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Artificial Intelligence in Medicine

Ye Xue, Diego Klabjan, Yuan Luo
BACKGROUND: Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmission prediction models are often built based on physiological variables. Intuitively, snapshot measurements, especially the last measurements, are effective predictors that are widely used by researchers...
September 10, 2018: Artificial Intelligence in Medicine
Clayton R Pereira, Danilo R Pereira, Silke A T Weber, Christian Hook, Victor Hugo C de Albuquerque, João P Papa
BACKGROUND AND OBJECTIVE: In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinson's Disease (PD), as well as an analysis of future trends on new approaches to this end. METHODS: In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation...
September 7, 2018: Artificial Intelligence in Medicine
Sam Henry, Alex McQuilkin, Bridget T McInnes
Association 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 of biomedical terminologies and ontololgies. In this paper we propose a method of generating association scores between biomedical concepts to estimate semantic relatedness...
September 6, 2018: Artificial Intelligence in Medicine
Massimo W Rivolta, Md Aktaruzzaman, Giovanna Rizzo, Claudio L Lafortuna, Maurizio Ferrarin, Gabriele Bovi, Daniela R Bonardi, Andrea Caspani, Roberto Sassi
Gait 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 patients and eleven volunteers were enrolled in two rehabilitation centers and underwent a full Tinetti test, while wearing a triaxial accelerometer at the chest...
September 5, 2018: Artificial Intelligence in Medicine
Jose Bernal, Kaisar Kushibar, Daniel S Asfaw, Sergi Valverde, Arnau Oliver, Robert Martí, Xavier Lladó
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. 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...
September 5, 2018: Artificial Intelligence in Medicine
Daniel Sonntag, Hans-Jürgen Profitlich
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...
September 5, 2018: Artificial Intelligence in Medicine
Germain Forestier, François Petitjean, Pavel Senin, Fabien Despinoy, Arnaud Huaulmé, Hassan Ismail Fawaz, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Pierre Jannin
OBJECTIVE: The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care. MATERIAL AND METHOD: In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions...
August 29, 2018: Artificial Intelligence in Medicine
Kerstin Denecke, Frank van Harmelen
No abstract text is available yet for this article.
August 3, 2018: Artificial Intelligence in Medicine
Pedro Pereira Rodrigues, Daniela Ferreira-Santos, Ana Silva, Jorge Polónia, Inês Ribeiro-Vaz
In 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 network was developed, for which the structure was defined by experts while the parameters were learnt from 593 completely filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre medical expert between 2000 and 2012...
August 2, 2018: Artificial Intelligence in Medicine
Elena V Epure, Dario Compagno, Camille Salinesi, Rébecca Deneckere, Marko Bajec, Slavko Žitnik
Being 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 required. In this paper, we propose an automatic method to reveal process models of interrelated speech intentions from conversations...
July 17, 2018: Artificial Intelligence in Medicine
Yasunori Yamada, Masatomo Kobayashi
Health 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, previous models were mainly tested by user groups that did not include older adults, although age-related changes in eye-tracking measures have been reported especially in older adults...
July 16, 2018: Artificial Intelligence in Medicine
Siqi Liu, Adam Wright, Milos Hauskrecht
A clinical decision support system (CDSS) helps clinicians to manage patients, but malfunctions of its components or other systems on which it depends may affect its intended functions. Monitoring the system and detecting changes in its behavior that may indicate the malfunction can help to avoid any potential costs associated with its improper operation. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts...
July 2, 2018: Artificial Intelligence in Medicine
Christopher C Yang, Haodong Yang
Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals has drawn many researchers' attention and efforts. Currently, methods proposed for ADR and DDI detection are mainly based on traditional data sources such as spontaneous reporting data, electronic health records, pharmaceutical databases, and biomedical literature...
August 2018: Artificial Intelligence in Medicine
Jun Guo, Xuan Yuan, Xia Zheng, Pengfei Xu, Yun Xiao, Baoying Liu
Data 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 of the existing multi-label learning algorithms that take correlations among diseases into consideration, our model learns disease specific features to benefit the discrimination of different diseases...
August 2018: Artificial Intelligence in Medicine
Tadeu Junior Gross, Renata Bezerra Araújo, Francisco Assis Carvalho Vale, Michel Bessani, Carlos Dias Maciel
Globally, the proportion of elderly individuals in the population has increased substantially in the last few decades. However, the risk factors that should be managed in advance to ensure a natural process of mental decline due to aging remain unknown. 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)...
August 2018: Artificial Intelligence in Medicine
Fan Liang, Pengjiang Qian, Kuan-Hao Su, Atallah Baydoun, Asha Leisser, Steven Van Hedent, Jung-Wen Kuo, Kaifa Zhao, Parag Parikh, Yonggang Lu, Bryan J Traughber, Raymond F Muzic
BACKGROUND: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring. METHODS/MATERIALS: Our algorithm is based on two types of information flow, i.e. top-down and bottom-up...
August 2018: Artificial Intelligence in Medicine
Shahzad Akbar, Muhammad Usman Akram, Muhammad Sharif, Anam Tariq, Shoab A Khan
Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR).The presented system includes three modules i.e. main component extraction, artery/vein (A/V) classification and finally AVR calculation and grading of HR...
August 2018: Artificial Intelligence in Medicine
Aaron N Richter, Taghi M Khoshgoftaar
Advancements 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 determine if new patients are at high risk for cancer development or recurrence...
August 2018: Artificial Intelligence in Medicine
Elham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad Teshnehlab
The 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). The model is designed by cellular neural networks and it uses the proper features of electroencephalography...
July 2018: Artificial Intelligence in Medicine
Minxia Luo, Ruirui Zhao
The intuitionistic fuzzy set, as a generation of fuzzy set, can express and process uncertainty much better. Distance measures between intuitionistic fuzzy sets are used to indicate the difference degree between the information carried by intuitionistic fuzzy sets. Although some distance measures have been proposed in previous studies, they can not satisfy the axioms of distance measure, or exist counter-intuitive cases. In this paper, we give a new distance measure between intuitionistic fuzzy sets, which is based on a matrix norm and a strictly increasing (or decreasing) binary function...
July 2018: Artificial Intelligence in Medicine
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