Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach

Verma, Anju; Fiasché, Maurizio; Cuzzola, Maria; Morabito, Francesco Carlo; Irrera, Giuseppe

doi:10.1007/978-3-642-23957-1_31

Cited by 4 publications

(10 citation statements)

References 28 publications

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“…14,15 The main objective of personalized modeling is to create a model for each patient (sample), which is able to reveal the most important information specifically for each sample, focusing attention on the individual patient (sample) rather than simply on the global problem space. 16,17 Previous works have reported that personalized modeling can produce better classification results than those obtained from classical global modeling, 14,16,18 making it more appropriate to build clinical decision support systems for new patients. The framework proposed by Fiasché et al 11,19 used a "personalized" wrapping method, a PMGS described in detail in recent papers 20,21 for gene expression data analysis, integrating new data with the existing models; the block diagram is reported in Figure 1.…”

Section: Personalized Modeling For Identification Of Target Genes In mentioning

confidence: 99%

“…Since simple statistical analysis or classical data-mining techniques failed to identify a specific gene pattern, we used computational intelligence methods according to previously published algorithms. 17,19,20 The PMGS approach was applied to the data obtained from the TLDA-card to identify a group of genes to represent the data set. In our framework the most important genes selected for each patient may differ (personalized subset), but during our analysis, it was evident that for each run, four genes were always present in the entire subset of patients who did develop aGvHD.…”

Section: Personalized Modeling-based Gene Selection Identified Foxp3mentioning

confidence: 99%

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A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

Cuzzola¹,

Fiasché²,

Iacopino

et al. 2012

Haematologica

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The online version of this article has a Supplementary Appendix. BackgroundAcute graft-versus-host disease is a severe complication of allogeneic stem cell transplantation in which the functional immune cells of the donor recognize the recipient as foreign and mount an immunological attack. There is an urgent need for better diagnostic instruments for the assessment of acute graft-versus-host disease. In the present study, a novel bioinformatics framework was used to identify gene expression patterns associated with acute graft-versushost disease in patients undergoing allogeneic hematopoietic stem cell transplantation. Design and MethodsPeripheral blood cells were collected prospectively from patients who did develop acute graftversus-host disease (YES) and from those who did not (NO). Gene expression profiling was performed using a panel of 47 candidate genes potentially involved in alloreactive responses. The entire population of YES/NO acute graft-versus-host disease patients formed the experimental validation set. Personalized modeling based on a gene selection technique was applied to identify the most significant mRNA transcripts, which were then used to profile individual data samples for training and testing the classification/prediction framework. ResultsA leave-one-out cross-validation procedure was performed to investigate the robustness of the classification framework producing the following results: 100% on the training dataset and 97% on the testing dataset. According to our integrated methodology, transcripts for FOXP3, ICOS, CD52 and CASP1, genes involved in immune alloreactive responses and participating in immune cell interactions, were identified as the most informative biomarkers in allogeneic stem cell transplant recipients experiencing acute graft-versus-host disease. ConclusionsThis study demonstrates that the integrated methodology proposed is useful for the selection of valid gene targets for the diagnosis of acute graft-versus-host disease, producing satisfactory accuracy over independent clinical features of the allogeneic transplanted population.

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Section: Personalized Modeling For Identification Of Target Genes In mentioning

confidence: 99%

Section: Personalized Modeling-based Gene Selection Identified Foxp3mentioning

confidence: 99%

A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

Cuzzola¹,

Fiasché²,

Iacopino

et al. 2012

Haematologica

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“…Ontological reasoning engines are being used to add deduction capabilities to these systems [62]. The commonest use of decision support is the diagnosis of disease decision support and it is increasingly used for detecting the risk of disease, prescribing or other intervention decisions and prevention [63][64][65].…”

Section: Decision Supportmentioning

confidence: 99%

The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review

et al. 2013

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SummaryBackground: Most chronic diseases are managed in primary and ambulatory care. The chronic care model (CCM) suggests a wide range of community, technological, team and patient factors contribute to effective chronic disease management. Ontologies have the capability to enable formalised linkage of heterogeneous data sources as might be found across the elements of the CCM. Objective: To describe the evidence base for using ontologies and other semantic integration methods to support chronic disease management. Method: We reviewed the evidence-base for the use of ontologies and other semantic integration methods within and across the elements of the CCM. We report them using a realist review describing the context in which the mechanism was applied, and any outcome measures. Results: Most evidence was descriptive with an almost complete absence of empirical research and important gaps in the evidence-base. We found some use of ontologies and semantic integration methods for community support of the medical home and for care in the community. Ubiquitous information technology (IT) and other IT tools were deployed to support self-management support, use of shared registries, health behavioural models and knowledge discovery tools to improve delivery system design. Data quality issues restricted the use of clinical data; however there was an increased use of interoperable data and health system integration. Conclusions: Ontologies and semantic integration methods are emergent with limited evidence-base for their implementation. However, they have the potential to integrate the disparate community wide data sources to provide the information necessary for effective chronic disease management.

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“…We need a formal representation of diabetes terminology, vocabulary and relationships to discover and extract, share, retrieve and reuse knowledge. Ontology is a technique to store semantic information and facilitate manipulating with data by applying different methods such as analysis and algorithms ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

“…The different stakeholders will access and share the most recent medical knowledge due to the scalability of the ontology and the frequent updates that will deliver. More advance queries could be applied by using Simple Protocol and RDF Query Language (SPARQL) ( 2 , 5 ) and intelligent algorithms for clustering, classifications and generate association rules in many applications ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Discovering Diabetes Complications: an Ontology Based Model

2015

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Background:Diabetes is a serious disease that spread in the world dramatically. The diabetes patient has an average of risk to experience complications. Take advantage of recorded information to build ontology as information technology solution will help to predict patients who have average of risk level with certain complication. It is helpful to search and present patient’s history regarding different risk factors. Discovering diabetes complications could be useful to prevent or delay the complications.Method:We designed ontology based model, using adult diabetes patients’ data, to discover the rules of diabetes with its complications in disease to disease relationship.Result:Various rules between different risk factors of diabetes Patients and certain complications generated. Furthermore, new complications (diseases) might be discovered as new finding of this study, discovering diabetes complications could be useful to prevent or delay the complications.Conclusion:The system can identify the patients who are suffering from certain risk factors such as high body mass index (obesity) and starting controlling and maintaining plan.

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Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach

Cited by 4 publications

References 28 publications

A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review

Discovering Diabetes Complications: an Ontology Based Model

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