听力与言语-语言病理学

行为科学

医学伦理学

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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.

    abstract: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 ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101997

    authors: Esmaili N,Buchlak QD,Piccardi M,Kruger B,Girosi F

    更新日期:2021-01-01 00:00:00

  • A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images.

    abstract::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 c...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101980

    authors: Bennai MT,Guessoum Z,Mazouzi S,Cormier S,Mezghiche M

    更新日期:2020-11-01 00:00:00

  • Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

    abstract::In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supportin...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101931

    authors: Van Woensel W,Roy PC,Abidi SSR,Abidi SR

    更新日期:2020-08-01 00:00:00

  • A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis.

    abstract::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...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101950

    authors: Reyes O,Pérez E,Luque RM,Castaño J,Ventura S

    更新日期:2020-08-01 00:00:00

  • Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection.

    abstract::Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning model, such as faster region convolutional neural network (faster R-CNN), has be...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101881

    authors: Chen S,Han Y,Lin J,Zhao X,Kong P

    更新日期:2020-07-01 00:00:00

  • Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

    abstract::Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101911

    authors: Lee WK,Wu CC,Lee CC,Lu CF,Yang HC,Huang TH,Lin CY,Chung WY,Wang PS,Wu HM,Guo WY,Wu YT

    更新日期:2020-07-01 00:00:00

  • Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis.

    abstract::Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101927

    authors: Xie J,Jiang J,Wang Y,Guan Y,Guo X

    更新日期:2020-07-01 00:00:00

  • ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network.

    abstract::Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method fo...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101856

    authors: Zhang J,Liu A,Gao M,Chen X,Zhang X,Chen X

    更新日期:2020-06-01 00:00:00

  • On the use of pairwise distance learning for brain signal classification with limited observations.

    abstract::The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral pr...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101852

    authors: Calhas D,Romero E,Henriques R

    更新日期:2020-05-01 00:00:00

  • A novel deep mining model for effective knowledge discovery from omics data.

    abstract::Knowledge discovery from omics data has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, high-throughput biomedical datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101821

    authors: Alzubaidi A,Tepper J,Lotfi A

    更新日期:2020-04-01 00:00:00

  • An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation.

    abstract::Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particl...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101790

    authors: Xiong H,Qiu B,Liu J

    更新日期:2020-04-01 00:00:00

  • Topic-informed neural approach for biomedical event extraction.

    abstract::As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101783

    authors: Zhang J,Liu M,Zhang Y

    更新日期:2020-03-01 00:00:00

  • Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI).

    abstract::Over the years, there has been growing interest in using machine learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc.) or the acquisition technology, which mi...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101804

    authors: Ferrari E,Retico A,Bacciu D

    更新日期:2020-03-01 00:00:00

  • Automatic computation of mandibular indices in dental panoramic radiographs for early osteoporosis detection.

    abstract:AIM:A new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101816

    authors: Aliaga I,Vera V,Vera M,García E,Pedrera M,Pajares G

    更新日期:2020-03-01 00:00:00

  • A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy.

    abstract:BACKGROUND:The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspec...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101751

    authors: Campos M,Jimenez F,Sanchez G,Juarez JM,Morales A,Canovas-Segura B,Palacios F

    更新日期:2020-01-01 00:00:00

  • State recognition of decompressive laminectomy with multiple information in robot-assisted surgery.

    abstract::The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons h...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101763

    authors: Sun Y,Wang L,Jiang Z,Li B,Hu Y,Tian W

    更新日期:2020-01-01 00:00:00

  • Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods.

    abstract::Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progr...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101742

    authors: Zahia S,Garcia Zapirain MB,Sevillano X,González A,Kim PJ,Elmaghraby A

    更新日期:2020-01-01 00:00:00

  • The social phenotype: Extracting a patient-centered perspective of diabetes from health-related blogs.

    abstract:MOTIVATIONS:It has recently been argued [1] that the effectiveness of a cure depends on the doctor-patient shared understanding of an illness and its treatment. Although a better communication between doctor and patient can be pursued through dedicated training programs, or by collecting patients' experiences and sympt...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101727

    authors: Lenzi A,Maranghi M,Stilo G,Velardi P

    更新日期:2019-11-01 00:00:00

  • The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

    abstract::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 p...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101706

    authors: Spänig S,Emberger-Klein A,Sowa JP,Canbay A,Menrad K,Heider D

    更新日期:2019-09-01 00:00:00

  • Retinal image assessment using bi-level adaptive morphological component analysis.

    abstract::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 trea...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.07.010

    authors: Javidi M,Harati A,Pourreza H

    更新日期:2019-08-01 00:00:00

  • Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

    abstract:OBJECTIVES:The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system. METHOD:Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorit...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.06.005

    authors: Alexiadis A

    更新日期:2019-07-01 00:00:00

  • Project INSIDE: towards autonomous semi-unstructured human-robot social interaction in autism therapy.

    abstract::This paper describes the INSIDE system, a networked robot system designed to allow the use of mobile robots as active players in the therapy of children with autism spectrum disorders (ASD). While a significant volume of work has explored the impact of robots in ASD therapy, most such work comprises remotely operated ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.12.003

    authors: Melo FS,Sardinha A,Belo D,Couto M,Faria M,Farias A,Gambôa H,Jesus C,Kinarullathil M,Lima P,Luz L,Mateus A,Melo I,Moreno P,Osório D,Paiva A,Pimentel J,Rodrigues J,Sequeira P,Solera-Ureña R,Vasco M,Veloso M,Vent

    更新日期:2019-05-01 00:00:00

  • Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

    abstract::The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive an...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.02.002

    authors: Ghosh S,Chattopadhyay BP,Roy RM,Mukherjee J,Mahadevappa M

    更新日期:2019-05-01 00:00:00

  • Predicting ICU readmission using grouped physiological and medication trends.

    abstract:BACKGROUND:Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmi...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.08.004

    authors: Xue Y,Klabjan D,Luo Y

    更新日期:2019-04-01 00:00:00

  • A frame reduction system based on a color structural similarity (CSS) method and Bayer images analysis for capsule endoscopy.

    abstract::A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an incr...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.12.008

    authors: Al-Shebani Q,Premaratne P,McAndrew DJ,Vial PJ,Abey S

    更新日期:2019-03-01 00:00:00

  • Accessing complex patient data from Arden Syntax Medical Logic Modules.

    abstract:OBJECTIVE:Arden Syntax is a standard for representing and sharing medical knowledge in form of independent modules and looks back on a history of 25 years. Its traditional field of application is the monitoring of clinical events such as generating an alert in case of occurrence of a critical laboratory result. Arden S...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2015.09.003

    authors: Kraus S,Enders M,Prokosch HU,Castellanos I,Lenz R,Sedlmayr M

    更新日期:2018-11-01 00:00:00

  • Using Arden Syntax for the creation of a multi-patient surveillance dashboard.

    abstract:OBJECTIVE:Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not native...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2015.09.009

    authors: Kraus S,Drescher C,Sedlmayr M,Castellanos I,Prokosch HU,Toddenroth D

    更新日期:2018-11-01 00:00:00

  • User-defined functions in the Arden Syntax: An extension proposal.

    abstract:BACKGROUND:The Arden Syntax is a knowledge-encoding standard, started in 1989, and now in its 10th revision, maintained by the health level seven (HL7) organization. It has constructs borrowed from several language concepts that were available at that time (mainly the HELP hospital information system and the Regenstrie...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2015.11.003

    authors: Karadimas H,Ebrahiminia V,Lepage E

    更新日期:2018-11-01 00:00:00

  • Symptoms and medications change patterns for Parkinson's disease patients stratification.

    abstract::Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients' medical conditions and changes in the presc...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.04.010

    authors: Valmarska A,Miljkovic D,Konitsiotis S,Gatsios D,Lavrač N,Robnik-Šikonja M

    更新日期:2018-09-01 00:00:00

  • Decision support system for detection of hypertensive retinopathy using arteriovenous ratio.

    abstract::Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated sys...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.06.004

    authors: Akbar S,Akram MU,Sharif M,Tariq A,Khan SA

    更新日期:2018-08-01 00:00:00

  • Approximate dynamic programming approaches for appointment scheduling with patient preferences.

    abstract::During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.02.001

    authors: Li X,Wang J,Fung RYK

    更新日期:2018-04-01 00:00:00

  • Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation.

    abstract:OBJECTIVE:In this paper, we propose a novel algorithm to extract the active intervals of intracardiac electrograms during atrial fibrillation. METHODS:First, we show that the characteristics of the signal waveform at its inflection points are prominent features that are implicitly used by human annotators for distingu...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.02.003

    authors: Hajimolahoseini H,Hashemi J,Gazor S,Redfearn D

    更新日期:2018-04-01 00:00:00

  • Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

    abstract:OBJECTIVE:Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs). METHODS AND MATERIAL:Different approaches exist to model GRNs; they are mostly simulated as mathematical models that represent relationships between ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.06.007

    authors: Erdogdu U,Polat F,Alhajj R

    更新日期:2017-11-01 00:00:00

  • Analyzing interactions on combining multiple clinical guidelines.

    abstract::Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability a...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.03.012

    authors: Zamborlini V,da Silveira M,Pruski C,Ten Teije A,Geleijn E,van der Leeden M,Stuiver M,van Harmelen F

    更新日期:2017-09-01 00:00:00

  • Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies.

    abstract:OBJECTIVE:Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.07.002

    authors: Lamy JB

    更新日期:2017-07-01 00:00:00

  • iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

    abstract::Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to peri...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.06.008

    authors: Akbar S,Hayat M,Iqbal M,Jan MA

    更新日期:2017-06-01 00:00:00

  • Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

    abstract::Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart soun...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.05.005

    authors: Eslamizadeh G,Barati R

    更新日期:2017-05-01 00:00:00

  • A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis.

    abstract:OBJECTIVE:We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their m...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.02.003

    authors: Gu D,Liang C,Zhao H

    更新日期:2017-03-01 00:00:00

  • An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem.

    abstract:OBJECTIVE:The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2016.10.002

    authors: Lusby RM,Schwierz M,Range TM,Larsen J

    更新日期:2016-11-01 00:00:00

  • Out of hours workload management: Bayesian inference for decision support in secondary care.

    abstract:OBJECTIVE:In this paper, we aim to evaluate the use of electronic technologies in out of hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2016.09.005

    authors: Perez I,Brown M,Pinchin J,Martindale S,Sharples S,Shaw D,Blakey J

    更新日期:2016-10-01 00:00:00

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