1,778 results match your criteria Artificial Intelligence In Medicine[Journal]


Understanding what patients think about hospitals: A deep learning approach for detecting emotions in patient opinions.

Artif Intell Med 2022 Jun 8;128:102298. Epub 2022 Apr 8.

Department of Information Technologies and Systems, Escuela Superior de Informática, University of Castilla-La Mancha, Ciudad Real, Spain. Electronic address:

Introduction: Most hospital assessment systems are based on the study of objective statistical variables as well as patient opinions on their experiences with respect to the services offered by each hospital. Nevertheless, studies have indicated that most of these assessment systems fail to detect patient emotions when they are assessing their stays in a hospital. This information is vital to understanding most of the patient reviews, which are very complex and convey several emotions per review. Read More

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Multilabel classification of medical concepts for patient clinical profile identification.

Artif Intell Med 2022 Jun 26;128:102311. Epub 2022 Apr 26.

Sorbonne Université, Inserm, Université Sorbonne Paris Nord, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé (LIMICS), 75006 Paris, France.

Background: The development of electronic health records has provided a large volume of unstructured biomedical information. Extracting patient characteristics from these data has become a major challenge, especially in languages other than English.

Methods: Inspired by the French Text Mining Challenge (DEFT 2021) [1] in which we participated, our study proposes a multilabel classification of clinical narratives, allowing us to automatically extract the main features of a patient report. Read More

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Medical checkup data analysis method based on LiNGAM and its application to nonalcoholic fatty liver disease.

Artif Intell Med 2022 Jun 22;128:102310. Epub 2022 Apr 22.

Shiga University of Medical Science, Japan.

Although medical checkup data would be useful for identifying unknown factors of disease progression, a causal relationship between checkup items should be taken into account for precise analysis. Missing values in medical checkup data must be appropriately imputed because checkup items vary from person to person, and items that have not been tested include missing values. In addition, the patients with target diseases or disorders are small in comparison with the total number of persons recorded in the data, which means medical checkup data is an imbalanced data analysis. Read More

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Intelligent and strong robust CVS-LVAD control based on soft-actor-critic algorithm.

Artif Intell Med 2022 Jun 22;128:102308. Epub 2022 Apr 22.

Key Laboratory for Precision and Nontraditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, China. Electronic address:

Left ventricular assist device (LVAD) is an effective method to treat ventricular failure. According to the physiological conditions of different patients, the device adaptively adjusts its rotation speed to change LVAD output. In this study, a physiological control system for LVAD based on deep reinforcement learning (DRL) is proposed. Read More

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Assigning diagnosis codes using medication history.

Artif Intell Med 2022 Jun 20;128:102307. Epub 2022 Apr 20.

Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark; Unit of Clinical Biostatistics, Department of Research and Innovation, Aalborg University Hospital, Aalborg, Denmark. Electronic address:

Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniques utilizing medical notes but are inapplicable in the absence of these notes. Read More

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It's the data, stupid: Inflection point for Artificial Intelligence in Indian healthcare.

Artif Intell Med 2022 Jun 6;128:102300. Epub 2022 Apr 6.

Employees' State Insurance Corporation, Head Quarter, New Delhi, India.

Indian healthcare is fast growing and with significant chunk of it being in small, fragmented, informal sector; Artificial Intelligence (AI) is pegged as a magical tool for a better healthcare system. There is an inclination to merely mimic the US approach in the on-going policy making and legislative exercises, which can have serious fallouts for Indian healthcare. India needs a different approach to suite her unique requirements. Read More

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Machine learning-based heart disease diagnosis: A systematic literature review.

Artif Intell Med 2022 Jun 29;128:102289. Epub 2022 Mar 29.

Dept. of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73071, USA. Electronic address:

Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patients' data, detecting heart disease during the early stage is feasible. However, both ECG and patients' data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Read More

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Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.

Artif Intell Med 2022 Jun 28;128:102286. Epub 2022 Mar 28.

National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Rende, Italy. Electronic address:

The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. Read More

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Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations.

Artif Intell Med 2022 Jun 22;128:102283. Epub 2022 Mar 22.

Universidade Federal de Minas Gerais, Brazil; CIIA-Health, Innovation Center on Artificial Intelligence for Health, Brazil.

The aim of this study is to build machine learning models to predict severe complications using administrative and clinical elements that are collected immediately after patient admission to the intensive care unit (ICU). Risk models are of increasing importance in the ICU setting. However, they generally present the black-box issue because they do not provide meaningful information about the logic involved in patient-specific predictions. Read More

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Radiology report generation for proximal femur fractures using deep classification and language generation models.

Artif Intell Med 2022 Jun 26;128:102281. Epub 2022 Mar 26.

University of Twente, Enschede, the Netherlands; University of Duisburg-Essen, Essen, Germany. Electronic address:

Proximal femur fractures represent a major health concern, and substantially contribute to the morbidity of elderly. Correct classification and diagnosis of hip fractures has a significant impact on mortality, costs and hospital stay. In this paper, we present a method and empirical validation for automatic subclassification of proximal femur fractures and Dutch radiological report generation that does not rely on manually curated data. Read More

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Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection.

Artif Intell Med 2022 May 18;127:102278. Epub 2022 Mar 18.

Department of Mathematics, Istanbul Commerce University, Istanbul, Turkey. Electronic address:

ELECTRE is a family of multi-criteria decision analysis techniques, which has the ability to provide as much as possible precise and suitable set of actions or alternatives to the underlying problem by eliminating the alternatives, which are outranked by others. Group decision-making is an effective process to provide the most appropriate solution to real-world decision-making scenarios by considering and merging the expert opinions of multiple individuals on the problem. The aim of this study is to present an extended version of the ELECTRE I model called the Fermatean fuzzy ELECTRE I method for of multi-criteria group decision-making with Fermatean fuzzy human assessments. Read More

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CapillaryNet: An automated system to quantify skin capillary density and red blood cell velocity from handheld vital microscopy.

Artif Intell Med 2022 May 28;127:102287. Epub 2022 Mar 28.

Department of Informatics, University of Oslo, Gaustadalléen 21, 0349 Oslo, Norway. Electronic address:

Capillaries are the smallest vessels in the body which are responsible for delivering oxygen and nutrients to surrounding cells. Various life-threatening diseases are known to alter the density of healthy capillaries and the flow velocity of erythrocytes within the capillaries. In previous studies, capillary density and flow velocity were manually assessed by trained specialists. Read More

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BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions.

Artif Intell Med 2022 May 29;127:102285. Epub 2022 Mar 29.

Institute for Systems and Robotics, Avenida Rovisco Pais 1, 1049-001 Lisbon, Portugal.

In this paper, we developed BreastScreening-AI within two scenarios for the classification of multimodal beast images: (1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the introduction of a deep learning method into a real clinical workflow for medical imaging diagnosis. We attempt to address three high-level goals in the two above scenarios. Read More

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Word-level text highlighting of medical texts for telehealth services.

Artif Intell Med 2022 May 23;127:102284. Epub 2022 Mar 23.

Data Science Lab at Ryerson University, Toronto, ON M5B 1G3, Canada.

The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This creates additional workload for medical professionals who are heavily dependent on medical data to complete their research and consult their patients. Read More

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Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF.

Artif Intell Med 2022 May 18;127:102282. Epub 2022 Mar 18.

School of Computer Science and Engineering, Central South University, Changsha 410083, PR China. Electronic address:

Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinical free texts. In recent years, with the development of deep learning technology, deep neural networks (DNNs) have been widely used in Chinese clinical named entity recognition and many other clinical NLP tasks. However, these state-of-the-art models failed to make full use of the global information and multi-level semantic features in clinical texts. Read More

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Fall risk assessment through a synergistic multi-source DNN learning model.

Artif Intell Med 2022 May 18;127:102280. Epub 2022 Mar 18.

Department of Engineering, College of Science and Mathematics, United States of America; University of Massachusetts Boston, Boston, MA, United States of America. Electronic address:

Falls are a complex problem and play a leading role in the development of disabilities in the older population. While fall detection systems are important, it is also essential to work on fall preventive strategies, which will have the most significant impact in reducing disability in the elderly. In this work, we explore a prospective cohort study, specifically designed for examining novel risk factors for falls in community-living older adults. Read More

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Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features.

Artif Intell Med 2022 May 9;127:102279. Epub 2022 Mar 9.

Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Electronic address:

This work proposed a novel method for automatic sleep stage classification based on the time, frequency, and fractional Fourier transform (FRFT) domain features extracted from a single-channel electroencephalogram (EEG). Bidirectional long short-term memory was applied to the proposed model to train it to learn the sleep stage transition rules according to the American Academy of Sleep Medicine's manual for automatic sleep stage classification. Results indicated that the features extracted from the fractional Fourier-transformed single-channel EEG may improve the performance of sleep stage classification. Read More

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Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control.

Artif Intell Med 2022 May 26;127:102288. Epub 2022 Mar 26.

College of Computer Engineering and Science, Prince Sattam Bin ABdulaziz University, Al-Kharj, Saudi Arabia. Electronic address:

COVID-19 is a life-threatening contagious virus that has spread across the globe rapidly. To reduce the outbreak impact of COVID-19 virus illness, continual identification and remote surveillance of patients are essential. Medical service delivery based on the Internet of Things (IoT) technology backed up by the fog-cloud paradigm is an efficient and time-sensitive solution for remote patient surveillance. Read More

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A novel multi-objective medical feature selection compass method for binary classification.

Artif Intell Med 2022 May 9;127:102277. Epub 2022 Mar 9.

Exercise and Sports medicine, University Hospital of Angers, 4 Rue Larrey, 49100 Angers, France; INSERM 1083, CNRS 6015, University of Angers, 40 Rue de Rennes, BP 73532 - 49035 Angers CEDEX 01, France. Electronic address:

The use of Artificial Intelligence in medical decision support systems has been widely studied. Since a medical decision is frequently the result of a multi-objective optimization problem, a popular challenge combining Artificial Intelligence and Medicine is Multi-Objective Feature Selection (MOFS). This article proposes a novel approach for MOFS applied to medical binary classification. Read More

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Breast cancer detection using artificial intelligence techniques: A systematic literature review.

Artif Intell Med 2022 May 5;127:102276. Epub 2022 Mar 5.

University of Sharjah, United Arab Emirates. Electronic address:

Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer Foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. Read More

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Exemplar Darknet19 feature generation technique for automated kidney stone detection with coronal CT images.

Artif Intell Med 2022 May 5;127:102274. Epub 2022 Mar 5.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia, University, Taichung, Taiwan. Electronic address:

Kidney stone is a commonly seen ailment and is usually detected by urologists using computed tomography (CT) images. It is difficult and time-consuming to detect small stones in CT images. Hence, an automated system can help clinicians to detect kidney stones accurately. Read More

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FlauBERT vs. CamemBERT: Understanding patient's answers by a French medical chatbot.

Artif Intell Med 2022 May 2;127:102264. Epub 2022 Mar 2.

Université de Lyon, Lyon, France; Université Lyon 1, Villeurbanne, France; Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France; Équipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR 5558, Villeurbanne, France.

In a number of circumstances, obtaining health-related information from a patient is time-consuming, whereas a chatbot interacting efficiently with that patient might help saving health care professional time and better assisting the patient. Making a chatbot understand patients' answers uses Natural Language Understanding (NLU) technology that relies on 'intent' and 'slot' predictions. Over the last few years, language models (such as BERT) pre-trained on huge amounts of data achieved state-of-the-art intent and slot predictions by connecting a neural network architecture (e. Read More

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A novel methodology for head and neck carcinoma treatment stage detection by means of model checking.

Artif Intell Med 2022 May 21;127:102263. Epub 2022 Mar 21.

Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.

Context: Head and neck cancers are diagnosed at an annual rate of 3% to 7% with respect to the total number of cancers, and 50% to 75% of such new tumours occur in the upper aerodigestive tract.

Purpose: In this paper we propose formal methods based approach aimed to identify the head and neck tumour treatment stage by means of model checking. We exploit a set of radiomic features to model medical imaging as a labelled transition system to verify treatment stage properties. Read More

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A method for the early prediction of chronic diseases based on short sequential medical data.

Artif Intell Med 2022 May 3;127:102262. Epub 2022 Mar 3.

Health Management Center, The First Affiliated Hospital, Medical School of Zhejiang University, No.79 Qingchun Rd., Hangzhou 310003, China. Electronic address:

Noncommunicable diseases (NCDs) have become the leading cause of death worldwide. NCDs' chronicity, hiddenness, and irreversibility make patients' disease self-awareness extremely important in disease control but hard to achieve. With an accumulation of electronic health record (EHR) data, it has become possible to predict NCDs early through machine learning approaches. Read More

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Lesion-attention pyramid network for diabetic retinopathy grading.

Artif Intell Med 2022 04 25;126:102259. Epub 2022 Feb 25.

Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway. Electronic address:

As one of the most common diabetic complications, diabetic retinopathy (DR) can cause retinal damage, vision loss and even blindness. Automated DR grading technology has important clinical significance, which can help ophthalmologists achieve rapid and early diagnosis. With the popularity of deep learning, DR grading based on the convolutional neural networks (CNNs) has become the mainstream method. Read More

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Detection of bladder cancer with feature fusion, transfer learning and CapsNets.

Artif Intell Med 2022 04 6;126:102275. Epub 2022 Mar 6.

CMEMS-UMinho Research Unit, University of Minho, Campus Azurém, 4800-058 Guimarães, Portugal; LABBELS -Associate Laboratory, Braga, Guimarães, Portugal. Electronic address:

This paper confronts two approaches to classify bladder lesions shown in white light cystoscopy images when using small datasets: the classical one, where handcrafted-based features feed pattern recognition systems and the modern deep learning-based (DL) approach. In between, there are alternative DL models that had not received wide attention from the scientific community, even though they can be more appropriate for small datasets such as the human brain motivated capsule neural networks (CapsNets). However, CapsNets have not yet matured hence presenting lower performances than the most classic DL models. Read More

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Weak label based Bayesian U-Net for optic disc segmentation in fundus images.

Artif Intell Med 2022 04 26;126:102261. Epub 2022 Feb 26.

Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

Fundus images have been widely used in routine examinations of ophthalmic diseases. For some diseases, the pathological changes mainly occur around the optic disc area; therefore, detection and segmentation of the optic disc are critical pre-processing steps in fundus image analysis. Current machine learning based optic disc segmentation methods typically require manual segmentation of the optic disc for the supervised training. Read More

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A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Artif Intell Med 2022 04 24;126:102260. Epub 2022 Feb 24.

Guangdong Key Lab of Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. Electronic address:

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods. However, these multi-modal methods are restricted to a single task (e. Read More

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Spatio-temporal mixture process estimation to detect dynamical changes in population.

Artif Intell Med 2022 04 23;126:102258. Epub 2022 Feb 23.

Center for Applied Mathematics - Ecole Polytechnique, Palaiseau, France; UMR S1138, University of Paris, INRIA, INSERM, Sorbonne University, Paris, France. Electronic address:

Population monitoring is a challenge in many areas such as public health and ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data changes. Assuming that mixture models can correctly model populations, we propose a new version of the Expectation-Maximization (EM) algorithm to better estimate the number of clusters and their parameters at the same time. Read More

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Automatic pediatric congenital heart disease classification based on heart sound signal.

Artif Intell Med 2022 04 19;126:102257. Epub 2022 Feb 19.

Department of Cardiac Surgery, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 310057 Hangzhou, China. Electronic address:

Congenital heart diseases (CHD) are the most common birth defects, and the early diagnosis of CHD is crucial for CHD therapy. However, there are relatively few studies on intelligent auscultation for pediatric CHD, due to the fact that effective cooperation of the patient is required for the acquisition of useable heart sounds by electronic stethoscopes, yet the quality of heart sounds in pediatric is poor compared to adults due to the factors such as crying and breath sounds. This paper presents a novel pediatric CHD intelligent auscultation method based on electronic stethoscope. Read More

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