Read by QxMD icon Read

Machine learning

Juan Jose Saldaña Barrios, Luis Mendoza, Edgardo Pitti, Miguel Vargas
In this work, the authors present two eHealth platforms that are examples of how health systems are migrating from client-server architecture to the web-based and ubiquitous paradigm. These two platforms were modeled, designed, developed and implemented with positive results. First, using ambient-assisted living and ubiquitous computing, the authors enhance how palliative care is being provided to the elderly patients and patients with terminal illness, making the work of doctors, nurses and other health actors easier...
October 21, 2016: Health Informatics Journal
Zhiwei Zhou, Xiaotao Shen, Jia Tu, Zheng-Jiang Zhu
The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility - mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites...
October 21, 2016: Analytical Chemistry
Mutlu Mete, Unal Sakoglu, Jeffrey S Spence, Michael D Devous, Thomas S Harris, Bryon Adinoff
BACKGROUND: Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented...
October 6, 2016: BMC Bioinformatics
Ryan Eshleman, Rahul Singh
BACKGROUND: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention...
October 6, 2016: BMC Bioinformatics
Nidhi Singh, Priyanka Shah, Hemlata Dwivedi, Shikha Mishra, Renu Tripathi, Amogh A Sahasrabuddhe, Mohammad Imran Siddiqi
N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protein interactions. It is a studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, a machine learning approach, docking studies and CoMFA analysis have been integrated with the objective of translation of knowledge into a pipelined workflow towards the identification of putative hits through the screening of large compound libraries...
October 21, 2016: Molecular BioSystems
Alistair E W Johnson, Mohammad M Ghassemi, Shamim Nemati, Katherine E Niehaus, David A Clifton, Gari D Clifford
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms...
February 2016: Proceedings of the IEEE
G Guidi, N Maffei, B Meduri, E D'Angelo, G M Mistretta, P Ceroni, A Ciarmatori, A Bernabei, S Maggi, M Cardinali, V E Morabito, F Rosica, S Malara, A Savini, G Orlandi, C D'Ugo, F Bunkheila, M Bono, S Lappi, C Blasi, F Lohr, T Costi
PURPOSE: To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset. MATERIALS AND METHODS: 90 patients (pts) treated for head-neck cancer (H&N) formed a multicenter data-set. 41 H&N pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG)...
October 17, 2016: Physica Medica: PM
Fayyaz Ul Amir Afsar Minhas, Amina Asif, Muhammad Arif
: Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map...
October 11, 2016: Computers in Biology and Medicine
Chao Zhang, Lei Du, Dacheng Tao
The techniques of random matrices have played an important role in many machine learning models. In this letter, we present a new method to study the tail inequalities for sums of random matrices. Different from other work (Ahlswede & Winter, 2002; Tropp, 2012; Hsu, Kakade, & Zhang, 2012), our tail results are based on the largest singular value (LSV) and independent of the matrix dimension. Since the LSV operation and the expectation are noncommutative, we introduce a diagonalization method to convert the LSV operation into the trace operation of an infinitely dimensional diagonal matrix...
October 20, 2016: Neural Computation
Jiangming Sun, Yang De Marinis, Peter Osmark, Pratibha Singh, Annika Bagge, Bérengère Valtat, Petter Vikman, Peter Spégel, Hindrik Mulder
RNA editing is a post-transcriptional alteration of RNA sequences that, via insertions, deletions or base substitutions, can affect protein structure as well as RNA and protein expression. Recently, it has been suggested that RNA editing may be more frequent than previously thought. A great impediment, however, to a deeper understanding of this process is the paramount sequencing effort that needs to be undertaken to identify RNA editing events. Here, we describe an in silico approach, based on machine learning, that ameliorates this problem...
2016: PloS One
A H Mattsson, J V Kringelum, C Garde, M Nielsen
Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical. Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor-ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding motifs, to others with no or very limited experimental characterization...
October 20, 2016: HLA
Jörn Lötsch, Thomas Hummel, Alfred Ultsch
The human sense of smell is often analyzed as being composed of three main components comprising olfactory threshold, odor discrimination and the ability to identify odors. A relevant distinction of the three components and their differential changes in distinct disorders remains a research focus. The present data-driven analysis aimed at establishing a cluster structure in the pattern of olfactory subtest results. Therefore, unsupervised machine-learning was applied onto olfactory subtest results acquired in 10,714 subjects with nine different olfactory pathologies...
October 20, 2016: Scientific Reports
Hasseeb Azzawi, Jingyu Hou, Yong Xiang, Russul Alanni
Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models...
October 2016: IET Systems Biology
Yasheng Chen, Rajat Dhar, Laura Heitsch, Andria Ford, Israel Fernandez-Cadenas, Caty Carrera, Joan Montaner, Weili Lin, Dinggang Shen, Hongyu An, Jin-Moo Lee
Although cerebral edema is a major cause of death and deterioration following hemispheric stroke, there remains no validated biomarker that captures the full spectrum of this critical complication. We recently demonstrated that reduction in intracranial cerebrospinal fluid (CSF) volume (∆ CSF) on serial computed tomography (CT) scans provides an accurate measure of cerebral edema severity, which may aid in early triaging of stroke patients for craniectomy. However, application of such a volumetric approach would be too cumbersome to perform manually on serial scans in a real-world setting...
2016: NeuroImage: Clinical
Hans Lehrach
Every human is unique. We differ in our genomes, environment, behavior, disease history, and past and current medical treatment-a complex catalog of differences that often leads to variations in the way each of us responds to a particular therapy. We argue here that true personalization of drug therapies will rely on "virtual patient" models based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. The models will be based, wherever possible, on the molecular mechanisms of disease processes and drug action but can also expand to hybrid models including statistics/machine learning/artificial intelligence-based elements trained on available data to address therapeutic areas or therapies for which insufficient information on mechanisms is available...
September 2016: Dialogues in Clinical Neuroscience
Salma Jamal, Sukriti Goyal, Asheesh Shanker, Abhinav Grover
BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics...
October 18, 2016: BMC Genomics
Jacqueline Kerr, Catherine R Marinac, Katherine Ellis, Suneeta Godbole, Aaron Hipp, Karen Glanz, Jonathan Mitchell, Francine Laden, Peter James, David Berrigan
PURPOSE: To compare physical activity estimates across different accelerometer wear locations, wear time protocols, and data processing techniques. METHODS: A convenience sample of middle aged to older women wore a GT3X+ accelerometer at the wrist and hip for 7 days. Physical activity estimates were calculated using three data processing techniques: single axis cut points, raw vector magnitude thresholds, and machine learning algorithms applied to the raw data from the three axes...
October 14, 2016: Medicine and Science in Sports and Exercise
Timothy B Lannin, Fredrik I Thege, Brian J Kirby
Advances in rare cell capture technology have made possible the interrogation of circulating tumor cells (CTCs) captured from whole patient blood. However, locating captured cells in the device by manual counting bottlenecks data processing by being tedious (hours per sample) and compromises the results by being inconsistent and prone to user bias. Some recent work has been done to automate the cell location and classification process to address these problems, employing image processing and machine learning (ML) algorithms to locate and classify cells in fluorescent microscope images...
October 2016: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
Ji Li, Guoqing Hu, Yonghong Zhou, Chong Zou, Wei Peng, Jahangir Alam Sm
A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine...
October 14, 2016: Sensors
Nicholas K Schiltz, David F Warner, Jiayang Sun, Paul M Bakaki, Avi Dor, Charles W Given, Kurt C Stange, Siran M Koroukian
BACKGROUND: Multimorbidity affects the majority of elderly adults and is associated with higher health costs and utilization, but how specific patterns of morbidity influence resource use is less understood. OBJECTIVE: The objective was to identify specific combinations of chronic conditions, functional limitations, and geriatric syndromes associated with direct medical costs and inpatient utilization. DESIGN: Retrospective cohort study using the Health and Retirement Study (2008-2010) linked to Medicare claims...
October 6, 2016: Medical Care
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"