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STQS: Interpretable Multi-modal Spatial-Temporal-seQuential Model for Automatic Sleep Scoring Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-27 Shreyasi Pathak; Changqing Lu; Sunil Belur Nagaraj; Michel van Putten; Christin Seifert
Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. In this paper, we propose a deep learning architecture for multi-modal sleep scoring,
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Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-12 Nuttapong Sanglerdsinlapachai; Anon Plangprasopchok; Tu Bao Ho; Ekawit Nantajeewarawat
Sentiments associated with assessments and observations recorded in a clinical narrative can often indicate a patient's health status. To perform sentiment analysis on clinical narratives, domain-specific knowledge concerning meanings of medical terms is required. In this study, semantic types in the Unified Medical Language System (UMLS) are exploited to improve lexicon-based sentiment classification
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DeepAISE – An interpretable and recurrent neural survival model for early prediction of sepsis Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-13 Supreeth P. Shashikumar; Christopher S. Josef; Ashish Sharma; Shamim Nemati
Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU). Early prediction of sepsis can improve situational awareness among clinicians and facilitate timely, protective interventions. While the application of predictive analytics in ICU patients has shown early promising results, much of the
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A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-17 Xin Yuan; Lingxiao Zhou; Shuyang Yu; Miao Li; Xiang Wang; Xiujuan Zheng
Glaucoma is the leading cause of irreversible blindness. For glaucoma screening, the cup to disc ratio (CDR) is a significant indicator, whose calculation relies on the segmentation of optic disc(OD) and optic cup(OC) in color fundus images. This study proposes a residual multi-scale convolutional neural network with a context semantic extraction module to jointly segment the OD and OC. The proposed
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AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-13 Avdesh Mishra; Reecha Khanal; Wasi Ul Kabir; Tamjidul Hoque
Identification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules is an important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA localization
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Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-11 Larry Hernandez; Renaid Kim; Neriman Tokcan; Harm Derksen; Ben E. Biesterveld; Alfred Croteau; Aaron M. Williams; Michael Mathis; Kayvan Najarian; Jonathan Gryak
Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time
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Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-19 Carla Barros; Carlos A. Silva; Ana P. Pinheiro
The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients’ quality of life
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Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-23 Jin Li; Yu Tian; Runze Li; Tianshu Zhou; Jun Li; Kefeng Ding; Jingsong Li
Background and objective Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, research on multicenter clinical collaborative networks, which can provide external medical data, has received increasing attention. With the increasing availability
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Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis Artif. Intell. Med. (IF 4.383) Pub Date : 2021-02-02 Sergei Bedrikovetski; Nagendra N. Dudi-Venkata; Gabriel Maicas; Hidde M. Kroon; Warren Seow; Gustavo Carneiro; James W. Moore; Tarik Sammour
Purpose Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies. Methodology Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify
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Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-23 Minyoung Chung; Jingyu Lee; Sanguk Park; Chae Eun Lee; Jeongjin Lee; Yeong-Gil Shin
Objective: Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limited studies have focused on the performance of generalization. In this study, we introduce a CNN for liver segmentation on abdominal computed tomography
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Multiscale CNN with compound fusions for false positive reduction in lung nodule detection Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-12 Pardha Saradhi Mittapalli; Thanikaiselvan V
Pulmonary lung nodules are often benign at the early stage but they could easily become malignant and metastasize to other locations in later stages. Morphological characteristics of these nodule instances vary largely in terms of their size, shape, and texture. There are also other co-existing lung anatomical structures such as lung walls and blood vessels surrounding these nodules resulting in complex
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Modeling multivariate clinical event time-series with recurrent temporal mechanisms Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-18 Jeong Min Lee; Milos Hauskrecht
In this work, we propose a novel autoregressive event time-series model that can predict future occurrences of multivariate clinical events. Our model represents multivariate event time-series using different temporal mechanisms aimed to fit different temporal characteristics of the time-series. In particular, information about distant past is modeled through the hidden state space defined by an LSTM-based
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A novel computational method for assigning weights of importance to symptoms of COVID-19 patients Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-15 Mohammad A. Alzubaidi; Mwaffaq Otoom; Nesreen Otoum; Yousef Etoom; Rudaina Banihani
Background and objective The novel coronavirus disease 2019 (COVID-19) is considered a pandemic by the World Health Organization (WHO). As of April 3, 2020, there were 1,009,625 reported confirmed cases, and 51,737 reported deaths. Doctors have been faced with a myriad of patients who present with many different symptoms. This raises two important questions. What are the common symptoms, and what are
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A method based on cardiopulmonary coupling analysis for sleep quality assessment with FPGA implementation Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-15 Fábio Mendonça; Sheikh Shanawaz Mostafa; Fernando Morgado-Dias; Antonio G. Ravelo-García
The relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constraint since the subject is a poor self-observer of sleep
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A survey of deep learning models in medical therapeutic areas Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-15 Alberto Nogales; Álvaro J. García-Tejedor; Diana Monge; Juan Serrano Vara; Cristina Antón
Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane
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BertMCN: Mapping colloquial phrases to standard medical concepts using BERT and highway network Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-07 Katikapalli Subramanyam Kalyan; Sivanesan Sangeetha
In the last few years, people started to share lots of information related to health in the form of tweets, reviews and blog posts. All these user generated clinical texts can be mined to generate useful insights. However, automatic analysis of clinical text requires identification of standard medical concepts. Most of the existing deep learning based medical concept normalization systems are based
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Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-05 Pablo Castillo-Segura; Carmen Fernández-Panadero; Carlos Alario-Hoyos; Pedro J. Muñoz-Merino; Carlos Delgado Kloos
The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a
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A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder Artif. Intell. Med. (IF 4.383) Pub Date : 2021-01-05 Wenjun Kou; Dustin A. Carlson; Alexandra J. Baumann; Erica Donnan; Yuan Luo; John E. Pandolfino; Mozziyar Etemadi
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A new framework for classification of multi-category hand grasps using EMG signals Artif. Intell. Med. (IF 4.383) Pub Date : 2020-12-28 Firas Sabar Miften; Mohammed Diykh; Shahab Abdulla; Siuly Siuly; Jonathan H. Green; Ravinesh C. Deo
Electromyogram (EMG) signals have had a great impact on many applications, including prosthetic or rehabilitation devices, human-machine interactions, clinical and biomedical areas. In recent years, EMG signals have been used as a popular tool to generate device control commands for rehabilitation equipment, such as robotic prostheses. This intention of this study was to design an EMG signal-based
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MitPlan: A planning approach to mitigating concurrently applied clinical practice guidelines Artif. Intell. Med. (IF 4.383) Pub Date : 2020-12-16 Martin Michalowski; Szymon Wilk; Wojtek Michalowski; Marc Carrier
As the population ages, patients’ complexity and the scope of their care is increasing. Over 60% of the population is 65 years of age or older and suffers from multi-morbidity, which is associated with two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these two competing issues, previously we developed a framework
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DRNet: Segmentation and localization of optic disc and Fovea from diabetic retinopathy image Artif. Intell. Med. (IF 4.383) Pub Date : 2020-12-13 Md. Kamrul Hasan; Md. Ashraful Alam; Md. Toufick E Elahi; Shidhartho Roy; Robert Martí
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Estimating sparse functional connectivity networks via hyperparameter-free learning model Artif. Intell. Med. (IF 4.383) Pub Date : 2020-12-18 Lei Sun; Yanfang Xue; Yining Zhang; Lishan Qiao; Limei Zhang; Mingxia Liu
Functional connectivity networks (FCNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Currently, researchers have proposed many methods for FCN construction, among which the most classic example is Pearson's correlation (PC). Despite its simplicity and popularity, PC always results in dense FCNs, and thus a thresholding strategy is
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Training data enhancements for improving colonic polyp detection using deep convolutional neural networks Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-21 Victor de Almeida Thomaz; Cesar A. Sierra-Franco; Alberto B. Raposo
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Using interpretability approaches to update “black-box” clinical prediction models: an external validation study in nephrology Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-07 Harry Freitas da Cruz; Boris Pfahringer; Tom Martensen; Frederic Schneider; Alexander Meyer; Erwin Böttinger; Matthieu-P. Schapranow
Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III
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Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis Artif. Intell. Med. (IF 4.383) Pub Date : 2020-12-15 Luca Roggeveen; Ali el Hassouni; Jonas Ahrendt; Tingjie Guo; Lucas Fleuren; Patrick Thoral; Armand RJ Girbes; Mark Hoogendoorn; Paul WG Elbers
Introduction In recent years, reinforcement learning (RL) has gained traction in the healthcare domain. In particular, RL methods have been explored for haemodynamic optimization of septic patients in the Intensive Care Unit. Most hospitals however, lack the data and expertise for model development, necessitating transfer of models developed using external datasets. This approach assumes model generalizability
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EPTs-TL: A two-level approach for efficient event prediction in healthcare Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-30 Soheila Mehrmolaei
Recently, the event prediction on time series (EPTs) was discussed as one of the important and interesting research trends that its usage is growing for taking proper decisions in the various sciences. In the real-world, time series event-based analysis can pose as one of the challenging prediction problems in healthcare, which have a direct impact and a key role in supporting health management. In
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Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-27 Nazanin Esmaili; Quinlan D. Buchlak; Massimo Piccardi; Bernie Kruger; Federico Girosi
Background Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns of MVA patients. To fill this gap, this study has been designed to investigate temporal
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A Decision Tree-Initialised Neuro-fuzzy Approach for Clinical Decision Support Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-12 Tianhua Chen; Changjing Shang; Pan Su; Elpida Keravnou-Papailiou; Yitian Zhao; Grigoris Antoniou; Qiang Shen
Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand the deployment of interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based models are generally considered interpretable that are able to reflect the associations between medical conditions and associated symptoms, through
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Interactive medical image segmentation via a point-based interaction Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-28 Jian Zhang; Yinghuan Shi; Jinquan Sun; Lei Wang; Luping Zhou; Yang Gao; Dinggang Shen
Due to low tissue contrast, irregular shape, and large location variance, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, a novel method is presented for interactive medical image segmentation with the following merits. (1) Its design is fundamentally different from previous pure patch-based and image-based
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Individual tooth detection and identification from dental panoramic X-ray images via point-wise localization and distance regularization Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-21 Minyoung Chung; Jusang Lee; Sanguk Park; Minkyung Lee; Chae Eun Lee; Jeongjin Lee; Yeong-Gil Shin
Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization
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Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-20 Gilles Vandewiele; Isabelle Dehaene; György Kovács; Lucas Sterckx; Olivier Janssens; Femke Ongenae; Femke De Backere; Filip De Turck; Kristien Roelens; Johan Decruyenaere; Sofie Van Hoecke; Thomas Demeester
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource, called the Term/Preterm Electrohysterogram database
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EKNN: Ensemble classifier incorporating connectivity and density into kNN with application to cancer diagnosis Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-08 Mohamed A. Mahfouz; Amin Shoukry; Mohamed A. Ismail
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Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-07 A.S. Albahri; Rula A. Hamid; O.S. Albahri; A.A. Zaidan
Context and background Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk
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Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-10 A. Parziale; R. Senatore; A. Della Cioppa; A. Marcelli
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Sleep stage classification for child patients using DeConvolutional Neural Network Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-02 Xinyu Huang; Kimiaki Shirahama; Frédéric Li; Marcin Grzegorzek
Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort has been made on sleep stage classification for adults, children have significantly different characteristics of sleep stages. Therefore, there is an urgent need for sleep stage classification targeting children in particular. Our method focuses on two
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Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography Artif. Intell. Med. (IF 4.383) Pub Date : 2020-10-21 Luca Corinzia; Fabian Laumer; Alessandro Candreva; Maurizio Taramasso; Francesco Maisano; Joachim M. Buhmann
The segmentation of the mitral valve annulus and leaflets specifies a crucial first step to establish a machine learning pipeline that can support physicians in performing multiple tasks, e.g. diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. Current methods for mitral valve segmentation on 2D echocardiography videos require extensive interaction with annotators
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A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-02 Mohamed T. Bennai; Zahia Guessoum; Smaine Mazouzi; Stéphane Cormier; Mohamed Mezghiche
According to functional or anatomical modalities, medical imaging provides a visual representation of complex structures or activities in the human body. One of the most common processing methods applied to those images is segmentation, in which an image is divided into a set of regions of interest. Human anatomical complexity and medical image acquisition artifacts make segmentation of medical images
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Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping Artif. Intell. Med. (IF 4.383) Pub Date : 2020-09-07 Qiang Zhang; Evan Hann; Konrad Werys; Cody Wu; Iulia Popescu; Elena Lukaschuk; Ahmet Barutcu; Vanessa M. Ferreira; Stefan K. Piechnik
Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious
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Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network Artif. Intell. Med. (IF 4.383) Pub Date : 2020-11-01 Hanyin Wang; Yikuan Li; Seema A Khan; Yuan Luo
Distant recurrence of breast cancer results in high lifetime risks and low 5-year survival rates. Early prediction of distant recurrent breast cancer could facilitate intervention and improve patients’ life quality. In this study, we designed an EHR-based predictive model to estimate the distant recurrent probability of breast cancer patients. We studied the pathology reports and progress notes of
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Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance Artif. Intell. Med. (IF 4.383) Pub Date : 2020-10-22 Laura Macías-García; María Martínez-Ballesteros; José María Luna-Romera; José M. García-Heredia; Jorge García-Gutiérrez; José C. Riquelme-Santos
Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an interesting primary source for supervised knowledge extraction regarding breast
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Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos Artif. Intell. Med. (IF 4.383) Pub Date : 2020-10-06 Stefan Williams; Samuel D. Relton; Hui Fang; Jane Alty; Rami Qahwaji; Christopher D. Graham; David C. Wong
Background Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim We propose a low-cost, contactless system
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Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design Artif. Intell. Med. (IF 4.383) Pub Date : 2020-10-07 Marija D. Ivanović; Julius Hannink; Matthias Ring; Fabio Baronio; Vladan Vukčević; Ljupco Hadžievski; Bjoern Eskofier
Objective Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically
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The automation of bias in medical Artificial Intelligence (AI): Decoding the past to create a better future Artif. Intell. Med. (IF 4.383) Pub Date : 2020-10-06 Isabel Straw
Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence (AI) into the healthcare field the future care of our patients will depend on the decisions we make now. Demographic healthcare inequalities continue to persist worldwide and the impact of medical biases on different patient groups is still being uncovered by the research community. At a time when clinical
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Reinforcement learning for intelligent healthcare applications: A survey Artif. Intell. Med. (IF 4.383) Pub Date : 2020-09-28 Antonio Coronato; Muddasar Naeem; Giuseppe De Pietro; Giovanni Paragliola
Discovering new treatments and personalizing existing ones is one of the major goals of modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the realization of advanced intelligent systems able to learn about clinical treatments and discover new medical knowledge from the huge amount of data collected. Reinforcement Learning (RL), which is a branch of Machine Learning
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GANs for medical image analysis Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-09 Salome Kazeminia; Christoph Baur; Arjan Kuijper; Bram van Ginneken; Nassir Navab; Shadi Albarqouni; Anirban Mukhopadhyay
Generative adversarial networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of
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Recommendations for enhancing the usability and understandability of process mining in healthcare Artif. Intell. Med. (IF 4.383) Pub Date : 2020-09-28 Niels Martin; Jochen De Weerdt; Carlos Fernández-Llatas; Avigdor Gal; Roberto Gatta; Gema Ibáñez; Owen Johnson; Felix Mannhardt; Luis Marco-Ruiz; Steven Mertens; Jorge Munoz-Gama; Fernando Seoane; Jan Vanthienen; Moe Thandar Wynn; David Baltar Boilève; Jochen Bergs; Mieke Joosten-Melis; Stijn Schretlen; Bart Van Acker
Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life
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Exploiting complex medical data with interpretable deep learning for adverse drug event prediction Artif. Intell. Med. (IF 4.383) Pub Date : 2020-09-15 Jonathan Rebane; Isak Samsten; Panagiotis Papapetrou
A variety of deep learning architectures have been developed for the goal of predictive modelling and knowledge extraction from medical records. Several models have placed strong emphasis on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability
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Ensemble neural network approach detecting pain intensity from facial expressions Artif. Intell. Med. (IF 4.383) Pub Date : 2020-09-07 Ghazal Bargshady, Xujuan Zhou, Ravinesh C. Deo, Jeffrey Soar, Frank Whittaker, Hua Wang
This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis
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Skeletal scintigraphy image enhancement based neutrosophic sets and salp swarm algorithm Artif. Intell. Med. (IF 4.383) Pub Date : 2020-09-06 Mohammed M. Nasef, Fatma T. Eid, Amr M. Sauber
Recently, several schemes are proposed for enhancing the dark regions of the skeletal scintigraphy image. Nevertheless, most of them are flawed by some performance problems. This paper presents an adaptive scheme based on Salp Swarm algorithm (SSA) and a neutrosophic set (NS) under multi-criteria to enhance the dark regions of the skeletal scintigraphy image efficiently. Enhancing the dark regions
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The natural language explanation algorithms for the lung cancer computer-aided diagnosis system Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-28 Anna Meldo, Lev Utkin, Maxim Kovalev, Ernest Kasimov
Two algorithms for explaining decisions of a lung cancer computer-aided diagnosis system are proposed. Their main peculiarity is that they produce explanations of diseases in the form of special sentences via natural language. The algorithms consist of two parts. The first part is a standard local post-hoc explanation model, for example, the well-known LIME, which is used for selecting important features
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Grouping attributes zero-shot learning for tongue constitution recognition Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-21 Guihua Wen, Jiajiong Ma, Yang Hu, Huihui Li, Lijun Jiang
Traditional Chinese Medicine (TCM) considers that the personal constitution determines the occurrence trend and therapeutic effects of certain diseases, which can be recognized by machine learning through tongue images. However, current machine learning methods are confronted with two challenges. First, there are not some larger tongue image databases available. Second, they do not use the domain knowledge
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A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-20 Oscar Reyes, Eduardo Pérez, Raúl M. Luque, Justo Castaño, Sebastián Ventura
Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability
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Automated ICD-10 code assignment of nonstandard diagnoses via a two-stage framework Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-15 Chengjie Mou, Jiangtao Ren
An electronic medical record (EMR) is a rich source of clinical information for medical studies. Each physician usually has his or her own way to describe a patient's diagnosis. This results in many different ways to describe the same disease, which produces a large number of informal nonstandard diagnoses in EMRs. The Tenth Revision of International Classification of Diseases (ICD-10) is a medical
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G-Forest: An ensemble method for cost-sensitive feature selection in gene expression microarrays Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-14 Mai Abdulla, Mohammad T. Khasawneh
Microarray gene expression profiling has emerged as an efficient technique for cancer diagnosis, prognosis, and treatment. One of the major drawbacks of gene expression microarrays is the “curse of dimensionality”, which hinders the usefulness of information in datasets and leads to computational instability. In recent years, feature selection techniques have emerged as effective tools to identify
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EBM+: Advancing Evidence-Based Medicine via two level automatic identification of Populations, Interventions, Outcomes in medical literature Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-13 Nikolaos Stylianou, Gerasimos Razis, Dimitrios G. Goulis, Ioannis Vlahavas
Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available and make an informative treatment plan for their patients. A variety of frameworks, including the PICO framework which is named after its elements (Population
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Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-11 Jinwang Feng, Shao-Wu Zhang, Luonan Chen
Alzheimer's disease (AD) is now difficult to be identified for clinicians, especially, at its prodromal stage, mild cognitive impairment (MCI), because of no obvious clinical symptom and few impacts on daily life at this phase. In addition, energy distribution differences of brain atrophies reflected in structural magnetic resonance imaging (sMRI) images between MCI patients and older healthy controls
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Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-09 Yuan Li, Pingjun Chen, Zhiyuan Li, Hai Su, Lin Yang, Dingrong Zhong
Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the diagnoses often challenge pathologists. Here we propose a rule-based system to differentiate thyroid nodules from intraoperative frozen sections using deep learning techniques. The proposed system consists of three components: (1) automatically locating tissue regions in the whole slide images (WSIs)
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Handling imbalanced medical image data: A deep-learning-based one-class classification approach Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-07 Long Gao, Lei Zhang, Chang Liu, Shandong Wu
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Deep learning to find colorectal polyps in colonoscopy: A systematic literature review Artif. Intell. Med. (IF 4.383) Pub Date : 2020-08-01 Luisa F. Sánchez-Peralta, Luis Bote-Curiel, Artzai Picón, Francisco M. Sánchez-Margallo, J. Blas Pagador
Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality
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Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network Artif. Intell. Med. (IF 4.383) Pub Date : 2020-07-24 Zhan Wu, Gonglei Shi, Yang Chen, Fei Shi, Xinjian Chen, Gouenou Coatrieux, Jian Yang, Limin Luo, Shuo Li
Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet)
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