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Artificial Intelligence in Medicine

Yihan Deng, André Sander, Lukas Faulstich, Kerstin Denecke
Classification systems such as ICD-10 for diagnoses or the Swiss Operation Classification System (CHOP) for procedure classification in the clinical treatment are essential for clinical management and information exchange. Traditionally, classification codes are assigned manually or by systems that rely upon concept-based or rule-based classification methods. Such methods can reach their limit easily due to the restricted coverage of handcrafted rules and of the vocabulary in underlying terminological systems...
October 29, 2018: Artificial Intelligence in Medicine
Yushan Qiu, Hao Jiang, Wai-Ki Ching, Michael K Ng
Identifying 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 factors. We apply an extended LASSO model, L1/2 -regularization model, as a feature selector, to identify significant RNA-binding proteins (RBPs) that contribute to regulating the EMT...
October 20, 2018: Artificial Intelligence in Medicine
Roberto Gatta, Mauro Vallati, Nicola Dinapoli, Carlotta Masciocchi, Jacopo Lenkowicz, Davide Cusumano, Calogero Casá, Alessandra Farchione, Andrea Damiani, Johan van Soest, Andre Dekker, Vincenzo Valentini
Following 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 exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics...
October 3, 2018: Artificial Intelligence in Medicine
Mirka Saarela, Olli-Pekka Ryynänen, Sami Äyrämö
Hospitalization 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 patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algorithms, it is shown that the need for help and supervision are the most important features predicting whether these patients will return home after hospitalization...
October 3, 2018: Artificial Intelligence in Medicine
Asis Roy, Sourangshu Bhattacharya, Kalyan Guin
BACKGROUND: Clinical 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 expensive or uncomfortable tests...
September 29, 2018: Artificial Intelligence in Medicine
M Khalid Khan Niazi, Y Lin, F Liu, A Ashok, M W Marcellin, G Tozbikian, M N Gurcan, A Bilgin
In 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 proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine...
September 25, 2018: 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
Annette Ten Teije, Christian Popow, John H Holmes, Lucia Sacchi
No abstract text is available yet for this article.
September 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
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
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