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 Syntax Medical Logic Modules must be able to retrieve patient data from the electronic medical record in order to enable automated decision making. For patient data with a simple structure, for instance a list of laboratory results, or, in a broader view, any patient data with a list or table structure, this mapping process is straightforward. Nevertheless, if patient data are of a complex nested structure the mapping process may become tedious. Two clinical requirements - to process complex microbiology data and to decrease the time between a critical laboratory event and its alerting by monitoring Health Level 7 (HL7) communication - have triggered the investigation of approaches for providing complex patient data from electronic medical records inside Arden Syntax Medical Logic Modules. METHODS AND MATERIALS:The data mapping capabilities of current versions of the Arden Syntax standard as well as interfaces and data mapping capabilities of three different Arden Syntax environments have been analyzed. We found and implemented three different approaches to map a test sample of complex microbiology data for 22 patients and measured their execution times and memory usage. Based on one of these approaches, we mapped entire HL7 messages onto congruent Arden Syntax objects. RESULTS:While current versions of Arden Syntax support the mapping of list and table structures, complex data structures are so far unsupported. We identified three different approaches to map complex data from electronic patient records onto Arden Syntax variables; each of these approaches successfully mapped a test sample of complex microbiology data. The first approach was implemented in Arden Syntax itself, the second one inside the interface component of one of the investigated Arden Syntax environments. The third one was based on deserialization of Extended Markup Language (XML) data. Mean execution times of the approaches to map the test sample were 497ms, 382ms, and 84ms. Peak memory usage amounted to 3MB, 3MB, and 6MB. CONCLUSION:The most promising approach by far was to map arbitrary XML structures onto congruent complex data types of Arden Syntax through deserialization. This approach is generic insofar as a data mapper based on this approach can transform any patient data provided in appropriate XML format. Therefore it could help overcome a major obstacle for integrating clinical decision support functions into clinical information systems. Theoretically, the deserialization approach would even allow mapping entire patient records onto Arden Syntax objects in one single step. We recommend extending the Arden Syntax specification with an appropriate XML data format.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Kraus S,Enders M,Prokosch HU,Castellanos I,Lenz R,Sedlmayr Mdoi
10.1016/j.artmed.2015.09.003subject
Has Abstractpub_date
2018-11-01 00:00:00pages
95-102eissn
0933-3657issn
1873-2860pii
S0933-3657(15)00120-7journal_volume
92pub_type
杂志文章abstract:OBJECTIVES:The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinic...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章,多中心研究
doi:10.1016/j.artmed.2015.09.007
更新日期:2016-01-01 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:MOTIVATION:Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2016.06.002
更新日期:2016-07-01 00:00:00
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: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
更新日期:2020-03-01 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::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
更新日期:2018-09-01 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: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:OBJECTIVE:Suitable techniques for microarray analysis have been widely researched, particularly for the study of marker genes expressed to a specific type of cancer. Most of the machine learning methods that have been applied to significant gene selection focus on the classification ability rather than the selection ab...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2011.06.008
更新日期:2011-09-01 00:00:00
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
更新日期:2020-01-01 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:OBJECTIVES:A well-developed appointment system can help increase the utilization of medical facilities in an outpatient department. This paper outlines the development of an appointment system that can make an outpatient department work more efficiently and improve patient satisfaction level. METHODS:A Markov decision...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2014.12.002
更新日期:2015-01-01 00:00:00
abstract:OBJECTIVE:Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.07.004
更新日期:2005-06-01 00:00:00
abstract::We introduce a novel approach to couple temporal similarity with spatial neighborhood information. This is achieved by concatenating the K nearest, spatially contiguous neighbors of a pixel time-course (TC) of T time-instances. This produces a new TC of (K+1)T time instances. Depending on how "nearest" is defined, we ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(02)00005-2
更新日期:2002-05-01 00:00:00
abstract::To explore the design of computer-supported collaborative work in health care, a case study is described addressing the social contexts and conditions influencing the development process. The data set covers 13 consecutive meetings held in a systems design group over a 2-year period, in total approximately 24 h of vid...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(97)00046-8
更新日期:1998-02-01 00:00:00
abstract:OBJECTIVE:Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applicat...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2006.07.010
更新日期:2007-02-01 00:00:00
abstract::INTERNIST-I was an expert system designed in the early 1970's to diagnose multiple diseases in internal medicine by modelling the behaviour of clinicians. Its form and operation are described, and evaluations of the system are surveyed. The major result of the project was its knowledge base which has been used in succ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/0933-3657(94)00028-q
更新日期:1995-04-01 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::As part of a plan to promote semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA, this study sought to explore and cope with the variability of results that may be anticipated when performing knowledge acquisition with patient data from different patient settings. Patient data were ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(02)00025-8
更新日期:2002-07-01 00:00:00
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
更新日期:2017-07-01 00:00:00
abstract:OBJECTIVE:The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing int...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2006.08.005
更新日期:2007-02-01 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:OBJECTIVE:We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. METHODS:We cast both models and background knowledge in terms of processes that interact to account for behavior. We a...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2006.04.003
更新日期:2006-07-01 00:00:00
abstract::Allowing exchange of information and cooperation among network-wide distributed and heterogeneous applications is a major need of current health-care information systems. The European project SynEx aims at developing an integration platform for both new and legacy applications on each partner's site. We developed, in ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(03)00007-1
更新日期:2003-03-01 00:00:00
abstract:OBJECTIVES:We propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of nasopharyngeal carcinoma (NPC) on a case-control epidemiologic study that consists of 1289 subjects and 150 risk factors. METHODS:This framework build...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2011.09.002
更新日期:2012-01-01 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:We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS:Both time and f...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2016.01.005
更新日期:2016-02-01 00:00:00
abstract:OBJECTIVE:To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. METHODS:We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent ag...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2012.04.005
更新日期:2012-07-01 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