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Artificial Intelligence in Medicine

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https://www.readbyqxmd.com/read/29129481/image-processing-strategies-based-on-saliency-segmentation-for-object-recognition-under-simulated-prosthetic-vision
#1
Heng Li, Xiaofan Su, Jing Wang, Han Kan, Tingting Han, Yajie Zeng, Xinyu Chai
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...
November 9, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29111222/ssel-ade-a-semi-supervised-ensemble-learning-framework-for-extracting-adverse-drug-events-from-social-media
#2
Jing Liu, Songzheng Zhao, Gang Wang
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.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction - distinguishing ADE relationship from other relation types - necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space's high-dimensionality attributed to intrinsic characteristics of social media data...
October 27, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29054572/chaotic-genetic-algorithm-and-adaboost-ensemble-metamodeling-approach-for-optimum-resource-planning-in-emergency-departments
#3
Milad Yousefi, Moslem Yousefi, Ricardo Poley Martins Ferreira, Joong Hoon Kim, Flavio S Fogliatto
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...
October 17, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29042162/pronation-and-supination-analysis-based-on-biomechanical-signals-from-parkinson-s-disease-patients
#4
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 to evaluate several aspects of the disease. Some previous work on this subject does not contemplate the MDS-UPDRS guidelines. The method proposed in this work quantifies the biomechanical features examiners evaluate...
October 14, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28993124/learning-ensemble-classifiers-for-diabetic-retinopathy-assessment
#5
Emran Saleh, Jerzy Błaszczyński, Antonio Moreno, Aida Valls, Pedro Romero-Aroca, Sofia de la Riva-Fernández, Roman Słowiński
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. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble...
October 6, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28986108/temporal-case-based-reasoning-for-type-1-diabetes-mellitus-bolus-insulin-decision-support
#6
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 parameters, a process that often requires the input of an expert...
October 3, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28943335/single-nucleotide-polymorphism-relevance-learning-with-random-forests-for-type-2-diabetes-risk-prediction
#7
Beatriz López, Ferran Torrent-Fontbona, Ramón Viñas, José Manuel Fernández-Real
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...
September 21, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28943334/special-section-on-artificial-intelligence-for-diabetes
#8
EDITORIAL
Beatriz López, Clare Martin, Pau Herrero Viñas
No abstract text is available yet for this article.
September 21, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28935226/machine-learning-and-graph-analytics-in-computational-biomedicine
#9
EDITORIAL
Quan Zou, Lei Chen, Tao Huang, Zhenguo Zhang, Yungang Xu
No abstract text is available yet for this article.
September 7, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28958803/evaluation-of-an-automated-knowledge-based-textual-summarization-system-for-longitudinal-clinical-data-in-the-intensive-care-domain
#10
Ayelet Goldstein, Yuval Shahar, Efrat Orenbuch, Matan J Cohen
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...
October 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28943333/finding-discriminative-and-interpretable-patterns-in-sequences-of-surgical-activities
#11
Germain Forestier, François Petitjean, Pavel Senin, Laurent Riffaud, Pierre-Louis Henaux, Pierre Jannin
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. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries...
October 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28939302/a-hierarchical-classifier-based-on-human-blood-plasma-fluorescence-for-non-invasive-colorectal-cancer-screening
#12
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 related to its high dimensionality and inherent variability, especially when analyzing a small number of samples...
October 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28911905/gaussian-process-classification-of-superparamagnetic-relaxometry-data-phantom-study
#13
Javad Sovizi, Kelsey B Mathieu, Sara L Thrower, Wolfgang Stefan, John D Hazle, David Fuentes
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...
October 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28882544/gene2disco-gene-to-disease-using-disease-commonalities
#14
Marco Frasca
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.g. genetic or therapeutic profiles), and exploiting disease commonalities may significantly enhance the performance of GP methods...
October 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28939301/reprint-of-updating-markov-models-to-integrate-cross-sectional-and-longitudinal-studies
#15
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 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...
September 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28733119/artificial-intelligence-in-medicine-aime-2015
#16
EDITORIAL
John H Holmes, Lucia Sacchi, Riccardo Bellazzi, Niels Peek
No abstract text is available yet for this article.
September 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28456512/inter-labeler-and-intra-labeler-variability-of-condition-severity-classification-models-using-active-and-passive-learning-methods
#17
Nir Nissim, Yuval Shahar, Yuval Elovici, George Hripcsak, Robert Moskovitch
BACKGROUND AND OBJECTIVES: Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning...
September 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28416144/feasibility-of-spirography-features-for-objective-assessment-of-motor-function-in-parkinson-s-disease
#18
Aleksander Sadikov, Vida Groznik, Martin Možina, Jure Žabkar, Dag Nyholm, Mevludin Memedi, Ivan Bratko, Dejan Georgiev
OBJECTIVE: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations...
September 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28410780/analyzing-interactions-on-combining-multiple-clinical-guidelines
#19
Veruska Zamborlini, Marcos da Silveira, Cedric Pruski, Annette Ten Teije, Edwin Geleijn, Marike van der Leeden, Martijn Stuiver, Frank van Harmelen
Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity...
September 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28343742/automatic-matching-of-surgeries-to-predict-surgeons-next-actions
#20
Germain Forestier, François Petitjean, Laurent Riffaud, Pierre Jannin
OBJECTIVE: More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery. MATERIAL AND METHOD: We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities...
September 2017: Artificial Intelligence in Medicine
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