An ontological analysis of medical Bayesian indicators of performance

Barton, Adrien; Éthier, Jean-François; Duvauferrier, R; Burgun, Anita

doi:10.1186/s13326-016-0099-4

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…Section editors achieved a first selection of 100 papers based on titles and abstracts. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 . Five reviewers scored these 15 pre-selected papers and finally selected five final best papers 2 3 4 5 6 .…”

Section: Resultsmentioning

confidence: 99%

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As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Dhombres

Charlet

2018

Yearb Med Inform

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Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.

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Section: Resultsmentioning

confidence: 99%

“…The last methodological challenge was addressed by Barton et al 8 , who provided an elegant theoretical basis for the use of ontologies for Bayesian indicators calculation, accounting for the granularity represented in these ontologies (i.e. the “spectrum effect”).…”

Section: Resultsmentioning

confidence: 99%

As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Dhombres

Charlet

2018

Yearb Med Inform

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show abstract

“…Additional statistical measures of models' performance were calculated, including sensitivity, specificity, positive predictive value (PPV) and negative predictive values (NPV), with the latter (PPV and NPV) adjusted in accordance with the fixed prevalence of exponential response in STAR*D (0.5) [44]. Statistical measures of the studies themselves were likewise calculated, including STAR*D's and PGRN-AMPS's response rates and null-information rates (NIRs).…”

Section: Statistical Analysesmentioning

confidence: 99%

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

Taliaz¹,

Spinrad²,

Barzilay

et al. 2021

Transl Psychiatry

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Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42–53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm’s capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm’s citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p’s < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

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“…Many studies have been conducted that have successfully utilized the OHDSI consortium, including a treatment pathways study [ 8 ], a birth season – disease risk study [ 9 , 10 ] and several pharmacovigilance studies [ 11 ]. Using multiple sites allows researchers to study geographic variation [ 8 , 10 ], which can be caused by regional changes in pollution and other exposures [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites

et al. 2018

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Electronic Health Records (EHR) contain extensive information on various health outcomes and risk factors, and therefore have been broadly used in healthcare research. Integrating EHR data from multiple clinical sites can accelerate knowledge discovery and risk prediction by providing a larger sample size in a more general population which potentially reduces clinical bias and improves estimation and prediction accuracy. To overcome the barrier of patient-level data sharing, distributed algorithms are developed to conduct statistical analyses across multiple sites through sharing only aggregated information. The current distributed algorithm often requires iterative information evaluation and transferring across sites, which can potentially lead to a high communication cost in practical settings. In this study, we propose a privacy-preserving and communication-efficient distributed algorithm for logistic regression without requiring iterative communications across sites. Our simulation study showed our algorithm reached comparative accuracy comparing to the oracle estimator where data are pooled together. We applied our algorithm to an EHR data from the University of Pennsylvania health system to evaluate the risks of fetal loss due to various medication exposures.

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An ontological analysis of medical Bayesian indicators of performance

Cited by 11 publications

References 21 publications

As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites

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