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    1267 results match your criteria Artificial Intelligence in Medicine [Journal]

    1 OF 26

    Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading.
    Artif Intell Med 2018 Apr 18. Epub 2018 Apr 18.
    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

    Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.
    Artif Intell Med 2018 Apr 18. Epub 2018 Apr 18.
    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

    Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.
    Artif Intell Med 2018 Apr 16. 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

    What matters in a transferable neural network model for relation classification in the biomedical domain?
    Artif Intell Med 2018 Apr 13. 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

    A two-step approach for mining patient treatment pathways in administrative healthcare databases.
    Artif Intell Med 2018 Apr 6. Epub 2018 Apr 6.
    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

    Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.
    Artif Intell Med 2018 Mar 29. Epub 2018 Mar 29.
    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

    Co-occurrence graphs for word sense disambiguation in the biomedical domain.
    Artif Intell Med 2018 Mar 21. 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

    Position-aware deep multi-task learning for drug-drug interaction extraction.
    Artif Intell Med 2018 Mar 17. 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

    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

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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    Different approaches for identifying important concepts in probabilistic biomedical text summarization.
    Artif Intell Med 2018 01 6;84:101-116. Epub 2017 Dec 6.
    Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. Electronic address:
    Automatic text summarization tools help users in the biomedical domain to acquire their intended information from various textual resources more efficiently. Some of biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the raw frequency for identifying valuable contents within an input document, or considering correlations existing between concepts, may be more useful for this type of summarization. Read More

    isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection.
    Artif Intell Med 2018 01 26;84:90-100. Epub 2017 Nov 26.
    Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205, Bangladesh. Electronic address:
    The Golgi Apparatus (GA) is a key organelle for protein synthesis within the eukaryotic cell. The main task of GA is to modify and sort proteins for transport throughout the cell. Proteins permeate through the GA on the ER (Endoplasmic Reticulum) facing side (cis side) and depart on the other side (trans side). Read More

    An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.
    Artif Intell Med 2018 01 21;84:79-89. Epub 2017 Nov 21.
    Center for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak, Malaysia. Electronic address:
    Background: The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. Read More

    Development of an intelligent surgical training system for Thoracentesis.
    Artif Intell Med 2018 01 21;84:50-63. Epub 2017 Nov 21.
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, Milan, 20133, Italy. Electronic address:
    Surgical training improves patient care, helps to reduce surgical risks, increases surgeon's confidence, and thus enhances overall patient safety. Current surgical training systems are more focused on developing technical skills, e.g. Read More

    Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.
    Artif Intell Med 2018 01 10;84:64-78. Epub 2017 Nov 10.
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
    Background And Objective: Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. Read More

    SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.
    Artif Intell Med 2018 01 27;84:34-49. Epub 2017 Oct 27.
    School of Management, Hefei University of Technology, Hefei, Anhui, 230009, PR China. Electronic address:
    With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e. Read More

    Identifying Parkinson's Patients: A Functional Gradient Boosting Approach.
    Artif Intell Med (2017) 2017 Jun 30;10259:332-337. Epub 2017 May 30.
    Indiana University Bloomington.
    Parkinson's, a progressive neural disorder, is difficult to identify due to the hidden nature of the symptoms associated. We present a machine learning approach that uses a definite set of features obtained from the Parkinsons Progression Markers Initiative(PPMI) study as input and classifies them into one of two classes: PD(Parkinson's disease) and HC(Healthy Control). As far as we know this is the first work in applying machine learning algorithms for classifying patients with Parkinson's disease with the involvement of domain expert during the feature selection process. Read More

    Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments.
    Artif Intell Med 2018 01 18;84:23-33. Epub 2017 Oct 18.
    Industrial & Transportation Engineering Department, Universidade Federal do Rio Grande do Sul - UFRGS, 90035-190, Porto Alegre, RS, Brazil.
    Long length of stay and overcrowding in emergency departments (EDs) are two common problems in the healthcare industry. To decrease the average length of stay (ALOS) and tackle overcrowding, numerous resources, including the number of doctors, nurses and receptionists need to be adjusted, while a number of constraints are to be considered at the same time. In this study, an efficient method based on agent-based simulation, machine learning and the genetic algorithm (GA) is presented to determine optimum resource allocation in emergency departments. Read More

    Pronation and supination analysis based on biomechanical signals from Parkinson's disease patients.
    Artif Intell Med 2018 01 16;84:7-22. Epub 2017 Oct 16.
    Instituto Politécnico Nacional, Escuela Nacional de Medicina y Homeopatía, Guillermo Massieu, 07320 México City, Mexico.
    In this work, a fuzzy inference model to evaluate hands pronation/supination exercises during the MDS-UPDRS motor examination is proposed to analyze different extracted features from the bio-mechanical signals acquired from patients with Parkinson's disease (PD) in different stages of severity. Expert examiners perform visual assessments to evaluate several aspects of the disease. Some previous work on this subject does not contemplate the MDS-UPDRS guidelines. Read More

    Learning ensemble classifiers for diabetic retinopathy assessment.
    Artif Intell Med 2018 04 6;85:50-63. Epub 2017 Oct 6.
    Institute of Computing Sciences, Poznań University of Technology, 60-965 Poznań, Poland; Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland. Electronic address:
    Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. Read More

    Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support.
    Artif Intell Med 2018 04 3;85:28-42. Epub 2017 Oct 3.
    Oxford Brookes University, Oxford, United Kingdom. Electronic address:
    Individuals with type 1 diabetes have to monitor their blood glucose levels, determine the quantity of insulin required to achieve optimal glycaemic control and administer it themselves subcutaneously, multiple times per day. To help with this process bolus calculators have been developed that suggest the appropriate dose. However these calculators do not automatically adapt to the specific circumstances of an individual and require fine-tuning of parameters, a process that often requires the input of an expert. Read More

    Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain.
    Artif Intell Med 2017 Oct 27;82:20-33. Epub 2017 Sep 27.
    Clalit Health Services, affiliated with the Hebrew University School of Medicine, Jerusalem, Israel.
    Objectives: To examine the feasibility of the automated creation of meaningful free-text summaries of longitudinal clinical records, using a new general methodology that we had recently developed; and to assess the potential benefits to the clinical decision-making process of using such a method to generate draft letters that can be further manually enhanced by clinicians.

    Methods: We had previously developed a system, CliniText (CTXT), for automated summarization in free text of longitudinal medical records, using a clinical knowledge base. In the current study, we created an Intensive Care Unit (ICU) clinical knowledge base, assisted by two ICU clinical experts in an academic tertiary hospital. Read More

    Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction.
    Artif Intell Med 2018 04 22;85:43-49. Epub 2017 Sep 22.
    Biomedical Research Institute of Girona, Avda. de França, s/n, 17007 Girona, Spain; CIBERobn Pathophysiology of Obesity and Nutrition, Instituto de Salud Carlos III, Madrid, Spain. Electronic address:
    Objective: The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction.

    Methods: We use the Random Forest (RF) technique in order to search for the most important attributes (SNPs) related to diabetes, giving a weight (degree of importance), ranging between 0 and 1, to each attribute. Read More

    Finding discriminative and interpretable patterns in sequences of surgical activities.
    Artif Intell Med 2017 Oct 22;82:11-19. Epub 2017 Sep 22.
    INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France. Electronic address:
    Objective: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries. Read More

    A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening.
    Artif Intell Med 2017 Oct 20;82:1-10. Epub 2017 Sep 20.
    Department of Industrial Engineering - Universidade Federal do Rio Grande do Sul, Av. Osvaldo Aranha, 99-5° andar, Porto Alegre, RS, Brazil. Electronic address:
    Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effective diagnosis methods are required. Blood fluorescence spectroscopy provides rich information that can be used for cancer identification. Read More

    Reprint of "Updating Markov models to integrate cross-sectional and longitudinal studies".
    Artif Intell Med 2017 Sep 19;81:33-40. Epub 2017 Sep 19.
    Moorfields Eye Hospital and UCL Institute of Ophthalmology, University College London, UK.
    Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. Read More

    Gaussian process classification of superparamagnetic relaxometry data: Phantom study.
    Artif Intell Med 2017 Oct 24;82:47-59. Epub 2017 Jul 24.
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States.
    Motivation: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty. Read More

    Gene2DisCo: Gene to disease using disease commonalities.
    Artif Intell Med 2017 Oct 4;82:34-46. Epub 2017 Sep 4.
    Università Degli Studi di Milano, Dipartimento di Informatica, Via Comelico 39/41, Milano, Italy. Electronic address:
    Objective: Finding the human genes co-causing complex diseases, also known as "disease-genes", is one of the emerging and challenging tasks in biomedicine. This process, termed gene prioritization (GP), is characterized by a scarcity of known disease-genes for most diseases, and by a vast amount of heterogeneous data, usually encoded into networks describing different types of functional relationships between genes. In addition, different diseases may share common profiles (e. Read More

    Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies.
    Artif Intell Med 2017 07 14;80:11-28. Epub 2017 Aug 14.
    LIMICS, Université Paris 13, Sorbonne Paris Cité, 93017 Bobigny, INSERM UMRS 1142, UPMC Université Paris 6, Sorbonne Universités, Paris, France. Electronic address:
    Objective: Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. Read More

    Change-Point Detection Method for Clinical Decision Support System Rule Monitoring.
    Artif Intell Med (2017) 2017 Jun 30;10259:126-135. Epub 2017 May 30.
    Department of Computer Science, University of Pittsburgh.
    A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. 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

    A new preprocessing parameter estimation based on geodesic active contour model for automatic vestibular neuritis diagnosis.
    Artif Intell Med 2017 07 23;80:48-62. Epub 2017 Jul 23.
    University of Tunis, The National Engineering School of Tunis (ENSIT), Laboratory of Signal Image and Energy Mastery, LR13ES03 (SIME), Tunis, Tunisia. Electronic address:
    The diagnostic of the vestibular neuritis (VN) presents many difficulties to traditional assessment methods This paper deals with a fully automatic VN diagnostic system based on nystagmus parameter estimation using a pupil detection algorithm. A geodesic active contour model is implemented to find an accurate segmentation region of the pupil. Hence, the novelty of the proposed algorithm is to speed up the standard segmentation by using a specific mask located on the region of interest. Read More

    Machine learning based identification of protein-protein interactions using derived features of physiochemical properties and evolutionary profiles.
    Artif Intell Med 2017 05 13;78:61-71. Epub 2017 Jun 13.
    Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan. Electronic address:
    Proteins are the central constitute of a cell or biological system. Proteins execute their functions by interacting with other molecules such as RNA, DNA and other proteins. The major functionality of protein-protein interactions (PPIs) is the execution of biochemical activities in living species. Read More

    Automatic detection of surgical haemorrhage using computer vision.
    Artif Intell Med 2017 05 10;78:55-60. Epub 2017 Jun 10.
    Systems and Automatics Engineering Department, Miguel Hernández University, Avinguda de la Universitat d'Elx, Elche, 03202, Spain.
    Background And Objectives: On occasions, a surgical intervention can be associated with serious, potentially life-threatening complications. One of these complications is a haemorrhage during the operation, an unsolved issue that could delay the intervention or even cause the patient's death. On laparoscopic surgery this complication is even more dangerous, due to the limited vision and mobility imposed by the minimally invasive techniques. Read More

    Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence.
    Artif Intell Med 2017 05 10;78:47-54. Epub 2017 Jun 10.
    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
    Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. Read More

    Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine.
    Artif Intell Med 2017 05 24;78:41-46. Epub 2017 May 24.
    College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China; School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China. Electronic address:
    Objectives: In this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins.

    Methods: In the proposed methodology, the novel evolutionary-conservative information is introduced to represent protein sequences. Meanwhile, based on the proportion of golden section in mathematics, position-specific scoring matrix (PSSM) is divided into several blocks. Read More

    Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.
    Artif Intell Med 2017 05 13;78:23-40. Epub 2017 May 13.
    Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran. Electronic address:
    Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Read More

    Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods.
    Artif Intell Med 2017 05 10;78:14-22. Epub 2017 May 10.
    Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan. Electronic address:
    Golgi is one of the core proteins of a cell, constitutes in both plants and animals, which is involved in protein synthesis. Golgi is responsible for receiving and processing the macromolecules and trafficking of newly processed protein to its intended destination. Dysfunction in Golgi protein is expected to cause many neurodegenerative and inherited diseases that may be cured well if they are detected effectively and timely. Read More

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