Artificial Intelligence in Medicine | Page 3

V Jahmunah, Shu Lih Oh, V Rajinikanth, Edward J Ciaccio, Kang Hao Cheong, N Arunkumar, U Rajendra Acharya
Examination of the brain's condition with the Electroencephalogram (EEG) can be helpful to predict abnormality and cerebral activities. The purpose of this study was to develop an Automated Diagnostic Tool (ADT) to investigate and classify the EEG signal patterns into normal and schizophrenia classes. The ADT implements a sequence of events, such as EEG series splitting, non-linear features mining, t-test assisted feature selection, classification and validation. The proposed ADT is employed to evaluate a 19-channel EEG signal collected from normal and schizophrenia class volunteers...
September 2019: Artificial Intelligence in Medicine
U Rajendra Acharya, Kristen M Meiburger, Joel En Wei Koh, Jahmunah Vicnesh, Edward J Ciaccio, Oh Shu Lih, Sock Keow Tan, Raja Rizal Azman Raja Aman, Filippo Molinari, Kwan Hoong Ng
Cardiovascular diseases are the primary cause of death globally. These are often associated with atherosclerosis. This inflammation process triggers important variations in the coronary arteries (CA) and can lead to coronary artery disease (CAD). The presence of CA calcification (CAC) has recently been shown to be a strong predictor of CAD. In this clinical setting, computed tomography angiography (CTA) has begun to play a crucial role as a non-intrusive imaging method to characterize and study CA plaques. Herein, we describe an automated algorithm to classify plaque as either normal, calcified, or non-calcified using 2646 CTA images acquired from 73 patients...
September 2019: Artificial Intelligence in Medicine
Helena A Watson, Rachel M Tribe, Andrew H Shennan
INTRODUCTION: The now ubiquitous smartphone has huge potential to assist clinical decision-making across the globe. However, the rapid pace of digitalisation contrasts starkly with the slower rate of medical research and publication. This review explores the evidence base that exists to validate and evaluate the use of medical decision-support apps. The resultant findings will inform appropriate and pragmatic evaluation strategies for future clinical app developers and provide a scientific and cultural context for research priorities in this field...
September 2019: Artificial Intelligence in Medicine
David Riaño, Mor Peleg, Annette Ten Teije
BACKGROUND: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide...
September 2019: Artificial Intelligence in Medicine
Shigehiko Schamoni, Holger A Lindner, Verena Schneider-Lindner, Manfred Thiel, Stefan Riezler
Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches...
September 2019: Artificial Intelligence in Medicine
Mohamed Abdel-Basset, Mohamed El-Hoseny, Abduallah Gamal, Florentin Smarandache
This research suggests an approach constructed on the connotation of plithogenic theory and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) technique to come up with a methodical procedure to assess the infirmary serving under a framework of plithogenic theory, where the ambiguity, incomplete information, qualitative information, approximate evaluation, imprecision and uncertainty are addressed with semantic expressions determined by plithogenic numbers and computing of contradiction degrees of attribute values...
September 2019: Artificial Intelligence in Medicine
Yanpeng Qu, Guanli Yue, Changjing Shang, Longzhi Yang, Reyer Zwiggelaar, Qiang Shen
CONTEXT AND BACKGROUND: Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution. MOTIVATION: Computer aided risk analysis of breast cancer typically works with a set of extracted mammographic features which may contain significant redundancy and noise, thereby requiring technical developments to improve runtime performance in both computational efficiency and classification accuracy...
September 2019: Artificial Intelligence in Medicine
Javier Puente, Fernando Gascon, Borja Ponte, David de la Fuente
OBJECTIVES: We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk...
September 2019: Artificial Intelligence in Medicine
Yu Shi, Weng Kee Wong, Jonathan G Goldin, Matthew S Brown, Grace Hyun J Kim
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive decline in lung function. Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosis of IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictive model for the radiological progression pattern at voxel-wise level using only baseline HRCT scans...
September 2019: Artificial Intelligence in Medicine
Sebastian Spänig, Agnes Emberger-Klein, Jan-Peter Sowa, Ali Canbay, Klaus Menrad, Dominik Heider
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction. In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e...
September 2019: Artificial Intelligence in Medicine
Norah Asiri, Muhammad Hussain, Fadwa Al Adel, Nazih Alzaidi
Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deep-learning-based methods have been introduced...
August 2019: Artificial Intelligence in Medicine
Xiaowei Li, Xin Zhang, Jing Zhu, Wandeng Mao, Shuting Sun, Zihan Wang, Chen Xia, Bin Hu
The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method that predicts clinical outcomes in depression is essential for increasing the accuracy of depression recognition and treatments. This paper aims at better recognizing depression using the transformation of EEG features and machine learning methods. An experiment based on emotional face stimuli task was conducted, and twenty-eight subjects' EEG data were recorded from 128-channel HydroCel Geodesic Sensor Net (HCGSN) by Net Station software...
August 2019: Artificial Intelligence in Medicine
Tahira Nazir, Aun Irtaza, Zain Shabbir, Ali Javed, Usman Akram, Muhammad Tariq Mahmood
Diabetic retinopathy (DR) is an eye disease that victimize the people suffering from diabetes from many years. The severe form of DR results in form of the blindness that can initially be controlled by the DR-screening oriented treatment. The effective screening programs require the trained human resource that manually grade the fundus images to understand the severity of the disease. But due to the complexity of this process, and the insufficient number of the trained workers, the precise manual grading is an expensive process...
August 2019: Artificial Intelligence in Medicine
A M Mutawa, Mariam A Alzuwawi
Uveitis is a condition caused by inflammation of the uvea, which is the middle layer of the eye. Uveitis can result in swelling or destruction of the eye tissue, which can lead to visual impairment or blindness [1]. Many diseases, either systemic or localized to the eye, are associated with the symptoms of uveitis. Thus, it is often hard to determine the underlying disease responsible for uveitis, especially when the signs and symptoms are unclear. Additionally, there are few experts on uveitis, especially in poor and developing countries...
August 2019: Artificial Intelligence in Medicine
Nassim Versbraegen, Aziz Fouché, Charlotte Nachtegael, Sofia Papadimitriou, Andrea Gazzo, Guillaume Smits, Tom Lenaerts
In order to gain insight into oligogenic disorders, understanding those involving bi-locus variant combinations appears to be key. In prior work, we showed that features at multiple biological scales can already be used to discriminate among two types, i.e. disorders involving true digenic and modifier combinations. The current study expands this machine learning work towards dual molecular diagnosis cases, providing a classifier able to effectively distinguish between these three types. To reach this goal and gain an in-depth understanding of the decision process, game theory and tree decomposition techniques are applied to random forest predictors to investigate the relevance of feature combinations in the prediction...
August 2019: Artificial Intelligence in Medicine
Anders Reenberg Andersen, Wim Vancroonenburg, Greet Vanden Berghe
Providing patients with the best possible care is the most essential function of any hospital. In an increasing number of countries, hospitals are governed by the number of patients they are able to attract and the corresponding services they provide for patients. One such service, which is often of significant importance for patients, is the option to choose their room type. Hospital decision makers would benefit from a strategic method for optimizing the configuration of room types among nursing wards by distinguishing between patients who prefer private rooms and those who have no preference concerning whether they are assigned to a private or shared room...
August 2019: Artificial Intelligence in Medicine
Malihe Javidi, Ahad Harati, HamidReza Pourreza
The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as features of these structures are directly related to the diagnosis and treatment process of diabetic retinopathy. The complexity of the retinal image contents especially in images with severe diabetic retinopathy makes detection of vascular structure and lesions difficult...
August 2019: Artificial Intelligence in Medicine
Andy M Y Tai, Alcides Albuquerque, Nicole E Carmona, Mehala Subramanieapillai, Danielle S Cha, Margarita Sheko, Yena Lee, Rodrigo Mansur, Roger S McIntyre
INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems. METHODS: Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria...
August 2019: Artificial Intelligence in Medicine
Yi-Peng Liu, Zhanqing Li, Cong Xu, Jing Li, Ronghua Liang
Diabetic retinopathy (DR) is the most common cause of blindness in middle-age subjects and low DR screening rates demonstrates the need for an automated image assessment system, which can benefit from the development of deep learning techniques. Therefore, the effective classification performance is significant in favor of the referable DR identification task. In this paper, we propose a new strategy, which applies multiple weighted paths into convolutional neural network, called the WP-CNN, motivated by the ensemble learning...
August 2019: Artificial Intelligence in Medicine
Nicolas Houy, François Le Grand
PURPOSE: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability. METHODS: Our optimizations are based on a Pharmacokinetics/Pharmacodynamics (PK/PD) model with population variability for temozolomide, inspired by Faivre et al. [10] and Panetta et al. [25,26]. The patient pharmacokinetic parameters can only be partially observed at admission and are progressively learned by Bayesian inference during treatment...
August 2019: Artificial Intelligence in Medicine
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