4,074 results match your criteria Computer Methods and Programs in Biomedicine [Journal]


A hospital wide predictive model for unplanned readmission using hierarchical ICD data.

Comput Methods Programs Biomed 2019 Feb 13. Epub 2019 Feb 13.

Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.

Background And Objective: Hospitals already acquire a large amount of data, mainly for administrative, billing and registration purposes. Tapping on these already available data for additional purposes, aiming at improving care, without significant incremental effort and cost. This potential of secondary patient data is explored through modeling administrative and billing data, as well as the hierarchical structure of pathology codes of the International Classification of Diseases (ICD) in the prediction of unplanned readmissions, as a clinically relevant outcome parameter that can be impacted on in a quality improvement program. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183039
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http://dx.doi.org/10.1016/j.cmpb.2019.02.007DOI Listing
February 2019
1 Read

An automated data verification approach for improving data quality in a clinical registry.

Comput Methods Programs Biomed 2019 Jan 31. Epub 2019 Jan 31.

College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, 310027 Hanghzou, China; Key Laboratory for Biomedical Engineering, Ministry of Education, China. Electronic address:

Background And Objective: The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (CRFs) and further transcribing them onto registry databases. To ensure the quality of data, verifying data in the registry is necessary. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.012DOI Listing
January 2019

A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications.

Comput Methods Programs Biomed 2019 Mar 11;170:95-106. Epub 2019 Jan 11.

PIMM, ENSAM ParisTech, CNRS, France.

Background And Objective: This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response at tissue and organ scale and the capability of integrating patient-specific anatomy from medical images to pinpoint the individual variations from standard anatomical models.

Methods: We propose an automated procedure to create mechanical models of the human liver with patient-specific geometry and real time capabilities. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.003DOI Listing

ECG based human identification using Second Order Difference Plots.

Comput Methods Programs Biomed 2019 Mar 16;170:81-93. Epub 2019 Jan 16.

Yildirim Beyazit University, Turkey. Electronic address:

Background And Objective: ECG is one of the biometric signals that has been studied in peer-reviewed over past years. The developments on the signal analysis methods show that the studies on the ECG would continue unabatedly. It has a common use on cardiac diseases with high rates of classification performances by integrating it with signal analysis methods. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.010DOI Listing
March 2019
1 Read

Speeding up the discovery of combinations of differentially expressed genes for disease prediction and classification.

Comput Methods Programs Biomed 2019 Mar 12;170:69-80. Epub 2019 Jan 12.

ECE Dept., Southern Illinois University, Carbondale, IL 62901, USA. Electronic address:

Background And Objective: Finding combinations (i.e., pairs, or more generally, q-tuples with q ≥ 2) of genes whose behavior as a group differs significantly between two classes has received a lot of attention in the quest for the discovery of simple, accurate, and easily interpretable decision rules for disease classification and prediction. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.004DOI Listing
March 2019
1 Read

Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks.

Comput Methods Programs Biomed 2019 Mar 15;170:53-67. Epub 2019 Jan 15.

Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, São Luís, 65085-580, MA, Brazil. Electronic address:

Background And Objective: The spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients who are undergoing radiotherapy. For the planning of treatments with RT, computed tomography (CT) scans are commonly used to delimit the OARs and to analyze the impact of rays in these organs. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.005DOI Listing
March 2019
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Concentration-dependent viscosity and thermal radiation effects on MHD peristaltic motion of Synovial Nanofluid: Applications to rheumatoid arthritis treatment.

Comput Methods Programs Biomed 2019 Mar 11;170:39-52. Epub 2019 Jan 11.

Mathematics Department, Faculty of Science, Imam Abdulrahman Bin Faisal University, Al-Dammam, Saudi Arabia.

Background And Objective: The biomedical fluid which fills the Synovial joint cavity is called Synovial fluid which behaves as in the fluid classifications to Non-Newtonian fluids. Also it's described as a several micrometers thick layer among the interstitial cartilages with very low friction coefficient. Consequently, the present paper opts to investigate the influence of the concentration-dependent viscosity on Magnetohydrodynamic peristaltic flow of Synovial Nanofluid in an asymmetric channel in presence of thermal radiation effect. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.001DOI Listing

A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data.

Comput Methods Programs Biomed 2019 Mar 30;170:31-38. Epub 2018 Dec 30.

Graduate Institute of Biomedical Informatics, College of Medical Science & Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Taipei Medical University Wan-Fang Hospital, Taipei, Taiwan. Electronic address:

Objectives: Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data.

Methods: We first built a knowledge base that consisted of 2. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.033DOI Listing

Prediction of fatty liver disease using machine learning algorithms.

Comput Methods Programs Biomed 2019 Mar 29;170:23-29. Epub 2018 Dec 29.

Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan. Electronic address:

Background And Objective: Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.032DOI Listing
March 2019
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A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation.

Comput Methods Programs Biomed 2019 Mar 29;170:11-21. Epub 2018 Dec 29.

Biomedical and Multimedia Information Technology Research Group, School of Computer Science, University of Sydney, Sydney, Australia.

Background And Objective: Prostate segmentation on Magnetic Resonance (MR) imaging is problematic because disease changes the shape and boundaries of the gland and it can be difficult to separate the prostate from surrounding tissues. We propose an automated model that extracts and combines multi-level features in a deep neural network to segment prostate on MR images.

Methods: Our proposed model, the Propagation Deep Neural Network (P-DNN), incorporates the optimal combination of multi-level feature extraction as a single model. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183109
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http://dx.doi.org/10.1016/j.cmpb.2018.12.031DOI Listing
March 2019
4 Reads

Adaptive color deconvolution for histological WSI normalization.

Comput Methods Programs Biomed 2019 Mar 15;170:107-120. Epub 2019 Jan 15.

Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.

Background And Objective: Color consistency of histological images is significant for developing reliable computer-aided diagnosis (CAD) systems. However, the color appearance of digital histological images varies across different specimen preparations, staining, and scanning situations. This variability affects the diagnosis and decreases the accuracy of CAD approaches. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.008DOI Listing

Prediction of sepsis patients using machine learning approach: A meta-analysis.

Comput Methods Programs Biomed 2019 Mar 26;170:1-9. Epub 2018 Dec 26.

Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan; TMU Research Center of Cancer Translational Medicine, Taipei, Taiwan.

Study Objective: Sepsis is a common and major health crisis in hospitals globally. An innovative and feasible tool for predicting sepsis remains elusive. However, early and accurate prediction of sepsis could help physicians with proper treatments and minimize the diagnostic uncertainty. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.027DOI Listing

PROs in the wild: Assessing the validity of patient reported outcomes in an electronic registry.

Comput Methods Programs Biomed 2019 Jan 17. Epub 2019 Jan 17.

IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.

Background And Objectives: Collecting Patient-Reported Outcomes (PROs) is an important way to get first-hand information by patients on the outcome of treatments and surgical procedure they have undergone, and hence about the quality of the care provided. However, the quality of PRO data cannot be given for granted and cannot be traced back to the dimensions of timeliness and completeness only. While the reliability of these data can be guaranteed by adopting standard and validated questionnaires that are used across different health care facilities all over the world, these facilities must take responsibility to assess, monitor and ensure the validity of PROs that are collected from their patients. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.009DOI Listing
January 2019

An automatic diagnostic network using skew-robust adversarial discriminative domain adaptation to evaluate the severity of depression.

Comput Methods Programs Biomed 2019 Jan 17. Epub 2019 Jan 17.

The Intelligent Computing and Software Research Center, Information Science and Technology College, Beijing Normal University, 19 xinjiekou street, Beijing 100875, China.

Background And Objective: Deep learning provides an automatic and robust solution to depression severity evaluation. However, despite it is powerful, there is a trade-off between robust performance and the cost of manual annotation.

Methods: Motivated by knowledge evolution and domain adaptation, we propose a deep evaluation network using skew-robust adversarial discriminative domain adaptation (SRADDA), which adaptively shifts its domain from a large-scale Twitter dataset to a small-scale depression interview dataset for evaluating the severity of depression. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.01.006DOI Listing
January 2019

Semi-supervised encoding for outlier detection in clinical observation data.

Comput Methods Programs Biomed 2019 Jan 12. Epub 2019 Jan 12.

Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Partners Healthcare, Somerville, MA, USA. Electronic address:

Background And Objective: Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible observations are frequent in records of laboratory test results and vital signs. Outlier detection methods can offer low-cost solutions to flagging implausible EHR observations. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183075
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http://dx.doi.org/10.1016/j.cmpb.2019.01.002DOI Listing
January 2019
3 Reads

TAQIH, a tool for tabular data quality assessment and improvement in the context of health data.

Comput Methods Programs Biomed 2018 Dec 29. Epub 2018 Dec 29.

South Eastern Health and Social Care Trust, Upper Newtownards Road, Belfast, BT16 1RH, United Kingdom.

Background And Objectives: Data curation is a tedious task but of paramount relevance for data analytics and more specially in the health context where data-driven decisions must be extremely accurate. The ambition of TAQIH is to support non-technical users on 1) the exploratory data analysis (EDA) process of tabular health data, and 2) the assessment and improvement of its quality.

Methods: A web-based tool has been implemented with a simple yet powerful visual interface. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.029DOI Listing
December 2018
1 Read

Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention.

Comput Methods Programs Biomed 2019 Feb 24;169:9-18. Epub 2018 Nov 24.

Neuroscience & Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran. Electronic address:

Background And Objective: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.11.006DOI Listing
February 2019
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Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection.

Comput Methods Programs Biomed 2019 Feb 27;169:59-69. Epub 2018 Dec 27.

Telecommunication and Aeronautic Engineering, São Paulo State University (UNESP), São João da Boa Vista, Brazil. Electronic address:

Background And Objective: Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computational tools can provide essential assistance to specialists. This paper presents a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes (Q, R and S waves). Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.028DOI Listing
February 2019
2 Reads

Satisfaction and perceived usefulness with newly-implemented Electronic Health Records System among pediatricians at a university hospital.

Comput Methods Programs Biomed 2019 Feb 26;169:51-57. Epub 2018 Dec 26.

Pediatrics Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia; Clinical Practice Guidelines Unit, Quality Management Department, King Saud University Medical City, Riyadh, Saudi Arabia; Research Chair for Evidence-Based Health Care and Knowledge Translation, King Saud University, Riyadh, Saudi Arabia.

Background: Apposite implementation of Electronic Health Records (EHR) is anchoring standards of care in healthcare settings by reducing long-run operational costs, improving healthcare quality, and enhancing patient safety.

Objective: This study aims to explore factors that might influence Pediatricians' satisfaction with an implemented EHR system and its perceived usefulness at a tertiary-care teaching hospital, Riyadh, Saudi Arabia.

Methods: A cross-sectional survey distributed to all physicians working in the pediatric department of King Saud University Medical City (KSUMC) in the period from June to November 2015, two months after the launch of the EHR system, internally branded as electronic system for integrated health information (eSiHi). Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.026DOI Listing
February 2019
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Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI.

Comput Methods Programs Biomed 2019 Feb 12;169:37-50. Epub 2018 Dec 12.

CONICET - Departamento de Física Médica, Centro Atómico Bariloche, Av. Bustillo 9500, S. C. de Bariloche, Río Negro, 8400 Argentina; Comisión Nacional de Energía Atómica (CNEA) Argentina.

Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).

Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.002DOI Listing
February 2019
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An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal.

Comput Methods Programs Biomed 2019 Feb 7;169:19-36. Epub 2018 Dec 7.

Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran.

Background And Objective: Taking into consideration the critical importance of Sudden cardiac death (SCD), as it could be the first and the last heart condition to be diagnosed in a person while continuing to claim millions of lives around the world, prediction of sudden cardiac death has increasingly been regarded as a matter of substantive significance. This study does not seek to once again define features to predict and detect SCD, as there already has been adequate discussion addressing feature extraction in our previous works and other recent studies. What seems to be lacking attention is the need for an appropriate strategy to manage the extracted features to such an extent that the best classification is presented. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.12.001DOI Listing
February 2019
3 Reads

Automated real-time method for ventricular heartbeat classification.

Comput Methods Programs Biomed 2019 Feb 20;169:1-8. Epub 2018 Nov 20.

Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (CSIC-UIB), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain. Electronic address:

Background And Objective: In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.11.005DOI Listing
February 2019
1 Read

What can millions of laboratory test results tell us about the temporal aspect of data quality? Study of data spanning 17 years in a clinical data warehouse.

Comput Methods Programs Biomed 2018 Dec 29. Epub 2018 Dec 29.

INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université Paris Descartes, Sorbonne Paris Cité, Paris, France; Hôpital Européen Georges Pompidou, Department of Medical Informatics, Assistance Publique - Hôpitaux de Paris (AP-HP), Université Paris Descartes, 20 rue Leblanc, 75015 Paris, France. Electronic address:

Objective: To identify common temporal evolution profiles in biological data and propose a semi-automated method to these patterns in a clinical data warehouse (CDW).

Materials And Methods: We leveraged the CDW of the European Hospital Georges Pompidou and tracked the evolution of 192 biological parameters over a period of 17 years (for 445,000 + patients, and 131 million laboratory test results).

Results: We identified three common profiles of evolution: discretization, breakpoints, and trends. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183070
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http://dx.doi.org/10.1016/j.cmpb.2018.12.030DOI Listing
December 2018
7 Reads

Deep into Patient care: An automated deep learning approach for reshaping patient care in clinical setting.

Comput Methods Programs Biomed 2019 Jan;168:A1-A2

Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Chair, Dept. of Dermatology, Wan Fang Hospital, Taipei, Taiwan. Electronic address:

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http://dx.doi.org/10.1016/j.cmpb.2018.11.007DOI Listing
January 2019
1 Read

compound.Cox: Univariate feature selection and compound covariate for predicting survival.

Comput Methods Programs Biomed 2019 Jan 27;168:21-37. Epub 2018 Oct 27.

Institute of Statistical Science, Academia Sinica, 128 Academia Road Sec.2, Nankang Taipei 115, Taiwan.

Background And Objective: Univariate feature selection is one of the simplest and most commonly used techniques to develop a multigene predictor for survival. Presently, there is no software tailored to perform univariate feature selection and predictor construction.

Methods: We develop the compound. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.020DOI Listing
January 2019
1 Read

Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding.

Comput Methods Programs Biomed 2019 Jan 20;168:11-19. Epub 2018 Nov 20.

Deustotech-LIFE Unit (eVIDA), University of Deusto Avda. Universidades, 24. 48007 Bilbao, Spain. Electronic address:

Background And Objective: To ensure proper functioning of a Computer Aided Diagnosis (CAD) system for melanoma detection in dermoscopy images, it is important to accurately detect the border of the lesion. This paper proposes a method developed by the authors to address this problem.

Methods: The algorithm for segmentation of skin lesions in dermoscopy images is based on fuzzy classification of pixels and subsequent histogram thresholding. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.11.001DOI Listing
January 2019
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Predicting combinative drug pairs via multiple classifier system with positive samples only.

Comput Methods Programs Biomed 2019 Jan 15;168:1-10. Epub 2018 Nov 15.

Department of Computer Science, The University of Hong Kong, Hong Kong, China. Electronic address:

Background And Objective: Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.11.002DOI Listing
January 2019
1 Read

Development of user-friendly tools for biomedical research and healthcare.

Comput Methods Programs Biomed 2018 Dec;167:A1

Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Chair, Dept. of Dermatology, Wan Fang Hospital, Taipei, Taiwan. Electronic address:

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183165
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http://dx.doi.org/10.1016/j.cmpb.2018.11.004DOI Listing
December 2018
2 Reads

A novel retinal vessel detection approach based on multiple deep convolution neural networks.

Comput Methods Programs Biomed 2018 Dec 30;167:43-48. Epub 2018 Oct 30.

Electrical and Electronics Engineering Department, Firat University, Elazig, Turkey.

Background And Objective: Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detection is still a challenging problem due to variations in morphology of the vessels on noisy and low contrast fundus images. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.021DOI Listing
December 2018
2 Reads

Patient satisfaction with dermatology teleconsultation by using MedX.

Comput Methods Programs Biomed 2018 Dec 18;167:37-42. Epub 2018 Oct 18.

Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; School of Health Care Administration, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan. Electronic address:

Background: The development of telecommunication has strongly affected comprehensive scientific disciplines, including medical sciences.

Objective: This study aims to assess the patient satisfaction of the teleconsultation system used for the consultation of dermatological follow-up care.

Methods: The study was performed cross sectional patient satisfaction survey method conducted between February and April 2017 to determine patient satisfaction using MedX teleconsultation system. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.015DOI Listing
December 2018
4 Reads
1.897 Impact Factor

BioSimulator.jl: Stochastic simulation in Julia.

Comput Methods Programs Biomed 2018 Dec 10;167:23-35. Epub 2018 Oct 10.

Department of Biomathematics, David Geffen School of Medicine at UCLA, USA. Electronic address:

Background And Objectives: Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.09.009DOI Listing
December 2018
1 Read

Automated identification and grading of coronary artery stenoses with X-ray angiography.

Comput Methods Programs Biomed 2018 Dec 24;167:13-22. Epub 2018 Oct 24.

Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China. Electronic address:

Background And Objective: X-ray coronary angiography (XCA) remains the gold standard imaging technique for the diagnosis and treatment of cardiovascular disease. Automatic detection and grading of coronary stenoses in XCA are challenging problems due to the complex overlap of different background structures with intensity inhomogeneities. We present a new computerized image based method to accurately identify and quantify the stenosis severity on XCA. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183072
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http://dx.doi.org/10.1016/j.cmpb.2018.10.013DOI Listing
December 2018
10 Reads

Automated 2D-3D quantitative analysis of corneal graft detachment post DSAEK based on AS-OCT images.

Comput Methods Programs Biomed 2018 Dec 9;167:1-12. Epub 2018 Oct 9.

Tej Kohli Cornea Institute, L. V. Prasad Eye Institute, Hyderabad, Telangana 500034, India.

Background And Objective: In current ophthalmological practices, assessment of graft condition post Descemet's stripping automated endothelial keratoplasty (DSAEK) is performed qualitatively using few (four) anterior-segment optical coherence tomography (AS-OCT) radial B-scans. From those scans, clinicians need to mentally synthesize the graft in 3D, and estimate its overall condition. In contrast, quantitative representation of 360° thickness profile would facilitate better visualization of graft condition, and hence medical decision making. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.003DOI Listing
December 2018
1 Read

Initializing a hospital-wide data quality program. The AP-HP experience.

Comput Methods Programs Biomed 2018 Nov 9. Epub 2018 Nov 9.

DSI WIND, AP-HP, Paris, France; INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, Paris, France.

Background And Objectives: Data Quality (DQ) programs are recognized as a critical aspect of new-generation research platforms using electronic health record (EHR) data for building Learning Healthcare Systems. The AP-HP Clinical Data Repository aggregates EHR data from 37 hospitals to enable large-scale research and secondary data analysis. This paper describes the DQ program currently in place at AP-HP and the lessons learned from two DQ campaigns initiated in 2017. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.016DOI Listing
November 2018
10 Reads

Analyzing hedyotis diffusa mechanisms of action from the genomics perspective.

Comput Methods Programs Biomed 2018 Oct 31. Epub 2018 Oct 31.

School of Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China. Electronic address:

Background And Objective: Hedyotis diffusa is an herb used for anti-cancer, anti-oxidant, anti-inflammatory, and anti-fibroblast treatment in the clinical practice of Traditional Chinese Medicine. However, its pharmacological mechanisms have not been fully established and there is a lack of modern scientific verification. One of the best ways to further understand Hedyotis diffusa's mechanisms of action is to analyze it from the genomics perspective. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607173066
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http://dx.doi.org/10.1016/j.cmpb.2018.10.019DOI Listing
October 2018
11 Reads

Artificial Intelligence in Clinical Implications.

Comput Methods Programs Biomed 2018 Nov;166:A1

International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan;; Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan;; Chair, Dept. of Dermatology, Wan Fang Hospital, Taipei, Taiwan. Electronic address:

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http://dx.doi.org/10.1016/j.cmpb.2018.10.022DOI Listing
November 2018
1 Read

A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data.

Comput Methods Programs Biomed 2018 Nov 5;166:99-105. Epub 2018 Oct 5.

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:

Background And Objective: Cancer has become a complex health problem due to its high mortality. Over the past few decades, with the rapid development of the high-throughput sequencing technology and the application of various machine learning methods, remarkable progress in cancer research has been made based on gene expression data. At the same time, a growing amount of high-dimensional data has been generated, such as RNA-seq data, which calls for superior machine learning methods able to deal with mass data effectively in order to make accurate treatment decision. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183045
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http://dx.doi.org/10.1016/j.cmpb.2018.10.004DOI Listing
November 2018
8 Reads

Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features.

Comput Methods Programs Biomed 2018 Nov 2;166:91-98. Epub 2018 Oct 2.

Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia.

Background And Objective: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183051
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http://dx.doi.org/10.1016/j.cmpb.2018.10.006DOI Listing
November 2018
14 Reads

Nutrition delivery, workload and performance in a model-based ICU glycaemic control system.

Comput Methods Programs Biomed 2018 Nov 11;166:9-18. Epub 2018 Sep 11.

Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand. Electronic address:

Background And Objective: Hyperglycaemia is commonplace in the adult intensive care unit (ICU), and has been associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a model-based GC protocol that uniquely maintains normoglycaemia by changing both insulin and nutrition interventions, and has been proven effective in controlling blood glucose (BG) in the ICU. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.09.005DOI Listing
November 2018
3 Reads

Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics.

Authors:
Aydin Kaya

Comput Methods Programs Biomed 2018 Nov 3;166:77-89. Epub 2018 Oct 3.

Hacettepe University, Computer Engineering Department, 06800 Ankara, Turkey. Electronic address:

Background And Objectives: Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.009DOI Listing
November 2018
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Accurate liver vessel segmentation via active contour model with dense vessel candidates.

Comput Methods Programs Biomed 2018 Nov 4;166:61-75. Epub 2018 Oct 4.

School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.

Background And Objective: The purpose of this paper is to propose a fully automated liver vessel segmentation algorithm including portal vein and hepatic vein on contrast enhanced CTA images.

Methods: First, points of a vessel candidate region are extracted from 3-dimensional (3D) CTA image. To generate accurate points, we reduce 3D segmentation problem to 2D problem by generating multiple maximum intensity (MI) images. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183064
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http://dx.doi.org/10.1016/j.cmpb.2018.10.010DOI Listing
November 2018
2 Reads

Diagnosis of urinary tract infection based on artificial intelligence methods.

Comput Methods Programs Biomed 2018 Nov 2;166:51-59. Epub 2018 Oct 2.

Department of Urology, Necmettin Erbakan University, Konya, Turkey.

Background And Objective: Urinary tract infection (UTI) is a common disease affecting the vast majority of people. UTI involves a simple infection caused by urinary tract inflammation as well as a complicated infection that may be caused by an inflammation of other urinary tract organs. Since all of these infections have similar symptoms, it is difficult to identify the cause of primary infection. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183028
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http://dx.doi.org/10.1016/j.cmpb.2018.10.007DOI Listing
November 2018
19 Reads

Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.

Comput Methods Programs Biomed 2018 Nov 21;166:39-49. Epub 2018 Sep 21.

Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France. Electronic address:

Background And Objective: Nowadays, getting an efficient Brain Tumor Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical diagnosis, treatment and follow-up. The aim of this study is to develop a new deep learning model for the segmentation of brain tumors. The proposed models are used to segment the brain tumors of Glioblastomas (with both high and low grade). Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183090
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http://dx.doi.org/10.1016/j.cmpb.2018.09.007DOI Listing
November 2018
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Fusion based Glioma brain tumor detection and segmentation using ANFIS classification.

Comput Methods Programs Biomed 2018 Nov 12;166:33-38. Epub 2018 Sep 12.

Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu 624001, India.

The detection of tumor regions in Glioma brain image is a challenging task due to its low sensitive boundary pixels. In this paper, Non-Sub sampled Contourlet Transform (NSCT) is used to enhance the brain image and then texture features are extracted from the enhanced brain image. These extracted features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) approach to classify the brain image into normal and Glioma brain image. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.09.006DOI Listing
November 2018
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Trends and characteristics of global medical informatics conferences from 2007 to 2017: A bibliometric comparison of conference publications from Chinese, American, European and the Global Conferences.

Comput Methods Programs Biomed 2018 Nov 27;166:19-32. Epub 2018 Aug 27.

Center for Medical Informatics, Peking University, 38 Xueyuan Rd, Haidian District, Beijing 100191, China; School of Medical Informatics and Engineering, Southwest Medical University, Luzhou, Sichuan, China. Electronic address:

Background: As the second-largest economy in the world, China has invested considerable financial and policy support into hospital informatization since health care reform in 2010. However, the results and experience of such investments have not been compared with relevant research and applications in the United States and Europe.

Objectives: From the perspective of professional conference proceedings, we comparatively analyzed the current situations, characteristics, hotspots, and trends of medical informatics (MI) development in China, the United States and Europe to help Chinese MI researchers and practitioners summarize their experiences and determine gaps compared to their American and European peers. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.08.017DOI Listing
November 2018
4 Reads

Global optimal constrained ICA and its application in extraction of movement related cortical potentials from single-trial EEG signals.

Comput Methods Programs Biomed 2018 Nov 11;166:155-169. Epub 2018 Aug 11.

Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. Electronic address:

Background And Objective: The constrained ICA (cICA) is a recent approach which can extract the desired source signal by using prior information. cICA employs gradient-based algorithms to optimize non convex objective functions and therefore global optimum solution is not guaranteed. In this study, we propose the Global optimal constrained ICA (GocICA) algorithm for solving the conventional cICA problems. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183058
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http://dx.doi.org/10.1016/j.cmpb.2018.07.013DOI Listing
November 2018
11 Reads

Towards an efficient and Energy-Aware mobile big health data architecture.

Comput Methods Programs Biomed 2018 Nov 4;166:137-154. Epub 2018 Oct 4.

Department of Information Systems and Security, College of IT, United Arab Emirates University, Al Ain 15551, UAE.

Background And Objectives: Mobile and ubiquitous devices are everywhere, generating an exorbitant amount of data. New generations of healthcare systems are using mobile devices to continuously collect large amounts of different types of data from patients with chronic diseases. The challenge with such Mobile Big Data in general, is how to meet the growing performance demands of the mobile resources handling these tasks, while simultaneously minimizing their consumption. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183056
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http://dx.doi.org/10.1016/j.cmpb.2018.10.008DOI Listing
November 2018
12 Reads

An improved support vector machine-based diabetic readmission prediction.

Comput Methods Programs Biomed 2018 Nov 12;166:123-135. Epub 2018 Oct 12.

School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China; Department of Computer Science, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom.

Background And Objective: In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01692607183080
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http://dx.doi.org/10.1016/j.cmpb.2018.10.012DOI Listing
November 2018
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Application of multiscale Poincaré short-time computation versus multiscale entropy in analyzing fingertip photoplethysmogram amplitudes to differentiate diabetic from non-diabetic subjects.

Comput Methods Programs Biomed 2018 Nov 2;166:115-121. Epub 2018 Oct 2.

Department of Emergency Medicine, E-Da Hospital, I-Shou University School of Medicine for International students, Kaohsiung, Taiwan. Electronic address:

Background And Objectives: Multiscale Poincaré (MSP) plots have recently been introduced to facilitate the visualization of time series of physiological signals. This study aimed at investigating the feasibility of MSP application in distinguishing subjects with and without diabetes.

Methods: Using photoplethysmogram (PPG) waveform amplitudes acquired from unilateral fingertip of non-diabetic (n = 34) and diabetic (n = 30) subjects, MSP indices (MSPI) of the two groups were compared using 1000, 500, 250, 100 data points. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.001DOI Listing
November 2018
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Classification of auditory selective attention using spatial coherence and modular attention index.

Comput Methods Programs Biomed 2018 Nov 2;166:107-113. Epub 2018 Oct 2.

Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Pampulha, Belo Horizonte, MG 31270-901, Brazil. Electronic address:

Background And Objective: Brain-Computer Interfaces (BCIs) based on auditory selective attention have been receiving much attention because i) they are useful for completely paralyzed users since they do not require muscular effort or gaze and ii) focusing attention is a natural human ability. Several techniques - such as recently developed Spatial Coherence (SC) - have been proposed in order to optimize the BCI procedure. Thus, this work aims at investigating and comparing two strategies based on spatial coherence detection: contralateral and modular classifiers. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.10.002DOI Listing
November 2018
1 Read