Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments

Begum, Shahina; Ahmed, Mobyen Uddin; Funk, Peter; Xiong, Naixue; Folke, Mia

doi:10.1109/tsmcc.2010.2071862

Cited by 184 publications

(107 citation statements)

References 35 publications

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“…However, the underlying content is static, lacks formally defined semantics, and lacks decision support methods, thus making it difficult for the content to be reused, reasoned across and recombined for different purposes. Most prior work in representing generalized knowledge for medical decision support methods [1][2][3][4]6] use some non-standard formalisms or proprietary formats which hinder integration, interoperability and efficient knowledge reasoning. They also lead to unjustified results by fusing all generalized knowledge into a black box system or assume a mature established domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, some of these previous methods cannot evolve over time, due to their shallow knowledge representation formalisms. Case-based reasoning [6], on the other hand, cannot combine past evidences to form a new evidence for a given problem where no past similar evidence exists. This scenario is typical for rare diseases like skeletal dysplasias.…”

Section: Related Workmentioning

confidence: 99%

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DC Proposal: Decision Support Methods in Community-Driven Knowledge Curation Platforms

Paul

2011

The Semantic Web – ISWC 2011

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Abstract. Skeletal dysplasias comprise a group of genetic diseases characterized by highly complex, heterogeneous and sparse data. Performing efficient and automated knowledge discovery in this domain poses serious challenges, one of the main issues being the lack of a proper formalization. Semantic Web technologies can, however, provide the appropriate means for encoding the knowledge and hence enabling complex forms of reasoning. We aim to develop decision support methods in the skeletal dysplasia domain by applying uncertainty reasoning over Semantic Web data. More specifically, we devise techniques for semi-automated diagnosis and key disease feature inferencing from an existing pool of patient cases -that are shared and discussed in the SKELETOME community-driven knowledge curation platform. The outcome of our research will enable clinicians and researchers to acquire a critical mass of structured knowledge that will sustain a better understanding of these genetic diseases and foster advances in the field.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DC Proposal: Decision Support Methods in Community-Driven Knowledge Curation Platforms

Paul

2011

The Semantic Web – ISWC 2011

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“…Chronic diseases such as diabetes, hepatitis, breast cancer and heart disease are the main cause of death in many countries [1,2]. Early diagnosis and treatment of these diseases can provide a tremendous effect on improving patients' quality of life and reducing the costs of their treatment.…”

Section: Introductionmentioning

confidence: 99%

A Modified Model Based on Flower Pollination Algorithm and K-Nearest Neighbor for Diagnosing Diseases

Gashti¹

2018

IIUMEJ

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Exponential growth of medical data and recorded resources from patients with different diseases can be exploited to establish an optimal association between disease symptoms and diagnosis. The main issue in diagnosis is the variability of the features that can be attributed for particular diseases, since some of these features are not essential for the diagnosis and may even lead to a delay in diagnosis. For instance, diabetes, hepatitis, breast cancer, and heart disease, that express multitudes of clinical manifestations as symptoms, are among the diseases with higher morbidity rate. Timely diagnosis of such diseases can play a critical role in decreasing their effect on patients' quality of life and on the costs of their treatment. Thanks to the large data set available, computer aided diagnosis can be an advanced option for early diagnosis of the diseases. In this paper, using a Flower Pollination Algorithm (FPA) and K-Nearest Neighbor (KNN), a new method is suggested for diagnosis. The modified model can diagnose diseases more accurately by reducing the number of features. The main purpose of the modified model is that the Feature Selection (FS) should be done by FPA and data classification should be performed using KNN. The results showed higher efficiency of the modified model on diagnosis of diabetes, hepatitis, breast cancer, and heart diseases compared to the KNN models. ABSTRAK:Pertumbuhan eksponen dalam data perubatan dan sumber direkodkan daripada pesakit dengan penyakit berbeza boleh disalah guna bagi membentuk kebersamaan optimum antara simptom penyakit dan mengenal pasti gejala penyakit (diagnosis). Isu utama dalam diagnosis adalah kepelbagaian ciri yang dimiliki pada penyakit tertentu, sementara ciri-ciri ini tidak penting untuk didiagnosis dan boleh mengarah kepada penangguhan dalam diagnosis. Sebagai contoh, penyakit kencing manis, radang hati, barah payudara dan penyakit jantung, menunjukkan banyak klinikal simptom jelas dan merupakan penyakit tertinggi berlaku dalam masyarakat. Diagnosis tepat pada penyakit tersebut boleh memainkan peranan penting dalam mengurangkan kesan kualiti hidup dan kos rawatan pesakit. Terima kasih kepada set data yang banyak, diagnosis dengan bantuan komputer boleh menjadi pilihan maju menuju ke arah diagnosis awal kepada penyakit. Kertas ini menggunakan Algoritma Flower Pollination (FPA) dan K-Nearest Neighbor (KNN), iaitu kaedah baru dicadangkan bagi diagnosis. Model yang diubah suai boleh mendiagnosis penyakit lebih tepat dengan mengurangkan bilangan ciri-ciri. Tujuan utama model yang diubah suai ini adalah bagi Pemilihan Ciri (FS) perlu dilakukan menggunakan FPA and pengkhususan data perlu dijalankan menggunakan KNN. Keputusan menunjukkan model yang diubah suai lebih cekap dalam mendiagnosis penyakit kencing manis, radang hati, barah payudara dan penyakit jantung berbanding model KNN.

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“…However, the message is not personal as well as not provides overall status of user"s health condition. A case-based reasoning (CBR) [17], [19] approach can work in a way close to human reasoning e.g. solves a new problem applying previous experiences, which is more common for doctors, clinicians or engineers.…”

Section: Introductionmentioning

confidence: 99%

“…Aamodt and Plaza has introduced a life cycle of CBR [17] with four main steps Retrieve, Reuse, Revise and Retain present key tasks to implement such kind of cognitive model.The advantages of CBR in the medical domain have identified in several research works i.e. in [17], [18], [19], [20] and [27]. The proposed approach applies CBR to combine several different health parameters, user"s contextual, medical, medicine related information, which generates an overall classification of health condition such as "current health condition is healthy".…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Case Study on Health-IoT

Ahmed¹

2017

IJERA

Self Cite

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Vital signs monitoring for elderly in daily life environment is a promising concept that efficiently can provide medical services to people at home. However, make the system self-served and functioning as personalized provision makes the challenge even larger. This paper presents a case study on a Health-IoT system where an intelligent healthcare service is developed to monitor vital signs in daily life. Here, a generic Health-IoT framework with a Clinical Decision Support System (CDSS) is presented. The generic framework is mainly focused on the supporting sensors, communication media, secure and safe data communication, cloud-based storage, and remote accesses of the data. The CDSS is used to provide a personalized report on persons" health condition based on daily basis observation on vital signs. Six participants, from Spain (n=3) and Slovenia (n=3) have been using the proposed healthcare system for eight weeks (e.g. 300+ health measurements) in their home environments to monitor their health. The sensitivity, specificity and overall accuracy of the DSS"s classification are achieved as 90%, 97% and 96% respectively while k=2 i.e., top 2 most similar retrieved cases are considered. The initial user evaluation resultdemonstrates the feasibility and performance of the implemented system through the proposed framework.

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Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments

Cited by 184 publications

References 35 publications

DC Proposal: Decision Support Methods in Community-Driven Knowledge Curation Platforms

DC Proposal: Decision Support Methods in Community-Driven Knowledge Curation Platforms

A Modified Model Based on Flower Pollination Algorithm and K-Nearest Neighbor for Diagnosing Diseases

An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Case Study on Health-IoT

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