1,349 results match your criteria Artificial intelligence in medicine[Journal]


Profiling continuous sleep representations for better understanding of the dynamic character of normal sleep.

Artif Intell Med 2018 Dec 29. Epub 2018 Dec 29.

Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia. Electronic address:

The amount and quality of sleep substantially influences health, daily behaviour and overall quality of life. The main goal of this study was to investigate to what extent sleep structure, as derived from the polysomnographic (PSG) recordings of nocturnal human sleep, can provide information about sleep quality in terms of correlating with a set of variables representing the daytime subjective, neurophysiological and cognitive states of a healthy population without serious sleep problems. We focused on a continuous sleep representation derived from the probabilistic sleep model (PSM), which describes the microstructure of sleep by a set of sleep probabilistic curves representing a finite number of sleep microstates. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.009DOI Listing
December 2018

Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods.

Artif Intell Med 2019 Apr 2;95:1-15. Epub 2019 Mar 2.

Department of Computer Engineering, Tabriz Branch, Azad University, Tabriz, Iran. Electronic address:

In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employing a set of effective image features and combination of supervised and unsupervised machine learning techniques. Further to the common features used in extracting blood vessels, three strong features having a significant influence on the accuracy of the vessel extraction are utilized. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.03.001DOI Listing

Joint segmentation and classification of retinal arteries/veins from fundus images.

Artif Intell Med 2019 Mar 19;94:96-109. Epub 2019 Feb 19.

Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.

Objective: Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.02.004DOI Listing
March 2019
4 Reads

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks.

Artif Intell Med 2019 Mar 10;94:88-95. Epub 2019 Feb 10.

Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, UK.

In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.01.005DOI Listing
March 2019
3 Reads

Predicting lab values for gastrointestinal bleeding patients in the intensive care unit: A comparative study on the impact of comorbidities and medications.

Artif Intell Med 2019 Mar 23;94:79-87. Epub 2019 Jan 23.

Department of Computer Engineering, Yazd University, Yazd, Iran. Electronic address:

Since a significant number of frequent laboratory blood tests are unnecessary and these tests may have complications, developing a system that could identify unnecessary tests is essential. In this paper, a value prediction approach is presented to predict the values of Calcium and Hematocrit laboratory blood tests for upper gastrointestinal bleeding patients and patients with unspecified hemorrhage in their gastrointestinal tract. The data have been extracted from the MIMIC-II database. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.01.004DOI Listing
March 2019
3 Reads

Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography.

Artif Intell Med 2019 Mar 8;94:67-78. Epub 2019 Jan 8.

Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta GA, 30332, USA.

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.01.002DOI Listing

Normal and pathological gait classification LSTM model.

Artif Intell Med 2019 Mar 11;94:54-66. Epub 2019 Jan 11.

Le2i, FRE CNRS 2005, Univ. Bourgogne Franche-Comté, France. Electronic address:

Computer vision-based clinical gait analysis is the subject of permanent research. However, there are very few datasets publicly available; hence the comparison of existing methods between each other is not straightforward. Even if the test data are in an open access, existing databases contain very few test subjects and single modality measurements, which limit their usage. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.007DOI Listing
March 2019
2 Reads

Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach.

Artif Intell Med 2019 Mar 14;94:42-53. Epub 2019 Jan 14.

LIMICS, Université Paris 13, Sorbonne Universités, INSERM UMRS 1142, 93017 Bobigny, France; AP-HP, Hôpital Tenon, Département de Santé Publique, Paris, France. Electronic address:

Case-Based Reasoning (CBR) is a form of analogical reasoning in which the solution for a (new) query case is determined using a database of previous known cases with their solutions. Cases similar to the query are retrieved from the database, and then their solutions are adapted to the query. In medicine, a case usually corresponds to a patient and the problem consists of classifying the patient in a class of diagnostic or therapy. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09333657183048
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http://dx.doi.org/10.1016/j.artmed.2019.01.001DOI Listing
March 2019
1 Read
2.019 Impact Factor

Antigenic: An improved prediction model of protective antigens.

Artif Intell Med 2019 Mar 3;94:28-41. Epub 2019 Jan 3.

Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205, Bangladesh. Electronic address:

An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance in vaccine preparation and drug design. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09333657183027
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http://dx.doi.org/10.1016/j.artmed.2018.12.010DOI Listing
March 2019
9 Reads
2.019 Impact Factor

A frame reduction system based on a color structural similarity (CSS) method and Bayer images analysis for capsule endoscopy.

Artif Intell Med 2019 Mar 29;94:18-27. Epub 2018 Dec 29.

Department of Gastroenterology, The Hospital of Wollongong, Wollongong, NSW, Australia.

A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an increased probability of overlooking an image that may contain an abnormality. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.008DOI Listing
March 2019
2.019 Impact Factor

Using classification techniques for statistical analysis of Anemia.

Artif Intell Med 2019 Mar 19;94:138-152. Epub 2019 Feb 19.

Computer Science and Engineering, India; Indira Gandhi Delhi Technical University for Women, India. Electronic address:

Anemia in children is becoming a worldwide problem owing to the unawareness among people regarding the disease, its causes and preventive measures. This study develops a decision support system using data mining techniques that are applied to a database containing data about nutritional factors for children. The data set was taken from NFHS-4, a survey conducted by the Government of India in 2015-16. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.02.005DOI Listing

Execution-time integration of clinical practice guidelines to provide decision support for comorbid conditions.

Artif Intell Med 2019 Mar 20;94:117-137. Epub 2019 Feb 20.

Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada. Electronic address:

Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.02.003DOI Listing

Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis.

Artif Intell Med 2019 Mar 25;94:110-116. Epub 2019 Feb 25.

Department of Industrial & Management Engineering, POSTECH, Pohang, South Korea. Electronic address:

Introduction: Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the SAP data is provided. In order to reflect structural-functional patterns of glaucoma on the automated diagnostic models, we propose composite variables derived from anatomically grouped visual field clusters to improve the prediction performance. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.02.006DOI Listing
March 2019
3 Reads

Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization.

Artif Intell Med 2019 Mar 31;94:1-17. Epub 2018 Dec 31.

Department of Electronics & Communication Engineering, National Institute of Technology Calicut, India.

Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre- and/or post-processing step for better disease diagnosis. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.006DOI Listing
March 2019
4 Reads

Autonomous agents and multi-agent systems applied in healthcare.

Artif Intell Med 2019 Feb 27. Epub 2019 Feb 27.

eHealth Unit - Eurecat, Centre Tecnològic de Catalunya, Spain. Electronic address:

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http://dx.doi.org/10.1016/j.artmed.2019.02.007DOI Listing
February 2019

Real-time multi-agent systems for telerehabilitation scenarios.

Artif Intell Med 2019 Feb 14. Epub 2019 Feb 14.

University of Applied Science Western Switzerland (HES-SO), Sierre, Switzerland.

Telerehabilitation in older adults is most needed in the patient environments, rather than in formal ambulatories or hospitals. Supporting such practices brings significant advantages to patients, their family, formal and informal caregivers, clinicians, and researchers. This paper presents a focus group with experts in physiotherapy and telerehabilitation, debating on the requirements, current techniques and technologies developed to facilitate and enhance the effectiveness of telerehabilitation, and the still open challenges. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.02.001DOI Listing
February 2019

A survey of neural network-based cancer prediction models from microarray data.

Artif Intell Med 2019 Jan 30. Epub 2019 Jan 30.

University of Waikato, Hamilton, New Zealand. Electronic address:

Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Read More

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http://dx.doi.org/10.1016/j.artmed.2019.01.006DOI Listing
January 2019
1 Read

MRI denoising by NeighShrink based on chi-square unbiased risk estimation.

Artif Intell Med 2019 Jan 31. Epub 2019 Jan 31.

College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China.

NeighShrink is an efficient image denoising algorithm for the reduction of additive white Gaussian noise. However, it does not perform well in terms of Rician noise removal for MRI (Magnetic Resonance Imaging). Allowing for the characteristics of squared-magnitude MR (Magnetic Resonance) images, which follow a non-central chi-square distribution, the CURE (Chi-Square Unbiased Risk Estimation) is used to determine an optimal threshold for NeighShrink. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.001DOI Listing
January 2019
1 Read

A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity.

Artif Intell Med 2019 Apr 22;95:104-117. Epub 2019 Jan 22.

Radboud University Nijmegen Medical Centre, The Netherlands. Electronic address:

Background: Recently, mobile devices, such as smartphones, have been introduced into healthcare research to substitute paper diaries as data-collection tools in the home environment. Such devices support collecting patient data at different time points over a long period, resulting in clinical time-series data with high temporal complexity, such as time irregularities. Analysis of such time series poses new challenges for machine-learning techniques. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09333657173049
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http://dx.doi.org/10.1016/j.artmed.2018.10.002DOI Listing
April 2019
14 Reads

Project INSIDE: towards autonomous semi-unstructured human-robot social interaction in autism therapy.

Artif Intell Med 2018 Dec 28. Epub 2018 Dec 28.

Instituto Superior Técnico, University of Lisbon, Portugal.

This paper describes the INSIDE system, a networked robot system designed to allow the use of mobile robots as active players in the therapy of children with autism spectrum disorders (ASD). While a significant volume of work has explored the impact of robots in ASD therapy, most such work comprises remotely operated robots and/or well-structured interaction dynamics. In contrast, the INSIDE system allows for complex, semi-unstructured interaction in ASD therapy while featuring a fully autonomous robot. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.003DOI Listing
December 2018
1 Read
2.019 Impact Factor

A novel dynamical approach in continuous cuffless blood pressure estimation based on ECG and PPG signals.

Artif Intell Med 2018 Dec 23. Epub 2018 Dec 23.

Biomedical Engineering Department, Hamedan University of Technology, Hamedan, 6516913733, Iran. Electronic address:

Continuous cuffless blood pressure (BP) monitoring has attracted much interest in finding the ideal treatment of diseases and the prevention of premature death. This paper presents a novel dynamical method, based on pulse transit time (PTT) and photoplethysmogram intensity ratio (PIR), for the continuous cuffless BP estimation. By taking the advantages of both the modeling and the prediction approaches, the proposed framework effectively estimates diastolic BP (DBP), mean BP (BP), and systolic BP (SBP). Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.005DOI Listing
December 2018
1 Read

BDI personal medical assistant agents: The case of trauma tracking and alerting.

Artif Intell Med 2018 Dec 19. Epub 2018 Dec 19.

Intensive Care Unit/Trauma Center, M. Bufalini Hospital, Cesena, Italy. Electronic address:

Personal assistant agents can have an important role in healthcare as a smart technology to support physicians in their daily work, helping to tackle the increasing complexity of their task environment. In this paper we present and discuss a personal medical assistant agent technology for trauma documentation and management, based on the Belief-Desire-Intention (BDI) architecture. The purpose of the personal assistant agent is twofold: to assist the Trauma Team in doing precision tracking during a trauma resuscitation, so as to (automatically) produce an accurate documentation of the trauma, and to generate alerts at real-time, to be eventually displayed either on smart-glasses or room-display. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.002DOI Listing
December 2018
1 Read

Fuzzy logic based approaches for gene regulatory network inference.

Authors:
Khalid Raza

Artif Intell Med 2018 Dec 17. Epub 2018 Dec 17.

Department of Computer Science, Jamia Millia Islamia, New Delhi, India. Electronic address:

The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.12.004DOI Listing
December 2018
1 Read

CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm.

Artif Intell Med 2018 Dec 14. Epub 2018 Dec 14.

Faculty of Computers and Information, Cairo University, Cairo, Egypt; Scientific Research Group in Egypt (SRGE), Egypt(1).

Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.11.007DOI Listing
December 2018
1 Read

Optimal testing policies for diagnosing patients with intermediary probability of disease.

Artif Intell Med 2018 Dec 5. Epub 2018 Dec 5.

Instituto Nacional de Cardiologia, Rua das Laranjeiras 374, Laranjeiras, Rio de Janeiro, RJ, 22.240-006, Brazil. Electronic address:

This paper proposes a stochastic shortest path approach to find an optimal sequence of tests to confirm or discard a disease, for any prescribed optimality criterion. The idea is to select the best sequence in which to apply a series of available tests, with a view at reaching a diagnosis with minimum expenditure of resources. The proposed approach derives an optimal policy whereby the decision maker is provided with a test strategy for each a priori probability of disease, aiming to reach posterior probabilities that warrant either immediate treatment or a not-ill diagnosis. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.11.005DOI Listing
December 2018
1 Read

Arden Syntax: Then, now, and in the future.

Artif Intell Med 2018 Nov 25;92:1-6. Epub 2018 Oct 25.

Department of Medicine & Clinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Center for Biomedical Informatics, Charles Drew University, 1748 E 118th Street, LSRNE N238, Los Angeles, CA 90059, USA.

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http://dx.doi.org/10.1016/j.artmed.2018.09.001DOI Listing
November 2018
4 Reads

An efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images.

Artif Intell Med 2018 Dec 4. Epub 2018 Dec 4.

Computer Engineering Department, Yıldız Technical University, İstanbul, Turkey. Electronic address:

Menisci are structures that directly affect movement, so early detection of meniscus tears also helps to prevent progressive knee disorders such as osteoarthritis. Manual segmentation of the menisci and diagnosis of the meniscal tear is a costly process in terms of time and effort for a radiologist. The aim of this study is to automatically determine the location and the type of meniscal tears that are important in the diagnosis and effective treatment of this problem. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.11.008DOI Listing
December 2018
2 Reads

A computer-aided diagnosis system for HEp-2 fluorescence intensity classification.

Artif Intell Med 2018 Nov 28. Epub 2018 Nov 28.

Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy. Electronic address:

Background And Objective: The indirect immunofluorescence (IIF) on HEp-2 cells is the recommended technique for the detection of antinuclear antibodies. However, it is burdened by some limitations, as it is time consuming and subjective, and it requires trained personnel. In other fields the adoption of deep neural networks has provided an effective high-level abstraction of the raw data, resulting in the ability to automatically generate optimized high-level features. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.11.002DOI Listing
November 2018
1 Read

Recursive model identification for the analysis of the autonomic response to exercise testing in Brugada syndrome.

Artif Intell Med 2018 Nov 28. Epub 2018 Nov 28.

Univ Rennes, CHU Rennes, Inserm, LTSI UMR 1099, F-35000 Rennes, France.

This paper proposes the integration and analysis of a closed-loop model of the baroreflex and cardiovascular systems, focused on a time-varying estimation of the autonomic modulation of heart rate in Brugada syndrome (BS), during exercise and subsequent recovery. Patient-specific models of 44 BS patients at different levels of risk (symptomatic and asymptomatic) were identified through a recursive evolutionary algorithm. After parameter identification, a close match between experimental and simulated signals (mean error = 0. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09333657183044
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http://dx.doi.org/10.1016/j.artmed.2018.11.006DOI Listing
November 2018
7 Reads

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

Artif Intell Med 2018 Nov 23. Epub 2018 Nov 23.

Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost. We proposed two distinct deep learning models - (i) CNN Word - Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7370 clinical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09333657173062
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http://dx.doi.org/10.1016/j.artmed.2018.11.004DOI Listing
November 2018
13 Reads

Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning.

Artif Intell Med 2018 Nov 22. Epub 2018 Nov 22.

Hospital San Agustín, Linares, Spain.

The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.11.003DOI Listing
November 2018
2 Reads

Diabetic retinopathy techniques in retinal images: A review.

Artif Intell Med 2018 Nov 15. Epub 2018 Nov 15.

Military College of Signals, Department of Information Security, National University of Science and Technology, Rawalpindi, Pakistan. Electronic address:

The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.10.009DOI Listing
November 2018
1 Read

Neural network modelling of soft tissue deformation for surgical simulation.

Artif Intell Med 2018 Nov 13. Epub 2018 Nov 13.

Department of Mechanical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia.

This paper presents a new neural network methodology for modelling of soft tissue deformation for surgical simulation. The proposed methodology formulates soft tissue deformation and its dynamics as the neural propagation and dynamics of cellular neural networks for real-time, realistic, and stable simulation of soft tissue deformation. It develops two cellular neural network models; based on the bioelectric propagation of biological tissues and principles of continuum mechanics, one cellular neural network model is developed for propagation and distribution of mechanical load in soft tissues; based on non-rigid mechanics of motion in continuum mechanics, the other cellular neural network model is developed for governing model dynamics of soft tissue deformation. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09333657173019
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http://dx.doi.org/10.1016/j.artmed.2018.11.001DOI Listing
November 2018
15 Reads

Indexing the Event Calculus: Towards practical human-readable Personal Health Systems.

Artif Intell Med 2018 Nov 12. Epub 2018 Nov 12.

Open University of the Netherlands, Heerlen, The Netherlands; BISS Institute, Heerlen, The Netherlands. Electronic address:

Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. In general, a patient affected by a chronic disease can generate large amounts of events: for example, in Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. Just by itself, without considering other physiological parameters, it would be impossible for medical doctors to individually and accurately follow every patient, highlighting the need of simple approaches towards querying physiological time series. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.10.003DOI Listing
November 2018
16 Reads

Computational methods for Gene Regulatory Networks reconstruction and analysis: A review.

Artif Intell Med 2019 Apr 9;95:133-145. Epub 2018 Nov 9.

Division of Computer Science, Pablo de Olavide University, ES-41013 Seville, Spain. Electronic address:

In the recent years, the vast amount of genetic information generated by new-generation approaches, have led to the need of new data handling methods. The integrative analysis of diverse-nature gene information could provide a much-sought overview to study complex biological systems and processes. In this sense, Gene Regulatory Networks (GRN) arise as an increasingly-promising tool for the modelling and analysis of biological processes. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.10.006DOI Listing
April 2019
1 Read

Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours.

Artif Intell Med 2018 Nov 9. Epub 2018 Nov 9.

School of Health Sciences, Ulster University, Newtownabbey, Northern Ireland BT37 0QB, United Kingdom.

In medical computer aided diagnosis systems, image segmentation is one of the major pre-processing steps used to ensure only the region of interest, such as the breast region, will be processed in subsequent steps. Nevertheless, breast segmentation is a difficult task due to low contrast and inhomogeneity, especially when estimating the chest wall in magnetic resonance (MR) images. In fact, the chest wall comprises fat, skin, muscles, and the thoracic skeleton, which can misguide automatic methods when attempting to estimate its location. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09333657183025
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http://dx.doi.org/10.1016/j.artmed.2018.10.007DOI Listing
November 2018
17 Reads

Computational normalization of H&E-stained histological images: Progress, challenges and future potential.

Artif Intell Med 2019 Apr 9;95:118-132. Epub 2018 Nov 9.

Center of Mathematics, Computing and Cognition, Federal University of ABC, Av. dos Estados, 5001, 09210-580, Santo André, São Paulo, Brazil; Faculty of Computer Science, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, 38400-902, Uberlândia, Minas Gerais, Brazil. Electronic address:

Different types of cancer can be diagnosed with the analysis of histological samples stained with hematoxylin-eosin (H&E). Through this stain, it is possible to identify the architecture of tissue components and analyze cellular morphological aspects that are essential for cancer diagnosis. However, preparation and digitization of histological samples can lead to color variations that influence the performance of segmentation and classification algorithms in histological image analysis systems. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.10.004DOI Listing
April 2019
1 Read

Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks.

Artif Intell Med 2018 Nov 9. Epub 2018 Nov 9.

Ghent University - imec, IDLab, Department of Information Technology, Technologiepark 15, B-9052, Ghent, Belgium. Electronic address:

Introduction: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death.

Problem Statement: The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.10.008DOI Listing
November 2018
3 Reads

Early anomaly detection in smart home: A causal association rule-based approach.

Artif Intell Med 2018 Sep 29;91:57-71. Epub 2018 Jun 29.

CNRS Paris Saclay, Telecom SudParis, SAMOVAR, France. Electronic address:

As the world's population grows older, an increasing number of people are facing health issues. For the elderly, living alone can be difficult and dangerous. Consequently, smart homes are becoming increasingly popular. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.06.001DOI Listing
September 2018
17 Reads

Preface: AIME 2017.

Artif Intell Med 2018 Sep;91:1-2

University of Pavia, Pavia, Italy.

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September 2018
1 Read

Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders.

Artif Intell Med 2019 Jan 29;93:29-42. Epub 2018 Oct 29.

Bern University of Applied Sciences, Medical Informatics, Biel, Switzerland.

Classification systems such as ICD-10 for diagnoses or the Swiss Operation Classification System (CHOP) for procedure classification in the clinical treatment are essential for clinical management and information exchange. Traditionally, classification codes are assigned manually or by systems that rely upon concept-based or rule-based classification methods. Such methods can reach their limit easily due to the restricted coverage of handcrafted rules and of the vocabulary in underlying terminological systems. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.10.001DOI Listing
January 2019
7 Reads

On predicting epithelial mesenchymal transition by integrating RNA-binding proteins and correlation data via L-regularization method.

Artif Intell Med 2019 Apr 21;95:96-103. Epub 2018 Oct 21.

Department of Mathematics, Hong Kong Baptist University, Hong Kong. Electronic address:

Identifying tumor metastasis signatures from gene expression data at the whole genome level remains an arduous challenge, particularly so when the number of genes is huge and the number of experimental samples is small. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than on tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. We apply an extended LASSO model, L-regularization model, as a feature selector, to identify significant RNA-binding proteins (RBPs) that contribute to regulating the EMT. Read More

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April 2019
1 Read
2.020 Impact Factor

Towards a modular decision support system for radiomics: A case study on rectal cancer.

Artif Intell Med 2018 Oct 3. Epub 2018 Oct 3.

Polo Scienze Oncologiche ed Ematologiche, Fondazione Policlinico Universitario Agostino Gemelli, Largo A. Gemelli, 8, 00168 Rome, Italy.

Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.09.003DOI Listing
October 2018
4 Reads

Predicting hospital associated disability from imbalanced data using supervised learning.

Artif Intell Med 2019 Apr 3;95:88-95. Epub 2018 Oct 3.

University of Jyvaskyla, Faculty of Information Technology, P.O. Box 35, FI-40014, University of Jyvaskyla, Finland.

Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.09.004DOI Listing
April 2019
8 Reads

A methodology for customizing clinical tests for esophageal cancer based on patient preferences.

Artif Intell Med 2019 Apr 29;95:16-26. Epub 2018 Sep 29.

Indian Institute of Technology (IIT), Kharagpur, India.

Background: Clinical tests for diagnosis of any disease may be expensive, uncomfortable, time consuming and can have side effects e.g. barium swallow test for esophageal cancer. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.08.001DOI Listing
April 2019
13 Reads

Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer.

Artif Intell Med 2019 Apr 25;95:82-87. Epub 2018 Sep 25.

University of Arizona, Tucson, AZ, USA.

In this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine. Read More

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April 2019
2 Reads

Predicting ICU readmission using grouped physiological and medication trends.

Artif Intell Med 2019 Apr 10;95:27-37. Epub 2018 Sep 10.

Department of Preventive Medicine, Northwestern University, Chicago, IL, USA. Electronic address:

Background: Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmission prediction models are often built based on physiological variables. Read More

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http://dx.doi.org/10.1016/j.artmed.2018.08.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6474661PMC
April 2019
17 Reads

A survey on computer-assisted Parkinson's Disease diagnosis.

Artif Intell Med 2019 Apr 7;95:48-63. Epub 2018 Sep 7.

São Paulo State University, School of Sciences, Bauru, Brazil.

Background And Objective: In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinson's Disease (PD), as well as an analysis of future trends on new approaches to this end.

Methods: In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation. Each selected work has been carefully analyzed in order to identify its objective, methodology and results. Read More

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April 2019
2 Reads

Association measures for estimating semantic similarity and relatedness between biomedical concepts.

Artif Intell Med 2019 Jan 7;93:1-10. Epub 2018 Sep 7.

Virginia Commonwealth University, Richmond, VA, United States.

Association measures quantify the observed likelihood a term pair co-occurs versus their predicted co-occurrence together if by chance. This is based both on the terms' individual occurrence frequencies, and their mutual co-occurrence frequencies. One application of association scores is estimating semantic relatedness, which is critical for many natural language processing applications, such as clustering of biomedical and clinical documents and the development of biomedical terminologies and ontololgies. Read More

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January 2019
1 Read

Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis.

Artif Intell Med 2019 Apr 6;95:38-47. Epub 2018 Sep 6.

Dipartimento di Informatica, Università degli Studi di Milano, Crema (CR) 26013, Italy. Electronic address:

Gait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Read More

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April 2019
17 Reads