Computer-based clinical decision support for laryngopathies using recurrent neural networks

2010 ◽  
Author(s):  
Jaroslaw Szkola ◽  
Krzysztof Pancerz ◽  
Jan Warchol
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Recurrent Neural Networks ◽  
Clinical Decision ◽  
Computer Based

scholarly journals Recurrent Neural Networks in Computer-Based Clinical Decision Support for Laryngopathies: An Experimental Study

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Jarosław Szkoła ◽  
Krzysztof Pancerz ◽  
Jan Warchoł
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Recurrent Neural Networks ◽  
Clinical Decision ◽  
Learning Ability ◽  
Series Data ◽  
Control Group ◽  
Target Pattern ◽  
Computer Based

The main goal of this paper is to give the basis for creating a computer-based clinical decision support (CDS) system for laryngopathies. One of approaches which can be used in the proposed CDS is based on the speech signal analysis using recurrent neural networks (RNNs). RNNs can be used for pattern recognition in time series data due to their ability of memorizing some information from the past. The Elman networks (ENs) are a classical representative of RNNs. To improve learning ability of ENs, we may modify and combine them with another kind of RNNs, namely, with the Jordan networks. The modified Elman-Jordan networks (EJNs) manifest a faster and more exact achievement of the target pattern. Validation experiments were carried out on speech signals of patients from the control group and with two kinds of laryngopathies.

DESIGN OF DECOUPLED CLINICAL DECISION SUPPORT FOR SERVICE-ORIENTED ARCHITECTURES

2009 ◽  
Vol 19 (02) ◽  
pp. 159-183 ◽  
Author(s):  
JENS WEBER-JAHNKE
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Service Oriented Architecture ◽  
Empirical Evaluation ◽  
Cost Savings ◽  
Clinical Decision ◽  
Service Oriented Architectures ◽  
Distributed Services ◽  
Service Oriented ◽  
Computer Based

Computer-based clinical decision support (CDS) contributes to cost savings, increased patient safety and quality of medical care. Most existing CDS systems are stand-alone products (first generation) or part of complete electronic medical record packages (second generation). Experience shows that creating and maintaining CDS systems is expensive and requires effort that should be economized by sharing them among multiple users. It makes good economic sense to share CDS service installations among a larger set of client systems. The paradigm of a service-oriented architecture (SOA) embraces this idea of sharing distributed services. Some attempts making CDS services available to distributed health information systems exist. However, these approaches have not gained much adoption. We argue that they do not provide a sufficient level of decoupling between client and CDS in order to be broadly reusable in SOAs. In this paper, we present a new CDS service component called EGADSS, which has been designed and implemented with the declared objective to minimize the coupling between client and CDS server. We present our key design decisions, which are guided by empirical research in SOA development. We evaluate our result theoretically by measuring the level of decoupling achieved compared to existing CDS approaches. Furthermore, we report on an empirical evaluation of the resulting design, integrating the EGADSS service with an example client system.

scholarly journals Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

2011 ◽  
Vol 02 (03) ◽  
pp. 284-303 ◽  
Author(s):  
A. Wright ◽  
M. Burton ◽  
G. Fraser ◽  
M. Krall ◽  
S. Maviglia ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Health Care ◽  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Diabetes Care ◽  
Clinical Decision ◽  
The United States ◽  
Computer Based

SummaryBackground: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known.Objective: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care.Methods: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites.Results: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines.Conclusion: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

scholarly journals Computer-based clinical decision support for general practitioners

2014 ◽  
Vol 31 (5) ◽  
pp. 497-498 ◽  
Author(s):  
M. E. Murphy ◽  
T. Fahey ◽  
S. M. Smith
Keyword(s):  
Decision Support ◽  
General Practitioners ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Computer Based
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