Clinical Decision Support Systems and Predictive Analytics

Lourdusamy, Ravi; Mattam, Xavierlal J.

doi:10.1007/978-3-030-40850-3_14

Cited by 4 publications

(3 citation statements)

References 116 publications

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“…Apriori is a representative algorithm designed to focus on the occurrence of events, and it makes it easy to analyze the comorbidities of the target dataset [18,19]. The application of it has been expanded to medical fields, such as clinical decision-making to diagnose diseases in recent years [20][21][22]. It is also known as an unsupervised machine learning method.…”

Section: Discussionmentioning

confidence: 99%

Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining

Cha

Kim

2021

Healthcare

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This study explored physical and psychiatric comorbidities of mood disorders using association rule mining. There were 7709 subjects who were patients (19 years old) diagnosed with mood disorders and included in the data collected by the Korean National Hospital Discharge In-depth Injury Survey (KNHDS) between 2006 and 2018. Physical comorbidities (46.17%) were higher than that of psychiatric comorbidities (27.28%). The frequent comorbidities of mood disorders (F30–F39) were hypertensive diseases (I10–I15), neurotic, stress-related and somatoform disorders (F40–F48), diabetes mellitus (E10–E14), and diseases of esophagus, stomach, and duodenum (K20–K31). The bidirectional association path of mood disorders (F30–F39) with hypertensive diseases (I10–I15) and diabetes mellitus (E10–E14) were the strongest. Depressive episodes (F32) and recurrent depressive disorders (F33) revealed strong bidirectional association paths with other degenerative diseases of the nervous system (G30-G32) and organic, including symptomatic and mental disorders (F00–F09). Bipolar affective disorders (F31) revealed strong bidirectional association paths with diabetes mellitus (E10–E14) and hypertensive diseases (I10–I15). It was found that different physical and psychiatric disorders are comorbid according to the sub-classification of mood disorders. Understanding the comorbidity patterns of major comorbidities for each mood disorder can assist mental health providers in treating and managing patients with mood disorders.

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Section: Discussionmentioning

confidence: 99%

Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining

Cha

Kim

2021

Healthcare

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“…13 In particular, clinical decision support (CDS) is a promising approach to informing the care of chronic conditions leveraging AI. [14][15][16] Indeed, machine learning (ML)-based CDS tools have been explored for providing pharmacotherapy recommendations 17,18 and predicting the risk of complications 19,20 in the context of chronic disease. Interoperability standards have also been explored as a means to facilitate the dissemination of CDS tools for common chronic diseases, 21 including for providing AI-driven care recommendations.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

et al. 2021

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Objectives Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. Methods Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. Results The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. Conclusion A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

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“…The study of CDSS acceptance contributes significantly to revealing many of the barriers and advantages in adopting the system and provides a significant opportunity for the success of the implementation of the CDSS. The investigation and discovery of the factors that influence the acceptance of CDSS by the end-user are crucial to its successful implementation [10]. Several previous studies have indicated the need to conduct high-quality studies to determine the factors that influence the acceptance of CDSS by physicians.…”

Section: Introductionmentioning

confidence: 99%

An Integration of UTAUT and Task-Technology Fit Frameworks for Assessing the Acceptance of Clinical Decision Support Systems in the Context of a Developing Country

Aljarboa

Miah

2021

Proceedings of Sixth International Congress on Information and Communication Technology

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This paper is to create a basis of theoretical contribution for a new PhD thesis in the area of Clinical Decision Support Systems (CDSS) acceptance. Over the past three years we conducted the qualitative research into three distinctive phases to develop an extended Task-Technology Fit (TTF) Framework. These phases are for initiating requirement generation of the framework, discovering the factors of the framework through perspectives, and evaluating the new proposed framework. The new condition is related to developing country in which various sectors such as healthcare is mostly under attention. We conduct a new inspective for assisting decisions support technology and its usefulness in this sector to integrate with other frameworks for assisting the value, use and how can be better accepted in context of healthcare professionals.

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Clinical Decision Support Systems and Predictive Analytics

Cited by 4 publications

References 116 publications

Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining

Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

An Integration of UTAUT and Task-Technology Fit Frameworks for Assessing the Acceptance of Clinical Decision Support Systems in the Context of a Developing Country

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