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Artificial Intelligence in Medicine

Ashenafi Zebene Woldaregay, Eirik Årsand, Ståle Walderhaug, David Albers, Lena Mamykina, Taxiarchis Botsis, Gunnar Hartvigsen
BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabetes. However, self-management of the disease, especially keeping BG in the recommended range, is central to the treatment. This includes actively tracking BG levels and managing physical activity, diet, and insulin intake. The recent advancements in diabetes technologies and self-management applications have made it easier for patients to have more access to relevant data...
July 26, 2019: Artificial Intelligence in Medicine
Zhi Li, Jinshan Yang, Xu Gou, Xiaorong Qi
At present, great progress has been achieved on the relation extraction for clinical texts, but we have noticed that the current models have great drawbacks when dealing with long sentences and multiple entities in a sentence. In this paper, we propose a novel neural network architecture based on Bidirectional Long Short-Term Memory Networks for relation classification. Firstly, we utilize a concat-attention mechanism for capturing the most important context words for relation extraction in a sentence. In addition, a segment attention mechanism is proposed to improve the performance of the model processing long sentences...
June 2019: Artificial Intelligence in Medicine
Utkarsh Agrawal, Daniele Soria, Christian Wagner, Jonathan Garibaldi, Ian O Ellis, John M S Bartlett, David Cameron, Emad A Rakha, Andrew R Green
Breast Cancer is one of the most common causes of cancer death in women, representing a very complex disease with varied molecular alterations. To assist breast cancer prognosis, the classification of patients into biological groups is of great significance for treatment strategies. Recent studies have used an ensemble of multiple clustering algorithms to elucidate the most characteristic biological groups of breast cancer. However, the combination of various clustering methods resulted in a number of patients remaining unclustered...
June 2019: Artificial Intelligence in Medicine
Loris Nanni, Sheryl Brahnam, Christian Salvatore, Isabella Castiglioni
BACKGROUND AND OBJECTIVE: Early and accurate diagnosis of Alzheimer's Disease (AD) is critical since early treatment effectively slows the progression of the disease thereby adding productive years to those afflicted by this disease. A major problem encountered in the classification of MRI for the automatic diagnosis of AD is the so-called curse-of-dimensionality, which is a consequence of the high dimensionality of MRI feature vectors and the low number of training patterns available in most MRI datasets relevant to AD...
June 2019: Artificial Intelligence in Medicine
Toan Duc Bui, Jae-Joon Lee, Jitae Shin
Bone age assessment plays an important role in the endocrinology and genetic investigation of patients. In this paper, we proposed a deep learning-based approach for bone age assessment by integration of the Tanner-Whitehouse (TW3) methods and deep convolution networks based on extracted regions of interest (ROI)-detection and classification using Faster-RCNN and Inception-v4 networks, respectively. The proposed method allows exploration of expert knowledge from TW3 and features engineering from deep convolution networks to enhance the accuracy of bone age assessment...
June 2019: Artificial Intelligence in Medicine
Alessio Bottrighi, Luca Piovesan, Paolo Terenziani
Clinical guidelines (GLs) are widely adopted in order to improve the quality of patient care, and to optimize it. To achieve such goals, their application on a specific patient usually requires the interventions of different agents, with different roles (e.g., physician, nurse), abilities (e.g., specialist in the treatment of alcohol-related problems) and contexts (e.g., many chronic patients may be treated at home). Additionally, the responsibility of the application of a guideline to a patient is usually retained by a physician, but delegation of responsibility (of the whole guideline, or of a part of it) is often used\required (e...
May 18, 2019: Artificial Intelligence in Medicine
Ramon Pires, Sandra Avila, Jacques Wainer, Eduardo Valle, Michael D Abramoff, Anderson Rocha
Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize. OBJECTIVE: We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector. METHODS: We gradually build the solution based on convolutional neural networks, adding data augmentation, multi-resolution training, robust feature-extraction augmentation, and a patient-basis analysis, testing the effectiveness of each improvement...
May 2019: Artificial Intelligence in Medicine
Christopher C Yang, Mengnan Zhao
Drug repositioning has drawn significant attention for drug development in pharmaceutical research and industry, because of its advantages in cost and time compared with the de novo drug development. The availability of biomedical databases and online health-related information, as well as the high-performance computing, empowers the development of computational drug repositioning methods. In this work, we developed a systematic approach that identifies repositioning drugs based on heterogeneous network mining using both pharmaceutical databases (PharmGKB and SIDER) and online health community (MedHelp)...
May 2019: Artificial Intelligence in Medicine
Jianfei Zhang, Shengrui Wang, Josiane Courteau, Lifei Chen, Gongde Guo, Alain Vanasse
Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmission and death in the United States, Canada and worldwide. COPD failure imposes a significant social and economic burden on society, and predicting such failure is crucial to early intervention and decision-making, making this a very important research issue. Current analysis methods address all risk factors in medical records indiscriminately and therefore generally suffer from ineffectiveness in real applications, mainly because many of these factors relate weakly to prediction...
May 2019: Artificial Intelligence in Medicine
Olivier Palombi, Fabrice Jouanot, Nafissetou Nziengam, Behrooz Omidvar-Tehrani, Marie-Christine Rousset, Adam Sanchez
We introduce OntoSIDES, the core of an ontology-based learning management system in Medicine, in which the educational content, the traces of students' activities and the correction of exams are linked and related to items of an official reference program in a unified RDF data model. OntoSIDES is an RDF knowledge base comprised of a lightweight domain ontology that serves as a pivot high-level vocabulary of the query interface with users, and of a dataset made of factual statements relating individual entities to classes and properties of the ontology...
May 2019: Artificial Intelligence in Medicine
Sudipta Ghosh, Bhabani Prasad Chattopadhyay, Ram Mohan Roy, Jayanta Mukherjee, Manjunatha Mahadevappa
The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive and non-invasive hemodynamic monitoring techniques. These methods require expert supervision and costly clinical set-ups, and cannot be employed by a common individual to perform a self diagnosis of one's cardiac health, unassisted...
May 2019: Artificial Intelligence in Medicine
Mohammad Saber Iraji
The combination of machine vision and soft computing approaches in the clinical decisions, using training data, can improve medical decisions and treatments. The cardiotocography (CTG) monitoring and uterine activity (UA) provides useful information about the condition of the fetus and the cesarean or natural delivery. The visual assessment by the pathologists takes a lot of time and may be incompatible. Therefore, creating a computer intelligent method to assess fetal wellbeing before the mother labour is very important...
May 2019: Artificial Intelligence in Medicine
Hongliang Zou, Jian Yang
BACKGROUND AND OBJECTIVE: Functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) is an effective approach to describe the neural interaction between distributed brain regions. Recent progress in neuroimaging study reported that the connection between regions is time-varying, which may enhance understanding of normal cognition and alterations that result from brain disorders. However, conventional sliding window based dynamic FC (DFC) analysis has several drawbacks, including arbitrary choice of window length, inaccurate descriptor of FC, and the fact that many spurious connections were included in the fully-connected networks due to noise...
May 2019: Artificial Intelligence in Medicine
Zhenqiu Liu, David Elashoff, Steven Piantadosi
Omics data usually have ultra-high dimension (p) and small sample size (n). Standard support vector machines (SVMs), which minimize the L2 norm for the primal variables, only lead to sparse solutions for the dual variables. L1 based SVMs, directly minimizing the L1 norm, have been used for feature selection with omics data. However, most current methods directly solve the primal formulations of the problem, which are not computationally scalable. The computational complexity increases with the number of features...
May 2019: Artificial Intelligence in Medicine
Jiajiong Ma, Guihua Wen, Changjun Wang, Lijun Jiang
The body constitution is much related to the diseases and the corresponding treatment programs in Traditional Chinese Medicine. It can be recognized by the tongue image diagnosis, so that it is essentially regarded as a problem of tongue image classification, where each tongue image is classified into one of nine constitution types. This paper first presents a system framework to automatically identify the constitution through natural tongue images, where deep convolutional neural networks are carefully designed for tongue coating detection, tongue coating calibration, and constitution recognition...
May 2019: Artificial Intelligence in Medicine
Liang Sun, Chen Zu, Wei Shao, Junye Guang, Daoqiang Zhang, Mingxia Liu
Label fusion is one of the key steps in multi-atlas based segmentation of structural magnetic resonance (MR) images. Although a number of label fusion methods have been developed in literature, most of those existing methods fail to address two important problems, i.e., (1) compared with boundary voxels, inner voxels usually have higher probability (or reliability) to be correctly segmented, and (2) voxels with high segmentation reliability (after initial segmentation) can help refine the segmentation of voxels with low segmentation reliability in the target image...
May 2019: Artificial Intelligence in Medicine
Anthony Rios, Ramakanth Kavuluru
OBJECTIVE: Electronic medical records (EMRs) are manually annotated by healthcare professionals and specialized medical coders with a standardized set of alphanumeric diagnosis and procedure codes, specifically from the International Classification of Diseases (ICD). Annotating EMRs with ICD codes is important for medical billing and downstream epidemiological studies. However, manually annotating EMRs is both time-consuming and error prone. In this paper, we explore the use of convolutional neural networks (CNNs) for automatic ICD coding...
May 2019: Artificial Intelligence in Medicine
Xiaoxia Liu, Zhihao Yang, Shengtian Sang, Hongfei Lin, Jian Wang, Bo Xu
Cellular processes are typically carried out by protein complexes rather than individual proteins. Identifying protein complexes is one of the keys to understanding principles of cellular organization and function. Also, protein complexes are a group of interacting genes underlying similar diseases, which points out the therapeutic importance of protein complexes. With the development of life science and computing science, an increasing amount of protein-protein interaction (PPI) data becomes available, which makes it possible to predict protein complexes from PPI networks...
May 2019: Artificial Intelligence in Medicine
Xin Fan, Yuzhuo Duan, Shichao Cheng, Yuxi Zhang, Hua Cheng
Clustering white matter (WM) tracts from diffusion tensor imaging (DTI) is primarily important for quantitative analysis on pediatric brain development. A recently developed algorithm, density peaks (DP) clustering, demonstrates great robustness to the complex structural variations of WM tracts without any prior templates. Nevertheless, the calculation of densities, the core step of DP, is time consuming especially when the number of WM fibers is huge. In this paper, we propose a fast algorithm that accelerates the density computation about 50 times over the original one...
May 2019: Artificial Intelligence in Medicine
Mahdi Hashemzadeh, Baharak Adlpour Azar
In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employing a set of effective image features and combination of supervised and unsupervised machine learning techniques. Further to the common features used in extracting blood vessels, three strong features having a significant influence on the accuracy of the vessel extraction are utilized...
April 2019: Artificial Intelligence in Medicine
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