Intelligent Technique for Knowledge Reuse of Dental Medical Records Based on Case-Based Reasoning

2008 ◽  
Vol 34 (2) ◽  
pp. 213-222 ◽  
Author(s):  
Dong-xiao Gu ◽  
Chang-yong Liang ◽  
Xing-guo Li ◽  
Shan-lin Yang ◽  
Pei Zhang
Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 28-37
Author(s):  
Abdul Rahim Jalil ◽  
Muhammad Sharfi Najib ◽  
Suhaimi Mohd Daud ◽  
Mujahid Mohamad

The pollination period is one of the crucial steps needed to ensure crop yield increases, especially in palm oil palm plantations. Most of the research has difficulty determining the pollination period of palm oil. Many problems contribute to this problem, such as difficut to reach and depedency of the polination insect as the insect activity is influenced by the surrounding enviroment.E-Nose can help determine the period by classifiy odour pattern of the male and female palm oil flower. The pattern of each of the flowers were classified using cased – based reasoning artificial intelligent technique. This paper shows the research of the palm oil pollination flower odour profile pattern using case-based reasoning (CBR) classifier.


2003 ◽  
Vol 03 (03n04) ◽  
pp. 231-245 ◽  
Author(s):  
S. C. FOK ◽  
E. Y. K. NG ◽  
G. L. THIMM

The treatment of early development of breast tumor has a higher success rate. This paper presents a framework for the early discovery of breast cancer. The objective is to assist the general practitioners and specialists in the detection of breast tumor. The proposed detection process consists of a preliminary screening process and a prediction process. The preliminary screening process using thermography aims to complement the detailed screening operation using mammography. The prediction process using artificial intelligence techniques aims to use past records of other similar cases to enhance the forecast of breast cancer development. The paper discusses the issues and techniques for the implementation of the proposed framework. These include the preliminary screening process, the retrieval of the relevant cases, and the prediction of the risk of developing breast cancer based on the thermographs, environmental/social data, physiological information, genetic factors, and medical records. This work constitutes initial effort to lessen the burden of medical professionals and increase the chances of successful treatment for patients in the fight against breast cancer.


Author(s):  
Niloofar Jalali ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
Ibrahim Zeid ◽  
Sagar Kamarthi

Most of the current problems can be solved by referring to the solutions of the previous problems. Case Based reasoning (CBR) is one of the methods that solves a problem by retrieving the similar problems from the past and adapting the solutions of the past problems to solve the new problem. Recent studies that apply CBR include time as a parameter to retrieve most effective solutions that vary with time. This approach is more helpful in healthcare area in which one needs to look at historical evidence to find an accurate diagnostic or treatment regime. Hence, in this study, a time-based CBR is applied to track the outcomes of the drug therapy on hypertensive patients and find the most effective drug as a prescription. Initially, episodes in each patient’s medical records are chronologically ordered such that the oldest episode is placed first in the episode sequence and the latest episode is placed the last. It is assumed that the first episode of each patient is the first instance of diagnose; so when a new patient comes for checkup, his/her state (health condition) is compared with the initial state of the past patients. Therefore, the retrieval process calculates the similarity between the new patient’s current state and the most similar patients at their first episodes in the patient records. Due to the diversity of therapies for matching patients, the best treatment couldn’t be determined without knowing the efficacy of the different treatments. Therefore, the subsequent episodes of matching patients are examined to find the best treatment for the new patient. This might even require using a combination of treatments from all matching patients to find a good treatment for the new patient. After the treatment is defined for the first visit, the record of the new patient is stored in the library for future case retrieval. This method is a novel approach to personalized treatment of patients having chronic disease by tracking the medical records past patients over a long period of time. The current approach for treating the hypertensive patients uses evidence-based guidelines for managing the disease. However, this approach is more general and doesn’t take into account all the patient characteristics such as lab results and physical examination parameters. In the current approach the similarity between patients can’t be leveraged; the change of the treatment regime is based only on the risk parameter. However, in this method several parameters are being checked for efficiency of the medication. In contrast, the proposed CBR-based method personalizes the treatment based on what worked well for similar patients. In this paper, the clinical records of hypertensive patients are provided by a Boston based hospital. The preliminary results confirm that the proposed approach will give good recommendation for hypertension treatment.


2020 ◽  
Vol 165 ◽  
pp. 04007
Author(s):  
Ru Wang ◽  
Wei Chen

At present, China has a large number of ancient buildings, and correspondingly, it also faces many problems. Staff of different professions have inconsistent perceptions of ancient buildings, resulting in inability to interact with information. In addition, the ancient architecture case did not carry out efficient reuse of knowledge. Therefore, this article applies ontology to the field of ancient architecture, and proposes the knowledge expression of ancient architecture based on ontology. And SWRL is used to describe the ancient building rules. Secondly, through the application of case-reasoning technology, the reuse of case knowledge of ancient architecture is realized. Ontology-based case representation can provide a unified definition of ancient building knowledge for different participants and lay the foundation for information interaction. Case-based reasoning provides an implementation method for knowledge reuse of ancient building damage cases.


Author(s):  
Eka Wahyudi ◽  
Novi Indah Pradasari

Case Based Reasoning is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR  for diagnosing heart disease. The diagnosis process  is done by inserting new cases containing symptoms into the system, then  the similarity value calculation between cases  uses the minkowski distance similarity. Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold less than 0.80, the case will be revised by experts. Revised successful cases are stored to add the system knowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis. The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using minskowski distance similarity correctly of 100 percent. Using minkowski get accuracy of 100 percent.  Keywords : Case Based Reasoning, Minkowski Distance Similarity.


2008 ◽  
Vol 47 (02) ◽  
pp. 98-106 ◽  
Author(s):  
R. A. Verheij ◽  
D. H. de Bakker ◽  
G. A. Zielhuis ◽  
P. F. de Vries Robbé ◽  
M. C. J. Biermans

Summary Objectives: In this study, we evaluated the internal validity of EPICON, an application for grouping ICPCcoded diagnoses from electronic medical records into episodes of care. These episodes are used to estimate morbidity rates in general practice. Methods: Morbidity rates based on EPICON were compared to a gold standard; i.e. the rates from the second Dutch National Survey of General Practice. We calculated the deviation from the gold standard for 677 prevalence and 681 incidence rates, based on the full dataset. Additionally, we examined the effect of casebased reasoning within EPICON using a comparison to a simple, not case-based method (EPI-0). Finally, we used a split sample procedure to evaluate the performance of EPICON. Results: Morbidity rates that are based on EPICON deviate only slightly from the gold standard and show no systematic bias. The effect of case-based reasoning within EPICON is evident. The addition of case-based reasoning to the grouping system reduced both systematic and random error. Although the morbidity rates that are based on the split sample procedure show no systematic bias, they do deviate more from the gold standard than morbidity rates for the full dataset. Conclusions: Results from this study indicate that the internal validity of EPICON is adequate. Assuming that the standard is gold, EPICON provides valid outcomes for this study population. EPICON seems useful for registries in general practice for the purpose of estimating morbidity rates.


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