Diagnostic Decision Support System Research Articles

Study on the Efficiency of a Multi-layer Perceptron Neural Network Based on the Number of Hidden Layers and Nodes for Diagnosing Coronary- Artery Disease

Background: Through the diagnostic decision support systems, potential patients or those who are on the threshold succumbing to a disease can be diagnosed early; thus, the prevention of unnecessary angiography for people not suffering from the coronary-artery disease as well as its dangers and costs can be avoided. The present study aimed at the efficiency evaluation of a multilayer perceptron neural network based on the number of hidden layers and nodes to diagnose coronary heart disease. Methods: A fundamental analysis was conducted on the provided data related to 13,228 patients who had undergone coronary angiography and the database (nine risk factors including age, gender, BMI, body fat, family history, smoking, blood cholesterol, diabetes, and high blood pressure) was investigated in this research using SPSS statistics (17.0) and R (2.13.2) software. In the next stage, through utilizing MATLAB (R2014a), 1332 different MLP neural networks were created. Results: Based on the largest area under the ROC curve, the best model of MLP neural network was selected involving two hidden layers; the first layer had 34 and the second one had 18 hidden nodes. This model had the highest efficiency of 82% in the diagnosis of coronary artery disease. Conclusions: The obtained results demonstrated that the MLP makes an acceptable approach to the diagnosis of coronary artery disease in patients without the need for performing angiography. The development of this model will result in creating an algorithm for decision support systems to diagnose coronary artery disease, as well

The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients

BackgroundClinical decision support systems (DSS) aimed at supporting diagnosis are not widely used. This is mainly due to usability issues and lack of integration into clinical work and the electronic health record (EHR). In this study we examined the usability and acceptability of a diagnostic DSS prototype integrated with the EHR and in comparison with the EHR alone.MethodsThirty-four General Practitioners (GPs) consulted with 6 standardised patients (SPs) using only their EHR system (baseline session); on another day, they consulted with 6 different but matched for difficulty SPs, using the EHR with the integrated DSS prototype (DSS session). GPs were interviewed twice (at the end of each session), and completed the Post-Study System Usability Questionnaire at the end of the DSS session. The SPs completed the Consultation Satisfaction Questionnaire after each consultation.ResultsThe majority of GPs (74%) found the DSS useful: it helped them consider more diagnoses and ask more targeted questions. They considered three user interface features to be the most useful: (1) integration with the EHR; (2) suggested diagnoses to consider at the start of the consultation and; (3) the checklist of symptoms and signs in relation to each suggested diagnosis. There were also criticisms: half of the GPs felt that the DSS changed their consultation style, by requiring them to code symptoms and signs while interacting with the patient. SPs sometimes commented that GPs were looking at their computer more than at them; this comment was made more often in the DSS session (15%) than in the baseline session (3%). Nevertheless, SP ratings on the satisfaction questionnaire did not differ between the two sessions.ConclusionsTo use the DSS effectively, GPs would need to adapt their consultation style, so that they code more information during rather than at the end of the consultation. This presents a potential barrier to adoption. Training GPs to use the system in a patient-centred way, as well as improvement of the DSS interface itself, could facilitate coding. To enhance patient acceptability, patients should be informed about the potential of the DSS to improve diagnostic accuracy.

A Diagnostic Decision Support System for BMP Selection in Small Urban Watershed

Best Management Practices (BMPs) have become the most effective way to mitigate non-point source pollution (NPS) issues. Much attention has been paid to NPS in rural areas, where agricultural activities increase nutrients, toxics, and sediments in surface water. Stormwater from urban areas is also a major contributor to NPS pollution. For watersheds bearing various soil types and land uses, a single type of BMP cannot be the panacea to all stormwater problems. To solve these problems, a Diagnostic Decision Support System (DDSS) was developed in this research. The DDSS can identify and locate the most critical NPS areas (hotspots) within a watershed in high spatial resolution. The DDSS can provide a series of spatially distributed small-scale BMPs which are effective in treating the NPS and are suitable for the physical environment. The BMPs, varying in types and locations, are recommended at HRU (Hydrologic Response Unit) level. The DDSS was tested in Watts Branch, a small urban watershed of the Anacostia River in metropolitan Washington D.C., USA. The process-based hydrologic model, Soil and Water Assessment Tool (SWAT), was used to simulate watershed responses. The simulation results were then used by the DDSS for BMP recommendation. Hotspots of different NPS were successfully located and prescribed with spatially distributed BMPs. The DDSS serves as a useful tool to better understand urban watersheds and to make proper stormwater management plans.

Templates as a method for implementing data provenance in decision support systems

Decision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates – abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project.

Application of visually based, computerised diagnostic decision support system in dermatological medical education: a pilot study

BackgroundMedical education has shifted from memory-based practice to evidence-based decisions. The question arises: how can we ensure that all students get correct and systematic information? Visually based, computerised diagnostic decision...

Mining balance disorders' data for the development of diagnostic decision support systems

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts.

Application of visually-based, computerized diagnostic decision support systems in dermatological medical education: A prospective study

Application of visually-based, computerized diagnostic decision support systems in dermatological medical education: A prospective study

Breast Cancer Detection with Reduced Feature Set.

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%–40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

Translational Medicine and Patient Safety in Europe: TRANSFoRm—Architecture for the Learning Health System in Europe

The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care. Methods. The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath. Results. Four models were developed (clinical research, clinical data, provenance, and diagnosis) that form the basis of the projects approach to interoperability. These models are maintained as ontologies with binding of terms to define precise data elements. CDISC ODM and SDM standards are extended using an archetype approach to enable a two-level model of individual data elements, representing both research content and clinical content. Separate configurations of the TRANSFoRm tools serve each use case. Conclusions. The project has been successful in using ontologies and archetypes to develop a highly flexible solution to the problem of heterogeneity of data sources presented by the LHS.

On the Algorithm of the Medical Diagnostic Decision Support System under the Mobile Platform

A mobile platform usually refers to a complete platform that provides a reference design, a hardware chipset and upper communication protocols and supporting software and development tools. The downstream vendors can design and manufacture appropriate mobile products in a shorter time as required. The app developers mustn’t pay attention to the content of platform hardware, but systematically understand the types of mobile devices, supporting software and appropriate development tools in mobile platform. The key for the medical experts to deal with the patients is decisionmaking. They make the decisions in diagnosing the patient’s status, adjusting the therapeutic plan and monitoring the changing disease status. In recent decades, a series of methods and tools have been developed to assist the clinicians for decision. Typically, these tools and methods are not intended to substitute the human’s job, but used for assistant and supportive purpose. The doctors, healthcare professionals and other healthcare staffs have been being aware of the importance of these algorithms and techniques in order to support the decision-making process as the information and communication technology has become increasingly important for the infrastructure construction in healthcare organizations.

An improved hybrid feature reduction for increased breast cancer diagnostic performance

Purpose Early and correct diagnosis of a disease is vital for the success of treatment. Medical diagnostic decision support system can be used to improve the accuracy of the traditional diagnosis. As such, various pattern recognition methods are studied and applied to develop medical diagnostic decision support system. In this study, the effects of dimensionality reduction techniques with probabilistic neural network (PNN) on breast cancer classification are examined.

A Refined Continuous Ant Colony Optimization Based FP-Growth Association Rule Technique on Type 2 Diabetes

In recent years, Diabetes mellitus almost tops the list of chronic diseases worldwide among the major public health challenges. Diagnosing diabetes at the preliminary stage is undoubtedly challenging as it involves varying complexities and inter relation and dependence on several factors that affect it directly or indirectly. Due to large number of diabetic patients in recent years, desperate measures have to be devised and developed to facilitate medical diagnostic decision support systems that help doctors, researchers and medical practitioners during and after the process of diagnosing Diabetes. In this paper, the Association Rule Mining and Enhanced FP-Growth Algorithm has been used as a reference to propose a new algorithm that is basically has similar functionalities as that of the Ant Colony Optimization algorithm and one that improvises association rule mining results. The Refined Continuous Ant Colony Optimization or CACO deploys a meta-heuristic approach and has been devised and enthused by actual ant colonies behavior along with the sustained continuous domains. Preliminarily association rules so produced by the Enhanced FP-Growth algorithm are deployed thereafter which rules from weakest set are found on the basis of threshold value and then further used by the Ant Colony algorithm so that association rules are reduced and a better quality of rules are discovered as a result of the efforts. The study as well the research presented here aims at reducing database scanning by optimizing as well by improving the quality of rules that are produced for CACO.

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics.

In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available. The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features. The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p<0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively. Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.

Performance-power consumption tradeoff in wearable epilepsy monitoring systems.

Automated seizure detection methods can be used to reduce time and costs associated with analyzing large volumes of ambulatory EEG recordings. These methods however have to rely on very complex, power hungry algorithms, implemented on the system backend, in order to achieve acceptable levels of accuracy. In size, and therefore power-constrained EEG systems, an alternative approach to the problem of data reduction is online data selection, in which simpler algorithms select potential epileptiform activity for discontinuous recording but accurate analysis is still left to a medical practitioner. Such a diagnostic decision support system would still provide doctors with information relevant for diagnosis while reducing the time taken to analyze the EEG. For wearable systems with limited power budgets, data selection algorithm must be of sufficiently low complexity in order to reduce the amount of data transmitted and the overall power consumption. In this paper, we present a low-power hardware implementation of an online epileptic seizure data selection algorithm with encryption and data transmission and demonstrate the tradeoffs between its accuracy and the overall system power consumption. We demonstrate that overall power savings by data selection can be achieved by transmitting less than 40% of the data. We also show a 29% power reduction when selecting and transmitting 94% of all seizure events and only 10% of background EEG.

An update and further testing of a knowledge-based diagnostic clinical decision support system for musculoskeletal disorders of the shoulder for use in a primary care setting.

A prototype diagnostic clinical decision support system (CDSS) was developed to assist primary care clinicians (general practitioners) in clinical decision making, aimed at reducing diagnostic errors. The prototype CDSS showed some promise with high levels of validity and reliability; however, issues regarding the underlying Bayesian belief network (BBN), small sample size and use of radiological imaging as a gold standard measure were highlighted that required further investigation before considering clinical testing. The prototype CDSS was reviewed and updated based on computer science literature and expert (orthopaedic consultant) opinion. The updated CDSS was tested by comparing its diagnostic outcome against the diagnosis of 93 case studies as determined by expert opinion combined with arthroscopy findings or radiological imaging. The updated CDSS showed significant high levels of sensitivity (91%), specificity (98%), positive likelihood ratio (53.12) and negative likelihood ratio (0.08) with a kappa value of 0.88 to a confidence level of 99% compared with expert diagnosis combined with arthroscopy findings or radiological imaging. The results suggest that the updated CDSS has addressed the issues highlighted from the initial research while maintaining high levels of validity and reliability. The updated CDSS is now ready for clinical testing.

Guided Integrated Remote and Workshop Troubleshooting of Heavy Trucks

When a truck or bus suffers from a breakdown it is important that the vehicle comes back on the road as soon as possible. In this paper we present a prototype diagnostic decision support system cap ...

What can be done to increase the use of diagnostic decision support systems?

This essay explores the reasons why diagnostic decision support systems are underutilized despite growing concern about diagnostic errors. Factors related to the motivation to use the systems, clinician cognition, system design and implementation, as well as the absence of feedback in routine clinical care are discussed. Suggestions for design and implementation strategies for diagnostic decision support systems that can increase appropriate utilization are discussed.

Quality and Cost Improvement of Healthcare via Complementary Measurement and Diagnosis of Patient General Health Outcome Using Electronic Health Record Data: Research Rationale and Design

In this evolving 'third era of health', one of the US Health Care Reform Act's goals is to effectively facilitate the primary care physician's ability to better diagnose and manage the health outcome of the outpatient. That goal must include research on the complementary quantitative-qualitative assessment and rating of the patient's health status. This paper proposes an overview of the rationale and design of a research program for a balanced measurement and diagnostic clinical decision support system (CDSS) of the changing general health status of the patient -including disease- using electronic health record (EHR) data. The rationale, objectives, health metric-diagnostic tools architecture, simulation-optimization, and clinical trials are outlined. Resources, time frames, costs, feasibility, healthcare benefits and data-integration of the project are delineated. The basis and components of the research program to achieve an automated-CDSS to complement physician's clinical judgment, calculating a mathematical 'health equation' from each patient's EHR database, assisting physician-patient collaboration to diagnose, and improve general health outcomes is described. Use of multiple dimensional index, ways of classification, and causal factors' assessments, to arrive at the EHR-based CDSS algorithm-software providing a general health level and state rating of the patient are proposed. Its application could provide a compass for the general practitioner's best choice and use of the myriad of healthcare educational and technological options available with lower costs for everyday clinical practice and research. It could advance the approaches and focus of the 'eras of diseases', to the promising 'era of health', in an integrated, general approach to 'health.'

Using experts feedback in clinical case resolution and arbitration as accuracy diagnosis methodology

This paper proposes a new methodology for assessing the efficiency of medical diagnostic systems and clinical decision support systems by using the feedback/opinions of medical experts. The methodology behind this work is based on a comparison between the expert feedback that has helped solve different clinical cases and the expert system that has evaluated these same cases. Once the results are returned, an arbitration process is carried out in order to ensure the correctness of the results provided by both methods. Once this process has been completed, the results are analyzed using Precision, Recall, Accuracy, Specificity and Matthews Correlation Coefficient (MCC) (PRAS-M) metrics. When the methodology is applied, the results obtained from a real diagnostic system allow researchers to establish the accuracy of the system based on objective facts. The methodology returns enough information to analyze the system's behavior for each disease in the knowledge base or across the entire knowledge base. It also returns data on the efficiency of the different assessors involved in the evaluation process, analyzing their behavior in the diagnostic process. The proposed work facilitates the evaluation of medical diagnostic systems, having a reliable process based on objective facts. The methodology presented in this research makes it possible to identify the main characteristics that define a medical diagnostic system and their values, allowing for system improvement. A good example of the results provided by the application of the methodology is shown in this paper. A diagnosis system was evaluated by means of this methodology, yielding positive results (statistically significant) when comparing the system with the assessors that participated in the evaluation process of the system through metrics such as recall (+27.54%) and MCC (+32.19%). These results demonstrate the real applicability of the methodology used.

The utility of an online diagnostic decision support system (Isabel) in general practice: a process evaluation.

ObjectivesTo evaluate the utility of Isabel, an online diagnostic decision support system developed by Isabel Healthcare primarily for secondary medical care, in the general practice setting.DesignFocus groups were conducted with clinicians to understand why and how they used the system. A modified online post-use survey asked practitioners about its impact on their decision-making. Normalization process theory (NPT) was used as a theoretical framework to determine whether the system could be incorporated into routine clinical practice.SettingThe system was introduced by NHS County Durham and Darlington in the UK in selected general practices as a three-month pilot.ParticipantsGeneral practitioners and nurse practitioners who had access to Isabel as part of the Primary Care Trust's pilot.Main outcome measuresGeneral practitioners’ views, experiences and usage of the system.ResultsSeven general practices agreed to pilot Isabel. Two practices did not subsequently use it. The remaining five practices conducted searches on 16 patients. Post-use surveys (n = 10) indicated that Isabel had little impact on diagnostic decision-making. Focus group participants stated that, although the diagnoses produced by Isabel in general did not have an impact on their decision-making, they would find the tool useful if it were better tailored to the primary care setting. Our analysis concluded that normalization was not likely to occur in its current form.ConclusionsIsabel was of limited utility in this short pilot study and may need further modification for use in general practice.