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https://www.readbyqxmd.com/read/29344895/bioinformatics-approaches-to-predict-drug-responses-from-genomic-sequencing
#1
Neel S Madhukar, Olivier Elemento
Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers...
2018: Methods in Molecular Biology
https://www.readbyqxmd.com/read/29343797/artificial-intelligence-estimation-of-carotid-femoral-pulse-wave-velocity-using-carotid-waveform
#2
Peyman Tavallali, Marianne Razavi, Niema M Pahlevan
In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal processing inputs to this machine learning algorithm are sensor agnostic, the presented method can accompany any medical instrument that provides a calibrated or uncalibrated carotid pressure waveform. Our results show that, for an unseen hold back test set population in the age range of 20 to 69, our model can estimate PWV with a Root-Mean-Square Error (RMSE) of 1...
January 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29341325/characterization-of-adrenal-lesions-on-unenhanced-mri-using-texture-analysis-a-machine-learning-approach
#3
Valeria Romeo, Simone Maurea, Renato Cuocolo, Mario Petretta, Pier Paolo Mainenti, Francesco Verde, Milena Coppola, Serena Dell'Aversana, Arturo Brunetti
BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest...
January 17, 2018: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/29337804/the-association-between-ventilator-dyssynchrony-delivered-tidal-volume-and-sedation-using-a-novel-automated-ventilator-dyssynchrony-detection-algorithm
#4
Peter D Sottile, David Albers, Carrie Higgins, Jeffery Mckeehan, Marc M Moss
OBJECTIVE: Ventilator dyssynchrony is potentially harmful to patients with or at risk for the acute respiratory distress syndrome. Automated detection of ventilator dyssynchrony from ventilator waveforms has been difficult. It is unclear if certain types of ventilator dyssynchrony deliver large tidal volumes and whether levels of sedation alter the frequency of ventilator dyssynchrony. DESIGN: A prospective observational study. SETTING: A university medical ICU...
February 2018: Critical Care Medicine
https://www.readbyqxmd.com/read/29336344/-the-urologist-of-the-future-and-new-technologies
#5
Francois Peinado, Atanasio Fernández, Fernando Teba, Guillermo Celada, Marco Antonio Acosta
The last 25 years have brought about revolutionary changes for medicine and in particular for urology: internet was only in its infancy, medical records were written on paper, searches for medical information were done in the hospital library, medical articles were photocopied and our relationship with patients only existed face to face. Social networks had not yet appeared and even Google did not exist. Just imagine what might happen during the next 25 years, we're going to see even more radical changes. The urologist of the future is going to see the arrival of artificial intelligence, collaborative medicine, telemedicine, machine learning, the Internet of Things and personalized robotics; in the meantime, social media will continue to transform the interaction between physician and patient...
January 2018: Archivos Españoles de Urología
https://www.readbyqxmd.com/read/29331250/inferred-joint-multigram-models-for-medical-term-normalization-according-to-icd
#6
Alicia Pérez, Aitziber Atutxa, Arantza Casillas, Koldo Gojenola, Álvaro Sellart
BACKGROUND: Electronic Health Records (EHRs) are written using spontaneous natural language. Often, terms do not match standard terminology like the one available through the International Classification of Diseases (ICD). OBJECTIVE: Information retrieval and exchange can be improved using standard terminology. Our aim is to render diagnostic terms written in spontaneous language in EHRs into the standard framework provided by the ICD. METHODS: We tackle diagnostic term normalization employing Weighted Finite-State Transducers (WFSTs)...
February 2018: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29326129/machine-learning-algorithm-predicts-cardiac-resynchronization-therapy-outcomes-lessons-from-the-companion-trial
#7
Matthew M Kalscheur, Ryan T Kipp, Matthew C Tattersall, Chaoqun Mei, Kevin A Buhr, David L DeMets, Michael E Field, Lee L Eckhardt, C David Page
BACKGROUND: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to predict outcomes after CRT. METHODS AND RESULTS: Models were developed with machine learning algorithms to predict all-cause mortality or heart failure hospitalization at 12 months post-CRT in the COMPANION trial (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure)...
January 2018: Circulation. Arrhythmia and Electrophysiology
https://www.readbyqxmd.com/read/29323142/an-application-of-machine-learning-to-haematological-diagnosis
#8
Gregor Gunčar, Matjaž Kukar, Mateja Notar, Miran Brvar, Peter Černelč, Manca Notar, Marko Notar
Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to predict a haematologic disease. One predictive model used all the available blood test parameters and the other used only a reduced set that is usually measured upon patient admittance. Both models produced good results, obtaining prediction accuracies of 0.88 and 0.86 when considering the list of five most likely diseases and 0...
January 11, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29321268/computational-techniques-for-ecg-analysis-and-interpretation-in-light-of-their-contribution-to-medical-advances
#9
REVIEW
Aurore Lyon, Ana Mincholé, Juan Pablo Martínez, Pablo Laguna, Blanca Rodriguez
Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data...
January 2018: Journal of the Royal Society, Interface
https://www.readbyqxmd.com/read/29320910/predicting-and-explaining-inflammation-in-crohn-s-disease-patients-using-predictive-analytics-methods-and-electronic-medical-record-data
#10
Bhargava K Reddy, Dursun Delen, Rupesh K Agrawal
Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease...
January 1, 2018: Health Informatics Journal
https://www.readbyqxmd.com/read/29305594/egbmmda-extreme-gradient-boosting-machine-for-mirna-disease-association-prediction
#11
Xing Chen, Li Huang, Di Xie, Qi Zhao
Associations between microRNAs (miRNAs) and human diseases have been identified by increasing studies and discovering new ones is an ongoing process in medical laboratories. To improve experiment productivity, researchers computationally infer potential associations from biological data, selecting the most promising candidates for experimental verification. Predicting potential miRNA-disease association has become a research area of growing importance. This paper presents a model of Extreme Gradient Boosting Machine for MiRNA-Disease Association (EGBMMDA) prediction by integrating the miRNA functional similarity, the disease semantic similarity, and known miRNA-disease associations...
January 5, 2018: Cell Death & Disease
https://www.readbyqxmd.com/read/29305324/real-world-outcomes-in-patients-with-neovascular-age-related-macular-degeneration-treated-with-intravitreal-vascular-endothelial-growth-factor-inhibitors
#12
REVIEW
Hemal Mehta, Adnan Tufail, Vincent Daien, Aaron Lee, Vuong Nguyen, Mehmet Ozturk, Daniel Barthelmes, Mark C Gillies
Clinical trials identified intravitreal vascular endothelial growth factor inhibitors (anti-VEGF agents) have the potential to stabilise or even improve visual acuity outcomes in neovascular age-related macular degeneration (AMD), a sight-threatening disease. Real-world evidence allows us to assess whether results from randomised controlled trials can be applied to the general population. We describe the development of global registries, in particular the Fight Retinal Blindness! registry that originated in Australia, the United Kingdom AMD Electronic Medical Records User Group and the IRIS registry in the USA...
January 2, 2018: Progress in Retinal and Eye Research
https://www.readbyqxmd.com/read/29297288/deep-learning-architectures-for-multi-label-classification-of-intelligent-health-risk-prediction
#13
Andrew Maxwell, Runzhi Li, Bei Yang, Heng Weng, Aihua Ou, Huixiao Hong, Zhaoxian Zhou, Ping Gong, Chaoyang Zhang
BACKGROUND: Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases...
December 28, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29290259/radiomics-and-radiogenomics-in-lung-cancer-a-review-for-the-clinician
#14
REVIEW
Rajat Thawani, Michael McLane, Niha Beig, Soumya Ghose, Prateek Prasanna, Vamsidhar Velcheti, Anant Madabhushi
Lung cancer is responsible for a large proportion of cancer-related deaths across the globe, with delayed detection being perhaps the most significant factor for its high mortality rate. Though the National Lung Screening Trial argues for screening of certain at-risk populations, the practical implementation of these screening efforts has not yet been successful and remains in high demand. Radiomics refers to the computerized extraction of data from radiologic images, and provides unique potential for making lung cancer screening more rapid and accurate using machine learning algorithms...
January 2018: Lung Cancer: Journal of the International Association for the Study of Lung Cancer
https://www.readbyqxmd.com/read/29289703/enhancing-interpretability-of-automatically-extracted-machine-learning-features-application-to-a-rbm-random-forest-system-on-brain-lesion-segmentation
#15
Sérgio Pereira, Raphael Meier, Richard McKinley, Roland Wiest, Victor Alves, Carlos A Silva, Mauricio Reyes
Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes"...
December 20, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/29286945/an-interpretable-machine-learning-model-for-accurate-prediction-of-sepsis-in-the-icu
#16
Shamim Nemati, Andre Holder, Fereshteh Razmi, Matthew D Stanley, Gari D Clifford, Timothy G Buchman
OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. DESIGN: Observational cohort study. SETTING: Academic medical center from January 2013 to December 2015...
December 26, 2017: Critical Care Medicine
https://www.readbyqxmd.com/read/29283402/real-time-monitoring-and-analysis-of-zebrafish-electrocardiogram-with-anomaly-detection
#17
Michael Lenning, Joseph Fortunato, Tai Le, Isaac Clark, Ang Sherpa, Soyeon Yi, Peter Hofsteen, Geethapriya Thamilarasu, Jingchun Yang, Xiaolei Xu, Huy-Dung Han, Tzung K Hsiai, Hung Cao
Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses...
December 28, 2017: Sensors
https://www.readbyqxmd.com/read/29279299/characteristics-associated-with-decreased-or-increased-mortality-risk-from-glycemic-therapy-among-patients-with-type-2-diabetes-and-high-cardiovascular-risk-machine-learning-analysis-of-the-accord-trial
#18
Sanjay Basu, Sridharan Raghavan, Deborah J Wexler, Seth A Berkowitz
OBJECTIVE: Identifying patients who may experience decreased or increased mortality risk from intensive glycemic therapy for type 2 diabetes remains an important clinical challenge. We sought to identify characteristics of patients at high cardiovascular risk with decreased or increased mortality risk from glycemic therapy for type 2 diabetes using new methods to identify complex combinations of treatment effect modifiers. RESEARCH DESIGN AND METHODS: The machine learning method of gradient forest analysis was applied to understand the variation in all-cause mortality within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251), whose participants were 40-79 years old with type 2 diabetes, hemoglobin A1c (HbA1c) ≥7...
December 26, 2017: Diabetes Care
https://www.readbyqxmd.com/read/29276071/enhancement-of-force-patterns-classification-based-on-gaussian-distributions
#19
Thomas Ertelt, Ilja Solomonovs, Thomas Gronwald
Description of the patterns of ground reaction force is a standard method in areas such as medicine, biomechanics and robotics. The fundamental parameter is the time course of the force, which is classified visually in particular in the field of clinical diagnostics. Here, the knowledge and experience of the diagnostician is relevant for its assessment. For an objective and valid discrimination of the ground reaction force pattern, a generic method, especially in the medical field, is absolutely necessary to describe the qualities of the time-course...
December 13, 2017: Journal of Biomechanics
https://www.readbyqxmd.com/read/29274047/quantitative-analysis-of-uncertainty-in-medical-reporting-creating-a-standardized-and-objective-methodology
#20
Bruce I Reiner
Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizing and quantifying uncertainty language, which could provide both the report author and reader with context related to the perceived level of diagnostic confidence and accuracy. A number of computerized strategies could be employed in the creation of this analysis including string search, natural language processing and understanding, histogram analysis, topic modeling, and machine learning...
December 22, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
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