Smart CDSS for Smart Homes

Hussain, Maqbool; Khan, Wajahat Ali; Afzal, Muhammad; Lee, Sungyoung

doi:10.1007/978-3-642-30779-9_41

Cited by 17 publications

(5 citation statements)

References 4 publications

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“…The proposed method was tested by applying the knowledge base of Smart CDSS [ 26 , 33 ] to the oral cavity site within the head and neck cancer domain. The knowledge base for the oral cavity site considered for this consists of four MLMs based on a calling mechanism consisting of a sub-MLM approach invoked by the Root MLM.…”

Section: Treatment Recommendation Service Scenario: Results and Evmentioning

confidence: 99%

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval

Afzal

Hussain

Ali

et al. 2015

Sensors

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Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important.

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Section: Treatment Recommendation Service Scenario: Results and Evmentioning

confidence: 99%

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval

Afzal

Hussain

Ali

et al. 2015

Sensors

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“…CDSSs play an important role in detecting abnormalities and giving alerts or feedback to patients and providers at an appropriate time. For multimorbidity, integration of CDSSs with telemonitoring could be helpful for patients who need close monitoring of their health status but do not have an indication for hospital admission, as they can be monitored at home and can keep a sense of contact with their providers [ 39 ]; this includes patients with cardiovascular disease [ 40 ], allergic diseases [ 41 ], and COVID-19 [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

The Integration of Clinical Decision Support Systems Into Telemedicine for Patients With Multimorbidity in Primary Care Settings: Scoping Review

Wiwatkunupakarn¹,

Aramrat²,

Pliannuom³

et al. 2023

J Med Internet Res

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Background Multimorbidity, the presence of more than one condition in a single individual, is a global health issue in primary care. Multimorbid patients tend to have a poor quality of life and suffer from a complicated care process. Clinical decision support systems (CDSSs) and telemedicine are the common information and communication technologies that have been used to reduce the complexity of patient management. However, each element of telemedicine and CDSSs is often examined separately and with great variability. Telemedicine has been used for simple patient education as well as more complex consultations and case management. For CDSSs, there is variability in data inputs, intended users, and outputs. Thus, there are several gaps in knowledge about how to integrate CDSSs into telemedicine and to what extent these integrated technological interventions can help improve patient outcomes for those with multimorbidity. Objective Our aims were to (1) broadly review system designs for CDSSs that have been integrated into each function of telemedicine for multimorbid patients in primary care, (2) summarize the effectiveness of the interventions, and (3) identify gaps in the literature. Methods An online search for literature was conducted up to November 2021 on PubMed, Embase, CINAHL, and Cochrane. Searching from the reference lists was done to find additional potential studies. The eligibility criterion was that the study focused on the use of CDSSs in telemedicine for patients with multimorbidity in primary care. The system design for the CDSS was extracted based on its software and hardware, source of input, input, tasks, output, and users. Each component was grouped by telemedicine functions: telemonitoring, teleconsultation, tele–case management, and tele-education. Results Seven experimental studies were included in this review: 3 randomized controlled trials (RCTs) and 4 non-RCTs. The interventions were designed to manage patients with diabetes mellitus, hypertension, polypharmacy, and gestational diabetes mellitus. CDSSs can be used for various telemedicine functions: telemonitoring (eg, feedback), teleconsultation (eg, guideline suggestions, advisory material provisions, and responses to simple queries), tele–case management (eg, sharing information across facilities and teams), and tele-education (eg, patient self-management). However, the structure of CDSSs, such as data input, tasks, output, and intended users or decision-makers, varied. With limited studies examining varying clinical outcomes, there was inconsistent evidence of the clinical effectiveness of the interventions. Conclusions Telemedicine and CDSSs have a role in supporting patients with multimorbidity. CDSSs can likely be integrated into telehealth services to improve the quality and accessibility of care. However, issues surrounding such interventions need to be further explored. These issues include expanding the spectrum of medical conditions examined; examining tasks of CDSSs, particularly for screening and diagnosis of multiple conditions; and exploring the role of the patient as the direct user of the CDSS.

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“…One of the projects utilizes the services of KnowledgeButton is Smart CDSS [5] which consists of three major components; knowledge authoring, knowledge base, and research evidence support. Knowledge authoring component is covered in our previous work [6] that provides the environment for physicians to create their clinical knowledge in the form of knowledge rules.…”

Section: School Of Comp and Information Systemsmentioning

confidence: 99%

KnowledgeButton: An evidence adaptive tool for CDSS and clinical research

Afzal

Hussain

Khan

et al. 2014

2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings

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Healthcare domain is continuously growing with new knowledge emerged at different levels of clinical interest. At the same time, there is an increasing interest in the use of clinical decision support systems (CDSSs) to increase the healthcare quality and efficiency. Majorly the existing CDSSs are not designed to adapt scientific research in a weD-established and automatic manner. Clinicians and researchers access the online resources on frequent basis for un met questions during the course of patient care. They usuaDy foDow a dis-integrated approach to search for their required information from resources of their interest. Additionally, there is lack of defined mechanism to integrate the relevant knowledge for future use. To overcome the disintegrated and non-automatic approach, we introduce the concept of KnowledgeButton; a comprehensive model for evidence adaption from online credible knowledge sources in a well-defined and established manner. It saves the time of clinicians spend unnecessary in searching research evidence using disintegratedand manual mechanism. In this paper, we provide architecture design, workflows, and scenarios complemented with primary results. It covers walk-through from search query generation to evaluation of search results.

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Smart CDSS for Smart Homes

Cited by 17 publications

References 4 publications

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval

The Integration of Clinical Decision Support Systems Into Telemedicine for Patients With Multimorbidity in Primary Care Settings: Scoping Review

KnowledgeButton: An evidence adaptive tool for CDSS and clinical research

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