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Jarrod D Frizzell, Li Liang, Phillip J Schulte, Clyde W Yancy, Paul A Heidenreich, Adrian F Hernandez, Deepak L Bhatt, Gregg C Fonarow, Warren K Laskey
Importance: Several attempts have been made at developing models to predict 30-day readmissions in patients with heart failure, but none have sufficient discriminatory capacity for clinical use. Machine-learning (ML) algorithms represent a novel approach and may have potential advantages over traditional statistical modeling. Objective: To develop models using a ML approach to predict all-cause readmissions 30 days after discharge from a heart failure hospitalization and to compare ML model performance with models developed using "conventional" statistically based methods...
October 26, 2016: JAMA Cardiology
Paolo Fusar-Poli, Grazia Rutigliano, Daniel Stahl, André Schmidt, Valentina Ramella-Cravaro, Shetty Hitesh, Philip McGuire
Importance: Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific determinants of pretest risk of psychosis onset in individuals undergoing clinical high risk (CHR) assessment are unknown. Objectives: To investigate the characteristics and determinants of pretest risk of psychosis onset in individuals undergoing CHR assessment and to develop and externally validate a pretest risk stratification model...
October 26, 2016: JAMA Psychiatry
Jorge Barros, Susana Morales, Orietta Echávarri, Arnol García, Jaime Ortega, Takeshi Asahi, Claudia Moya, Ronit Fischman, María P Maino, Catalina Núñez
Objective: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. Methods: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. Results: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0...
October 20, 2016: Revista Brasileira de Psiquiatria
Daniel Summer Gareau, Joel Correa da Rosa, Sarah Yagerman, John A Carucci, Nicholas Gulati, Ferran Hueto, Jennifer L DeFazio, Mayte Suárez-Farinas, Ashfaq Marghoob, James G Krueger
We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analyzed with a set of thirteen machine-learning algorithms to generate an overall risk score that is called a Q-score. These methods were applied to a set of 120 "difficult" dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified. This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation...
October 26, 2016: Experimental Dermatology
Andrik Rampun, Bernie Tiddeman, Reyer Zwiggelaar, Paul Malcolm
PURPOSE: In this paper the authors propose a texton based prostate computer aided diagnosis approach which bypasses the typical feature extraction process such as filtering and convolution which can be computationally expensive. The study focuses the peripheral zone because 75% of prostate cancers start within this region and the majority of prostate cancers arising within this region are more aggressive than those arising in the transitional zone. METHODS: For the model development, square patches were extracted at random locations from malignant and benign regions...
October 2016: Medical Physics
Francesco Ciompi, Simone Balocco, Juan Rigla, Xavier Carrillo, Josepa Mauri, Petia Radeva
PURPOSE: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during percutaneous coronary intervention (PCI), in order to prevent acute vessel occlusion. The identification of struts location and the definition of the stent shape is relevant for PCI planning and for patient follow-up. The authors present a fully automatic framework for computer-aided detection (CAD) of intracoronary stents in intravascular ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape...
October 2016: Medical Physics
S F Carr, R Garnett, C S Lo
Controlling molecule-surface interactions is key for chemical applications ranging from catalysis to gas sensing. We present a framework for accelerating the search for the global minimum on potential surfaces, corresponding to stable adsorbate-surface structures. We present a technique using Bayesian inference that enables us to predict converged density functional theory potential energies with fewer self-consistent field iterations. We then discuss how this technique fits in with the Bayesian Active Site Calculator, which applies Bayesian optimization to the problem...
October 21, 2016: Journal of Chemical Physics
Burak Himmetoglu
We present an application of the boosted regression tree algorithm for predicting ground state energies of molecules made up of C, H, N, O, P, and S (CHNOPS). The PubChem chemical compound database has been incorporated to construct a dataset of 16 242 molecules, whose electronic ground state energies have been computed using density functional theory. This dataset is used to train the boosted regression tree algorithm, which allows a computationally efficient and accurate prediction of molecular ground state energies...
October 7, 2016: Journal of Chemical Physics
Eunjeong Park, Sunghoon I Lee, Hyo Suk Nam, Jordan H Garst, Alex Huang, Andrew Campion, Monica Arnell, Nima Ghalehsariand, Sangsoo Park, Hyuk-Jae Chang, Daniel C Lu, Majid Sarrafzadeh
BACKGROUND: Alcohol ingestion influences sensory-motor function and the overall well-being of individuals. Detecting alcohol-induced impairments in gait in daily life necessitates a continuous and unobtrusive gait monitoring system. OBJECTIVES: This paper introduces the development and use of a non-intrusive monitoring system to detect changes in gait induced by alcohol intoxication. METHODS: The proposed system employed a pair of sensorized smart shoes that are equipped with pressure sensors on the insole...
October 26, 2016: Methods of Information in Medicine
Sheila John, Keerthi Ram, Mohanasankar Sivaprakasam, Rajiv Raman
BACKGROUND: Diabetic retinopathy (DR) is regarded as a major cause of preventable blindness, which can be detected and treated if the cases are identified by screening. Screening for DR is therefore being practiced in developed countries, and tele screening has been a prominent model of delivery of eye care for screening DR. AIM: Our study has been designed to provide inputs on the suitability of a computer-assisted DR screening solution, for use in a larger prospective study...
2016: Studies in Health Technology and Informatics
Yeliz Karaca, Yudong Zhang, Carlo Cattani
Our purpose is to develop a clinical decision support system to classify the patients' diagnostics based on features gathered from Magnetic Resonance Imaging (MRI) and Expanded Disability Status Scale (EDSS). We studied 120 patients and 19 healthy individuals (not afflicted with MS) have been studied for this study. Healthy individuals in the control group do not have any complaint or drug use history. For the kernel trick, efficient performance in non-linear classification, the Convex Combination of Infinite Kernels model was developed to measure the health status of patients based on features gathered from MRI and EDSS...
October 24, 2016: CNS & Neurological Disorders Drug Targets
Tim Lustberg, Johan van Soest, Arthur Jochems, Timo Deist, Yvonka van Wijk, Sean Walsh, Philippe Lambin, Andre Dekker
Data collected and generated by radiation oncology can be classified by the 4Vs of Big Data (Volume, Variety, Velocity, and Veracity) because it is spread across different care providers and not easily shared due to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially due to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers is understood and learned from, however this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability, and the large volume...
October 26, 2016: British Journal of Radiology
Peyvand Ghaderyan, Ataollah Abbasi
Automatic workload estimation has received much attention because of its application in error prevention, diagnosis, and treatment of neural system impairment. The development of a simple but reliable method using minimum number of psychophysiological signals is a challenge in automatic workload estimation. To address this challenge, this paper presented three different decomposition techniques (Fourier, cepstrum, and wavelet transforms) to analyze electrodermal activity (EDA). The efficiency of various statistical and entropic features was investigated and compared...
October 22, 2016: International Journal of Psychophysiology
Hamidreza Kavianpour, Mahdi Vasighi
Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods...
October 24, 2016: Amino Acids
Cheng Wang, Hongqian Chen, Xuebin Zhang, Chaoying Meng
BACKGROUND: Behavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts. There is an urgent need for automatic identification of individual animals and automatic tracking is a fundamental part of the solution to this problem. RESULTS: In this study, an algorithm based on a Hybrid Support Vector Machine (HSVM) was developed for the automated tracking of individual laying hens in a layer group...
2016: Journal of Animal Science and Biotechnology
Yi An, Jiawei Wang, Chen Li, André Leier, Tatiana Marquez-Lago, Jonathan Wilksch, Yang Zhang, Geoffrey I Webb, Jiangning Song, Trevor Lithgow
Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance...
October 24, 2016: Briefings in Bioinformatics
Raminta Daniulaityte, Lu Chen, Francois R Lamy, Robert G Carlson, Krishnaprasad Thirunarayan, Amit Sheth
BACKGROUND: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. OBJECTIVES: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets...
October 24, 2016: JMIR Public Health and Surveillance
E Rikandi, S Pamilo, T Mäntylä, J Suvisaari, T Kieseppä, R Hari, M Seppä, T T Raij
BACKGROUND: While group-level functional alterations have been identified in many brain regions of psychotic patients, multivariate machine-learning methods provide a tool to test whether some of such alterations could be used to differentiate an individual patient. Earlier machine-learning studies have focused on data collected from chronic patients during rest or simple tasks. We set out to unravel brain activation patterns during naturalistic stimulation in first-episode psychosis (FEP)...
October 25, 2016: Psychological Medicine
Laila Khedher, Ignacio A Illán, Juan M Górriz, Javier Ramírez, Abdelbasset Brahim, Anke Meyer-Baese
Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making...
July 22, 2016: International Journal of Neural Systems
Minglin Wu, Sheng Zhang, Yuhan Dong
In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction...
October 20, 2016: Sensors
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