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Computational Cloud Infrastructure for Patient Care
Published in Rishabha Malviya, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Bioinformatics Tools and Big Data Analytics for Patient Care, 2023
Urvashi Sharma, Deepika Bairagee, Nitu Singh, Neelam Jain
A clinical decision support system (CDSS) has been designed to upgrade health and patient care services by incorporating the expertise of clinicians, patient records, and several health-related information, to improve medical decisions. Conventional CDSS contained the software prepared for making the clinical decision directly by comparing the clinical database available in the computer with features of a particular patient; patient-focused advice was then given to the physician for a final decision. Nowadays, such CDSSs are being utilized for patient care where physicians blend their expertise with the data or recommendations given by the CDSS. Recently CDSSs have also been designed with the capacity to interpret their information or data which is uninterpretable by human beings [55,56].
Healthcare Data Analytics Over Big Data
Published in N. Jeyanthi, Kun Ma, Thinagaran Perumal, R. Thandeeswaran, Managing Security Services in Heterogenous Networks, 2020
Temporal relation extraction is also one of the methods in the information extraction process; it focuses on research direction because it is used to identify the complications, patient outcome predictions, and ADE detection. Previously, temporal relation was also utilized in the abstraction of emergency department (ED) computed tomography (CT) imaging reports. In addition to the NLP, we also using the data application and integration mechanism in EHR data to utilize and retrospectively assess treatment effectiveness in real-world settings, quality of care, and cost. A clinical decision support system (CDSS) helps to do multitasking on healthcare Big Data to make quick decisions.
Smart Applications of Internet of Things (IoT) in Healthcare
Published in Nishu Gupta, Srinivas Kiran Gottapu, Rakesh Nayak, Anil Kumar Gupta, Mohammad Derawi, Jayden Khakurel, Human-Machine Interaction and IoT Applications for a Smarter World, 2023
Praveen Kumar Gupta, Shweta Sudam Kallapur, Anusha Mysore Keerthi, Soujanya Ramapriya, A. H. Manjunatha Reddy, Sumathra Manokaran
Clinical decision support system (CDSS) is a computational system with a specialized algorithm that can be used to generate patient-specific treatment suggestions. CDSSs are integrated with electronic health records (EHRs), a database containing patients’ medical records that can be queried with keywords to find and retrieve individual patient records. The CDSS algorithm can then be used to obtain recommendations on treatment decisions. Data mining and relevant clinical research are employed in CDSS algorithms (Figure 7.3). The methodologies used in the implementation of CDSS are outlined below.
AI-CDSS Design Guidelines and Practice Verification
Published in International Journal of Human–Computer Interaction, 2023
Xin He, Xi Zheng, Huiyuan Ding, Yixuan Liu, Hongling Zhu
Clinical decision support systems (CDSS) are crucial to enhancing medical decision-making, such as assisting physicians in patient diagnosis (Mosquera-Lopez et al., 2015), drug suggestions (Pruszydlo et al., 2012), treatment options (Zamora et al., 2013), and flagging potential adverse reactions and allergies (Moxey et al., 2010). The conventional CDSS is rule-based, relying on experts to collate medical knowledge and formulate robust decision-making rules, with problems such as high cost, manual updating, poor system integration, and performance limited by the prior medical knowledge of experts (Middleton et al., 2016). In recent years, CDSS that utilize state-of-the-art AI technologies (such as deep learning or knowledge graphs) for decision-making have been designed and developed. These systems are expected to revolutionize the potential application scenarios and performance of CDSS. They can comprehensively process a large amount of complex unstructured data previously hard to integrate into clinically assisted decision-making without specifying decision rules for each specific task or considering complex interactions between input features. In addition, they can effectively identify potential disease risk that is difficult for human experts to discover (Bates et al., 2021).
Majority voting ensembled feature selection and customized deep neural network for the enhanced clinical decision support system
Published in International Journal of Computers and Applications, 2022
M. Dhilsath Fathima, S. Justin Samuel, R. Natchadalingam, V. Vijeya Kaveri
Clinical decision support systems (CDSS) are computer-based systems that analyze patient data from clinical datasets to predict disease and improve disease decision-making. CDSS can help doctors, patients, and others make the best evidence-based clinical recommendations at the point of care [1,2]. This clinical decision support system has the potential to improve complex disease decision-making significantly. The most significant feature of CDSS is its high prediction accuracy and disease diagnosis. Patients with type 2 diabetes mellitus have a much higher risk of heart disease morbidity and mortality than people without diabetes, and heart disease affects them more severely [3,4]. Diabetes mellitus (DM) is a metabolic disorder caused by high blood sugar levels. High blood sugar levels can damage blood vessels and nerves that regulate your heart over time. Heart disease has caused by damage to heart blood vessels. Diabetes patients are also more likely to have high blood pressure, excessive cholesterol levels (low-density lipoproteins), and high triglycerides, which increase their risk of heart disease. Early detection of heart disease and diabetes is necessary for lowering risk factors and preventing mortality. Several CDSS have been developed in the literature for heart and diabetes disease prediction. This system uses machine learning algorithms to predict disease; while these CDSS models execute predictions, their accuracy is insufficient. The proposed CDSS utilizes MVE feature selection and a customized DNN classifier to predict early cardiac and diabetes issues.
Classification Framework for Clinical Datasets Using Synergistic Firefly Optimization
Published in IETE Journal of Research, 2021
V. R. Elgin Christo, H. Khanna Nehemiah, S. Keerthana Sankari, Shiney Jeyaraj, A. Kannan
Clinical Decision Support System (CDSS) provides assistance to healthcare experts in decision-making. A CDSS is developed by extracting knowledge from clinical data and expert judgement. Computer-aided systems for the diagnosis of lung disorders from computed tomography (CT) slices are presented in [1–8]. The use of genetic algorithm, ant colony optimization and ant colony optimization with tandem-run recruitment for feature selection from the features extracted from CT slices to diagnose lung disorders, is presented in [9–11].