scholarly journals Complex Data-driven Predictive Modeling in Personalized Clinical Decision Support for Acute Coronary Syndrome Episodes

2016 ◽  
Vol 80 ◽  
pp. 518-529 ◽  
Author(s):  
Alexey V. Krikunov ◽  
Ekaterina V. Bolgova ◽  
Evgeniy Krotov ◽  
Tesfamariam M. Abuhay ◽  
Alexey N. Yakovlev ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Predictive Modeling ◽  
Clinical Decision ◽  
Data Driven ◽  
Complex Data ◽  
Coronary Syndrome

scholarly journals Contributions on Clinical Decision Support from the 2018 Literature

2019 ◽  
Vol 28 (01) ◽  
pp. 135-137 ◽  
Author(s):  
Vassilis Koutkias ◽  
Jacques Bouaud ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Metastatic Cancer ◽  
Clinical Decision ◽  
Data Driven ◽  
Editorial Team ◽  
Cancer Center ◽  
Bibliographic Databases ◽  
Free Text

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy. Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

scholarly journals From unstructured EHR text to data-driven clinical decision support

2015 ◽  
Vol 15 (7) ◽  
Author(s):  
Kasper Jensen ◽  
Knut Magne Augestad ◽  
Rolv-Ole Lindsetmo ◽  
Stein Olav Skrøvseth
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Data Driven

scholarly journals Argumentation schemes for clinical decision support

2021 ◽  
pp. 1-27
Author(s):  
Isabel Sassoon ◽  
Nadin Kökciyan ◽  
Sanjay Modgil ◽  
Simon Parsons
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Complex Data ◽  
Argumentation Schemes ◽  
Formal Reasoning ◽  
Rigorous Approach ◽  
Small Set ◽  
Computational Argumentation

This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created and illustrates the overall process on a small set of case studies.

scholarly journals Data-Driven Clinical Decision Support System for Medical Diagnosis and Treatment Recommendation

2019 ◽  
Vol 8 (11) ◽  
pp. 3660-3668
Keyword(s):  
Face Recognition ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Data Driven ◽  
Treatment Recommendation ◽  
Disease Prediction

This paper presents a Data-Driven Clinical Decision Support System (CDSS) using machine learning. The proposed system predicts the possibility of diseases based on the patient’s symptoms. It suggests lab tests and medication related to the disease. Lab test results are analyzed to check the probability of liver and kidney diseases. The proposed system uses face recognition to identify the patient. Face recognition module retrieves the Patient Health Record and provides patient information and health records access to the doctor and medical staff. The system is developed using Python Django for Backend, React.JS for User Interface and PostgreSQL as the relational database. The system uses Logistic Regression for possible disease prediction, Support Vector Machine for liver disease prediction, Random Forest for chronic kidney disease prediction. The result of the proposed data-driven clinical decision support system is compared with a doctor’s disease analysis to measure the effectiveness of the proposed system. This kind of system can help doctors in providing better care and predict the disease at an early stage.

scholarly journals A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis (Preprint)

2020 ◽  
Author(s):  
Lars Müller ◽  
Aditya Srinivasan ◽  
Shira R Abeles ◽  
Amutha Rajagopal ◽  
Francesca J Torriani ◽  
...  
Keyword(s):  
Decision Support ◽  
Antimicrobial Resistance ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Data Driven ◽  
Patient Specific ◽  
Tract Infections

BACKGROUND There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. OBJECTIVE The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. METHODS We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. RESULTS We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed <i>remaining risk</i> algorithm, these factors can be used to inform clinicians’ reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. CONCLUSIONS The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

scholarly journals Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

2019 ◽  
Vol 28 (01) ◽  
pp. 120-127 ◽  
Author(s):  
Stefania Montani ◽  
Manuel Striani
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Data Driven ◽  
Knowledge Based ◽  
Hybrid Approaches

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

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