Descriptive Analysis Report Assignment Sample

Pages: 10 Words: 2396

Introduction

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Decision Support System; are a subset of computerized data systems that aid in "decision-making (DSS). A DSS cloud graphically depicts data and information, and it may contain artificial intelligence (AI) or an expert system. Accessing all information such as relational sources of data and legacy; comparative figures of data; projected figures on the basis of assumption of new data; and consequences of various alternative decisions that can be given on the basis of the specific context are all examples of information that can be gathered through a typical decision-making system. In this report the theoretical background of the DSS system has been discussed along with the issue related to the descriptive analysis of the DSS system. Significant process of wearable medical devices and support system fort the diagnostic process has been discussed and differs diagnostic medical decision support tools has been discussed in this report.

Main body

Theoretical foundations of “Decision Support Systems (DSS)

A class of computerized data systems that helps in the decision-making is referred to as the Decision Support Systems (DSS). It is a computer-based interactive system as well as subsystems that are developed to assist the decision maker in the completer decision-making process through the application of communication technologies, and documents. Knowledge, data, or/and model. A DSS cloud represents data and information graphically or it could include artificial intelligence (AI) or an expert system (Alamanos, Rolston & Papaioannou, 2021). The information that can be gathered through the typical decision-making system is accessing all the information such as relational sources of data and legacy; comparative figures of data; projected figures on the basis of assumption of new data; consequences of various alternatives decision that can be given on the basis of the specific context.

There is different kind of DSSs and these can be categories into the following five types -

Communication-driven DSS - Most of this kind of DSS are focused on the internal team, including different partners. With the help of this type of DSS user can conduct meeting, or can collaborate. The client or web server is the common used technology that can be deployed in the DSS. For example - instant messaging software and chats, net-meeting system, and online collaboration are the most used communication driven DSS. Google Docs and Microsoft SharePoint Workspace are the integrated tools that are used in the “communication driven DSS.
Data-Driven DSS - This type of DSS mostly targeted at the service/product suppliers, manager, and staff. The data-driven DSS utilized to query data warehouse or database to get specific information about a specific purpose (Husain, 2019). It implemented through the main system of frame, web, or server/client link. It helps a organization to store and analyzed data for both internal and external of the organization. For instance- computer based database system which has a query framework for checking which included the incorporation of the data that helps to add value to the existing dataset.
Knowledge-driven DSS - Knowledgebase or Knowledge-driven database are at the catch-all categorizers that covers a huge range of framework covering clients in a organization environment it up, but it cloud also includes other different integrating system with the institute. Such as customer of a business. It is most importantly provide advice for management or to choose service/product. The typical technology deployment of such system can be server/silent framework, software or the web running on the computer.
Document-driven DSS - This type of DSS are most common and targeted at the user groups that are broad. This type of DSS used to find documents and search WebPages from the particular set of search item of keyword. The client/server system or the web system has been used to set up this type of technological DSS. With this type of DSS the manager of an organization managed unstructured data in various electronic formats.
Model-driven DSS - It is complex systems that assist to choose among different portions and analyze the decision. This process has been utilized by the staff member and danger of an organization.

The significant issue of descriptive analysis for DSS-

The DSS has different significant issue in the integration such as-

The cost of developing and implementation of the DSS system is very high, the organization has to invest huge amount (Guo et al. 2020). This is a major issue in the implementation of the DSS and is not accessible effort the small organization.
An organization can create a dependency on the DSS, as this is integrated in the regular decision making process which helps to improve the efficiency of the organization and the speed of secession making, for this reason the manager of the organization depends on the technology too much and theism reduced the human involvement in the decision-making (Husain, 2019).
The DSS system cloud overloads the information, because the information system tries to consider all aspect of the problem.

The significant process of wearable medical devices and diagnostic decision support systems

Bouteraa. (2021) present a new concept for a wearable navigation support system for visually impaired individuals (BVIP). Sensors, a “fuzzy logic-based decision support system, real-time processing boards, and a user interface are the primary components of the suggested navigation system. This takes sensor information as input and gives the BVIP with the required safety configuration. The choice is communicated to the user via a voice-hepatic connection. An integrated controller manages two wearable obstruction detecting sensors in the navigation assistance framework. The management system uses the Robot Operating System (ROS) framework, which is assisted by the Beagle Bone Black master board and satisfies the real-time requirements. Data collecting and avoidance of obstruction are performed by numerous ROS-managed nodes, culminating in the delivery of a mixed hectic-voice message for BVIP guiding (Bouteraa. 2021). Both sorts of participants thought the system was acceptable and saw it as a future potential navigation tool. The incorporation of the ROS into the created system enabled it to handle multiple tasks such as obstacle avoidance, acquisition, and real-time guiding. In comparison to the walking stick, the devised system performed better, particularly in detecting risky impediments.

Barnes & Zvarikova (2021) stated that the “Internet of Medical Things (IoMT) can collect, share, and visualise collected data by wearable and field sensor networks, helping in decision making via techniques of machine learning such as artificial intelligence-enabled wearable medical devices and machine learning-based automated diagnostic systems. Cloud services incorporate things-related diagnostics and equipment. Integrated programs help consumers follow their ailments in real - time basis while also optimizing diagnostic processes. The rapid evolution of the Internet of Medical Things expands the reach of omnipresent in-home healthcare monitoring networks. Wearable medical gadgets are capable of generating, storing, inspecting, and sharing health information. Smart healthcare focused on the edge, cloud, and Internet of Things aids in the optimization of medical care, illness management, and diagnosis through automatic signal analysis using artificial intelligence-enabled wearable medical equipment. Throughout the COVID 19 outbreak, medical professionals can employ interconnected electronic technology to remotely diagnose patients, manage medical devices, gadgets, and instruments, and monitor isolated persons by constructing Internet of Things and virtual medical facilities enabled medical network applications. The Internet of Medical Things, as informational networking technologies, has the potential to eliminate healthcare mistakes and enhance remote patient surveillance. Using patient information, Internet of Things-based intelligent medical systems may be used for COVID-19 prevention, diagnosis, and treatment. Action identification is critical in medical aid and monitoring patients in the Internet of Medical Things. The Internet of Medical Things enhances precision, dependability, and effectiveness in smart treatment by using artificial intelligence-based diagnostic algorithms to analyze patient information in real - time.

Medical diagnostic decision support tools

IOT and Cloud computing

The Internet of Things (IoT) is a somewhat new innovation that means to create and interconnect Internet-associated Things through PC organizations. IoT describes it as being more proficient to use a more noteworthy number of less b gadgets, for example, a wrist band, fridge, umbrella, etc as opposed to a couple of b processing contraptions like PCs, tablets, and cell phones (Lakshmanaprabu et al. 2019). These days, different items, like room purifiers and forced air systems, are modified by microcontrollers to give greater intricacy in day to day living assignments. Thus, interconnected devices or articles have the limit of b transmission and calculation in front of the necessities of less calculation contraptions like low power electric light, umbrella, and interlink structures utilizing PC organizations. These interesting IoT devices have the logical thinking potential to accomplish the relegated work without the requirement for a name or individuals (Cai et al. 2019). Since IoT works on an alternate range of Internet associations, the expression “omnipresent processing contrasts from IoT. At the point when IoT and Cloud Computing are utilized to make an application, they are beneficial in the two ways. An observing system might be laid out by joining IoT and cloud to effectively screen patient information even in far off districts, which will be exceptionally significant for clinical specialists (Saraf, Bartere & Lokulwar, 2022). By and large, IoT innovation is constantly upheld by a cloud stage to help its viability with regards to asset use, information capacity, handling, and processing abilities. Moreover, cloud Computing accepts advantages from the IoT through the expansion of the scope to handkle the present world as well as delivers different new services. The integration of the cloud-based service and the IoT performs well as compared to the traditional ones.

Electronic clinical decision support tools (eCDSTs)

Electronic clinical choice helps devices (eCDSTs) are electronic upheld clinical powerful systems. The GP enters patient-unequivocal information into the eCDST, or it will in general be normally created from the patient's electronic prosperity record. The eCDST produces thoughts, prompts, or alarms for the GP to ponder using affirmed computations. eCDSTs can be utilized really during a GP plan or expected to mine data reliably in the background. Electronic clinical decision help gadgets (eCDSTs) are PC upheld clinical powerful systems (Chima et al. 2019). The GP enters patient-unequivocal information into the eCDST, or it might be normally created from the patient's computerized prosperity record (Simpson et al. 2019). The eCDST produces thoughts, prompts, or cautions for the GP to contemplate using affirmed estimations. eCDSTs can be utilized successfully during a GP plan or wanted to mine data reliably in the background.

eCDSTs have been shown to increment specialist execution as well as determination precision in recreated patients for various ailments including dementia, osteoporosis, and HIV. A new efficient survey summed up the effect of eCDSTs on reference ways of behaving, yet it didn't especially concentrate on disease determination; subsequently, the meaning of eCDSTs in malignant growth conclusion has not been completely tended to (Fletcher et al. 2022). The viability of eCDSTs used in essential consideration for disease determination, as well as the variables deciding their powerful application. Endurance rates and only one assessed chance to conclusion accentuate the challenges of directing preliminaries of symptomatic medicines in essential consideration for moderately extraordinary sicknesses. To assess the sum or impact of eCDSTs on malignant growth results, far greater execution studies with long haul follow-up of disease analyses, stage, and endurance are fundamental.

Conclusion

There is different kind of DSSs and these could be categories into the five types such as “Communication-driven DSS, Data-Driven DSS, Knowledge-driven DSS, Document-driven DSS, and Model-driven DSS. A novel idea for a wearable navigation help device for visually challenged people (BVIP). Numerous ROS-managed nodes gather data and avoid obstacles, culminating in the delivery of a mixed hepatic-voice message for BVIP directing. The “Internet of Medical Things (IoMT) may gather, distribute, and visualize data acquired by wearable and field sensor networks, assisting in decision making using machine learning approaches such as AI-enabled wearable medical devices and machine learning-based automated diagnostic systems. The Internet of Things (IoT) is a relatively recent idea that refers to the creation and interconnection of Internet-connected Things via PC organizations. It is more capable of using a greater number of less powerful gadgets, according to IoT. Electronic clinical decision support tools (eCDSTs) are clinical dynamic frameworks that are assisted by technology. The GP inserts patient-specific data into the eCDST, or it is generated automatically from the patient's electronic health record.

References

Journals

Alamanos, A., Rolston, A., & Papaioannou, G. (2021). Development of a decision support system for sustainable environmental management and stakeholder engagement. Hydrology, 8(1), 40. Retrieve from: https://www.mdpi.com/2306-5338/8/1/40/pdf [Retrieve on: 08/07/2022]

Barnes, R., & Zvarikova, K. (2021). Artificial intelligence-enabled wearable medical devices, clinical and diagnostic decision support systems, and internet of things-based healthcare applications in COVID-19 prevention, screening, and treatment. American Journal of Medical Research, 8(2), 9-22.Retrieve from: doi:https://doi.org/10.22381/ajmr8220211 [Retrieve on: 08/07/2022]

Bouteraa. (2021). Design and Development of a Wearable Assistive Device Integrating a Fuzzy Decision Support System for Blind and Visually Impaired People. Micromachines (Basel), 12(9), 1082–Retrieve from: https://doi.org/10.3390/mi12091082 [Retrieve on: 08/07/2022]

Cai, Q., Wang, H., Li, Z., & Liu, X. (2019). A survey on multimodal data-driven smart healthcare systems: approaches and applications. IEEE Access, 7, 133583-133599. Retrieve from: https://ieeexplore.ieee.org/iel7/6287639/8600701/08836450.pdf [Retrieve on: 08/07/2022]

Chima, S., Reece, J. C., Milley, K., Milton, S., McIntosh, J. G., & Emery, J. D. (2019). Decision support tools to improve cancer diagnostic decision making in primary care: a systematic review. British Journal of General Practice, 69(689), e809-e818. Retrieve from: https://bjgp.org/content/bjgp/69/689/e809.full.pdf [Retrieve on: 08/07/2022]

Fletcher, E., Burns, A., Wiering, B., Lavu, D., Shephard, E., Hamilton, W., ... & Abel, G. (2022). Workload And Workflow Implications Associated With The Use Of Electronic Clinical Decision Support Tools Used By Health Professionals In General Practice: A Scoping Review. Retrieve from: https://www.researchsquare.com/article/rs-1401970/latest.pdf [Retrieve on: 08/07/2022]

Guo, Y., Wang, N., Xu, Z. Y., & Wu, K. (2020). The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mechanical Systems and Signal Processing, 142, 106630. Retrieve from: https://fardapaper.ir/mohavaha/uploads/2020/11/Fardapaper-The-internet-of-things-based-decision-support-system-for-information-processing-in-intelligent-manufacturing-using-data-mining-technology.pdf [Retrieve on: 08/07/2022]

Husain, T. (2019). An Analysis of Modeling Audit Quality Measurement Based on Decision Support Systems (DSS). measurement, 275, 310-326. Retrieve from: https://www.researchgate.net/profile/T-Husain/publication/341215761_An_Analysis_of_Modeling_Audit_Quality_Measurement_Based_on_Decision_Support_Systems_DSS/links/5eb42b9845851523bd4ad030/An-Analysis-of-Modeling-Audit-Quality-Measurement-Based-on-Decision-Support-Systems-DSS.pdf [Retrieve on: 08/07/2022]

Lakshmanaprabu, S. K., Mohanty, S. N., Krishnamoorthy, S., Uthayakumar, J., & Shankar, K. (2019). Online clinical decision support system using optimal deep neural networks. Applied Soft Computing, 81, 105487. Retrieve from: [Retrieve on: 08/07/2022]

Saraf, P. D., Bartere, M. M., & Lokulwar, P. P. (2022). Fog Enabled Intelligence Clinical Decision Support System (FICDSS) For Healthcare Applications Using Fuzzy Logic Inference System (FLIS). JOURNAL OF ALGEBRAIC STATISTICS, 13(3), 1515-1531. Retrieve from: [Retrieve on: 08/07/2022]

Simpson, S., Johnson, K., Hite, A., Frisbee, K., & Ghosh, A. (2019). Usability and Acceptability of an Electronic Clinical Decision Support Tool for Antibiotic Selection for Common Pediatric Infections in Outpatient Rural Healthcare Clinics. Retrieve from: https://digitalcommons.pittstate.edu/cgi/viewcontent.cgi?article=1055&context=posters_2019 [Retrieve on: 08/07/2022]