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Key Topics
- Requirement
- INTRODUCTION
- LITERATURE REVIEW
- Introduction of Case-Based Reasoning (CBR)
- Case design
- Case reuse
- Revise the solution
- Retain the new case
- Benefits of case-based reasoning in relation to diabetes treatment
- Challenges of case-based reasoning in relation to the diabetes treatment
- Other knowledge engineering system for diabetes support
- CONCLUSION
- REFERENCES
Requirement
Write a short article on Challenges in knowledge engineering for healthcare.
INTRODUCTION
Knowledge engineering is a very new and evolving concept in the healthcare. In today’s scenario, it is gaining major importance in the medical field. In this context, as per the viewpoint of Van Do, Le Thi, & Nguyen (2018) knowledge engineering is basically a branch of artificial intelligence that develops the rules which are used to resolve any kind of problem which incurred in different fields. The expert system, case-based reasoning, neural net are the common types of knowledge engineering systems. This article is based on the topic of “Use of case-based reasoning for diabetes management and treatment”. To get an effective solution to the diabetes problem is being regarded as one of the most significant issues faced by many healthcare firms. In this context, case-based reasoning model plays a very critical role. Here, in the present study detailed description will be given regarding the relationship between CBR and diabetes management. In addition to this, the study will also showcase benefits and disadvantages which are associated with CBR in healthcare.
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LITERATURE REVIEW
Introduction of Case-Based Reasoning (CBR)
According to the viewpoint of Zgheib, Conchon & Bastide (2017) case-based reasoning consists of a systematic process which assists in resolving the new problem on the basis of past solutions. Here, the name of the approach itself showcases its functioning. In this context, it can be said that here an individual will take assistance from a different type of cases for the purpose to solve the current problem. Besides this, Wang, (2017) has stated that case-based reasoning is the process of arriving at the solution of the new problem by taking assistance from previous solutions that are used with an aim to solve the similar type of problem. In simple words, it is correct to say that this approach uses past knowledge with an aim to develop a solution to the identified problem. This method is used by lawyers, doctors and mechanics etc. Thus, it is the type of problem solving approach that helps in resolving the varied type of complex problems with the help of past experience.
Use of case based reasoning in diabetes knowledge management
CBR has importance in the medical field because it helps in giving a quick solution to the problems which occurred in the respective field. In accordance with the given context, following steps examined which are used in order to manage the data of diabetes patients with the help of case-based reasoning.
Case design
The process of getting an answer to the problem begins with designing of cases. Here, knowledge is represented by storing different type of information such as describing the problem that has occurred, assessing the solution which was applied to the described problem and finally getting the information about the outcome of applied solution. In this regard, as per the viewpoint of Mezghani, Exposito & Drira (2017) during the process of representing problem, it is required by an individual that it should assemble full information about problem as well solution which is applied to the same. This is because it is through this way the only doctor can reach on to the effective solution which is similar to previous one. For example, there is some information which is used to describe diabetes management problems. These are actual blood glucose level, targeted blood glucose level, carbohydrate ratio, meal time, amount of carbohydrate consumed with each meal, type of insulin used and insulin sensitivity etc.
In a similar way, there is some information which is taken into account in order to solve assessed diabetes-related problems. These are changed in the time and location of insulin taken, mechanical issue related to pump, food consumed at each meal, menstrual cycle, consumption of alcohol and exercise etc. Herein, as per the viewpoint of Wang & Byrd (2017) Case-based reasoning will not only keep the information about problem and solution but it also possesses the record about the outcomes incurred from applied solutions. The outcomes for the diabetes treatment could be fixing the problem, not fixing the problem and condition of the patient has improved but not fully. Here, it is through this type of process only data for those patients are prepared who came up with a different type of diabetes-related problems and gone with a different type of solutions. This is how medical history is created in CBR for diabetes treatment and management.
Case reuse
It is being regarded as the second phase of CBR. In the given step, the doctor will reuse the solution from the cases which are similar to present a problem. According to the viewpoint of Gu, Li, Li & Liang (2017), it is the most important step as it gives an idea to the doctor that whether previous solutions from Case-Based Reasoning database are useful for current solution or not. In addition to this, here KNN tool is used with an aim to get an answer to the question that whether the new problem is matching with the existing solution or not. Thus, the effectiveness of respective phase totally depends upon the type and quality of diabetes information being feed by a doctor in the CBR system. For example, wrong information could lead to the wrong solution. Thus, the given thing will hamper the effectiveness of case-based reasoning system. Furthermore, the given thing will also affect the quality of services which is being provided by the healthcare firms. In this context, Smith Jervelund and et.al (2017) have depicted that success of healthcare company depends on this aspect that how well it is giving treatment to its patients. Furthermore, the given thing will also cause a direct effect on the satisfaction level of patients. Here, it is essential for the firm that it should put its maximum effort in the process of maintaining satisfaction level of its buyers. This is because; if it is not done then significant impact of the same will be seen on the sales and profit of the company. Furthermore, this will also have an effect on the brand image of the enterprise. Thus, ineffective action with regard to one thing will lead to affect the work of whole healthcare organization.
Revise the solution
It is the third step of CBR system in which the solution which is identified for the problem is revised. In the respective phase, the doctor has applied the previous solution to the problem and thereby has developed new solution too. According to the viewpoint of Mintzberg (2018), revise solution is kind of trial and error phase in which doctor test one solution and reject the same if it does not help to resolve the specific diabetes-related problem. However, Yang, Huang & Wang (2017) has evaluated that the given step is quite time-consuming. This is because here each solution which was applied to previous problems were tested with an aim to get an answer to the current problem. But, in case if no previous solution is applied to the current problem then in the given condition doctor will develop new solution and information about the same is recorded by it in the system. This new solution developed will be later used by the doctor. Thus, it can be said that case-based reasoning will not only state solution to the healthcare problem but it will also assist in the process of developing a new solution to the problem in an effectual manner.
Retain the new case
This step came into existence when the previous solution does not work in the assessed problem. Here, doctors who are treating diabetes patients will update the record of the new case along with the solution. In this context, Wang & Byrd (2017) have depicted that it is very beneficial for the healthcare firm that it should maintain and update the record of CBR. This is because it is by complying with the given type of activity only health care firm can assure to give high-quality services to its customers. Overall, it can be said that CBR system is based on these given steps only thus any kind of changes in the system will lead to affect services of healthcare enterprises in an effectual way.
Benefits of case-based reasoning in relation to diabetes treatment
Different authors have given varied opinions in relation to the advantages of case-based reasoning in the treatment of diabetes. In accordance with the given context, as per the viewpoint of Mezghani, Exposito & Drira (2017) case-based reasoning system helps in giving a quick solution to the problem which is being faced by healthcare firms. This is due to the fact that the process which is used in respective technique and simple and direct. Further, it does not involve any kind of complications also. It is the reason why given approach help in giving a quick solution to the examined problem in an effectual manner. However, Wang & Byrd (2017) have depicted that case-based reasoning is the fast approach to getting a solution to the problem but still it does not guarantee that the solution which is being given by it is accurate and effective.
In addition to this, as per the viewpoint of Mintzberg (2018) doctors can use the case-based reasoning system for ill-defined and open-ended concepts. In this context, it is examined that there are many cases related to diabetes which are not defined appropriately. Thus, it is the specialty of this system that it gets the solution from the uncompleted information. It is by complying with given type of activity only doctors get the best solution to the problem in an effectual manner. Moreover, Mezghani, Exposito & Drira (2017) have depicted that case-based reasoning helps in assessing the main features which are related to the specific problem. It is being regarded as a most effective approach for assessing the solution to the new problem. This is because with the help of highlighted feature doctor can prepare the base for the problem which is related to diabetes. Thus, the given thing will save lots of time and money of healthcare firms. It will also assist them in the process to deliver their best and effectual services to the buyers of healthcare enterprises in an effectual way. In addition to this, with the use of CBR features in relation to diabetes problem doctors can assess many more solution. Overall, it can be said that it is effective to comply with the concept of case-based reasoning in the healthcare services.
Challenges of case-based reasoning in relation to the diabetes treatment
There are some issues or limitations are also associated with the concept of case-based reasoning. Herein, according to the viewpoint of Wang (2017), this method totally relies upon old cases and does not use any other methodology with an aim to get an answer to the assessed problem. This thing could create a problem for the doctor. This is because; here doctor does not have guaranteed that the solution which is applied to the previous diabetes problem is effective. This raises the chances of not getting appropriate solution to the problem in an effectual manner. Furthermore, researchers have depicted that there are chances that the case library could represent a biased solution. Here, the solution examined are totally depends upon assumption. Here, case-based reasoning will only compare feature of old cases with the new one. However, by comparing the features doctors cannot reach on to some effective and appropriate solution. Thus, it is due to the presence of given aspect ineffectiveness of case-based reasoning approach occurred. In this regard, as per the viewpoint of Gu, Li, Li & Liang (2017) case-based reasoning is the type of tool in which human interaction is essential. Here, after describing the problem to the CBR system doctors will have to give an answer to many questions which are related with diabetes. Here, after giving an answer to a list of questions another list will come in front of doctors. This irritates doctor sometime and it also demands lots of time of doctors. Thus, it is due to the presence of given aspect there are many healthcare firm which resist applying the respective system because of the availability of given issue. But, if significant improvement will be made in the respective approach at that time effectiveness of the system will be enhanced in an effectual way.
Other knowledge engineering system for diabetes support
There is many other knowledge engineering systems examined which also provide support to the different healthcare firms. In this context, Mezghani, Exposito & Drira (2017) have depicted that Medtronic MiniMed is the company that manufacture pump and glucometer. Furthermore, respective company has also created software for the diabetes patients. Here, diabetes patients can download data relating to insulin dosage and blood glucose which are stored in their central site. Furthermore, it also allows interaction between patient and his respective physician. This is an effective way as the respective technique helps in saving time of patients as well as doctors. In addition to this, here data are represented in the graphical format. In addition to this, this software also includes application which is named as “Bolus Wizard”. This application uses numeric formula with an aim to recommend individual bolus dosage. Thus, it can be said that it is an effective approach for the purpose to treat the patients who are suffered from diabetes in an effectual manner.
Besides this, Wang (2017) has depicted that expert system is another most effective approach for the diabetes treatment. In the respective system, individual who have earned expertizes in the diabetes treatment sector will give advices to patients with regard to maintain their diabetes in an effective manner. Herein, this system includes data of number of reputed hospitals doctors which gives their fruitful advices to doctors. These all things tend to play very important role in today’s scenario. This is because; with the help of these modern ways of giving treatment quick and effectual services can be delivered by healthcare firms to the diabetes patients.
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Articulating all the facts and figures from the whole report, it can be stated that knowledge engineering is being regarded as one of the most useful concept. This is because; it is assisting in resolving many problems of healthcare firms. The problem could be related with difficulty in getting medical history of patients, not getting solution to the medical condition of patients and giving quick services to the customers etc. By taking significant action with regard to all these given problems healthcare firms can raise the effectiveness of the services which is being given by it. Further, through this way corporation can maintain satisfaction level of its patients. It is essential for the firm that it first priority should be to enhance satisfaction level of its customers. This is because, it is with the help of customers only firm can attain its strategic goals and objectives. In addition to this, it is the buyers only with the help of which firm can mark its effectual presence in a highly competitive environment. This thing will also assist corporation in the task of making improvement in the sales and profitability condition in an effectual manner.
REFERENCES
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Gu, D., Li, J., Li, X., & Liang, C. (2017). Visualizing the knowledge structure and evolution of big data research in healthcare informatics. International journal of medical informatics, 98, 22-32.
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Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K. M., & Sundarsekar, R. (2017). Big data knowledge system in healthcare. In Internet of things and big data technologies for next generation healthcare (pp. 133-157). Springer, Cham.
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Mezghani, E., Exposito, E., & Drira, K. (2017). A model-driven methodology for the design of autonomic and cognitive IoT-based systems: Application to healthcare. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(3), 224-234.
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Mintzberg, H. (2018). Managing the Myths of Health care. In The Myths of Health Care (pp. 3-11). Springer, Cham.
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Smith Jervelund, S., Maltesen, T., Wimmelmann, C. L., Petersen, J. H., & Krasnik, A. (2017). Ignorance is not bliss: the effect of systematic information on immigrants’ knowledge of and satisfaction with the Danish healthcare system. Scandinavian journal of public health, 45(2), 161-174.
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Van Do, T., Le Thi, H. A., & Nguyen, N. T. (2018). Advanced computational methods for knowledge engineering. Springer International Publishing:.
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Wang, Y. (2017). On cognitive foundations and mathematical theories of knowledge science. In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 889-914). IGI Global.
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Wang, Y., & Byrd, T. A. (2017). Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. Journal of Knowledge Management, 21(3), 517-539.
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Yang, D., Huang, C., & Wang, M. (2017). A social recommender system by combining social network and sentiment similarity: A case study of healthcare. Journal of Information Science, 43(5), 635-648.
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Zgheib, R., Conchon, E., & Bastide, R. (2017). Engineering IoT healthcare applications: towards a semantic data driven sustainable architecture. In eHealth 360° (pp. 407-418). Springer, Cham.
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