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.

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases Abstract (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

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

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.

scholarly journals Predicting with Confidence: A Case-Based Reasoning Framework for Predicting Survival in Breast Cancer

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Isabelle Bichindaritz ◽  
Christopher Bartlett ◽  
Guanghui Liu
Keyword(s):  
Breast Cancer ◽  
State Of The Art ◽  
Predictive Performance ◽  
Black Box ◽  
Optimal Number ◽  
Case Based Reasoning ◽  
Analysis Framework ◽  
Insight Into ◽  
Adaptation Phase ◽  
Case Based

There is usually a trade-off between predictive performance and transparency, where the reasoning process behind an algorithm is shielded behind a ”black-box.” In medical domains, experts being responsible for their decisions need to understand the reasons behind machine-generated recommendations. This paper presents a transparent case-based survival analysis framework that automatically retrieves an optimal number of solved survival cases and adapts them to predict the survival of a new case. With this methodology, retrieved and adapted survival cases lend an insight into which cases a prediction is based on. Our framework is capable of integrating DNA methylation, gene expression, and their combination in breast cancer. Additionally, we test our approach with and without feature selection and demonstrate the usefulness of the adaptation phase. We demonstrate that our framework performs at least as effectively as other state-of-the-art methods while affording greater explainability.

scholarly journals A Personalized Medical Decision Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data from the Real World

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1677
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Yi Xie ◽  
Xiaoyu Wang ◽  
Kaixiang Su ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Medical Decision ◽  
Case Based Reasoning ◽  
Medical Decision Support ◽  
Personalized System ◽  
The Impact ◽  
Case Based

Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the personalized medical decision support system. Our primary purpose is to study the impact of external case characteristics (ECC) on the effectiveness of the personalized medical decision support system for breast cancer assisted diagnosis (PMDSS-BCAD) in making accurate recommendations. Therefore, we designed a novel comprehensive framework for case-based reasoning (CBR) that takes the impact of external features of cases into account, made use of the naive Bayes and k-nearest neighbor (KNN) algorithms (CBR-ECC), and developed a PMDSS-BCAD system by using the CBR-ECC model and external features as system components. Under the new case-based reasoning framework, the accuracy of the combined model of naive Bayes and KNN with an optimal K value of 2 is 99.40%. Moreover, in a real hospital scenario, users rated the PMDSS-BCAD system, which takes into account the external characteristics of the case, better than the original personalized system. These results suggest that PMDSS-BCD can not only provide doctors with more personalized and accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the personalized system.

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