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Artificial Intelligence in Medicine

Lior Turgeman, Jerrold H May
OBJECTIVE: A hospital readmission is defined as an admission to a hospital within a certain time frame, typically thirty days, following a previous discharge, either to the same or to a different hospital. Because most patients are not readmitted, the readmission classification problem is highly imbalanced. MATERIALS AND METHODS: We developed a hospital readmission predictive model, which enables controlling the tradeoff between reasoning transparency and predictive accuracy, by taking into account the unique characteristics of the learned database...
September 2016: Artificial Intelligence in Medicine
Fuyuan Xiao, Masayoshi Aritsugi, Qing Wang, Rong Zhang
OBJECTIVE: For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper. METHODS AND MATERIAL: Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively...
September 2016: Artificial Intelligence in Medicine
Panayiotis Petousis, Simon X Han, Denise Aberle, Alex A T Bui
INTRODUCTION: Identifying high-risk lung cancer individuals at an early disease stage is the most effective way of improving survival. The landmark National Lung Screening Trial (NLST) demonstrated the utility of low-dose computed tomography (LDCT) imaging to reduce mortality (relative to X-ray screening). As a result of the NLST and other studies, imaging-based lung cancer screening programs are now being implemented. However, LDCT interpretation results in a high number of false positives...
September 2016: Artificial Intelligence in Medicine
Alessio Bottrighi, Paolo Terenziani
CONTEXT: Several different computer-assisted management systems of computer interpretable guidelines (CIGs) have been developed by the Artificial Intelligence in Medicine community. Each CIG system is characterized by a specific formalism to represent CIGs, and usually provides a manager to acquire, consult and execute them. Though there are several commonalities between most formalisms in the literature, each formalism has its own peculiarities. OBJECTIVE: The goal of our work is to provide a flexible support to the extension or definition of CIGs formalisms, and of their acquisition and execution engines...
September 2016: Artificial Intelligence in Medicine
Milos Jovanovic, Sandro Radovanovic, Milan Vukicevic, Sven Van Poucke, Boris Delibasic
OBJECTIVES: Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights...
September 2016: Artificial Intelligence in Medicine
Sebastian Pölsterl, Sailesh Conjeti, Nassir Navab, Amin Katouzian
BACKGROUND: In clinical research, the primary interest is often the time until occurrence of an adverse event, i.e., survival analysis. Its application to electronic health records is challenging for two main reasons: (1) patient records are comprised of high-dimensional feature vectors, and (2) feature vectors are a mix of categorical and real-valued features, which implies varying statistical properties among features. To learn from high-dimensional data, researchers can choose from a wide range of methods in the fields of feature selection and feature extraction...
September 2016: Artificial Intelligence in Medicine
Alexander Seitinger, Andrea Rappelsberger, Harald Leitich, Michael Binder, Klaus-Peter Adlassnig
INTRODUCTION: Clinical decision support systems (CDSSs) are being developed to assist physicians in processing extensive data and new knowledge based on recent scientific advances. Structured medical knowledge in the form of clinical alerts or reminder rules, decision trees or tables, clinical protocols or practice guidelines, score algorithms, and others, constitute the core of CDSSs. Several medical knowledge representation and guideline languages have been developed for the formal computerized definition of such knowledge...
August 12, 2016: Artificial Intelligence in Medicine
Robert A Jenders, Klaus-Peter Adlassnig, Karsten Fehre, Peter Haug
BACKGROUND: The initial version of the Arden Syntax for Medical Logic Systems was created to facilitate explicit representation of medical logic in a form that could be easily composed and interpreted by clinical experts in order to facilitate clinical decision support (CDS). Because of demand from knowledge engineers and programmers to improve functionality related to complex use cases, the Arden Syntax evolved to include features typical of general programming languages but that were specialized to meet the needs of the clinical decision support environment, including integration into a clinical information system architecture...
August 11, 2016: Artificial Intelligence in Medicine
Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Yoko Kominami, Shinji Tanaka
No abstract text is available yet for this article.
August 9, 2016: Artificial Intelligence in Medicine
Christos Dimitrakopoulos, Konstantinos Theofilatos, Andreas Pegkas, Spiros Likothanassis, Seferina Mavroudi
OBJECTIVE: Proteins are vital biological molecules driving many fundamental cellular processes. They rarely act alone, but form interacting groups called protein complexes. The study of protein complexes is a key goal in systems biology. Recently, large protein-protein interaction (PPI) datasets have been published and a plethora of computational methods that provide new ideas for the prediction of protein complexes have been implemented. However, most of the methods suffer from two major limitations: First, they do not account for proteins participating in multiple functions and second, they are unable to handle weighted PPI graphs...
July 2016: Artificial Intelligence in Medicine
Rachel L Richesson, Jimeng Sun, Jyotishman Pathak, Abel N Kho, Joshua C Denny
OBJECTIVE: The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale. METHODS: Based on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine...
July 2016: Artificial Intelligence in Medicine
Tao Hoang, Jixue Liu, Nicole Pratt, Vincent W Zheng, Kevin C Chang, Elizabeth Roughead, Jiuyong Li
MOTIVATION: Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention of detrimental PCs is essential as drug AEs are among the leading causes of hospitalization and deaths. Identifying detrimental PCs would enable warnings and contraindications to be disseminated and assist the detection of unknown drug AEs...
July 2016: Artificial Intelligence in Medicine
María Luisa Sánchez Brea, Noelia Barreira Rodríguez, Noelia Sánchez Maroño, Antonio Mosquera González, Carlos García-Resúa, María Jesús Giráldez Fernández
OBJECTIVE: The sudden increase of blood flow in the bulbar conjunctiva, known as hyperemia, is associated to a red hue of variable intensity. Experts measure hyperemia using levels in a grading scale, a procedure that is subjective, non-repeatable and time consuming, thus creating a need for its automatisation. However, the task is far from straightforward due to data issues such as class imbalance or correlated features. In this paper, we study the specific features of hyperemia and propose various approaches to address these problems in the context of an automatic framework for hyperemia grading...
July 2016: Artificial Intelligence in Medicine
Ane Alberdi, Asier Aztiria, Adrian Basarab
INTRODUCTION: The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient. OBJECTIVE: An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection...
July 2016: Artificial Intelligence in Medicine
Giulio Napolitano, Adele Marshall, Peter Hamilton, Anna T Gavin
BACKGROUND AND AIMS: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging. MATERIALS AND METHODS: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: 'semi-structured' and 'unstructured'...
June 2016: Artificial Intelligence in Medicine
Jing Liu, Songzheng Zhao, Xiaodi Zhang
OBJECTIVE: Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media...
June 2016: Artificial Intelligence in Medicine
Utku Sirin, Utku Erdogdu, Faruk Polat, Mehmet Tan, Reda Alhajj
OBJECTIVE: Overcome the lack of enough samples in gene expression data sets having thousands of genes but a small number of samples challenging the computational methods using them. METHODS AND MATERIAL: This paper introduces a multi-model artificial gene expression data generation framework where different gene regulatory network (GRN) models contribute to the final set of samples based on the characteristics of their underlying paradigms. In the first stage, we build different GRN models, and sample data from each of them separately...
June 2016: Artificial Intelligence in Medicine
Thomas Welchowski, Matthias Schmid
BACKGROUND AND OBJECTIVES: Kernel deep stacking networks (KDSNs) are a novel method for supervised learning in biomedical research. Belonging to the class of deep learning techniques, KDSNs are based on artificial neural network architectures that involve multiple nonlinear transformations of the input data. Unlike traditional artificial neural networks, KDSNs do not rely on backpropagation algorithms but on an efficient fitting procedure that is based on a series of kernel ridge regression models with closed-form solutions...
June 2016: Artificial Intelligence in Medicine
Marco E Molina, Aurora Perez, Juan P Valente
INTRODUCTION: Numeric time series are present in a very wide range of domains, including many branches of medicine. Data mining techniques have proved to be useful for knowledge discovery in this type of data and for supporting decision-making processes. OBJECTIVES: The overall objective is to classify time series based on the discovery of frequent patterns. These patterns will be discovered in symbolic sequences obtained from the time series data by means of a temporal abstraction process...
June 2016: Artificial Intelligence in Medicine
Pietro Lovato, Manuele Bicego, Maria Kesa, Nebojsa Jojic, Vittorio Murino, Alessandro Perina
OBJECTIVE: High-throughput technologies have generated an unprecedented amount of high-dimensional gene expression data. Algorithmic approaches could be extremely useful to distill information and derive compact interpretable representations of the statistical patterns present in the data. This paper proposes a mining approach to extract an informative representation of gene expression profiles based on a generative model called the Counting Grid (CG). METHOD: Using the CG model, gene expression values are arranged on a discrete grid, learned in a way that "similar" co-expression patterns are arranged in close proximity, thus resulting in an intuitive visualization of the dataset...
June 2016: Artificial Intelligence in Medicine
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