scholarly journals Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

2020 ◽  
Author(s):  
Shahab Pasha ◽  
Jan Lundgren ◽  
Marco Carratù ◽  
Patrik Wreeby ◽  
Consolatina Liguori
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Trees ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Cardiovascular Assessment

An artificial intelligence-based clinical decision support system for large kidney stone treatment

2019 ◽  
Vol 42 (3) ◽  
pp. 771-779 ◽  
Author(s):  
Tayyebe Shabaniyan ◽  
Hossein Parsaei ◽  
Alireza Aminsharifi ◽  
Mohammad Mehdi Movahedi ◽  
Amin Torabi Jahromi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Kidney Stone ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Stone Treatment

scholarly journals A Clinical Feasibility Study of an Artificial Intelligence-Powered Clinical Decision Support System

2021 ◽  
Author(s):  
Christina Popescu ◽  
Grace Golden ◽  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Dominique Slowey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Session Length ◽  
Positive Effects ◽  
Significant Difference

Objective: We examine the feasibility of an Artificial Intelligence (AI)-powered clinical decision support system (CDSS), which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. Methods: Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with major depressive disorder (MDD) as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. Results: Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. Conclusions: Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment: A Longitudinal Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Christina Popescu ◽  
Grace Golden ◽  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Dominique Slowey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Session Length ◽  
Positive Effects ◽  
Significant Difference

BACKGROUND Approximately two thirds of patients with major depressive disorder (MDD) do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence (AI)-powered clinical decision support systems (CDSS) to assist physicians in their treatment selection and management, improving personalization and use of best practices such as measurement-based care. Previous literature shows that in order for digital mental health tools to be successful, the tool must be easy to use for patients and physicians and feasible within existing clinical workflows. OBJECTIVE We examine the feasibility of an AI-powered clinical decision support system, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. METHODS Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with MDD as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. RESULTS Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. CONCLUSIONS Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies. CLINICALTRIAL NCT04061642

Online clinical decision support system using optimal deep neural networks

2019 ◽  
Vol 81 ◽  
pp. 105487 ◽  
Author(s):  
Lakshmanaprabu S.K. ◽  
Sachi Nandan Mohanty ◽  
Sheeba Rani S. ◽  
Sujatha Krishnamoorthy ◽  
Uthayakumar J. ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Deep Neural Networks ◽  
Clinical Decision

scholarly journals Evaluating the Perceived Utility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment Using a Simulation Centre

2021 ◽  
Author(s):  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Christina Popescu ◽  
Tamara Perez ◽  
Colleen Rollins ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Treatment Options ◽  
Clinical Decision ◽  
Perceived Utility ◽  
Clinical Interactions

AbstractAifred is a clinical decision support system (CDSS) that uses artificial intelligence to assist physicians in selecting treatments for major depressive disorder (MDD) by providing probabilities of remission for different treatment options based on patient characteristics. We evaluated the utility of the CDSS as perceived by physicians participating in simulated clinical interactions. Twenty psychiatry and family medicine staff and residents completed a study in which each physician had three 10-minute clinical interactions with standardized patients portraying mild, moderate, and severe episodes of MDD. During these scenarios, physicians were given access to the CDSS, which they could use in their treatment decisions. The perceived utility of the CDSS was assessed through self-report questionnaires, scenario observations, and interviews. 60% of physicians perceived the CDSS to be a useful tool in their treatment-selection process, with family physicians perceiving the greatest utility. Moreover, 50% of physicians would use the tool for all patients with depression, with an additional 35% noting they would reserve the tool for more severe or treatment-resistant patients. Furthermore, clinicians found the tool to be useful in discussing treatment options with patients. The efficacy of this CDSS and its potential to improve treatment outcomes must be further evaluated in clinical trials.

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