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. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. In this paper, we exploit recent advances in deep learning to mitigate against these limitations on the basis of automatically capturing enough of the meaningful abstractions latent with the available biological samples. Our deep feature learning model is proposed based on a set of non-linear sparse Auto-Encoders that are deliberately constructed in an under-complete manner to detect a small proportion of molecules that can recover a large proportion of variations underlying the data. However, since multiple projections are applied to the input signals, it is hard to interpret which phenotypes were responsible for deriving such predictions. Therefore, we also introduce a novel weight interpretation technique that helps to deconstruct the internal state of such deep learning models to reveal key determinants underlying its latent representations. The outcomes of our experiment provide strong evidence that the proposed deep mining model is able to discover robust biomarkers that are positively and negatively associated with cancers of interest. Since our deep mining model is problem-independent and data-driven, it provides further potential for this research to extend beyond its cognate disciplines.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Alzubaidi A,Tepper J,Lotfi Adoi
10.1016/j.artmed.2020.101821subject
Has Abstractpub_date
2020-04-01 00:00:00pages
101821eissn
0933-3657issn
1873-2860pii
S0933-3657(19)30993-5journal_volume
104pub_type
杂志文章abstract：OBJECTIVES:Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI experts around. But are the technology and the field mature enough f...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.08.004
更新日期：2013-10-01 00:00:00
abstract：OBJECTIVE:Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The ma...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.014
更新日期：2009-02-01 00:00:00
abstract：OBJECTIVE:One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.003
更新日期：2008-10-01 00:00:00
abstract：OBJECTIVE:We present a combined terminological resource for text mining over biomedical literature. The purpose of the resource is to allow the detection of mentions of specific biological entities in scientific publications, and their grounding to widely accepted identifiers. This is an essential process, useful in it...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.04.011
更新日期：2011-06-01 00:00:00
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
更新日期：2018-08-01 00:00:00
abstract：OBJECTIVE:Recently, fuzzy soft sets-based decision making has attracted more and more interest. Although plenty of works have been done, they cannot provide the uncertainty or certainty of their results. To manage uncertainty is one of the most important and toughest tasks of decision making especially in medicine. In ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.04.004
更新日期：2016-05-01 00:00:00
abstract：:Diagnosis of visual function losses in glaucomatous patients depends to a large extent on the analysis of the data collected from corresponding psychophysical tests. One of the main difficulties in obtaining reliable data from patients in these tests is the measurement noise caused by the learning effect, inattention,...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/0933-3657(94)90004-3
更新日期：1994-10-01 00:00:00
abstract：OBJECTIVE:The successful preparation of cells for therapy depends on the characterization of causal factors affecting cell quality. Ultra scale-down methods are used to characterize cells in terms of their response to process engineering causal factors of hydrodynamic shear stress and time. This response is in turn cha...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.07.003
更新日期：2014-10-01 00:00:00
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
更新日期：2020-01-01 00:00:00
abstract：CONTEXT:Temporal information plays a crucial role in medicine, so that in medical informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit trea...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2012.03.002
更新日期：2012-07-01 00:00:00
abstract：:Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.01.009
更新日期：2004-06-01 00:00:00
abstract：:In this paper, we propose an approach for managing clinical guidelines. We outline a modular architecture, allowing us to separate two conceptually distinct aspects: the representation (and acquisition) of clinical guidelines and their execution. We propose an expressive formalism, which allows one to deal with the co...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章,评审
doi：10.1016/s0933-3657(01)00087-2
更新日期：2001-11-01 00:00:00
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
更新日期：2020-08-01 00:00:00
abstract：OBJECTIVE:In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2012.12.003
更新日期：2013-01-01 00:00:00
abstract：OBJECTIVE:New medical systems may be rejected by staff because they do not integrate with local practice. An expert system, FLORENCE, is being developed to help staff in a neonatal intensive care unit (NICU) make decisions about ventilator settings when treating babies with respiratory distress syndrome. For FLORENCE t...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2005.01.004
更新日期：2005-11-01 00:00:00
abstract：:In case-based studies, controls are retrospectively assigned to patients in order to permit a statistical evaluation of the study results through a comparison of the main outcome measures for the patient and retrieved control groups. Inappropriate selection of the controls by using false retrieval parameters or a fals...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00084-2
更新日期：2002-11-01 00:00:00
abstract：:Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between node...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/0933-3657(93)90029-3
更新日期：1993-06-01 00:00:00
abstract：OBJECTIVE:In this paper a new nonlinear system identification approach is developed for dynamical quantification of cardiovascular regulation. This approach is specifically focused on the identification of the heart rate (HR) baroreflex mechanism. The principal objective of this paper is to improve the model accuracy i...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.01.002
更新日期：2011-05-01 00:00:00
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
更新日期：2018-04-01 00:00:00
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
更新日期：2019-08-01 00:00:00
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
更新日期：2020-04-01 00:00:00
abstract：:Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilaye...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(03)00053-8
更新日期：2003-09-01 00:00:00
abstract：OBJECTIVE:Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2009.03.004
更新日期：2009-10-01 00:00:00
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
更新日期：2019-05-01 00:00:00
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
更新日期：2016-10-01 00:00:00
abstract：OBJECTIVE:Coronary artery disease has been described as one of the curses of the western world, as it is one of its most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary arter...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.04.009
更新日期：2011-06-01 00:00:00
abstract：OBJECTIVE:This paper identifies and reviews ethical issues associated with artificial intelligent care providers (AICPs) in mental health care and other helping professions. Specific recommendations are made for the development of ethical codes, guidelines, and the design of AICPs. METHODS:Current developments in the ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.06.004
更新日期：2014-09-01 00:00:00
abstract：:Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting kn...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.01.015
更新日期：2004-09-01 00:00:00
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
更新日期：2019-03-01 00:00:00
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
更新日期：2019-04-01 00:00:00
