Dictionary-based monitoring of premature ventricular contractions: An ultra-low-cost point-of-care service
Publication date: Available online 26 April 2018 Source:Artificial Intelligence in Medicine Author(s): S. Chandra Bollepalli, S. Sastry Challa, Laxminarayana Anumandla, Soumya Jana 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 backdr...
Source: Artificial Intelligence in Medicine - April 26, 2018 Category: Bioinformatics Source Type: research

MuDeRN: Multi-category classification of breast histopathological image using deep residual networks
Conclusions MuDeRN can be helpful in the categorization of breast lesions. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - April 26, 2018 Category: Bioinformatics Source Type: research

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Conclusion Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - April 23, 2018 Category: Bioinformatics Source Type: research

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification
Conclusions The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - April 22, 2018 Category: Bioinformatics Source Type: research

Segmentation of corneal endothelium images using a U-Net-based convolutional neural network
Publication date: Available online 19 April 2018 Source:Artificial Intelligence in Medicine Author(s): Anna Fabijańska 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. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using ...
Source: Artificial Intelligence in Medicine - April 22, 2018 Category: Bioinformatics Source Type: research

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading
Conclusion We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - April 22, 2018 Category: Bioinformatics Source Type: research

How do we talk about doctors and drugs? Sentiment analysis in forums expressing opinions for medical domain
Conclusions Although opinions about physicians and drugs are written in most cases by non-professional users, reviews about physicians are characterized by the use of an informal language while reviews about drugs are characterized by a combination of informal language with specific terminology (e.g. adverse effects, drug names) with greater lexical diversity, making the task of sentiment analysis difficult. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - April 22, 2018 Category: Bioinformatics Source Type: research

What matters in a transferable neural network model for relation classification in the biomedical domain?
Publication date: Available online 13 April 2018 Source:Artificial Intelligence in Medicine Author(s): Sunil Kumar Sahu, Ashish Anand 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). In the biomedical and clinical domain, a lack of sufficient training data means that mach...
Source: Artificial Intelligence in Medicine - April 14, 2018 Category: Bioinformatics Source Type: research

A two-step approach for mining patient treatment pathways in administrative healthcare databases
Publication date: Available online 7 April 2018 Source:Artificial Intelligence in Medicine Author(s): Ahmed Najjar, Daniel Reinharz, Catherine Girouard, Christian Gagné 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. To analyze such data, we are first proposing t...
Source: Artificial Intelligence in Medicine - April 7, 2018 Category: Bioinformatics Source Type: research

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment
Publication date: Available online 30 March 2018 Source:Artificial Intelligence in Medicine Author(s): M. Eghbali-Zarch, R. Tavakkoli-Moghaddam, F. Esfahanian, M.M. Sepehri, A. Azaron 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 t...
Source: Artificial Intelligence in Medicine - March 30, 2018 Category: Bioinformatics Source Type: research

Co-occurrence graphs for word sense disambiguation in the biomedical domain
Publication date: Available online 21 March 2018 Source:Artificial Intelligence in Medicine Author(s): Andres Duque, Mark Stevenson, Juan Martinez-Romo, Lourdes Araujo 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. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientifi...
Source: Artificial Intelligence in Medicine - March 22, 2018 Category: Bioinformatics Source Type: research

Position-aware deep multi-task learning for drug –drug interaction extraction
Publication date: Available online 17 March 2018 Source:Artificial Intelligence in Medicine Author(s): Deyu Zhou, Lei Miao, Yulan He 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. Therefore, automatically extracting DDIs from biomedical texts is sore...
Source: Artificial Intelligence in Medicine - March 19, 2018 Category: Bioinformatics Source Type: research

Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation
Conclusion The proposed method can extract the active intervals of IEGMs during AF with a high accuracy and resolution close to manually annotated results. Significance In contrast with some of the conventional methods, no windowing technique is required in our approach resulting in significantly higher resolution in estimating the onset and offset of active intervals. Furthermore, since the signal characteristics at inflection points are analyzed instead of signal samples, the computational time is significantly low, ensuring the real-time application of our algorithm. The proposed method is also robust to noise and basel...
Source: Artificial Intelligence in Medicine - March 17, 2018 Category: Bioinformatics Source Type: research

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks
In this study, we present a patient-level sequential modeling approach utilizing the sequential dependencies to render a personalized prediction of the prescription efficacy. The prediction models are implemented using recurrent neural networks that use the sequence of all the previous records as inputs to predict the prescription efficacy at the time the current prescription is provided for each patient. Through this approach, each patient's historical records are effectively incorporated into the prediction. The experimental results of both the regression and classification analyses on real-world data demonstrate improve...
Source: Artificial Intelligence in Medicine - February 24, 2018 Category: Bioinformatics Source Type: research

Approximate dynamic programming approaches for appointment scheduling with patient preferences
Publication date: Available online 23 February 2018 Source:Artificial Intelligence in Medicine Author(s): Xin Li, Jin Wang, Richard Y.K. Fung 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 pa...
Source: Artificial Intelligence in Medicine - February 24, 2018 Category: Bioinformatics Source Type: research

Representing and querying now-relative relational medical data
Publication date: Available online 21 February 2018 Source:Artificial Intelligence in Medicine Author(s): Luca Anselma, Luca Piovesan, Bela Stantic, Paolo Terenziani 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. In this paper, we show that current approaches to relational data have remarkable limitations in the treatment of “now-relative” dat...
Source: Artificial Intelligence in Medicine - February 21, 2018 Category: Bioinformatics Source Type: research

Using echo state networks for classification: A case study in Parkinson's disease diagnosis
Publication date: Available online 21 February 2018 Source:Artificial Intelligence in Medicine Author(s): Stuart E. Lacy, Stephen L. Smith, Michael A. Lones 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 cla...
Source: Artificial Intelligence in Medicine - February 21, 2018 Category: Bioinformatics Source Type: research

Activities suggestion based on emotions in AAL environments
Publication date: Available online 14 February 2018 Source:Artificial Intelligence in Medicine Author(s): Angelo Costa, Jaime Andres Rincon, Carlos Carrascosa, Paulo Novais, Vicente Julian 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. The ambien...
Source: Artificial Intelligence in Medicine - February 14, 2018 Category: Bioinformatics Source Type: research

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription
Conclusions and significance This study attempted to bridge the patient/caregiver gap by building a sophisticated application that uses artificial intelligence and machine learning computational techniques to perform data-driven decision-making at the point of primary care. The first level of decision-making is conducted by the IDDAP and provides the patient with a first-line therapy. Patients can then make a subjective judgment, and if any questions arise, should consult a physician for subsequent decisions, particularly in complicated cases or in cases in which the necessary information is not yet available in the knowle...
Source: Artificial Intelligence in Medicine - February 10, 2018 Category: Bioinformatics Source Type: research

Seymour Aubrey Papert (1928 –2016)
Publication date: January 2018 Source:Artificial Intelligence in Medicine, Volume 84 (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - February 6, 2018 Category: Bioinformatics Source Type: research

Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework
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. We introduce LSM as a model for an automatic feature extraction and prediction from raw EEG with potential extension to a wider range of applications. We also elaborate on how to exploit the separation property in LSM to build a multipurpose and anytime recognition framework, where we used one trai...
Source: Artificial Intelligence in Medicine - February 2, 2018 Category: Bioinformatics Source Type: research

Improving the anesthetic process by a fuzzy rule based medical decision system
Conclusions The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - January 6, 2018 Category: Bioinformatics Source Type: research

Random ensemble learning for EEG classification
Publication date: Available online 3 January 2018 Source:Artificial Intelligence in Medicine Author(s): Mohammad-Parsa Hosseini, Dario Pompili, Kost Elisevich, Hamid Soltanian-Zadeh 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...
Source: Artificial Intelligence in Medicine - January 4, 2018 Category: Bioinformatics Source Type: research

Bayesian averaging over Decision Tree models for trauma severity scoring
Publication date: Available online 21 December 2017 Source:Artificial Intelligence in Medicine Author(s): V. Schetinin, L. Jakaite, W. Krzanowski 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. Models induced within such an app...
Source: Artificial Intelligence in Medicine - December 22, 2017 Category: Bioinformatics Source Type: research

Spatiotemporal Bayesian networks for malaria prediction
Publication date: Available online 11 December 2017 Source:Artificial Intelligence in Medicine Author(s): Peter Haddawy, A.H.M. Imrul Hasan, Rangwan Kasantikul, Saranath Lawpoolsri, Patiwat Sa-angchai, Jaranit Kaewkungwal, Pratap Singhasivanon 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 ...
Source: Artificial Intelligence in Medicine - December 12, 2017 Category: Bioinformatics Source Type: research

A novel method for predicting kidney stone type using ensemble learning
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. Information was collected from 936 patients with nephrolithiasis at the kidney center of the Razi Hospital in Rasht from 2012 through 2016. The prepared dataset included 42 features. Data pre-processing was the first step toward extracting the relevant features. The collected data was analyzed with Weka software, and various data mining models were used to prepare a predictive model. Various data mining algorithms such as the Bay...
Source: Artificial Intelligence in Medicine - December 12, 2017 Category: Bioinformatics Source Type: research

Different approaches for identifying important concepts in probabilistic biomedical text summarization
Publication date: Available online 6 December 2017 Source:Artificial Intelligence in Medicine Author(s): Milad Moradi, Nasser Ghadiri 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 betwe...
Source: Artificial Intelligence in Medicine - December 6, 2017 Category: Bioinformatics Source Type: research

isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection
Publication date: Available online 26 November 2017 Source:Artificial Intelligence in Medicine Author(s): M. Saifur Rahman, Md. Khaledur Rahman, M. Kaykobad, M. Sohel Rahman 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). Based on this phenomenon, we get two types of GA proteins, namely, cis-Golgi protein and trans-Golgi protein. Any dysfunction of GA proteins c...
Source: Artificial Intelligence in Medicine - November 27, 2017 Category: Bioinformatics Source Type: research

Development of an intelligent surgical training system for Thoracentesis
Publication date: Available online 21 November 2017 Source:Artificial Intelligence in Medicine Author(s): Hirenkumar Nakawala, Giancarlo Ferrigno, Elena De Momi 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. dexterity, of the surgeons while lacking the aspects of context-awareness and intra-operative real-time guidance. Context-aware intelligent training systems interpret the current surgical situation and help surgeons to train on...
Source: Artificial Intelligence in Medicine - November 22, 2017 Category: Bioinformatics Source Type: research

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder
Conclusion The SL features could be utilized as objective markers to screen the AUD patients and healthy controls. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - November 22, 2017 Category: Bioinformatics Source Type: research

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision
Conclusion The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - November 11, 2017 Category: Bioinformatics Source Type: research

SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media
This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed frame...
Source: Artificial Intelligence in Medicine - October 28, 2017 Category: Bioinformatics Source Type: research

Seymour Aubrey Papert (1928-2016)
Publication date: Available online 25 October 2017 Source:Artificial Intelligence in Medicine (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - October 26, 2017 Category: Bioinformatics Source Type: research

Pronation and supination analysis based on biomechanical signals from Parkinson ’s disease patients
Publication date: Available online 16 October 2017 Source:Artificial Intelligence in Medicine Author(s): Alejandro Garza-Rodríguez, Luis Pastor Sánchez-Fernández, Luis Alejandro Sánchez-Pérez, Christopher Ornelas-Vences, Mariane Ehrenberg-Inzunza 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 t...
Source: Artificial Intelligence in Medicine - October 18, 2017 Category: Bioinformatics Source Type: research

Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments
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. GA can effectively explore the entire domain of all 19 variables and identify the optimum resource allocation through evolution and mimicking the survival of the fittest concept. A chaotic mutation operator is used in this study to boost GA performance. A model of the system needs to be run several thousand times through the GA evolution process to evaluate each solution, hence the process is computationally expensive. To overcome...
Source: Artificial Intelligence in Medicine - October 18, 2017 Category: Bioinformatics Source Type: research

Learning ensemble classifiers for diabetic retinopathy assessment
This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - October 7, 2017 Category: Bioinformatics Source Type: research

Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support
Publication date: Available online 3 October 2017 Source:Artificial Intelligence in Medicine Author(s): Daniel Brown, Arantza Aldea, Rachel Harrison, Clare Martin, Ian Bayley 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 pa...
Source: Artificial Intelligence in Medicine - October 4, 2017 Category: Bioinformatics Source Type: research

Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain
Conclusions An automatic knowledge-based summarization system, such as the CTXT system, has the capability to model complex clinical domains, such as the ICU, and to support interpretation and summarization tasks such as the creation of a discharge summary letter. Based on the results, we suggest that the use of such systems could potentially enhance the standardization of the letters, significantly increase their completeness, and reduce the time to write the discharge summary. The results also suggest that using the resultant structured letters might reduce the decision time, and enhance the decision quality, of decision...
Source: Artificial Intelligence in Medicine - September 29, 2017 Category: Bioinformatics Source Type: research

Finding discriminative and interpretable patterns in sequences of surgical activities
Conclusions Identifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups. Graphical abstract (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - September 22, 2017 Category: Bioinformatics Source Type: research

Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction
Conclusions The Random Forest is a useful method for learning predictive models and the relevance of SNPs without any underlying assumption. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - September 22, 2017 Category: Bioinformatics Source Type: research

Special section on artificial intelligence for diabetes
Publication date: Available online 22 September 2017 Source:Artificial Intelligence in Medicine Author(s): Beatriz López, Clare Martin, Pau Herrero Viñas (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - September 22, 2017 Category: Bioinformatics Source Type: research

Reprint of “Updating Markov models to integrate cross-sectional and longitudinal studies”
Publication date: Available online 19 September 2017 Source:Artificial Intelligence in Medicine Author(s): Allan Tucker, Yuanxi Li, David Garway-Heath 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 processe...
Source: Artificial Intelligence in Medicine - September 20, 2017 Category: Bioinformatics Source Type: research

A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening
Publication date: Available online 20 September 2017 Source:Artificial Intelligence in Medicine Author(s): Felipe Soares, Karin Becker, Michel J. Anzanello 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. The main challenges in analyzing blood fluorescence data for CRC classification are re...
Source: Artificial Intelligence in Medicine - September 20, 2017 Category: Bioinformatics Source Type: research

Machine Learning and Graph Analytics in Computational Biomedicine
Publication date: Available online 7 September 2017 Source:Artificial Intelligence in Medicine Author(s): Quan Zou, Lei Chen, Tao Huang, Zhenguo Zhang, Yungang Xu (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - September 8, 2017 Category: Bioinformatics Source Type: research

Gene2DisCo: Gene to disease using disease commonalities
We present a novel network-based method, Gene2DisCo, based on generalized linear models (GLMs) to effectively prioritize genes by exploiting data regarding disease-genes, gene interaction networks and disease similarities. The scarcity of disease-genes is addressed by applying an efficient negative selection procedure, together with imbalance-aware GLMs. Gene2DisCo is a flexible framework, in the sense it is not dependent upon specific types of data, and/or upon specific disease ontologies. Results On a benchmark dataset composed of nine human networks and 708 medical subject headings (MeSH) diseases, Gene2DisCo largely ou...
Source: Artificial Intelligence in Medicine - September 6, 2017 Category: Bioinformatics Source Type: research

Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies
Conclusion Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - August 18, 2017 Category: Bioinformatics Source Type: research

Detecting masses in dense breast using independent component analysis
This study can help specialist to detect lesions in dense breast. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 27, 2017 Category: Bioinformatics Source Type: research

A New Preprocessing Parameter Estimation based on Geodesic Active Contour Model for Automatic Vestibular Neuritis Diagnosis
Publication date: Available online 23 July 2017 Source:Artificial Intelligence in Medicine Author(s): Amine Ben Slama, Aymen Mouelhi, Hanene Sahli, Sondes Manoubi, Chiraz Mbarek, Hedi Trabelsi, Farhat Fnaiech, Mounir Sayadi 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 ...
Source: Artificial Intelligence in Medicine - July 25, 2017 Category: Bioinformatics Source Type: research

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery
Conclusions The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 25, 2017 Category: Bioinformatics Source Type: research

Gaussian Process Classification of Superparamagnetic Relaxometry Data: Phantom Study
Conclusions The GP framework provides acceptable classification accuracies when dealing with in silico and phantom SPMR datasets and can outperform an image reconstruction method for binary classification of SPMR data. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 25, 2017 Category: Bioinformatics Source Type: research