Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria

Cohen, Aaron M.; Chamberlin, Steven; DeLoughery, Thomas G.; Nguyen, Michelle; Bedrick, Steven; Meninger, Stephen; Ko, John J.; Amin, Jigar; Wei, Anji; Hersh, William R.

doi:10.1371/journal.pone.0235574

Cited by 33 publications

(26 citation statements)

References 20 publications

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“…Aside from similarity-based methods, machine learning with quantitative features, as well as features derived from text directly or in the form of word embeddings, have been employed for rare disease diagnosis over EHRs [ 2 , 6 ]. Differential diagnosis of diseases through conversion of clinical pathway definitions to sets of rules mapped to ontologies has also been previously been explored, but did not involve clinical narratives or semantic similarity [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Towards similarity-based differential diagnostics for common diseases

Slater

Karwath

Williams

et al. 2021

Computers in Biology and Medicine

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Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.

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Section: Introductionmentioning

confidence: 99%

Towards similarity-based differential diagnostics for common diseases

Slater

Karwath

Williams

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…Aside from similarity-based methods, machine learning with quantitative features, as well as features derived from text directly or in the form of word embeddings, have been employed for rare disease diagnosis over EHRs [9, 16]. Differential diagnosis of diseases through conversion of clinical pathway definitions to sets of rules mapped to ontologies has also been previously been explored, but did not involve clinical narratives or semantic similarity [17].…”

Section: Introductionmentioning

confidence: 99%

Towards Similarity-based Differential Diagnostics For Common Diseases

Slater

Karwath

Williams

et al. 2021

Preprint

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Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. The results reveal a potentially powerful approach that can be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.

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“…9,10 Recently, machine-learning technologies are emerging and they are being utilized in the medical field. 11,12 Artificial intelligence (AI) -based ECG analyses are providing promising results in the detection of arrhythmias and myopathies. [13][14][15][16] We expected that this technology could be applied to the detection of PVC origins and then developed machinelearning models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of premature ventricular complex origins using artificial intelligence–enabled algorithms

Nakamura

Nagata

Nitta

et al. 2021

Cardiovascular Digital Health Journal

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Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria

Cited by 33 publications

References 20 publications

Towards similarity-based differential diagnostics for common diseases

Towards similarity-based differential diagnostics for common diseases

Towards Similarity-based Differential Diagnostics For Common Diseases

Prediction of premature ventricular complex origins using artificial intelligence–enabled algorithms

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