1,296 results match your criteria Artificial Intelligence in Medicine [Journal]


Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance.

Artif Intell Med 2018 Aug 6. Epub 2018 Aug 6.

College of Computing and Informatics, Drexel University, United States.

Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals has drawn many researchers' attention and efforts. Currently, methods proposed for ADR and DDI detection are mainly based on traditional data sources such as spontaneous reporting data, electronic health records, pharmaceutical databases, and biomedical literature. Read More

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Recent advances in extracting and processing rich semantics from medical texts.

Artif Intell Med 2018 Aug 3. Epub 2018 Aug 3.

Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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Causality assessment of adverse drug reaction reports using an expert-defined Bayesian network.

Artif Intell Med 2018 Aug 2. Epub 2018 Aug 2.

CINTESIS - Centre for Health Technology and Services Research, Rua Dr. Plácido Costa, 4200-450 Porto, Portugal; UFN - Northern Pharmacovigilance Centre (INFARMED), Rua Dr. Plácido Costa, 4200-450 Porto, Portugal.

In pharmacovigilance, reported cases are considered suspected adverse drug reactions (ADR). Health authorities have thus adopted structured causality assessment methods, allowing the evaluation of the likelihood that a drug was the causal agent of an adverse reaction. The aim of this work was to develop and validate a new causality assessment support system used in a regional pharmacovigilance centre. Read More

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Diagnosis labeling with disease-specific characteristics mining.

Artif Intell Med 2018 Jul 31. Epub 2018 Jul 31.

School of Information Science and Technology, Northwest University, Xian 710127, PR China. Electronic address:

Data analysis and management of huge volumes of medical data have attracted enormous attention, since discovering knowledge from the data can benefit both caregivers and patients. In this paper, we focus on learning disease labels from medical data of patients in Intensive Care Units (ICU). Specifically, we extract features from two main sources, medical charts and notes. Read More

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July 2018
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Dependence between cognitive impairment and metabolic syndrome applied to a Brazilian elderly dataset.

Artif Intell Med 2018 Jul 31. Epub 2018 Jul 31.

Department of Electrical and Computer Engineering, University of São Paulo, São Carlos, Brazil.

Globally, the proportion of elderly individuals in the population has increased substantially in the last few decades. However, the risk factors that should be managed in advance to ensure a natural process of mental decline due to aging remain unknown. In this study, a dataset consisting of a Brazilian elderly sample was modelled using a Bayesian Network (BN) approach to uncover connections between cognitive performance measures and potential influence factors. Read More

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Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach.

Artif Intell Med 2018 Jul 24. Epub 2018 Jul 24.

Department of Radiology, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Case Center for Imaging Research, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA. Electronic address:

Background: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring.

Methods/materials: Our algorithm is based on two types of information flow, i. Read More

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Decision support system for detection of hypertensive retinopathy using arteriovenous ratio.

Artif Intell Med 2018 Jul 2. Epub 2018 Jul 2.

Department of Computer & Software Engineering, College of E&ME, National University of Sciences and Technology, Islamabad, Pakistan. Electronic address:

Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR). Read More

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Change-point detection method for clinical decision support system rule monitoring.

Artif Intell Med 2018 Jul 2. Epub 2018 Jul 2.

Department of Computer Science, University of Pittsburgh, USA. Electronic address:

A clinical decision support system (CDSS) helps clinicians to manage patients, but malfunctions of its components or other systems on which it depends may affect its intended functions. Monitoring the system and detecting changes in its behavior that may indicate the malfunction can help to avoid any potential costs associated with its improper operation. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. Read More

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Process models of interrelated speech intentions from online health-related conversations.

Artif Intell Med 2018 Jul 17. Epub 2018 Jul 17.

University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia.

Being related to the adoption of new beliefs, attitudes and, ultimately, behaviors, analyzing online communication is of utmost importance for medicine. Multiple health care, academic communities, such as information seeking and dissemination and persuasive technologies, acknowledge this need. However, in order to obtain understanding, a relevant way to model online communication for the study of behavior is required. Read More

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July 2018
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Detecting mental fatigue from eye-tracking data gathered while watching video: Evaluation in younger and older adults.

Artif Intell Med 2018 Jul 16. Epub 2018 Jul 16.

IBM Research - Tokyo, 19-21, Nihonbashi, Hakozaki-cho, Chuo-ku, Tokyo 103-8510, Japan.

Health monitoring technology in everyday situations is expected to improve quality of life and support aging populations. Mental fatigue among health indicators of individuals has become important due to its association with cognitive performance and health outcomes, especially in older adults. Previous models using eye-tracking measures allow inference of fatigue during cognitive tasks, such as driving, but they require us to engage in specific cognitive tasks. Read More

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July 2018
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A review of statistical and machine learning methods for modeling cancer risk using structured clinical data.

Artif Intell Med 2018 Jul 14. Epub 2018 Jul 14.

Florida Atlantic University, United States. Electronic address:

Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers, they must be detected early. Additionally, there is a risk of cancers recurring after potentially curative treatments are performed. Read More

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July 2018
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Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis.

Artif Intell Med 2018 Jul 11;89:40-50. Epub 2018 Jul 11.

Department of Control Engineering, K. N. Toosi University of Technology, Tehran, Iran.

The brain connections in the different regions demonstrate the characteristics of brain activities. In addition, in various conditions and with neuropsychological disorders, the brain has special patterns in different regions. This paper presents a model to show and compare the connection patterns in different brain regions of children with autism (53 boys and 36 girls) and control children (61 boys and 33 girls). Read More

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July 2018
4 Reads

A distance measure between intuitionistic fuzzy sets and its application in medical diagnosis.

Artif Intell Med 2018 Jul 19;89:34-39. Epub 2018 Jun 19.

Department of Information and Computing Science, China Jiliang University, Hangzhou 310018, PR China.

The intuitionistic fuzzy set, as a generation of fuzzy set, can express and process uncertainty much better. Distance measures between intuitionistic fuzzy sets are used to indicate the difference degree between the information carried by intuitionistic fuzzy sets. Although some distance measures have been proposed in previous studies, they can not satisfy the axioms of distance measure, or exist counter-intuitive cases. Read More

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July 2018
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Automatic classification of radiological reports for clinical care.

Artif Intell Med 2018 Jun 7. Epub 2018 Jun 7.

Spedali Civili di Brescia, Italy.

Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia. Read More

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June 2018
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Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards.

Artif Intell Med 2018 Jul 2;89:61-69. Epub 2018 Jun 2.

Software Innovation Laboratory (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil. Electronic address:

Background: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. Read More

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July 2018
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Heart disease diagnosis based on mediative fuzzy logic.

Authors:
Ion Iancu

Artif Intell Med 2018 Jul 30;89:51-60. Epub 2018 May 30.

University of Craiova, Department of Computer Science, 13 A. I. Cuza Street, 200585, Romania. Electronic address:

Mediative fuzzy logic is an approach able to deal with inconsistent information providing a solution when contradiction exists. The aim of this paper is to design an expert system based on this type of fuzzy logic in order to diagnose a possible heart disease for a patient. Our proposed system is an extension of the standard Mamdani fuzzy logic controller and contains 44 rules of the type single input-single output. Read More

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July 2018
2 Reads

Symptoms and medications change patterns for Parkinson's disease patients stratification.

Artif Intell Med 2018 May 23. Epub 2018 May 23.

University of Ljubljana, Faculty of Computer and Information Science, Slovenia. Electronic address:

Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients' medical conditions and changes in the prescribed therapy when the medical conditions change. Read More

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May 2018
1 Read

Classifying medical relations in clinical text via convolutional neural networks.

Artif Intell Med 2018 May 16. Epub 2018 May 16.

Department of Mathematics, Harbin Institute of Technology, Harbin, China. Electronic address:

Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Read More

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May 2018
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Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy.

Artif Intell Med 2018 Jul 9;89:24-33. Epub 2018 Jul 9.

LIMICS, INSERM UMRS 1142, Université Paris 13, Sorbonne Université, F-75006 Paris, France. Electronic address:

Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also state that there is a high bacteria resistance in this context. Read More

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July 2018
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Extracting cancer mortality statistics from death certificates: A hybrid machine learning and rule-based approach for common and rare cancers.

Artif Intell Med 2018 Jul 10;89:1-9. Epub 2018 May 10.

Cancer Institute NSW, Sydney, Australia. Electronic address:

Objective: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality of certificates written in natural language. This paper proposes an automatic classification system for identifying all cancer related causes of death from death certificates. Read More

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July 2018
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An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR.

Artif Intell Med 2018 Jul 9;89:10-23. Epub 2018 May 9.

Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Germany.

Background: Clinical decision-support systems (CDSS) are designed to solve knowledge-intensive tasks for supporting decision-making processes. Although many approaches for designing CDSS have been proposed, due to high implementation costs, as well as the lack of interoperability features, current solutions are not well-established across different institutions. Recently, the use of standardized formalisms for knowledge representation as terminologies as well as the integration of semantically enriched clinical information models, as openEHR Archetypes, and their reuse within CDSS are theoretically considered as key factors for reusable CDSS. Read More

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July 2018
7 Reads

Identifying β-thalassemia carriers using a data mining approach: The case of the Gaza Strip, Palestine.

Artif Intell Med 2018 Jun 3;88:70-83. Epub 2018 May 3.

King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan. Electronic address:

Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly differentiate normal individuals from asymptomatic thalassemia carriers. Usually, thalassemia diagnosis is based on certain measurable characteristic changes to blood cell counts and related indices. Read More

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June 2018
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Leveraging Wikipedia knowledge to classify multilingual biomedical documents.

Artif Intell Med 2018 Jun 3;88:37-57. Epub 2018 May 3.

Department of Telematics Engineering, University of Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Spain. Electronic address:

This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with English documents. We propose the cross-language concept matching technique, which relies on Wikipedia interlanguage links to convert concept vectors between languages. The performance of the classifier is compared to a classifier based on machine translation, and two classifiers based on MetaMap. Read More

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June 2018
2 Reads

Lung sounds classification using convolutional neural networks.

Artif Intell Med 2018 Jun 1;88:58-69. Epub 2018 May 1.

Lareb Technologies, India.

Lung sounds convey relevant information related to pulmonary disorders, and to evaluate patients with pulmonary conditions, the physician or the doctor uses the traditional auscultation technique. However, this technique suffers from limitations. For example, if the physician is not well trained, this may lead to a wrong diagnosis. Read More

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June 2018
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Dictionary-based monitoring of premature ventricular contractions: An ultra-low-cost point-of-care service.

Artif Intell Med 2018 May 26;87:91-104. Epub 2018 Apr 26.

Department of Electrical Engineering, Indian Institute of Technology Hyderabad, 502285, India. Electronic address:

While cardiovascular diseases (CVDs) are prevalent across economic strata, the economically disadvantaged population is disproportionately affected due to the high cost of traditional CVD management, involving consultations, testing and monitoring at medical facilities. Accordingly, developing an ultra-low-cost alternative, affordable even to groups at the bottom of the economic pyramid, has emerged as a societal imperative. Against this backdrop, we propose an inexpensive yet accurate home-based electrocardiogram (ECG) monitoring service. Read More

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May 2018
1 Read

MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.

Artif Intell Med 2018 Jun 26;88:14-24. Epub 2018 Apr 26.

Image Optimisation and Perception, Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Sydney, NSW, Australia; Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.

Motivation: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of lesion subtypes is considered as the gold standard, however, sometimes strong disagreements among pathologists for distinction among lesion subtypes have been previously reported in the literature.

Objective: To propose a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. Read More

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June 2018
1 Read

An intelligent algorithm for identification of optimum mix of demographic features for trust in medical centers in Iran.

Artif Intell Med 2018 Jun 26;88:25-36. Epub 2018 Apr 26.

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Healthcare quality is affected by various factors including trust. Patients' trust to healthcare providers is one of the most important factors for treatment outcomes. The presented study identifies optimum mixture of patient demographic features with respect to trust in three large and busy medical centers in Tehran, Iran. Read More

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June 2018
2 Reads

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

Artif Intell Med 2018 May 23;87:49-59. Epub 2018 Apr 23.

School of Computer Science and Technology, Harbin, Heilongjiang 150001, China. Electronic address:

Objective: Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient.

Methods: We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Read More

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May 2018
1 Read

How do we talk about doctors and drugs? Sentiment analysis in forums expressing opinions for medical domain.

Artif Intell Med 2018 Apr 20. Epub 2018 Apr 20.

Department of Computer Science, Advanced Studies Center in Information and Communication Technologies (CEATIC), Universidad de Jaén Campus Las Lagunillas, E-23071 Jaén, Spain.

Objective: The main goal of this study is to examine how people express their opinion in medical forums. We analyze the language used in order to determine the best way to tackle sentiment analysis in this domain.

Methods: We have applied supervised learning and lexicon-based sentiment analysis approaches over two different corpora extracted from social web. Read More

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April 2018
1 Read

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading.

Artif Intell Med 2018 May 19;87:78-90. Epub 2018 Apr 19.

Department of Pathology, University Kebangsaan Malaysia Medical Center, 56000 Batu 9 Cheras, Malaysia. Electronic address:

Objective: Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. Read More

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May 2018
1 Read

Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.

Authors:
Anna Fabijańska

Artif Intell Med 2018 Jun 19;88:1-13. Epub 2018 Apr 19.

Institute of Applied Computer Science, Lodz University of Technology, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland. Electronic address:

Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracted from the image. Up to today, this has been performed manually or semi-automatically. Read More

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June 2018
2 Reads

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.

Artif Intell Med 2018 May 16;87:67-77. Epub 2018 Apr 16.

UNESP - São Paulo State University, School of Sciences, Bauru, Brazil. Electronic address:

Background And Objective: Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet.

Methods: In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment. Read More

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May 2018
3 Reads

What matters in a transferable neural network model for relation classification in the biomedical domain?

Artif Intell Med 2018 May 13;87:60-66. Epub 2018 Apr 13.

Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, India. Electronic address:

A lack of sufficient labeled data often limits the applicability of advanced machine learning algorithms to real life problems. However, the efficient use of transfer learning (TL) has been shown to be very useful across domains. TL make use of valuable knowledge learned in one task (source task), where sufficient data is available, in order to improve performance on the task of interest (target task). Read More

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May 2018
2 Reads

A two-step approach for mining patient treatment pathways in administrative healthcare databases.

Artif Intell Med 2018 May 7;87:34-48. Epub 2018 Apr 7.

Laboratoire de vision et systèmes numériques, Département de génie électrique et de génie informatique, Université Laval, Québec, QC G1V 0A6, Canada. Electronic address:

Clustering electronic medical records allows the discovery of information on healthcare practices. Entries in such medical records are usually composed of a succession of diagnostics or therapeutic steps. The corresponding processes are complex and heterogeneous since they depend on medical knowledge integrating clinical guidelines, the physician's individual experience, and patient data and conditions. Read More

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May 2018
4 Reads

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.

Artif Intell Med 2018 May 30;87:20-33. Epub 2018 Mar 30.

Beedie School of Business, Simon Fraser University, Vancouver, Canada; School of Business, Kwantlen Polytechnic University, Vancouver, Canada.

Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed to control the patient's blood glucose. The wide range of hyperglycemia lowering agents with varying effects and various side effects makes the decision quite difficult. This paper presents computer-aided medical decision support using a fuzzy Multi-Criteria Decision-Making (MCDM) model that hybridizes a Step-wise Weight Assessment Ratio Analysis (SWARA) method with a modification of Fuzzy Multi-Objective Optimization on the basis of a Ratio Analysis plus the full multiplicative form (FMULTIMOORA) method for pharmacological therapy selection of T2D. Read More

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May 2018
2 Reads

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

Artif Intell Med 2018 May 21;87:9-19. Epub 2018 Mar 21.

NLP & IR Group, Dpto. Lenguajes y Sistemas Informáticos. Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain. Electronic address:

Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. Read More

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May 2018
2 Reads

Position-aware deep multi-task learning for drug-drug interaction extraction.

Artif Intell Med 2018 May 17;87:1-8. Epub 2018 Mar 17.

School of Engineering and Applied Science, Aston University, UK. Electronic address:

Objective: A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Read More

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May 2018
3 Reads

Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation.

Artif Intell Med 2018 04;85:7-15

Cardiac Arrhythmia Signal Analysis Laboratory, School of Medicine, Queen's University, K7L 3N6 Kingston, Ontario, Canada.

Objective: In this paper, we propose a novel algorithm to extract the active intervals of intracardiac electrograms during atrial fibrillation.

Methods: First, we show that the characteristics of the signal waveform at its inflection points are prominent features that are implicitly used by human annotators for distinguishing between active and inactive intervals of IEGMs. Then, we show that the natural logarithm of features corresponding to active and inactive intervals exhibits a mixture of two Gaussian distributions in three dimensional feature space. Read More

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April 2018
2 Reads

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks.

Authors:
Seokho Kang

Artif Intell Med 2018 04 23;85:1-6. Epub 2018 Feb 23.

Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea. Electronic address:

Patients with type 2 diabetes mellitus are generally under continuous long-term medical treatment based on anti-diabetic drugs to achieve the desired glucose level. Thus, each patient is associated with a sequence of multiple records for prescriptions and their efficacies. Sequential dependencies are embedded in these records as personal factors so that previous records affect the efficacy of the current prescription for each patient. Read More

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April 2018
9 Reads

Approximate dynamic programming approaches for appointment scheduling with patient preferences.

Artif Intell Med 2018 04 23;85:16-25. Epub 2018 Feb 23.

Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Ave, Kowloon, Hong Kong.

During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. Read More

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April 2018
6 Reads

Representing and querying now-relative relational medical data.

Artif Intell Med 2018 03 21;86:33-52. Epub 2018 Feb 21.

Computer Science Institute, DISIT, Università del Piemonte Orientale, Alessandria, Italy. Electronic address:

Temporal information plays a crucial role in medicine. Patients' clinical records are intrinsically temporal. Thus, in Medical Informatics there is an increasing need to store, support and query temporal data (particularly in relational databases), in order, for instance, to supplement decision-support systems. Read More

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March 2018
4 Reads

Using echo state networks for classification: A case study in Parkinson's disease diagnosis.

Artif Intell Med 2018 03 21;86:53-59. Epub 2018 Feb 21.

Heriot-Watt University, Edinburgh, United Kingdom.

Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, we address this deficit with a case study that demonstrates how ESNs can be trained to predict disease labels when stimulated with movement data. Since there has been relatively little prior research into using ESNs for classification, we also consider a number of different approaches for realising input-output mappings. Read More

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March 2018
4 Reads

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription.

Artif Intell Med 2018 03 9;86:20-32. Epub 2018 Feb 9.

School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China. Electronic address:

Background: The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. Read More

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March 2018
5 Reads

Activities suggestion based on emotions in AAL environments.

Artif Intell Med 2018 03 14;86:9-19. Epub 2018 Feb 14.

Departamento de Sistemas Informáticos y Computación (DSIC), Universitat Politécnica de Valéncia, Spain. Electronic address:

The elderly population is increasing and the response of the society was to provide them with services directed to them to cope with their needs. One of the oldest solutions is the retirement home, providing housing and permanent assistance for the elderly. Furthermore, most of the retirement homes are inhabited by multiple elderly people, thus creating a community of people who are somewhat related in age and medical issues. Read More

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March 2018
3 Reads

Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework.

Artif Intell Med 2018 03 1;86:1-8. Epub 2018 Feb 1.

Auckland University of Technology, New Zealand. Electronic address:

Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data. LSM were applied to a previously validated EEG dataset where subjects view a battery of emotional film clips and then rate their degree of emotion during each film based on valence, arousal, and liking levels. Read More

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March 2018
3 Reads

Improving the anesthetic process by a fuzzy rule based medical decision system.

Artif Intell Med 2018 01 5;84:159-170. Epub 2018 Jan 5.

Universidad de La Laguna, La Laguna, Tenerife, Spain.

Objective: The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. Read More

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January 2018
4 Reads

Random ensemble learning for EEG classification.

Artif Intell Med 2018 01 3;84:146-158. Epub 2018 Jan 3.

CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Image Analysis Lab, Depts. of Radiology and Research Administration, Henry Ford Health System, MI 48202, United States.

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. Read More

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January 2018
3 Reads

Bayesian averaging over Decision Tree models for trauma severity scoring.

Artif Intell Med 2018 01 21;84:139-145. Epub 2017 Dec 21.

University of Exeter, United Kingdom.

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Read More

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January 2018
3 Reads

A novel method for predicting kidney stone type using ensemble learning.

Artif Intell Med 2018 01 11;84:117-126. Epub 2017 Dec 11.

Department of Computer Engineering, University of Guilan, Rasht, Iran. Electronic address:

The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of this disease and reduce its incidence and associated costs. The objective of the present study is to derive a model for the early detection of the type of kidney stone and the most influential parameters with the aim of providing a decision-support system. Read More

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January 2018
6 Reads

Spatiotemporal Bayesian networks for malaria prediction.

Artif Intell Med 2018 01 11;84:127-138. Epub 2017 Dec 11.

Faculty of Tropical Medicine, Mahidol University, 420/6 Ratchawithi Rd, Bangkok 10400 Thailand.

Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations. Read More

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January 2018
6 Reads