Abstract：
:Disagreement or inconsistencies in mammographic interpretation motivates utilizing computerized pattern recognition algorithms to aid the assessment of radiographic features. We have studied the potential for using artificial neural networks (ANNs) to analyze interpreted radiographic features from film screen mammograms. Attention was given to 216 cases (mammogram series) that presented suspicious characteristics. The domain expert (Wasson) quantified up to 12 radiographic features for each case based on guidelines from previous literature. Patient age was also included. The existence or absence of malignancy was confirmed in each case via open surgical biopsy (111 malignant, 105 benign). ANNs of various complexity were trained via evolutionary programming to indicate whether or not a malignancy was present given a vector of scored input features in a statistical cross validation procedure. For suspicious masses, the best evolved ANNs generated a mean area under the receiver operating characteristic curve (AZ) of 0.9196 +/- 0.0040 (1 S.E.), with a mean specificity of 0.6269 +/- 0.0272 at 0.95 sensitivity. Results when microcalcifications were included were not quite as good (AZ = 0.8464), however, ANNs with only two hidden nodes performed as well as more complex ANNs and better than ANNs with only one hidden node. The performance of the evolved ANNs was comparable to prior literature, but with an order of magnitude less complexity. The success of small ANNs in diagnosing breast cancer offers the promise that suitable explanations for the ANN's behavior can be induced, leading to a greater acceptance by physicians.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Fogel DB,Wasson EC 3rd,Boughton EM,Porto VWdoi
10.1016/s0933-3657(98)00040-2subject
Has Abstractpub_date
1998-11-01 00:00:00pages
317-26issue
3eissn
0933-3657issn
1873-2860pii
S0933365798000402journal_volume
14pub_type
杂志文章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
更新日期：2020-07-01 00:00:00
abstract：:In this study different substitution methods for the replacement of missing data values were inspected for the use of these cases in a neural network based decision support system for acute appendicitis. The leucocyte count had the greatest number of missing values and was used in the analyses. Four different methods ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00027-x
更新日期：1998-07-01 00:00:00
abstract：OBJECTIVE:There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the influence of abnormal spikes ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.06.007
更新日期：2011-10-01 00:00:00
abstract：OBJECTIVE:Connect-Four, a new sensorimotor rhythm (SMR) based brain-computer interface (BCI) gaming application, was evaluated by four severely motor restricted end-users; two were in the locked-in state and had unreliable eye-movement. METHODS:Following the user-centred approach, usability of the BCI prototype was ev...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.08.001
更新日期：2013-10-01 00:00:00
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
更新日期：2019-05-01 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：BACKGROUND:After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more inform...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.01.001
更新日期：2016-02-01 00:00:00
abstract：OBJECTIVE:Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an ess...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2005.02.003
更新日期：2005-09-01 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：:Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be ut...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00057-8
更新日期：1999-03-01 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: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: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
更新日期：2018-04-01 00:00:00
abstract：:The process of patient care performed by an anaesthesiologist during high invasive surgery requires fundamental knowledge of the physiologic processes and a long standing experience in patient management to cope with the inter-individual variability of the patients. Biomedical engineering research improves the patient...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(97)00020-1
更新日期：1997-09-01 00:00:00
abstract：:This work presents a hybrid expert system (HES) intended to minimise some complex problems pervasive to knowledge engineering such as: the knowledge elicitation process, known as the bottleneck of expert systems; the choice of a model for knowledge representation to codify human reasoning; the number of neurons in the...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(00)00090-7
更新日期：2001-01-01 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
abstract：OBJECTIVE:While EIRA has proved to be successful in the detection of anomalous patient responses to treatments in the Intensive Care Unit, it could not describe to clinicians the rationales behind the anomalous detections. The aim of this paper is to address this problem. METHODS:Few attempts have been made in the pas...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.02.003
更新日期：2013-05-01 00:00:00
abstract：OBJECTIVE:To assess whether a user-centred prototype clinical decision support system (CDSS) providing patient-specific advice better supports healthcare practitioners in terms of (a) types of usability problems detected and (b) effective and efficient retrieval of childhood cancer survivor's follow-up screening proced...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.04.004
更新日期：2013-09-01 00:00:00
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
更新日期：2020-06-01 00:00:00
abstract：OBJECTIVE:Gene Ontology (GO) has become a routine resource for functional analysis of gene lists. Although a number of tools have been provided to identify enriched GO terms in one or two gene lists, two technical challenges remain. First, how to handle multiple hypothesis testing in the analysis given that the tests a...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2007.08.002
更新日期：2007-10-01 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: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：OBJECTIVES:We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains. MATERIAL AND METHODS:We propose firstly a method for enriching mu...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.07.015
更新日期：2005-02-01 00:00:00
abstract：:We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relationships among sets of ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/0933-3657(94)90066-3
更新日期：1994-06-01 00:00:00
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
更新日期：2019-09-01 00:00:00
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
更新日期：2020-07-01 00:00:00
abstract：:The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual pro...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.01.017
更新日期：2004-07-01 00:00:00
abstract：OBJECTIVE:We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor classifiers. METHODS AND MATERIAL:Gene expression based cancer classificat...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.08.004
更新日期：2009-02-01 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：:A nursing database which records patient details and treatments as fields in a standard database format is transformed into a collection, in text form, of patient case days with history. Each case is represented as text strings encoding the patient details, the current problems, treatments and their associated history...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(96)00362-4
更新日期：1997-01-01 00:00:00
