A multiple measurements case-based reasoning method for predicting recurrent status of liver cancer patients

Ping, Xiao; Tseng, Yi-Ju; Lin, Yan; Chiu, Hsiang Ju; Lai, Feipei; Liang, Ja-Der; Huang, Guan-Tarn; Yang, Pei‐Ling

doi:10.1016/j.compind.2015.01.007

Cited by 36 publications

(13 citation statements)

References 19 publications

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“…Variations of this conceptual idea exist in the machine learning literature such as time interval related patterns [ 90 , 91 ], and sequential pattern mining [ 83 ]. These algorithms are usually embedded into an algorithmic framework which aims to match patterns over time in a current patient to historical patient information and infer the probability of the outcome of interest [ 90 , 92 ].…”

Section: Resultsmentioning

confidence: 99%

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

et al. 2020

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Background: Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers. Methods: MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method. Results: The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change. Conclusions: The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

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Section: Resultsmentioning

confidence: 99%

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

et al. 2020

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“…Similar to human reasoning systems, the predictive process of CBR allows users to easily understand the results generated (El-Sappagh, Elmogy, & Riad, 2015). Because of the advantages of CBR, in the last two decades, CBR has been successfully applied in the healthcare industry for designing systems for classification, planning, tutoring, and diagnosis (Holt, Bichindaritz, Schmidt, & Perner, 2005;Ping et al, 2015). Marling, Petot, and Sterling (1999) used CBR to design a nutritional menu planning system using commonsense knowledge that met the nutrition requirements and constraints of different people.…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

et al. 2019

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With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.

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“…Indeed, CBR provides the ability of solving new problems by reusing knowledge acquired from past experiences [12], i.e., CBR is used especially when similar cases have similar terms and solutions, even when they have different backgrounds [13]. Indeed, its use may be found in different arenas, namely in The Law, Online Dispute Resolution [14,15] or Medicine [16,17], just to name a few.…”

Section: Introductionmentioning

confidence: 99%

Screening a Case Base for Stroke Disease Detection

Neves

Gonçalves

Oliveira

et al. 2016

Lecture Notes in Computer Science

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Abstract. Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality figure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases retrieval one. On the other hand, and aiming at an improvement of the CBR theoretical basis, the predicates`attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were rewritten, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of oneʼs confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

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A multiple measurements case-based reasoning method for predicting recurrent status of liver cancer patients

Cited by 36 publications

References 19 publications

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

Screening a Case Base for Stroke Disease Detection

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