagnosis of healthcare provider anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical pre-diagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an efficient and privacy-preserving online medical pre-diagnosis framework, called eDiag, by using nonlinear kernel support vector machine (SVM)...
March 29, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28113636/purely-structural-protein-scoring-functions-using-support-vector-machine-and-ensemble-learning
#11
Shokoufeh Mirzaei, Tomer Sidi, Chen Keasar, Silvia Crivelli
The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a high price tag, computational predictions are highly desirable. Given a protein sequence, computational methods produce numerous 3D structures known as decoys. However, selection of the best quality decoys is challenging as the end users can handle only a few ones...
August 24, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28113458/marginal-space-deep-learning-efficient-architecture-for-volumetric-image-parsing
#12
Florin C Ghesu, Edward Krubasik, Bogdan Georgescu, Vivek Singh, Yefeng Zheng, Joachim Hornegger, Dorin Comaniciu
Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. There are two main challenges that need to be addressed, these are the efficiency in processing large volumetric input images and the need for strong, representative image features...
March 7, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28113454/cryo-balloon-catheter-localization-based-on-a-support-vector-machine-approach
#13
Tanja Kurzendorfer, Philip W Mewes, Andreas Maier, Norbert Strobel, Alexander Brost
Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction...
March 9, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28111643/mathematical-modeling-and-evaluation-of-human-motions-in-physical-therapy-using-mixture-density-neural-networks
#14
A Vakanski, J M Ferguson, S Lee
OBJECTIVE: The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient's exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient's physician with recommendations for improvement...
December 2016: J Physiother Phys Rehabil
https://www.readbyqxmd.com/read/28106853/significant-change-spotting-for-periodic-human-motion-segmentation-of-cleaning-tasks-using-wearable-sensors
#15
Kai-Chun Liu, Chia-Tai Chan
The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world...
January 19, 2017: Sensors
https://www.readbyqxmd.com/read/28104826/screening-for-medication-errors-using-an-outlier-detection-system
#16
Gordon D Schiff, Lynn A Volk, Mayya Volodarskaya, Deborah H Williams, Lake Walsh, Sara G Myers, David W Bates, Ronen Rozenblum
OBJECTIVE: The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening. MATERIALS AND METHODS: Five years of clinical data were extracted from an electronic health record system for 747 985 patients who had at least one visit during 2012-2013 at practices affiliated with 2 academic medical centers. Data were screened using the system to detect outliers suggestive of potential medication errors...
January 19, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/28092576/multiple-instance-learning-for-medical-image-and-video-analysis
#17
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-emergency-department-visits-and-high-system-costs
#18
David W Frost, Shankar Vembu, Jiayi Wang, Karen Tu, Quaid Morris, Howard B Abrams
BACKGROUND: A small proportion of patients account for a high proportion of healthcare use. Accurate preemptive identification may facilitate tailored intervention. We sought to determine whether machine learning techniques using text from a family practice electronic medical record can be used to predict future high emergency department use and total costs by patients who are not yet high emergency department users or high cost to the healthcare system. METHODS: Text from fields of the cumulative patient profile within an electronic medical record 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
#19
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
#20
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
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