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Artificial Intelligence in Medicine

Bin He, Yi Guan, Rui Dai
Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method...
May 16, 2018: Artificial Intelligence in Medicine
Rosy Tsopra, Jean-Baptiste Lamy, Karima Sedki
Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also state that there is a high bacteria resistance in this context. In this paper, we propose a method for the semi-automatic detection of inconsistencies in guidelines using preference learning, and we apply this method to antibiotherapy in primary care. The preference model was learned from the recommendations and from a knowledge base describing the domain...
May 15, 2018: Artificial Intelligence in Medicine
Bevan Koopman, Guido Zuccon, Anthony Nguyen, Anton Bergheim, Narelle Grayson
OBJECTIVE: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality of certificates written in natural language. This paper proposes an automatic classification system for identifying all cancer related causes of death from death certificates. METHODS: Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates...
May 10, 2018: Artificial Intelligence in Medicine
Antje Wulff, Birger Haarbrandt, Erik Tute, Michael Marschollek, Philipp Beerbaum, Thomas Jack
BACKGROUND: Clinical decision-support systems (CDSS) are designed to solve knowledge-intensive tasks for supporting decision-making processes. Although many approaches for designing CDSS have been proposed, due to high implementation costs, as well as the lack of interoperability features, current solutions are not well-established across different institutions. Recently, the use of standardized formalisms for knowledge representation as terminologies as well as the integration of semantically enriched clinical information models, as openEHR Archetypes, and their reuse within CDSS are theoretically considered as key factors for reusable CDSS...
May 9, 2018: Artificial Intelligence in Medicine
Alaa S AlAgha, Hossam Faris, Bassam H Hammo, Ala' M Al-Zoubi
Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly differentiate normal individuals from asymptomatic thalassemia carriers. Usually, thalassemia diagnosis is based on certain measurable characteristic changes to blood cell counts and related indices. These characteristic changes can be derived easily when performing a complete blood count test (CBC) using a special fully automated blood analyzer or counter...
May 2, 2018: Artificial Intelligence in Medicine
Marcos Antonio Mouriño García, Roberto Pérez Rodríguez, Luis Anido Rifón
This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with English documents. We propose the cross-language concept matching technique, which relies on Wikipedia interlanguage links to convert concept vectors between languages. The performance of the classifier is compared to a classifier based on machine translation, and two classifiers based on MetaMap...
May 2, 2018: Artificial Intelligence in Medicine
Dalal Bardou, Kun Zhang, Sayed Mohammad Ahmad
Lung sounds convey relevant information related to pulmonary disorders, and to evaluate patients with pulmonary conditions, the physician or the doctor uses the traditional auscultation technique. However, this technique suffers from limitations. For example, if the physician is not well trained, this may lead to a wrong diagnosis. Moreover, lung sounds are non-stationary, complicating the tasks of analysis, recognition, and distinction. This is why developing automatic recognition systems can help to deal with these limitations...
May 1, 2018: Artificial Intelligence in Medicine
S Chandra Bollepalli, S Sastry Challa, Laxminarayana Anumandla, Soumya Jana
While cardiovascular diseases (CVDs) are prevalent across economic strata, the economically disadvantaged population is disproportionately affected due to the high cost of traditional CVD management, involving consultations, testing and monitoring at medical facilities. Accordingly, developing an ultra-low-cost alternative, affordable even to groups at the bottom of the economic pyramid, has emerged as a societal imperative. Against this backdrop, we propose an inexpensive yet accurate home-based electrocardiogram (ECG) monitoring service...
April 25, 2018: Artificial Intelligence in Medicine
Ziba Gandomkar, Patrick C Brennan, Claudia Mello-Thoms
MOTIVATION: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of lesion subtypes is considered as the gold standard, however, sometimes strong disagreements among pathologists for distinction among lesion subtypes have been previously reported in the literature. OBJECTIVE: To propose a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each...
April 25, 2018: Artificial Intelligence in Medicine
R Yazdanparast, S Abdolhossein Zadeh, D Dadras, A Azadeh
Healthcare quality is affected by various factors including trust. Patients' trust to healthcare providers is one of the most important factors for treatment outcomes. The presented study identifies optimum mixture of patient demographic features with respect to trust in three large and busy medical centers in Tehran, Iran. The presented algorithm is composed of adaptive neuro-fuzzy inference system and statistical methods. It is used to deal with data and environmental uncertainty. The required data are collected from three large hospitals using standard questionnaires...
April 25, 2018: Artificial Intelligence in Medicine
Chao Zhao, Jingchi Jiang, Yi Guan, Xitong Guo, Bin He
OBJECTIVE: Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. METHODS: We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record...
April 22, 2018: Artificial Intelligence in Medicine
Salud María Jiménez-Zafra, M Teresa Martín-Valdivia, M Dolores Molina-González, L Alfonso Ureña-López
OBJECTIVE: The main goal of this study is to examine how people express their opinion in medical forums. We analyze the language used in order to determine the best way to tackle sentiment analysis in this domain. METHODS: We have applied supervised learning and lexicon-based sentiment analysis approaches over two different corpora extracted from social web. Specifically, we have focused on two aspects: drugs and doctors. We have selected two forums and we have collected corpora for each one: (i) DOS, a Spanish corpus of drug reviews and (ii) COPOS, a Spanish corpus of patients' opinions about physicians...
April 20, 2018: Artificial Intelligence in Medicine
Shahnorbanun Sahran, Dheeb Albashish, Azizi Abdullah, Nordashima Abd Shukor, Suria Hayati Md Pauzi
OBJECTIVE: Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components...
April 18, 2018: Artificial Intelligence in Medicine
Anna Fabijańska
Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracted from the image. Up to today, this has been performed manually or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based convolutional neural network...
April 18, 2018: Artificial Intelligence in Medicine
Clayton R Pereira, Danilo R Pereira, Gustavo H Rosa, Victor H C Albuquerque, Silke A T Weber, Christian Hook, João P Papa
BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. METHODS: In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment...
April 16, 2018: Artificial Intelligence in Medicine
Sunil Kumar Sahu, Ashish Anand
A lack of sufficient labeled data often limits the applicability of advanced machine learning algorithms to real life problems. However, the efficient use of transfer learning (TL) has been shown to be very useful across domains. TL make use of valuable knowledge learned in one task (source task), where sufficient data is available, in order to improve performance on the task of interest (target task). In the biomedical and clinical domain, a lack of sufficient training data means that machine learning models cannot be fully exploited...
April 13, 2018: Artificial Intelligence in Medicine
Ahmed Najjar, Daniel Reinharz, Catherine Girouard, Christian Gagné
Clustering electronic medical records allows the discovery of information on healthcare practices. Entries in such medical records are usually composed of a succession of diagnostics or therapeutic steps. The corresponding processes are complex and heterogeneous since they depend on medical knowledge integrating clinical guidelines, the physician's individual experience, and patient data and conditions. To analyze such data, we are first proposing to cluster medical visits, consultations, and hospital stays into homogeneous groups, and then to construct higher-level patient treatment pathways over these different groups...
April 6, 2018: Artificial Intelligence in Medicine
M Eghbali-Zarch, R Tavakkoli-Moghaddam, F Esfahanian, M M Sepehri, A Azaron
Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed to control the patient's blood glucose. The wide range of hyperglycemia lowering agents with varying effects and various side effects makes the decision quite difficult. This paper presents computer-aided medical decision support using a fuzzy Multi-Criteria Decision-Making (MCDM) model that hybridizes a Step-wise Weight Assessment Ratio Analysis (SWARA) method with a modification of Fuzzy Multi-Objective Optimization on the basis of a Ratio Analysis plus the full multiplicative form (FMULTIMOORA) method for pharmacological therapy selection of T2D...
March 29, 2018: Artificial Intelligence in Medicine
Andres Duque, Mark Stevenson, Juan Martinez-Romo, Lourdes Araujo
Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientific abstracts, and hence adapted to the specific domain...
March 21, 2018: Artificial Intelligence in Medicine
Deyu Zhou, Lei Miao, Yulan He
OBJECTIVE: A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed...
March 17, 2018: Artificial Intelligence in Medicine
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