Eliciting User Decision Requirements for Designing Computerized Diagnostic Support for Family Physicians

Porat, Talya; Kostopoulou, Olga; Woolley, Amanda; Delaney, Brendan

doi:10.1177/1555343415608973

Cited by 23 publications

(17 citation statements)

References 47 publications

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“…The evidence service was made available for use with the TRANSFoRm decision support tool that was developed separately. The design requirements and clinical motivation for implementing the decision support tool are described elsewhere and summarised here . The tool supported appropriate diagnostic cues for 78 diagnostic conditions relating to 3 presenting patient complaints: abdominal pain, chest pain, and dyspnoea.…”

Section: Methodsmentioning

confidence: 99%

Requirements and validation of a prototype learning health system for clinical diagnosis

Corrigan

Munnelly

Kazienko

et al. 2017

Learning Health Systems

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Section: Methodsmentioning

confidence: 99%

Requirements and validation of a prototype learning health system for clinical diagnosis

Corrigan

Munnelly

Kazienko

et al. 2017

Learning Health Systems

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“…Nesse contexto, uma parte significativa do treinamento médico é dedicada a aprender como identificar as informações relevantes desse montante, de modo que elas possam guiar a decisão sobre o tratamento [Bezemer et al 2019]. Sistemas computacionais de suporte à decisão clínica (SAD) vêm sendo amplamente utilizados para auxiliar os profissionais de saúde nessa análise [Horng et al 2017] [Porat et al 2016].…”

Section: Introductionunclassified

Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

Silva¹,

Novo²,

Souza³

et al. 2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

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Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.

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“…Clinical decision support systems (CDSSs) provide useful information and expert knowledge to assist healthcare providers to improve diagnosis and treatment outcomes, disease managements, and healthcare quality for patients in both home and clinical settings and have been shown to be capable of improving healthcare outcomes in medical practice [ 16 ]. CDSSs have been widely applied in disease diagnosis [ 17 , 18 ], disease treatment and management [ 19 – 21 ], medical alerting or event reminding [ 22 – 24 ], and drug dosing or medication prescribing [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset

Chen

Lin

Wang

et al. 2018

Journal of Healthcare Engineering

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More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002–2010, were retrieved from the National Health Insurance Research Database (NHIRD). After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7%) were diagnosed with hip fracture or vertebrate fracture. The statistical results showed that demographic information, chronic respiratory diseases and comorbidities, and corticosteroid-related variables (cumulative dose, mean exposed daily dose, follow-up duration, and exposed duration) were significantly different between fracture and nonfracture patients. The clinical decision support systems (CDSSs) were designed with integrated genetic algorithm (GA) and support vector machine (SVM) by training and validating the models with balanced training sets obtained by random and cluster-based undersampling methods and testing with the imbalanced NHIRD dataset. Two different objective functions were adopted for obtaining optimal models with best predictive performance. The predictive performance of the CDSSs exhibits a sensitivity of 69.84–77.00% and an AUC of 0.7495–0.7590. It was concluded that long-term use of inhaled corticosteroids may induce osteoporosis and exhibit higher incidence of hip or vertebrate fractures. The accumulated dose of ICS and OCS therapies should be continuously monitored, especially for patients with older age and women after menopause, to prevent from exceeding the maximum dosage.

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Eliciting User Decision Requirements for Designing Computerized Diagnostic Support for Family Physicians

Cited by 23 publications

References 47 publications

Requirements and validation of a prototype learning health system for clinical diagnosis

Requirements and validation of a prototype learning health system for clinical diagnosis

Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset

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