Patient Similarity: Emerging Concepts in Systems and Precision Medicine

Brown, Sherry‐Ann

doi:10.3389/fphys.2016.00561

Cited by 66 publications

(27 citation statements)

References 51 publications

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“…Recent advances in machine learning (ML) and big data analytics have led to the emergence of a new generation of clinical decision support systems (CDSSs) designed to exploit the potentials of data-driven decision making in patient monitoring, particularly in the area of internal medicine, general practice, and remote monitoring of vital signs (Gálvez et al, 2013, Helldén et al, 2015 Lisboa & Taktak, 2006, Skyttberg, Vicente, Chen, Blomqvist, & Koch, 2016). Improved access to large and heterogeneous healthcare data and an integration of advanced computational procedures into CDSSs has enabled the real-time discovery of similarity metrics for patient stratification, development of predictive analytics for risk assessment, and selection of patient-specific therapeutic interventions at the time of decision-making (Brown, 2016, Dagliati et al, 2018, Farran, Channanath, Behbehani, & Thanaraj, 2013). CDSSs provide clinical decision support at the time and location of care rather than prior to or after the patient encounter and therefore, help streamline the workflow for clinicians and assist real-time decision-making (diagnosis, prognosis, treatment) (Castaneda et al, 2015, Wright et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Bucholc

Ding

Wang

et al. 2019

Preprint

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1Computerized clinical decision support systems can help to provide objective, standardized, 2 and timely dementia diagnosis. However, current computerized systems are mainly based 3 on the group analysis, discrete classification of disease stages, or expensive and not readily 4 accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and 5 functional assessments (CFA). In this study, we developed a computational framework using 6 a suite of machine learning tools for identifying key markers in predicting the severity of 7 Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine 8 learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression 9 (SVR), and k-Nearest Neighbor (kNN reg ) for regression and Support Vector Machine (SVM), 10Random Forest (RF), and k-Nearest Neighbor (kNN class ) for classification, were used for the 11 development of predictive models. We demonstrated high predictive power of CFA. 12Predictive performance of models incorporating CFA was shown to be consistently higher 13 accuracy than those based solely on biomarker modalities. We found that KRR and SVM 14 were the best performing regression and classification methods respectively. The optimal 15 SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, 16MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the 17 best performance of the KRR model was reported with combined CFA and MRI 18 neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power 19 of CFA and their widespread use in clinical practice, we then designed a data-driven and 20 self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating 21 the severity of AD of an individual on a continuous spectrum. The system implemented an 22 automated computational approach for data pre-processing, modelling, and validation and 23 used exclusively the scores of selected cognitive measures as data entries. Taken together, 24we have developed an objective and practical CDSS to aid AD diagnosis. 25Keywords: dementia; Alzheimer's disease; decision support system; machine learning; 26 diagnosis support; cognitive impairment 27 97 CDSS for evaluating the severity of AD of an individual on a continuous spectrum. In order to 98 achieve this, we first utilize a suite of machine learning techniques to extract useful 99 information from large volumes of patient data and provide a disease outcome prediction for 100 different types and combinations of AD markers. We demonstrate that CFA can reliably and 101 accurately provide prediction of AD severity. Next, we design a CDSS that incorporates an 102 automated computational approach for data pre-processing, modelling, and validation and 103 uses selected CFA scores as data input. Since our system was designed to utilize 104 information from readily available and cost-effective CFA markers, it can be easily 105 implemented in general cli...

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Section: Introductionmentioning

confidence: 99%

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Bucholc

Ding

Wang

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…In this way, we identi ed the genetic clusters and the related biological processes. As already highlighted in scienti c literature [59], the use of a similarity metrics between patients in order to identify "patient similarity networks" is becoming a signi cant aspect of big data analytics in healthcare systems. By assisting patients' clustering, it allows researchers to detect homogeneous subgroups of individuals sharing similar characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…We rstly de ned a metric to measure the genetic similarity between patients according to their mutations and then we applied hierarchical clustering in order to identify groups of genetically similar individuals [22]. Afterwards, we proceeded with the enrichment analysis upon each cluster of patients, as to identify ASD-related biological pathways, which might have been disrupted by the mutations.…”

Section: Introductionmentioning

confidence: 99%

A novel data-driven approach reveals gene networks and biological processes underlying autism

Gialloreti¹,

Enea²,

Micco³

et al. 2020

Preprint

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Background: Developments in gene-hunting techniques identified several ASD associated genes. The considerable significance of cluster analysis associated with gene network studies has led to reveal many disrupted key pathways in ASD, even if its genetic underpinnings remain a challenging task. This study aims to determine, through a novel data-driven approach, how networks of mutated genes impact biological processes underlying autism. Methods: We analyzed the VariCarta dataset, which presents more than 200,000 genomic variant events collected from 13,069 people with ASD. Firstly, we created a whole-genome and an exome sequencing subset. Then, for each subset we compared pairwise patients of each group to build “patient similarity matrices”. Hierarchical-agglomerative-clustering and heatmap were performed to identify clusters of patients with common occurrences of gene networks within these matrices. The subsequent enrichment analysis (EA) highlighted biological processes that might be impacted by the mutated genes of each subgroup. Results: Considering the whole-genome matrix, we identified three main genetic clusters of ASD patients, each one characterized by a network of shared genetic variants. We isolated 11,609 genetic variants shared by at least two subjects in each cluster; 4,187 of these variants (36.1%) were common to the three clusters. Only 331 patients (2.5%) shared none or very few mutated genes with anyone else. The EA highlighted common or cluster-specific biological processes related to the variants. Most of the common abnormal processes were involved in neuron projections guidance and morphogenesis, cell junctions and synapse assembly. Exome sequencing alone was not effectual in identifying ASD subgroups. Limitations: Caution is warranted when interpreting our results, as we did not compare them with a control group and did not verify if the identified subgroups where actually associated with different phenotypes. Future work will have to ascertain the strength and reproducibility of these results. Conclusions: Itemizing not just single mutated genes, but also gene networks and specific biological processes that characterize different ASD subpopulations might allow to better understand which networks of genetic variants play a major role in the etiopathology of ASD. The proposed methodology may represent a novel approach to help disentangle ASD complexity and an instrument to boost more focused genotype-phenotype studies.

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“…The aggregation of clinical data for big data projects from electronic health records poses challenges [1][2][3][4][5]. Much of the clinical data in electronic health records (EHRs) are represented as free text.…”

Section: Introductionmentioning

confidence: 99%

A Neuro-ontology for the neurological examination

Hier

Brint

2020

BMC Med Inform Decis Mak

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Background: The use of clinical data in electronic health records for machine-learning or data analytics depends on the conversion of free text into machine-readable codes. We have examined the feasibility of capturing the neurological examination as machine-readable codes based on UMLS Metathesaurus concepts. Methods: We created a target ontology for capturing the neurological examination using 1100 concepts from the UMLS Metathesaurus. We created a dataset of 2386 test-phrases based on 419 published neurological cases. We then mapped the test-phrases to the target ontology. Results: We were able to map all of the 2386 test-phrases to 601 unique UMLS concepts. A neurological examination ontology with 1100 concepts has sufficient breadth and depth of coverage to encode all of the neurologic concepts derived from the 419 test cases. Using only pre-coordinated concepts, component ontologies of the UMLS, such as HPO, SNOMED CT, and OMIM, do not have adequate depth and breadth of coverage to encode the complexity of the neurological examination. Conclusion: An ontology based on a subset of UMLS has sufficient breadth and depth of coverage to convert deficits from the neurological examination into machine-readable codes using pre-coordinated concepts. The use of a small subset of UMLS concepts for a neurological examination ontology offers the advantage of improved manageability as well as the opportunity to curate the hierarchy and subsumption relationships.

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Patient Similarity: Emerging Concepts in Systems and Precision Medicine

Cited by 66 publications

References 51 publications

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

A novel data-driven approach reveals gene networks and biological processes underlying autism

A Neuro-ontology for the neurological examination

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