Temporal Case-Based Reasoning for Personalized Hypertensive Treatment

2016 ◽  
Author(s):  
Niloofar Jalali ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
Ibrahim Zeid ◽  
Sagar Kamarthi
Keyword(s):  
Medical Records ◽  
Current Approach ◽  
Treatment Regime ◽  
First Episode ◽  
Case Based Reasoning ◽  
Initial State ◽  
Hypertensive Patients ◽  
The Past ◽  
Clinical Records ◽  
Case Based

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.

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
Keyword(s):  
Medical Records ◽  
Case Based Reasoning ◽  
Knowledge Reuse ◽  
Intelligent Technique ◽  
Case Based

scholarly journals Retrieval, reuse, revision and retention in case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 215-240 ◽  
Author(s):  
RAMON LOPEZ DE MANTARAS ◽  
DAVID MCSHERRY ◽  
DEREK BRIDGE ◽  
DAVID LEAKE ◽  
BARRY SMYTH ◽  
...  
Keyword(s):  
Problem Solving ◽  
Analogical Reasoning ◽  
Case Based Reasoning ◽  
The Past ◽  
Future Problem ◽  
Considerable Success ◽  
Representative Selection ◽  
Case Based ◽  
Selection Of

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

DEVELOPING CASE-BASED REASONING FOR DISCOVERY OF BREAST CANCER

2003 ◽  
Vol 03 (03n04) ◽  
pp. 231-245 ◽  
Author(s):  
S. C. FOK ◽  
E. Y. K. NG ◽  
G. L. THIMM
Keyword(s):  
Breast Cancer ◽  
Medical Records ◽  
Breast Tumor ◽  
Genetic Factors ◽  
Medical Professionals ◽  
Case Based Reasoning ◽  
Screening Process ◽  
Preliminary Screening ◽  
Physiological Information ◽  
Case Based

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.

scholarly journals Penerapan Case Based Reasoning (Cbr) Pada Sistem Pakar Diagnosa Penyakit Otitis Media Supuratif Kronis (OMSK) Pada Orang Dewasa

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Gita Triswardani ◽  
Nelly Astuti Hasibuan
Keyword(s):  
Expert System ◽  
Otitis Media ◽  
Chronic Suppurative Otitis Media ◽  
Mucosal Inflammation ◽  
Case Based Reasoning ◽  
Suppurative Otitis Media ◽  
Lower Middle Class ◽  
The Past ◽  
Perforated Tympanic Membrane ◽  
Case Based

Chronic Suppurative Otitis Media (OMSK) is an infection of middle ear mucosal inflammation with perforated tympanic membrane and secret from the ear continuously, formerly called otitis media perforate (OMP) in everyday congek. Chronic suppurative otitis media (OMSK) is commonly found in lower middle-class people, and leads to increased costs for treatment. Therefore required a tool or system that has the ability of a doctor in diagnosing the disease. The expert system is used not to replace the physician's function but will be used only as a complement to a system and aids. An expert system is a system capable of mimicking the reasoning of a computer expert to solve problems as is usually done by experts. The method used in this expert system uses case-based reasoning (CBR) method, since this method yields the value of kmiripan a case with the former by recalling similar events that have occurred in the past then use the knowledge or information to solve a new problem, or in other words solve the problem by adopting solutions that have been used in the past.

scholarly journals A Conversational Approach to Process-oriented Case-based Reasoning

2018 ◽  
Author(s):  
Christian Zeyen ◽  
Gilbert Müller ◽  
Ralph Bergmann
Keyword(s):  
Structural Properties ◽  
Case Based Reasoning ◽  
Workflow Modeling ◽  
Target Problem ◽  
The Past ◽  
Labeled Graphs ◽  
Process Oriented ◽  
Case Based ◽  
Attribute Value

Process-oriented case-based reasoning (POCBR) supports workflow modeling by retrieving and adapting workflows that have proved useful in the past. Current approaches typically require users to specify detailed queries, which can be a demanding task. Conversational case-based reasoning (CCBR) particularly addresses this problem by proposing methods that incrementally elicit the relevant features of the target problem in an interactive dialog. However, no CCBR approaches exist that are applicable for workflow cases that go beyond attribute-value representations such as labeled graphs. This paper closes this gap and presents a conversational POCBR approach (C-POCBR) in which questions related to structural properties of the workflow cases are generated automatically. An evaluation with cooking workflows indicates that C-POCBR can reduce the communication effort for users during retrieval.

scholarly journals Case Based Reasoning untuk Diagnosis Penyakit Jantung Menggunakan Metode Minkowski Distance

2018 ◽  
Vol 1 (1) ◽  
pp. 119-123
Author(s):  
Eka Wahyudi ◽  
Novi Indah Pradasari
Keyword(s):  
Heart Disease ◽  
Computer System ◽  
Medical Records ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Test Results ◽  
Minkowski Distance ◽  
Heart Disease Diagnosis ◽  
System Knowledge ◽  
Case Based

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.

scholarly journals An Improved Case-Based Reasoning Model for Simulating Urban Growth

2021 ◽  
Vol 13 (11) ◽  
pp. 6146
Author(s):  
Xin Ye ◽  
Wenhui Yu ◽  
Lina Lv ◽  
Shuying Zang ◽  
Hongwei Ni
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Control Method ◽  
Growth Models ◽  
Case Based Reasoning ◽  
Initial State ◽  
Simulation Accuracy ◽  
Complex Dynamic Systems ◽  
Time Control ◽  
Case Based

Developing urban growth models enables a better understanding and planning of sustainable urban areas. Case-based reasoning (CBR), in which historical experience is used to solve problems, can be applied to the simulation of complex dynamic systems. However, when applying CBR to urban growth simulation, problems such as inaccurate case description, a single retrieval method, and the lack of a time control mechanism limit its application accuracy. In order to tackle these barriers, this study proposes a CBR model for simulating urban growth. This model includes three parts: (1) the case expression mode containing the “initial state-geographical feature-result” is proposed to adapt the case expression to the urban growth process; (2) in order to improve the reliability of the results, we propose a strategy to introduce the “retrieval quantity” parameter and retrieve multiple similar cases; and (3) a time factor control method based on demand constraints is proposed to improve the power of time control in the algorithm. Finally, the city of Jixi was used as the study area for simulation, and when the “retrieval quantity” is 10, the simulation accuracy reaches 97.02%, kappa is 85.51, and figure of merit (FoM) is 0.1699. The results showed that the proposed method could accurately analyze urban growth.

Estimating Morbidity Ratesfrom Electronic Medical Records in General Practice

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
Keyword(s):  
General Practice ◽  
Electronic Medical Records ◽  
Gold Standard ◽  
Medical Records ◽  
Internal Validity ◽  
Case Based Reasoning ◽  
Systematic Bias ◽  
Full Dataset ◽  
Split Sample ◽  
Case Based

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.

The Application of Case-Based Reasoning (CBR) in the Medical Management of Community Health

2014 ◽  
Vol 998-999 ◽  
pp. 340-345
Author(s):  
Jiang Bing ◽  
Deng Yan Mu
Keyword(s):  
Community Health ◽  
Health Management ◽  
Health Examination ◽  
Case Based Reasoning ◽  
The Past ◽  
Medical Expert System ◽  
Community Residents ◽  
Health And Disease ◽  
Medical Programs ◽  
Case Based

Currently, the technology of Case-Based Reasoning is used in the medical field,Such as the medical expert system that is developed for some large and medium-sized hospital, The patient 's medical programs are given by patient illness, The mainly property is descripted with non-numeric attributes about illness symptoms. It is the obstacles that the system can not become popularized for adaptive capacity.However,its adaptive capacity is not strong and non-intelligent , At current,the research about statistics of community health and disease prevention information is still limited ,An adaptive system model of CBR-based health management is presented , Case retrieval is important very mach . The system of community health management is developed to validate the model, The health examination data of five community are inputed in experimental platform in the past three years, The result indicating that the application of case-based reasoning makes the overall health of community residents to get continuous improvement .

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