Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable

Lewis-Smith, David; Galer, Peter D.; Balagura, Ganna; Kearney, Hugh; Ganesan, Shiva; Cosico, Mahgenn; O’Brien, Margaret; Vaidiswaran, Priya; Krause, Roland; Ellis, Colin A; Thomas, Rhys H.; Robinson, Peter N.; Helbig, Ingo

doi:10.1111/epi.16908

Cited by 19 publications

(18 citation statements)

References 45 publications

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“…[13][14][15] HPO terms are very useful for harmonising clinical features, in delineating longitudinal disease phenotypes, and in integrating phenotypic data into diagnostic workflows. 16 However, and despite the important overlap between the HPOs of GLUT1DS associated with SLC2A1 pathogenic variants and variants in the other genes here described, those associated with the former are rather distinct. For example, exercise-induced paroxysmal dyskinesia, fasting gait dyspraxia, and an excellent response of epileptic symptoms to a ketogenic diet, are suggestive of a SLC2A1 defect.…”

Section: Discussionmentioning

confidence: 76%

“…HPO terms are very useful for harmonising clinical features, in delineating longitudinal disease phenotypes, and in integrating phenotypic data into diagnostic workflows 16 . However, and despite the important overlap between the HPOs of GLUT1DS associated with SLC2A1 pathogenic variants and variants in the other genes here described, those associated with the former are rather distinct.…”

Section: Discussionmentioning

confidence: 97%

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The clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1

et al. 2022

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Glucose transporter 1 deficiency syndrome (GLUT1DS) is a neurometabolic disorder caused by haploinsufficiency of the GLUT1 glucose transporter (encoded by SLC2A1) leading to defective glucose transport across the blood-brain barrier. This work describes the genetic analysis of 56 patients with clinical or biochemical GLUT1DS hallmarks. 55.4% of these patients had a pathogenic variant of SLC2A1, and 23.2% had a variant in one of 13 different genes. No pathogenic variant was identified for the remaining patients. Expression analysis of SLC2A1 indicated a reduction in SLC2A1 mRNA in patients with pathogenic variants of this gene, as well as in one patient with a pathogenic variant in SLC9A6, and in three for whom no candidate variant was identified. Thus, the clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1.

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 97%

The clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1

et al. 2022

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“…For a seizure prediction model built using Cox’s proportional hazard regression, the AUC improved from 76.4% to 83% when clinical features, including gestational age, EEG indication and etiology/therapies, were added to EEG-based prediction alone. 16 Features such as gestational age, Apgar scores, diagnosis and phenotypic features such human phenotype ontology (HPO) codes, 30 physical exam findings, medication administration, and laboratory data are increasingly standardized within the EMR. As these data accrue over time, we anticipate that it will be feasible and beneficial to incorporate them into our predictive models.…”

Section: Discussionmentioning

confidence: 99%

Seizure prediction in 1117 neonates leveraging EMR-embedded standardized EEG reporting

McKee

Kaufman

Gonzalez

et al. 2022

Preprint

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Background Accurate prediction of seizures can help direct resource-intense continuous EEG (CEEG) monitoring to high-risk neonates. We aimed to use data extracted from standardized EEG reports to generate seizure prediction models for vulnerable neonates. Methods In 2018, we implemented a novel CEEG reporting system in the electronic medical record (EMR) that incorporated standardized terminology. We developed seizure prediction models using logistic regression, decision tree, and random forest models for neonates and specifically, neonates with hypoxic-ischemic encephalopathy (HIE), using EEG features on day 1 to predict future seizures. Findings We evaluated 1117 neonates, including 150 neonates with HIE, with CEEG data reported using standardized templates. Implementation of a consistent EEG reporting system, which documents discrete and standardized EEG variables, resulted in >95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered based on specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of high-risk neonates. Random forest models incorporating background features performed with classification accuracies of up to 90% for all neonates and 97% for neonates with HIE, and recall (sensitivity) of up to 97% for all neonates and >99% for neonates with HIE. Interpretation Using data extracted from the standardized EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of >90%. This information, incorporated into routine care, can guide decisions about the necessity of continuing CEEG beyond the first day and thereby improve the allocation of limited CEEG resources. Additionally, this analysis illustrates the benefits of standardized clinical data collection which can drive learning health system approaches to personalized CEEG utilization.

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“…However, it can be reasoned that these higher-level terms are often essential as they may capture large groups of individuals with phenotypically broad but clinically or biologically important similarities. Accordingly, we performed a method referred to as "propagation" that we have used extensively in past work [6,25,[41][42][43][44][45]. In brief, for each individual's set of explicitly annotated HPO terms, we add all HPO terms that can be inferred as applicable by taking the union of all those terms encountered following all possible paths along "is a" relationships to the root of the HPO.…”

Section: Assessing Hpo Term Frequencies Corrected For Frequencies Of ...mentioning

confidence: 99%

“…The HPO is a standardized representation of more than 15,000 clinical phenotypic concepts and their relationships based on expert knowledge. We have contributed to HPO terminology since 2010 [4][5][6][7]. The HPO has been widely used for harmonization of clinical features in various studies, including, but not limited to, semantic unification of common and rare diseases [8], genetic discoveries in pediatric epilepsy [9,10], and delineation of longitudinal phenotypes [11,12].…”

Section: Introductionmentioning

confidence: 99%

Enriching Representation Learning Using 53 Million Patient Notes through Human Phenotype Ontology Embedding

Daniali

Galer

Lewis‐Smith

et al. 2022

Preprint

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The Human Phenotype Ontology (HPO) is a dictionary of more than 15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize their representation for phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full- text health care notes from more than 1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity- measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass the current computational models. In addition, we show that our embedding technique aligns with domain experts' judgment at a level that exceeds their agreement. We show that our proposed technique efficiently represents complex and multidimensional phenotypes in HPO format, which can then be used as input for various downstream tasks that require deep phenotyping, including patient similarity analyses and disease trajectory prediction.

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Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable

Cited by 19 publications

References 45 publications

The clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1

The clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1

Seizure prediction in 1117 neonates leveraging EMR-embedded standardized EEG reporting

Enriching Representation Learning Using 53 Million Patient Notes through Human Phenotype Ontology Embedding

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