Improving Case-Based Reasoning Systems by Combining K-Nearest Neighbour Algorithm with Logistic Regression in the Prediction of Patients’ Registration on the Renal Transplant Waiting List

Campillo‐Gimenez, Boris; Jouini, Wassim; Bayat, Sahar; Cuggia, Marc

doi:10.1371/journal.pone.0071991

Cited by 20 publications

(25 citation statements)

References 31 publications

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“…The level of interest could be gauged by the increase in publication on this topic in recent years ( Figure 2 ). Fifteen studies of 22 were journal publications [ 16 , 17 , 19 , 21 , 23 , 25 , 26 , 30 , 32 , 33 , 36 , 37 , 39 , 40 , 42 , 43 ] and seven were conference articles [ 24 , 27 - 29 , 34 , 41 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most of the studies proposed an arbitrary list of predictors or limited their work to the available predictors, but selected predictors must be representative of patient’s condition in each particular application. Two studies [ 26 , 30 ] employed weighting schemes to adjust the importance of the predictors based on the outcome. One study [ 34 ] showed that predictors selected by a correlation-based feature algorithm could vary according to gender.…”

Section: Resultsmentioning

confidence: 99%

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Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Sharafoddini¹,

Dubin²,

Lee³

2017

JMIR Med Inform

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BackgroundPhysicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions..ObjectiveThe aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research.MethodsThe method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results.ResultsAfter duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods.ConclusionsInterest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Sharafoddini¹,

Dubin²,

Lee³

2017

JMIR Med Inform

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“…Case-based reasoning is a general decision-making paradigm used in the medical field [1]. A lot of work on CBR has been done in this area of research.…”

Section: State Of the Artmentioning

confidence: 99%

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

Benamina¹,

Atmani²,

Benbelkacem³

2018

IJIMAI

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In the medical field, experts' knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning.

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“…Campillo-Gimenez, Jouini, Bayat and Cuggia [22] used a medical decision-making method the modern case-based reasoning (CBR) technique. This method requires the help of a database of historical records to predict the patients' waiting list; but their work is limited, and does not consider the effective distribution of organs to the recipient during an emergency.…”

Section: Organ Procurement and Transplantationmentioning

confidence: 99%

Facility Location of Organ Procurement Organisations in Indian Health Care Supply Chain Management

Rajmohan

Theophilus

Sumalatha

et al. 2017

SAJIE

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In health care supply chain management, particularly in the area of organ transplantation, organ procurement and the transplantation network play an important role. The organ procurement organisation (OPO) should coordinate so that organs are prepared and transported to the recipients when donors become available. The scarcity of organ supply leads to life-challenging issues for the organ recipient. In this research, the importance of the location of OPOs to coordinate with the transplant centres in India is considered, and a solution is provided by facilitating the identification of locations where organs can be procured and distributed to the nearest transplant location. The location of the distribution centres of organs are identified, based on the p-median model. This model minimises the weighted distance of the organ recipients. Initially, the demand or the population density of organ recipients with respect to particular location is recognised. Then, based on the p-median model, the location of OPOs is effectively identified. Experimental analysis proves that the proposed model performs well in facilitating the location of OPOs. The robustness of the proposed work is validated using a sensitivity analysis of the differences in the selection of OPOs when the estimated demand for organs varies. OPSOMMINGOrgaanverkryging en die oorplantingsnetwerk speel ŉ belangrike rol in die voorsieningkettingbestuur van gesondheidsorg. Die orgaan verkrygingsorganisasie moet koördineer dat organe voorberei en vervoer word na ontvangers soos wat skenkers beskikbaar word. Die skaarsheid gekoppel aan orgaanverskaffing lei tot lewensgevaarlike situasies vir die orgaanontvanger. Die belangrikheid van die ligging van orgaan verkrygingsorganisasies word oorweeg en ŉ oplossing word verskaf deur die liggings waar organe verkry en versprei kan word na die naaste oorplantingsentrum te identifiseer. Die ligging van die verspreidingsentrums word deur middel van die p-mediaan model identifiseer. Die model minimeer die geweegde afstand tot by die orgaanontvangers. Eksperimentele analise bewys dat die voorgestelde model daarin slaag om die orgaan verkrygingsorganisasies se liggings te fasiliteer. Die robuustheid van die voorgestelde model is gevalideer deur middel van ŉ sensitiwiteitsanalise van die verskille in die seleksie van orgaan verkrygingsorganisasies wanneer die geskatte vraag na organe wissel.

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Improving Case-Based Reasoning Systems by Combining K-Nearest Neighbour Algorithm with Logistic Regression in the Prediction of Patients’ Registration on the Renal Transplant Waiting List

Cited by 20 publications

References 31 publications

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

Facility Location of Organ Procurement Organisations in Indian Health Care Supply Chain Management

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