Case-Based Reasoning in the Care of Alzheimer’s Disease Patients

Marling, Cindy; Whitehouse, Peter J.

doi:10.1007/3-540-44593-5_50

Cited by 65 publications

(43 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khan and Hoffmann [38] presented an approach that allows GPs to automatically construct a menu which is strongly tailored to the individual requirements and food preferences of a client. Concerning medical prescription practices, Marling and Whitehouse [31] developed AUGUSTE to support treatment planning in Alzheimer's disease by using CBR to determine if a neuroleptic drug should be prescribed and then to select the approved drugs for a patient via a rule-based mechanism. Hartge et al [32] proposed a similarity measurement algorithm for a CBR system to support drug-related events in minimizing inappropriate selection of drugs and inappropriate drug-drug interaction.…”

Section: Case-based Reasoning In Medical Prescriptionmentioning

confidence: 99%

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Ting

Kwok

Tsang

et al. 2011

Knowledge-Based Systems

View full text Add to dashboard Cite

ObjectiveWith the increased complexity and uncertainty in drug information, issuing medical prescriptions has become a vexing issue. As many as 240,000 medicines are available on the market, so this paper proposes a novel approach to the issuing of medical prescriptions. The proposed process will provide general practitioners (GPs) with medication advice and suggest a range of medicines for specific medical conditions by taking into consideration the collective pattern as well as the individual preferences of physicians' prescription decisions. Methods and MaterialA hybrid approach is described that uses a combination of case-based reasoning (CBR) and Bayesian reasoning. In the CBR process, all the previous knowledge retrieved via similarity measures is made available for the reference of physicians as to what medicines have been prescribed (to a particular patient) in the past. After obtaining the results from CBR, Bayesian reasoning is then applied to model the prescription experience of all physicians within the organization. By comparing the two sets of results, more refined recommendations on a range of medicines are suggested along with the ranking for each recommendation. ResultsTo validate the proposed approach, a Hong Kong medical center was selected as a testing site. Through application of the hybrid approach in the medical center for a period of one month, the results demonstrated that the approach produced satisfactory performance in terms of user satisfaction, ease of use, flexibility and effectiveness. In addition, the proposed approach yields better results and a faster learning rate than This is the Pre-Published Version. 2 when either CBR or Bayesian reasoning are applied alone. ConclusionEven with the help of a decision support system, the current approach to anticipating what drugs are to be prescribed is not flexible enough to cater for individual preferences of GPs, and provides little support for managing complex and dynamic changes in drug information. Therefore, with the increase in the amount of information about drugs, it is extremely difficult for physicians to write a good prescription. By integrating CBR and Bayesian reasoning, the general practitioners' prescription practices can be retrieved and compared with the collective prescription experience as modeled by probabilistic reasoning. As a result, physicians can select the drugs which are supported by informed evidential decisions. That is, they can take into consideration the pattern of decisions made by other physicians in similar cases.

show abstract

Section: Case-based Reasoning In Medical Prescriptionmentioning

confidence: 99%

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Ting

Kwok

Tsang

et al. 2011

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…Overviews of medical CBR have been published in [6,25,26,27]. Among the most closely related projects are MNAOMIA, in the domain of psychiatric eating disorders [10], CARE-PARTNER, for stem cell transplantation follow-up care [11], RHENE, in the domain of end-stage renal disease [12], and the Auguste Project, for the management of Alzheimer's Disease [13]. These research projects, like ours, aim to assist in managing long-term, or chronic, medical conditions.…”

Section: Related Researchmentioning

confidence: 99%

“…CBR systems have long integrated the quantitative with the qualitative for applications ranging from generating expressive music [7] to menu planning [8] to recommender systems [9]. Finally, CBR has been successfully applied to other long-term medical conditions that can not be cured but must nevertheless be managed [10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Case-Based Decision Support for Patients with Type 1 Diabetes on Insulin Pump Therapy

Marling

Shubrook

Schwartz

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. This paper presents a case-based approach to decision support for diabetes management in patients with Type 1 diabetes on insulin pump therapy. To avoid serious disease complications, including heart attack, blindness and stroke, these patients must continuously monitor their blood glucose levels and keep them as close to normal as possible. Achieving and maintaining good blood glucose control is a difficult task for these patients and their health care providers. A prototypical case-based decision support system was built to assist with this task. A clinical research study, involving 20 patients, yielded 50 cases of actual problems in blood glucose control, with their associated therapeutic adjustments and clinical outcomes, for the prototype's case base. The prototype operates by: (1) detecting problems in blood glucose control in large quantities of patient blood glucose and life event data; (2) finding similar past problems in the case base; and (3) offering the associated therapeutic adjustments stored in the case base to the physician as decision support. Results from structured evaluation sessions and a patient feedback survey encourage continued research and work towards a practical tool for diabetes management.

show abstract

“…Result shows that with dominant symptoms, the neural network is able to classify disorder with 98.96% average accuracy, compared to all symptoms, where the average accuracy is 98.91%. In work [27], authors proposed case-based reasoning and rule-based reasoning based system applied for the care and treatment of Alzheimer's disease Patients. The system used as a decision support tool for physicians, nurses and social workers.…”

Section: VImentioning

confidence: 99%