Search results for: clinical decision support sytem

Authors: Jacob Nortey

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Introduction: In recent years, there has been an enormous success in discoveries of scientific knowledge in medicine coupled with the advancement of technology. Despite all these successes, diagnoses and treatment of diseases have become complex. According to the Ibero – American Study of Adverse Effects (IBEAS), about 10% of hospital patients suffer from secondary damage during the care process, and approximately 2% die from this process. Many clinical decision support systems have been developed to help mitigate some healthcare medical errors. Method: Relevant databases were searched, including ones that were peculiar to the clinical decision support system (that is, using google scholar, Pub Med and general google searches). The articles were then screened for a comprehensive overview of the functionality, consultative style and statistical usage of Dxplain Clinical decision support systems. Results: Inferences drawn from the articles showed high usage of Dxplain clinical decision support system for problem-based learning among students in developed countries as against little or no usage among students in Low – and Middle – income Countries. The results also indicated high usage among general practitioners. Conclusion: Despite the challenges Dxplain presents, the benefits of its usage to clinicians and students are enormous.

Keywords: dxplain, clinical decision support sytem, diagnosis, support systems

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Authors: Luca Martignoni

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The increasing usage of clinical decision support systems in healthcare and the demand for software that enables doctors to take informed decisions is changing everyday clinical practice. However, as technology advances not only are the benefits of technology growing, but so are the potential risks. A growing danger is the doctors’ over-reliance on the proposed decision of the clinical decision support system, leading towards deskilling and rash decisions by doctors. In that regard, identifying doctors' requirements for software and developing approaches to prevent technological over-reliance is of utmost importance. In this paper, we report the results of a design science research study, focusing on the requirements and design principles of ultrasound software. We conducted a total of 15 interviews with experts about poten-tial ultrasound software functions. Subsequently, we developed meta-requirements and design principles to design future clinical decision support systems efficiently and as free from the occur-rence of technological over-reliance as possible.

Keywords: clinical decision support systems, technological over-reliance, design principles, design science research

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Authors: Rashida Suleiman, Asamoah Jnr. Boakye, Suleiman Ahmed Ibn Ahmed

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In recent years, there has been an enormous success in discoveries of scientific knowledge in medicine coupled with the advancement of technology. Despite all these successes, diagnoses and treatment of diseases have become complex. MYCIN is a groundbreaking illustration of a clinical decision support system (CDSS), which was developed to assist physicians in the diagnosis and treatment of bacterial infections by providing suggestions for antibiotic regimens. MYCIN was one of the earliest expert systems to demonstrate how CDSSs may assist human decision-making in complicated areas. Relevant databases were searched using google scholar, PubMed and general Google search, which were peculiar to clinical decision support systems. The articles were then screened for a comprehensive overview of the functionality, consultative style and statistical usage of MYCIN, a clinical decision support system. Inferences drawn from the articles showed some usage of MYCIN for problem-based learning among clinicians and students in some countries. Furthermore, the data demonstrated that MYCIN had completed clinical testing at Stanford University Hospital following years of research. The system (MYCIN) was shown to be extremely accurate and effective in diagnosing and treating bacterial infections, and it demonstrated how CDSSs might enhance clinical decision-making in difficult circumstances. Despite the challenges MYCIN presents, the benefits of its usage to clinicians, students and software developers are enormous.

Keywords: clinical decision support system, MYCIN, diagnosis, bacterial infections, support systems

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Authors: Pachawo Bisani, Goodall Nyirenda

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The Malawi National Antiretroviral Therapy (NART) Electronic Medical Record (EMR) system was designed and developed with guidance from the Ministry of Health through the Department of HIV and AIDS (DHA) with the aim of supporting the management of HIV patient data and reporting in high prevalence ART clinics. As of 2021, the system has been scaled up to over 206 facilities across the country. The system is integrated with the clinical decision support system (CDSS) to assist healthcare providers in making a decision about an individual patient at a particular point in time. Despite NART EMR undergoing several evaluations and assessments, little has been done to evaluate the clinical decision support system in the NART EMR system. Hence, the study aimed to evaluate the use of CDSS in the NART EMR system in Malawi. The study adopted a mixed-method approach, and data was collected through interviews, observations, and questionnaires. The study has revealed that the CDSS tools were integrated into the ART clinic workflow, making it easy for the user to use it. The study has also revealed challenges in system reliability and information accuracy. Despite the challenges, the study further revealed that the system is effective and efficient, and overall, users are satisfied with the system. The study recommends that the implementers focus more on the logic behind the clinical decision-support intervention in order to address some of the concerns and enhance the accuracy of the information supplied. The study further suggests consulting the system's actual users throughout implementation.

Keywords: clinical decision support system, electronic medical record system, usability, antiretroviral therapy

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Authors: Swati Kishor Zode, Rahul Ambekar

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Data mining is the extraction of fascinating examples on the other hand information from enormous measure of information and choice is made as indicated by the applicable information extracted. As of late, with the dangerous advancement in internet, stockpiling of information and handling procedures, privacy preservation has been one of the major (higher) concerns in data mining. Various techniques and methods have been produced for protection saving data mining. In the situation of Clinical Decision Support System, the choice is to be made on the premise of the data separated from the remote servers by means of Internet to diagnose the patient. In this paper, the fundamental thought is to build the precision of Decision Support System for multiple diseases for different maladies and in addition protect persistent information while correspondence between Clinician side (Client side) also, the Server side. A privacy preserving protocol for clinical decision support network is proposed so that patients information dependably stay scrambled amid diagnose prepare by looking after the accuracy. To enhance the precision of Decision Support System for various malady C5.0 classifiers and to save security, a Homomorphism encryption algorithm Paillier cryptosystem is being utilized.

Keywords: classification, homomorphic encryption, clinical decision support, privacy

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Authors: Ian McCulloh, Andrew Placona, Darren Stewart, Daniel Gause, Kevin Kiernan, Morgan Stuart, Christopher Zinner, Laura Cartwright

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An estimated 22-25% of viable deceased donor kidneys are discarded every year in the US, while waitlisted candidates are dying every day. As many as 85% of transplanted organs are refused at least once for a patient that scored higher on the match list. There are hundreds of clinical variables involved in making a clinical transplant decision and there is rarely an ideal match. Decision makers exhibit an optimism bias where they may refuse an organ offer assuming a better match is imminent. We propose a semi-parametric Cox proportional hazard model, augmented by an accelerated failure time model based on patient specific suitable organ supply and demand to estimate a time-to-next-offer. Performance is assessed with Cox-Snell residuals and decision curve analysis, demonstrating improved decision support for up to a 5-year outlook. Providing clinical decision makers with quantitative evidence of likely patient outcomes (e.g., time to next offer and the mortality associated with waiting) may improve decisions and reduce optimism bias, thus reducing discarded organs and matching more patients on the waitlist.

Keywords: decision science, KDPI, optimism bias, organ transplant

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Authors: Jonah Kenei, Elisha Opiyo

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Electronic health records, as a repository of patient information, is nowadays the most commonly used technology to record, store and review patient clinical records and perform other clinical tasks. However, the accurate identification and retrieval of relevant information from clinical records is a difficult task due to the unstructured nature of clinical documents, characterized in particular by a lack of clear structure. Therefore, medical practice is facing a challenge thanks to the rapid growth of health information in electronic health records (EHRs), mostly in narrative text form. As a result, it's becoming important to effectively manage the growing amount of data for a single patient. As a result, there is currently a requirement to visualize electronic health records (EHRs) in a way that aids physicians in clinical tasks and medical decision-making. Leveraging text visualization techniques to unstructured clinical narrative texts is a new area of research that aims to provide better information extraction and retrieval to support clinical decision support in scenarios where data generated continues to grow. Clinical datasets in electronic health records (EHR) offer a lot of potential for training accurate statistical models to classify facets of information which can then be used to improve patient care and outcomes. However, in many clinical note datasets, the unstructured nature of clinical texts is a common problem. This paper examines the very issue of getting raw clinical texts and mapping them into meaningful structures that can support healthcare professionals utilizing narrative texts. Our work is the result of a collaborative design process that was aided by empirical data collected through formal usability testing.

Keywords: classification, electronic health records, narrative texts, visualization

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Authors: Rolla Monib, Chris Ian Goodier, Alistair Gibb

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This paper aims to investigate decision support strategies in the EC sector to determine the most appropriate degree of modularization. This is achieved through three oil and gas (O&G) and two power plant case studies via semi-structured interviews (n=59 and n=27, respectively), analysis of project documents, and case study-specific semi-structured validation interviews (n=12 and n=8). New terminology to distinguish degrees of modularization is proposed, along with a decision-making support checklist and a diagrammatic decision-making support figure. Results indicate that the EC sub-sectors were substantially more satisfied with the application of component, structural, or traditional modularization compared with system modularization for some types of modules. Key drivers for decisions on the degree of modularization vary across module types. This paper can help the EC sector determine the most suitable degree of modularization via a decision-making support strategy.

Keywords: modularization, engineering construction, case study, decision support

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Authors: Ahmed Ouammi

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This paper presents an integrated decision framework to support decision makers in the selection and optimal allocation of wind power plants in the electric grid. The developed approach intends to maximize the benefice related to the project investment during the planning period. The proposed decision model considers the main cost components, meteorological data, environmental impacts, operation and regulation constraints, and territorial information. The decision framework is expressed as a stochastic constrained optimization problem with the aim to identify the suitable locations and related optimal wind turbine technology considering the operational constraints and maximizing the benefice. The developed decision support system is applied to a case study to demonstrate and validate its performance.

Keywords: decision support systems, electric power grid, optimization, wind energy

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Authors: Katejarinporn Chaiya, Thawit Janbanklong

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The purposes of this study were to design and find users’ satisfaction after using the decision support system for tourism in the Northern part of Thailand, which can provide tourists with touristic information and plan their personal voyage. Such information can be retrieved systematically based on personal budget and provinces. The samples of this study were five experts and users: 30 "white collars" in Bangkok. This decision support system was designed via ASP.NET. Its database was developed by using MySQL, for administrators to effectively manage the database. The application outcome revealed that the innovation works properly as sought in objectives. Specialists and white collars in Bangkok have evaluated the decision support system; the result was satisfactorily positive.

Keywords: decision Support System, ASP.NET, MySQL, white collars

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions

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Authors: J. Becker, R. Arnold

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The design of distribution logistics has a decisive impact on a company's logistics costs and performance. Hence, such solutions make an essential contribution to corporate success. This article describes a decision support system for analyzing the potential of distribution logistics in terms of logistics costs and performance. In contrast to previous procedures of business process re-engineering (BPR), this method maps distribution logistics holistically under variable distribution structures. Combined with qualitative measures the decision support system will contribute to a more efficient design of distribution logistics.

Keywords: decision support system, distribution logistics, potential analyses, supply chain management

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Authors: R. Tariq, R. Lee

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Background: Additional demands placed on senior clinical teams with ongoing COVID-19 management has accelerated the need to harness the wider healthcare professional resources and upskill them to take on greater clinical responsibility safely. The UK NHS Long Term Plan (2019)¹ emphasises the importance of expanding Advanced Practitioners’ (APs) roles to take on more clinical diagnostic responsibilities to cope with increased demand. In acute settings, APs are often the first point of care for patients and require training to take on initial triage responsibilities efficiently and safely. Critically, their roles include determining which onward services the patients may require, and assessing whether they can be treated at home, avoiding unnecessary admissions to the hospital. Dem Dx is a Clinical Reasoning Platform (CRP) that claims to help frontline healthcare professionals independently assess and triage patients. It guides the clinician from presenting complaints through associated symptoms to a running list of differential diagnoses, media, national and institutional guidelines. The objective of this study was to compare the clinical referral rates and guidelines adherence registered by the HMR Urgent Community Care Team (UCCT)² and Dem Dx recommendations using retrospective cases. Methodology: 192 cases seen by the UCCT were anonymised and reassessed using Dem Dx clinical pathways. We compared the UCCT’s performance with Dem Dx regarding the appropriateness of onward referrals. We also compared the clinical assessment regarding adherence to NICE guidelines recorded on the clinical notes and the presence of suitable guidance in each case. The cases were audited by two medical doctors. Results: Dem Dx demonstrated appropriate referrals in 85% of cases, compared to 47% in the UCCT team (p<0.001). Of particular note, Dem Dx demonstrated an almost 65% (p<0.001) improvement in the efficacy and appropriateness of referrals in a highly experienced clinical team. The effectiveness of Dem Dx is in part attributable to the relevant NICE and local guidelines found within the platform's pathways and was found to be suitable in 86% of cases. Conclusion: This study highlights the potential of clinical decision support, as Dem Dx, to improve the quality of onward clinical referrals delivered by a multidisciplinary team in primary care. It demonstrated that it could support healthcare professionals in making appropriate referrals, especially those that may be overlooked by providing suitable clinical guidelines directly embedded into cases and clear referral pathways. Further evaluation in the clinical setting has been planned to confirm those assumptions in a prospective study.

Keywords: advanced practitioner, clinical reasoning, clinical decision-making, management, multidisciplinary team, referrals, triage

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Authors: R. Tariq, R. Lee

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Background: Heywood, Middleton & Rochdale Urgent Community Care Team (UCCT)1 is a great example of using a multidisciplinary team to cope with demand. The service reduces unnecessary admissions to hospitals and ensures that patients can leave the hospital quicker by making care more readily available within the community and patient’s homes. The team comprises nurses, community practitioners, and allied health professions, including physiotherapy, occupational therapy, pharmacy, and GPs. The main challenge for a team with a range of experiences and skill sets is to maintain consistency of care, which technology can help address. Allied healthcare professionals (HCPs) are often used in expanded roles with duties mainly involving patient consultations and decision making to ease pressure on doctors. The Clinical Reasoning Platform (CRP) Dem Dx is used to support new as well as experienced professionals in the decision making process. By guiding HCPs through diagnosing patients from an expansive directory of differential diagnoses, patients can receive quality care in the community. Actions on the platform are determined using NICE guidelines along with local guidance influencing the assessment and management of a patient. Objective: To compare the clinical assessment, decisions, and actions taken by the UCCT multidisciplinary team in the community and Dem Dx, using retrospective clinical cases. Methodology: Dem Dx was used to analyse 192 anonymised cases provided by the HMR UCCT. The team’s performance was compared with Dem Dx regarding the quality of the documentation of the clinical assessment and the next steps on the patient’s journey, including the initial management, actions, and any onward referrals made. The cases were audited by two medical doctors. Results: The study found that the actions outlined by the Dem Dx platform were appropriate in almost 87% of cases. When in a direct comparison between DemDX and the actions taken by the clinical team, it was found that the platform was suitable 83% (p<0.001) of the time and could lead to a potential improvement of 66% in the assessment and management of cases. Dem Dx also served to highlight the importance of comprehensive and high quality clinical documentation. The quality of documentation of cases by UCCT can be improved to provide a detailed account of the assessment and management process. By providing step-by-step guidance and documentation at every stage, Dem Dx may ensure that legal accountability has been fulfilled. Conclusion: With the ever expanding workforce in the NHS, technology has become a key component in driving healthcare outcomes. To improve healthcare provision and clinical reasoning, a decision support platform can be integrated into HCPs’ clinical practice. Potential assistance with clinical assessments, the most appropriate next step and actions in a patient’s care, and improvements in the documentation was highlighted by this retrospective study. A further study has been planned to ascertain the effectiveness of improving outcomes using the clinical reasoning platform within the clinical setting by clinicians.

Keywords: allied health professional, assessment, clinical reasoning, clinical records, clinical decision-making, ocumentation

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Authors: Musarrat Jabeen

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Thoughtful intelligence offers a sustainable position to enhance the influence of decision-makers. Thoughtful Intelligence implies the understanding to realize the impact of one’s thoughts, words and actions on the survival, dignity and development of the individuals, groups and nations. Thoughtful intelligence has received minimal consideration in the area of Decision Support Systems, with an end goal to evaluate the quantity of knowledge and its viability. This pattern degraded the imbibed contribution of thoughtful intelligence required for sustainable decision making. Given the concern, this paper concentrates on the question: How to present a model of Thoughtful Decision Support System (TDSS)? The aim of this paper is to appreciate the concepts of thoughtful intelligence and insinuate a Decision Support System based on thoughtful intelligence. Thoughtful intelligence includes three dynamic competencies: i) Realization about long term impacts of decisions that are made in a specific time and space, ii) A great sense of taking actions, iii) Intense interconnectivity with people and nature and; seven associate competencies, of Righteousness, Purposefulness, Understanding, Contemplation, Sincerity, Mindfulness, and Nurturing. The study utilizes two methods: Focused group discussion to count prevailing Decision Support Systems; 70% results of focus group discussions found six decision support systems and the positive inexistence of thoughtful intelligence among decision support systems regarding sustainable decision making. Delphi focused on defining thoughtful intelligence to model (TDSS). 65% results helped to conceptualize (definition and description) of thoughtful intelligence. TDSS is offered here as an addition in the decision making literature. The clients are top leaders.

Keywords: thoughtful intelligence, sustainable decision making, thoughtful decision support system

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Authors: Alexander P. Eremeev, Ivan E. Kurilenko, Pavel R. Varshavskiy

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In this paper, the problem of the application of temporal reasoning and case-based reasoning in intelligent decision support systems is considered. The method of case-based reasoning with temporal dependences for the solution of problems of real-time diagnostics and forecasting in intelligent decision support systems is described. This paper demonstrates how the temporal case-based reasoning system can be used in intelligent decision support systems of the car access control. This work was supported by RFBR.

Keywords: analogous reasoning, case-based reasoning, intelligent decision support systems, temporal reasoning

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Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi

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Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, Diagnostic errors, Ontology, Pneumonia

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Authors: Abdallah A. Hlayel

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Operations research science (OR) deals with good success in developing and applying scientific methods for problem solving and decision-making. However, by using OR techniques, we can enhance the use of computer decision support systems to achieve optimal management for institutions. OR applies comprehensive analysis including all factors that affect on it and builds mathematical modeling to solve business or organizational problems. In addition, it improves decision-making and uses available resources efficiently. The adoption of OR by universities would definitely contributes to the development and enhancement of the performance of OR techniques. This paper provides an understanding of the structures, approaches and models of OR in problem solving and decision-making.

Keywords: best candidates' method, decision making, decision support system, operations research

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Authors: Shahram Payandeh

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This paper presents an overview of a model which can be used as a part of a decision support system when fusing information from multiple sensing environment. Data fusion has been widely studied in the past few decades and numerous frameworks have been proposed to facilitate decision making process under uncertainties. Multi-sensor data fusion technology plays an increasingly significant role during people tracking and activity recognition. This paper presents an overview of a quantum model as a part of a decision-making process in the context of multi-sensor data fusion. The paper presents basic definitions and relationships associating the decision-making process and quantum model formulation in the presence of uncertainties.

Keywords: quantum model, sensor space, sensor network, decision support

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Authors: Y. O’Connor, C. Heavin, S. O’ Connor, J. Gallagher, J. Wu, J. O’Donoghue

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Background: To improve the delivery of paediatric healthcare in resource-poor settings, Community Health Workers (CHW) have been provided with a paper-based set of protocols known as Community Case Management (CCM). Yet research has shown that CHW adherence to CCM guidelines is poor, ultimately impacting health service delivery. Digitising the CCM guidelines via mobile technology is argued in extant literature to improve CHW adherence. However, little research exist which outlines how (a) this process can be digitised and (b) adherence could be improved as a result. Aim: To explore how an electronic mobile version of CCM (eCCM) can overcome issues associated with the paper-based CCM protocol (poor adherence to guidelines) vis-à-vis service blueprinting. This service blueprint will outline how (a) the CCM process can be digitised using mobile Clinical Decision Support Systems software to support clinical decision-making and (b) adherence can be improved as a result. Method: Development of a single service blueprint for a standalone application which visually depicts the service processes (eCCM) when supporting the CHWs, using an application known as Supporting LIFE (Low cost Intervention For disEase control) as an exemplar. Results: A service blueprint is developed which illustrates how the eCCM solution can be utilised by CHWs to assist with the delivery of healthcare services to children. Leveraging smartphone technologies can (a) provide CHWs with just-in-time data to assist with their decision making at the point-of-care and (b) improve CHW adherence to CCM guidelines. Conclusions: The development of the eCCM opens up opportunities for the CHWs to leverage the inherent benefit of mobile devices to assist them with health service delivery in rural settings. To ensure that benefits are achieved, it is imperative to comprehend the functionality and form of the eCCM service process. By creating such a service blueprint for an eCCM approach, CHWs are provided with a clear picture regarding the role of the eCCM solution, often resulting in buy-in from the end-users.

Keywords: adherence, community health workers, developing countries, mobile clinical decision support systems, CDSS, service blueprint

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Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

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Authors: Norfiza Ibrahim, Norshuhada Shiratuddin, Siti Mahfuzah Sarif

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Decision-making system is used to facilitate people in making the right choice for their important daily activities. For the youth, proper guidance in making important decisions is needed. Their skills in decision-making aid decisions will indirectly affect their future. For that reason, this study focuses on the intelligent aspects in the development of intelligent decision support application. The aid apparently integrates Personality Traits (PT) and Multiple Intelligence (MI) data in development of a computerized personal decision aid for youth named as Youth Personal Decision Aid (Youth PDA). This study is concerned with the aid’s helpfulness based on the hybrid intelligent process. There are four main items involved which are reliability, decision making effort, confidence, as well as decision process awareness. Survey method was applied to the actual user of this system, namely the school and the Institute of Higher Education (IPT)’s students. An establish instrument was used to evaluate the study. The results of the analysis and findings in the assessment indicates a high mean value of the four dimensions in helping Youth PDA to be accepted as a useful tool for the youth in decision-making.

Keywords: decision support, multiple intelligent, personality traits, youth personal decision aid

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Authors: A. Bouamrane, M. T. Bouziane, K. Boutebba, Y. Djebbar

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This paper aims to develop a decision support tool to provide solutions to the problems of sewer networks management/maintenance in order to assist the manager to sort sections upon priority of intervention by taking account of the technical, economic, social and environmental standards as well as the managers’ strategy. This solution uses the Analytic Network Process (ANP) developed by Thomas Saaty, coupled with a set of tools for modelling and collecting integrated data from a geographic information system (GIS). It provides to the decision maker a tool adapted to the reality on the ground and effective in usage compared to the means and objectives of the manager.

Keywords: multi-criteria decision support, maintenance, Geographic Information System, modelling

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Authors: David Tresner-Kirsch

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Reconciling patient medication lists from multiple sources is a critical task supporting the safe delivery of patient care. Manual reconciliation is a time-consuming and error-prone process, and recently attempts have been made to develop efficiency- and safety-oriented automated support for professionals performing the task. An important capability of any such support system is automated alignment – finding which medications from a list correspond to which medications from a different source, regardless of misspellings, naming differences (e.g. brand name vs. generic), or changes in treatment (e.g. switching a patient from one antidepressant class to another). This work describes a new algorithmic solution to this alignment task, using a greedy matching approach based on string similarity, edit distances, concept extraction and normalization, and synonym search derived from the RxNorm nomenclature. The accuracy of this algorithm was evaluated against a gold-standard corpus of 681 medication records; this evaluation found that the algorithm predicted alignments with 99% precision and 91% recall. This performance is sufficient to support decision support applications for medication reconciliation.

Keywords: clinical decision support, medication reconciliation, natural language processing, RxNorm

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Authors: Abdelkader Mendas

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The integration of MultiCriteria Decision Making (MCDM) approaches in a Geographical Information System (GIS) provides a powerful spatial decision support system which offers the opportunity to efficiently produce the land suitability maps for agriculture. Indeed, GIS is a powerful tool for analyzing spatial data and establishing a process for decision support. Because of their spatial aggregation functions, MCDM methods can facilitate decision making in situations where several solutions are available, various criteria have to be taken into account and decision-makers are in conflict. The parameters and the classification system used in this work are inspired from the FAO (Food and Agriculture Organization) approach dedicated to a sustainable agriculture. A spatial decision support system has been developed for establishing the land suitability map for agriculture. It incorporates the multicriteria analysis method ELECTRE Tri (ELimitation Et Choix Traduisant la REalité) in a GIS within the GIS program package environment. The main purpose of this research is to propose a conceptual and methodological framework for the combination of GIS and multicriteria methods in a single coherent system that takes into account the whole process from the acquisition of spatially referenced data to decision-making. In this context, a spatial decision support system for developing land suitability maps for agriculture has been developed. The algorithm of ELECTRE Tri is incorporated into a GIS environment and added to the other analysis functions of GIS. This approach has been tested on an area in Algeria. A land suitability map for durum wheat has been produced. Through the obtained results, it appears that ELECTRE Tri method, integrated into a GIS, is better suited to the problem of land suitability for agriculture. The coherence of the obtained maps confirms the system effectiveness.

Keywords: multicriteria decision analysis, decision support system, geographical information system, land suitability for agriculture

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Authors: Gizelle K. Vianna, Gustavo S. Oliveira, Gabriel V. Cunha

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The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action.

Keywords: artificial neural networks, cellular automata, decision support system, pattern recognition

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well.

Keywords: data mining technique, the decision support system, knowledge and decision rules, education

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Authors: Amritpal Slaich, Mark Elshaw

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This paper explores the development of a decision support framework for the introduction of business intelligence (BI) through operational research techniques for application by SMEs. Aligned with the goals of the new Midlands Enterprise Initiative of improving the skill levels of the Midlands workforce and addressing high levels of regional unemployment, we have developed a framework to increase the level of business intelligence used by SMEs to improve business decision-making. Many SMEs in the Midlands fail due to the lack of high quality decision making. Our framework outlines how universities can: engage with SMEs in the use of BI through operational research techniques; develop appropriate and easy to use Excel spreadsheet models; and make use of a process to allow SMEs to feedback their findings of the models. Future work will determine how well the framework performs in getting SMEs to apply BI to improve their decision-making performance.

Keywords: SMEs, decision support framework, business intelligence, operational research techniques

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis

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Authors: Phang Moon Leng

Abstract:

Automated Early Warning Scores is a newly developed clinical decision tool that is used to streamline and improve the process of obtaining a patient’s vital signs so a clinical decision can be made at an earlier stage to prevent the patient from further deterioration. This technology provides immediate update on the score and clinical decision to be taken based on the outcome. This paper aims to study the use of an automated early warning score system on whether the technology has assisted the hospital in early detection and escalation of clinical condition and improve patient outcome. The hospital adopted the Modified Early Warning Scores (MEWS) Scoring System and MEWS Clinical Response into Philips IntelliVue Guardian Automated Early Warning Score equipment and studied whether the process has been leaned, whether the use of technology improved the usage & experience of the nurses, and whether the technology has improved patient care and outcome. It was found the steps required to obtain vital signs has been significantly reduced and is used more frequently to obtain patient vital signs. The number of deaths, and length of stay has significantly decreased as clinical decisions can be made and escalated more quickly with the Automated EWS. The automated early warning score equipment has helped improve work efficiency by removing the need for documenting into patient’s EMR. The technology streamlines clinical decision-making and allows faster care and intervention to be carried out and improves overall patient outcome which translates to better care for patient.

Keywords: automated early warning score, clinical quality and safety, patient safety, medical technology

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