On predicting epithelial mesenchymal transition by integrating RNA-binding proteins and correlation data via L1/2-regularization method
Publication date: Available online 21 October 2018Source: Artificial Intelligence in MedicineAuthor(s): Yushan Qiu, Hao Jiang, Wai-Ki Ching, Michael K. NgAbstractIdentifying tumor metastasis signatures from gene expression data at the whole genome level remains an arduous challenge, particularly so when the number of genes is huge and the number of experimental samples is small. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than on tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factor...
Source: Artificial Intelligence in Medicine - October 22, 2018 Category: Bioinformatics Source Type: research

Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer
Publication date: Available online 25 September 2018Source: Artificial Intelligence in MedicineAuthor(s): M. Khalid Khan Niazi, Y. Lin, F. Liu, A. Ashok, M.W. Marcellin, G. Tozbikian, M.N. Gurcan, A. BilginAbstractIn this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The ...
Source: Artificial Intelligence in Medicine - October 4, 2018 Category: Bioinformatics Source Type: research

A methodology for customizing clinical tests for esophageal cancer based on patient preferences
Publication date: Available online 29 September 2018Source: Artificial Intelligence in MedicineAuthor(s): Asis Roy, Sourangshu Bhattacharya, Kalyan GuinAbstractBackgroundClinical tests for diagnosis of any disease may be expensive, uncomfortable, time consuming and can have side effects e.g. barium swallow test for esophageal cancer. Although we can predict non-existence of esophageal cancer with near 100% certainty just using demographics, lifestyle, medical history information, and a few basic clinical tests but our objective is to devise a general methodology for customizing tests with user preferences to avoid expensiv...
Source: Artificial Intelligence in Medicine - October 4, 2018 Category: Bioinformatics Source Type: research

Predicting hospital associated disability from imbalanced data using supervised learning
Publication date: Available online 3 October 2018Source: Artificial Intelligence in MedicineAuthor(s): Mirka Saarela, Olli-Pekka Ryynänen, Sami ÄyrämöAbstractHospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patie...
Source: Artificial Intelligence in Medicine - October 4, 2018 Category: Bioinformatics Source Type: research

Towards a modular decision support system for radiomics: A case study on rectal cancer
Publication date: Available online 4 October 2018Source: Artificial Intelligence in MedicineAuthor(s): Roberto Gatta, Mauro Vallati, Nicola Dinapoli, Carlotta Masciocchi, Jacopo Lenkowicz, Davide Cusumano, Calogero Casá, Alessandra Farchione, Andrea Damiani, Johan van Soest, Andre Dekker, Vincenzo ValentiniAbstractFollowing the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitatio...
Source: Artificial Intelligence in Medicine - October 4, 2018 Category: Bioinformatics Source Type: research

Predicting ICU readmission using grouped physiological and medication trends
ConclusionsGrouped physiological and medication trends carry predictive information for ICU readmission risk. In order to build predictive models with higher accuracy, we should add grouped physiological and medication trends as complementary features to snapshot measurements. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - September 11, 2018 Category: Bioinformatics Source Type: research

A survey on computer-assisted Parkinson's Disease diagnosis
ConclusionsThe main focus of this survey is to consider computer-assisted diagnosis, and how effective they can be when handling the problem of PD identification. Also, the main contribution of this review is to consider very recent works only, mainly from 2015 and 2016. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - September 8, 2018 Category: Bioinformatics Source Type: research

Association measures for estimating semantic similarity and relatedness between biomedical concepts
Publication date: Available online 7 September 2018Source: Artificial Intelligence in MedicineAuthor(s): Sam Henry, Alex McQuilkin, Bridget T. McInnesAbstractAssociation measures quantify the observed likelihood a term pair co-occurs versus their predicted co-occurrence together if by chance. This is based both on the terms’ individual occurrence frequencies, and their mutual co-occurrence frequencies. One application of association scores is estimating semantic relatedness, which is critical for many natural language processing applications, such as clustering of biomedical and clinical documents and the development...
Source: Artificial Intelligence in Medicine - September 7, 2018 Category: Bioinformatics Source Type: research

An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation
This article presents our steps to integrate complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated faceted search tool, accompanied by the visualisation of results of automatic information extraction from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals. Our exemplary use cases are nephrology and mammography. The software was first developed in the nephrology domain and then adapted to the mammography use case....
Source: Artificial Intelligence in Medicine - September 6, 2018 Category: Bioinformatics Source Type: research

Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis
Publication date: Available online 6 September 2018Source: Artificial Intelligence in MedicineAuthor(s): Massimo W. Rivolta, Md. Aktaruzzaman, Giovanna Rizzo, Claudio L. Lafortuna, Maurizio Ferrarin, Gabriele Bovi, Daniela R. Bonardi, Andrea Caspani, Roberto SassiAbstractGait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Seventy-nine p...
Source: Artificial Intelligence in Medicine - September 6, 2018 Category: Bioinformatics Source Type: research

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective...
Source: Artificial Intelligence in Medicine - September 6, 2018 Category: Bioinformatics Source Type: research

Surgical motion analysis using discriminative interpretable patterns
ConclusionsThe proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - August 30, 2018 Category: Bioinformatics Source Type: research

Editorial Board
Publication date: August 2018Source: Artificial Intelligence in Medicine, Volume 90Author(s): (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - August 24, 2018 Category: Bioinformatics Source Type: research

Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance
In this study, we propose a framework for drug safety signal detection by harnessing online health community data, a timely, informative, and publicly available data source. Concretely, we used MedHelp as the data source to collect patient-contributed content based on which a weighted heterogeneous network was constructed. We extracted topological features from the network, quantified them with different weighting methods, and used supervised learning method for both ADR and DDI signal detection. In addition, after identifying DDI signals, we proposed a new metric, named Interaction Ratio, to identify associated ADRs due t...
Source: Artificial Intelligence in Medicine - August 7, 2018 Category: Bioinformatics Source Type: research

Recent advances in extracting and processing rich semantics from medical texts
Publication date: Available online 3 August 2018Source: Artificial Intelligence in MedicineAuthor(s): Kerstin Denecke, Frank van Harmelen (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - August 3, 2018 Category: Bioinformatics Source Type: research

Causality assessment of adverse drug reaction reports using an expert-defined Bayesian network
Publication date: Available online 2 August 2018Source: Artificial Intelligence in MedicineAuthor(s): Pedro Pereira Rodrigues, Daniela Ferreira-Santos, Ana Silva, Jorge Polónia, Inês Ribeiro-VazAbstractIn 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. A Bayesian...
Source: Artificial Intelligence in Medicine - August 2, 2018 Category: Bioinformatics Source Type: research

Dependence between cognitive impairment and metabolic syndrome applied to a Brazilian elderly dataset
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. Regarding its structure (a Directed Acyclic Graph), it was investigated the probabilistic dependence mechanism between two variables of medical interest: the suspected risk factor known as Metabolic Syndrome (MetS) and the indicator of mental decline referred to as Cognitive Impairment (CI). In this investigation, the concept known in the context of a BN as D-separation has been employed. Results of the conducted ...
Source: Artificial Intelligence in Medicine - August 1, 2018 Category: Bioinformatics Source Type: research

Diagnosis labeling with disease-specific characteristics mining
Publication date: Available online 31 July 2018Source: Artificial Intelligence in MedicineAuthor(s): Jun Guo, Xuan Yuan, Xia Zheng, Pengfei Xu, Yun Xiao, Baoying LiuAbstractData 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. We apply the Bag-of-Words (BoW) model to encode the features. Different from most ...
Source: Artificial Intelligence in Medicine - August 1, 2018 Category: Bioinformatics Source Type: research

Editorial Board
Publication date: July 2018Source: Artificial Intelligence in Medicine, Volume 89Author(s): (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 28, 2018 Category: Bioinformatics Source Type: research

Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach
ConclusionWith a DSI significantly higher than the usually reported 0.7, our novel algorithm yields a high segmentation accuracy. To our knowledge, this is the first fully automated contouring approach using T1 MRI images for adaptive radiotherapy. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 25, 2018 Category: Bioinformatics Source Type: research

Process models of interrelated speech intentions from online health-related conversations
Publication date: Available online 18 July 2018Source: Artificial Intelligence in MedicineAuthor(s): Elena V. Epure, Dario Compagno, Camille Salinesi, Rébecca Deneckere, Marko Bajec, Slavko ŽitnikAbstractBeing 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 requir...
Source: Artificial Intelligence in Medicine - July 18, 2018 Category: Bioinformatics Source Type: research

Detecting mental fatigue from eye-tracking data gathered while watching video: Evaluation in younger and older adults
Publication date: Available online 17 July 2018Source: Artificial Intelligence in MedicineAuthor(s): Yasunori Yamada, Masatomo KobayashiAbstractHealth 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. In addition, previo...
Source: Artificial Intelligence in Medicine - July 18, 2018 Category: Bioinformatics Source Type: research

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data
Publication date: Available online 14 July 2018Source: Artificial Intelligence in MedicineAuthor(s): Aaron N. Richter, Taghi M. KhoshgoftaarAbstractAdvancements 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. Predictive models can be built using historical patient data to model the characteristics of patients that developed cancer or relapsed. These models can then be deployed into clinical settings to ...
Source: Artificial Intelligence in Medicine - July 15, 2018 Category: Bioinformatics Source Type: research

Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis
Publication date: Available online 11 July 2018Source: Artificial Intelligence in MedicineAuthor(s): Elham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad TeshnehlabAbstractThe 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). Th...
Source: Artificial Intelligence in Medicine - July 11, 2018 Category: Bioinformatics Source Type: research

Origins of the Arden Syntax
Publication date: Available online 2 July 2015Source: Artificial Intelligence in MedicineAuthor(s): George Hripcsak, Ove B. Wigertz, Paul D. ClaytonAbstractThe Arden Syntax originated in the 1980's, when several knowledge-based systems began to show promise, but researchers recognized the burden of recreating these systems at every institution. Derived initially from Health Evaluation through Logical Processing (HELP) and the Regenstrief Medical Record System (RMRS), the Arden Syntax defines medical logic that can be encoded as independent rules, such as reminders and alerts, with the hope of creating a public library of r...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Accessing complex patient data from Arden Syntax Medical Logic Modules
ConclusionThe most promising approach by far was to map arbitrary XML structures onto congruent complex data types of Arden Syntax through deserialization. This approach is generic insofar as a data mapper based on this approach can transform any patient data provided in appropriate XML format. Therefore it could help overcome a major obstacle for integrating clinical decision support functions into clinical information systems. Theoretically, the deserialization approach would even allow mapping entire patient records onto Arden Syntax objects in one single step. We recommend extending the Arden Syntax specification with ...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Using Arden Syntax Medical Logic Modules to reduce overutilization of laboratory tests for detection of bacterial infections—Success or failure?
ConclusionWe observed an 18% reduction of PCT tests within the first four weeks of CDSS support in the investigated ICU. This reduction may have been influenced by raised awareness of the overutilization problem; the extent of this influence cannot be determined in our study design. No reduction of PCT tests could be observed during the second ON phase. The physician interviews indicated that time critical ICU situations can prevent extensive reflection about the necessity of individual tests. In order to achieve an enduring effect on PCT utilization, we will have to proceed to electronic order entry. (Source: Artificial I...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Pediatric decision support using adapted Arden Syntax
ConclusionsOur results show that the Arden Syntax standard provided us with an effective way to represent pediatric guidelines for use in routine care. We only required minor modifications to the standard to support our clinical workflow. Additionally, Arden Syntax implementation in CHICA facilitated the study of many pediatric guidelines in real clinical environments. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Using Arden Syntax for the creation of a multi-patient surveillance dashboard
ConclusionOur study demonstrated the general feasibility of creating multi-patient CDS routines in Arden Syntax. We believe that our prototypical dashboard also suggests that such implementations can be relatively easy, and may simultaneously hold promise for sharing dashboards between institutions and reusing elementary components for additional dashboards. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Assessing the feasibility of a mobile health-supported clinical decision support system for nutritional triage in oncology outpatients using Arden Syntax
We report on a novel clinical decision support system (CDSS) for the global assessment and nutritional triage of the nutritional condition of oncology outpatients. The system combines clinical and laboratory data collected in the clinical setting with patient-generated data from a smartphone application for monitoring the patients’ nutritional status. Our objective is to assess the feasibility of a CDSS that combines the aforementioned data sources and describe its integration into a hospital information system. Furthermore, we collected patients’ opinions on the value of the system, and whether they would rega...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax
ConclusionData-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration
In this report we take a look at the milestones achieved and the challenges faced in the creation and modification of CDSSs, and their integration into the HIS over the last three years.Materials and methodsWe introduce a three-stage development method, which is followed in nearly all CDSS projects at the Medical University of Vienna and the VGH. Stage one comprises requirements engineering and system conception. Stage two focuses on the implementation and testing of the system. Finally, stage three describes the deployment and integration of the system in the VGH HIS. The HIS provides a clinical work environment for healt...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

User-defined functions in the Arden Syntax: An extension proposal
ConclusionsIt is possible to add user-defined functions to the Arden Syntax in a way that remains coherent with the standard. We believe that this enhances the readability and the robustness of MLMs. A detailed proposal will be submitted by the end of the year to the HL7 workgroup on Arden Syntax. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Transformation of Arden Syntax's medical logic modules into ArdenML for a business rules management system
ConclusionWe have demonstrated that ArdenML can replace a compiler for transforming MLMs into commercial rule engine format. While the proposed XSLT stylesheet requires refinement for general use, we anticipate that the development of further XSLT stylesheets will support various rule engines. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Evolution of the Arden Syntax: Key Technical Issues from the Standards Development Organization Perspective
ConclusionIn response to user demand and to reflect its growing role in clinical decision support, the Arden Syntax has evolved to include a number of powerful features. These depart somewhat from the original vision of the syntax as simple and easily understandable but from the SDO perspective increase the utility of this standard for implementation of CDS. Backwards compatibility has been maintained, allowing continued support of the earlier, simpler decision support models. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Executable medical guidelines with Arden Syntax—Applications in dermatology and obstetrics
DiscussionToday, with the latest versions of Arden Syntax and the application of contemporary software development methods, Arden Syntax has become a powerful and versatile medical knowledge representation and processing language, well suited to implement a large range of CDSSs, including clinical-practice-guideline-based CDSSs. Moreover, such CDS is provided and can be shared as a service by different medical institutions, redefining the sharing of medical knowledge. Arden Syntax is also highly flexible and provides developers the freedom to use up-to-date software design and programming patterns for external patient data...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

How do we talk about doctors and drugs? Sentiment analysis in forums expressing opinions for medical domain
ConclusionsAlthough 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 - July 10, 2018 Category: Bioinformatics Source Type: research

Position-aware deep multi-task learning for drug–drug interaction extraction
Publication date: May 2018Source: Artificial Intelligence in Medicine, Volume 87Author(s): Deyu Zhou, Lei Miao, Yulan HeAbstractObjectiveA 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 sorely need...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

A two-step approach for mining patient treatment pathways in administrative healthcare databases
Publication date: May 2018Source: Artificial Intelligence in Medicine, Volume 87Author(s): Ahmed Najjar, Daniel Reinharz, Catherine Girouard, Christian GagnéAbstractClustering 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 to clu...
Source: Artificial Intelligence in Medicine - July 10, 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: May 2018Source: Artificial Intelligence in Medicine, Volume 87Author(s): M. Eghbali-Zarch, R. Tavakkoli-Moghaddam, F. Esfahanian, M.M. Sepehri, A. AzaronAbstractMedication 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 hy...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Editorial Board
Publication date: May 2018Source: Artificial Intelligence in Medicine, Volume 87Author(s): (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Co-occurrence graphs for word sense disambiguation in the biomedical domain
Publication date: May 2018Source: Artificial Intelligence in Medicine, Volume 87Author(s): Andres Duque, Mark Stevenson, Juan Martinez-Romo, Lourdes AraujoAbstractWord 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 scientific abst...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Dictionary-based monitoring of premature ventricular contractions: An ultra-low-cost point-of-care service
Publication date: May 2018Source: Artificial Intelligence in Medicine, Volume 87Author(s): S. Chandra Bollepalli, S. Sastry Challa, Laxminarayana Anumandla, Soumya JanaAbstractWhile 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...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading
ConclusionWe 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 - July 10, 2018 Category: Bioinformatics Source Type: research

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification
ConclusionsThe 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 - July 10, 2018 Category: Bioinformatics Source Type: research

What matters in a transferable neural network model for relation classification in the biomedical domain?
Publication date: May 2018Source: Artificial Intelligence in Medicine, Volume 87Author(s): Sunil Kumar Sahu, Ashish AnandAbstractA 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 machine le...
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
ConclusionCombining 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 - July 10, 2018 Category: Bioinformatics Source Type: research

An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR
ConclusionsWe found the use of openEHR Archetypes and AQL a feasible approach to bridge the interoperability gap between local infrastructures and CDSS. The designed concept was successfully transferred into a clinically evaluated openEHR based CDSS. To the authors’ knowledge, this is the first openEHR based CDSS, which is technically reliable and capable in a real context, and facilitates clinical decision-support for a complex task. Further activities will comprise enrichments of the knowledge base, the reasoning processes and cross-institutional evaluations.Graphical abstract (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Extracting cancer mortality statistics from death certificates: A hybrid machine learning and rule-based approach for common and rare cancers
ConclusionThe system proposed in this study provides automatic identification and characterisation of cancers from large collections of free-text death certificates. This allows organisations such as Cancer Registries to monitor and report on cancer mortality in a timely and accurate manner. In addition, the methods and findings are generally applicable beyond cancer classification and to other sources of medical text besides death certificates. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research

Classifying medical relations in clinical text via convolutional neural networks
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. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 10, 2018 Category: Bioinformatics Source Type: research