ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis

Deisseroth, Cole A.; Birgmeier, Johannes; Bodle, Ethan E.; Kohler, Jennefer N.; Matalon, Dena; Nazarenko, Yelena; Genetti, Casie A.; Brown, Donna M.; Schmitz-Abe, Klaus; Schoch, Kelly; Cope, Heidi; Signer, Rebecca; Martínez‐Agosto, Julian A.; Shashi, Vandana; Beggs, Alan H.; Wheeler, Matthew T.; Bernstein, Jonathan A.; Bejerano, Gill

doi:10.1038/s41436-018-0381-1

Cited by 84 publications

(67 citation statements)

References 35 publications

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“…It achieved more than 97% sensitivity on four network meta-analyses, decreasing the abstract review burden by 53% while only missing three of 124 eligible Evaluation (AMELIE) and ClinPhen, which can extract and prioritize candidate genes and patient phenotypes from literature and clinical notes. 34,35 However, none of these is like our procedure, which is designed specifically for gene-cancer penetrance studies and, as shown in this study, leads to a significant workload reduction in this context.…”

Section: Discussionmentioning

confidence: 96%

Validation of a Semiautomated Natural Language Processing–Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance

Deng

Yin

Bao

et al. 2019

JCO Clinical Cancer Informatics

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PURPOSE Quantifying the risk of cancer associated with pathogenic mutations in germline cancer susceptibility genes—that is, penetrance—enables the personalization of preventive management strategies. Conducting a meta-analysis is the best way to obtain robust risk estimates. We have previously developed a natural language processing (NLP) –based abstract classifier which classifies abstracts as relevant to penetrance, prevalence of mutations, both, or neither. In this work, we evaluate the performance of this NLP-based procedure. MATERIALS AND METHODS We compared the semiautomated NLP-based procedure, which involves automated abstract classification and text mining, followed by human review of identified studies, with the traditional procedure that requires human review of all studies. Ten high-quality gene–cancer penetrance meta-analyses spanning 16 gene–cancer associations were used as the gold standard by which to evaluate the performance of our procedure. For each meta-analysis, we evaluated the number of abstracts that required human review (workload) and the ability to identify the studies that were included by the authors in their quantitative analysis (coverage). RESULTS Compared with the traditional procedure, the semiautomated NLP-based procedure led to a lower workload across all 10 meta-analyses, with an overall 84% reduction (2,774 abstracts v 16,941 abstracts) in the amount of human review required. Overall coverage was 93%—we are able to identify 132 of 142 studies—before reviewing references of identified studies. Reasons for the 10 missed studies included blank and poorly written abstracts. After reviewing references, nine of the previously missed studies were identified and coverage improved to 99% (141 of 142 studies). CONCLUSION We demonstrated that an NLP-based procedure can significantly reduce the review workload without compromising the ability to identify relevant studies. NLP algorithms have promising potential for reducing human efforts in the literature review process.

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Section: Discussionmentioning

confidence: 96%

Validation of a Semiautomated Natural Language Processing–Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance

Deng

Yin

Bao

et al. 2019

JCO Clinical Cancer Informatics

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“…Deisseroth et al from the same group developed ClinPhen. This algorithm parses clinical notes, converts them into a prioritized list of phenotypes, and eventually aids the diagnosis of rare genetic diseases . In addition, NLP has been used to find genomic relations with diseases in the medical literature and to assist with variant interpretation.…”

Section: Implementations Of Nlp In Clinical Genetics and Breast Cancementioning

confidence: 99%

Natural language processing to facilitate breast cancer research and management

et al. 2019

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The medical literature has been growing exponentially, and its size has become a barrier for physicians to locate and extract clinically useful information. As a promising solution, natural language processing (NLP), especially machine learning (ML)-based NLP is a technology that potentially provides a promising solution. ML-based NLP is based on training a computational algorithm with a large number of annotated examples to allow the computer to "learn" and "predict" the meaning of human language.Although NLP has been widely applied in industry and business, most physicians still are not aware of the huge potential of this technology in medicine, and the implementation of NLP in breast cancer research and management is fairly limited. With a real-world successful project of identifying penetrance papers for breast and other cancer susceptibility genes, this review illustrates how to train and evaluate an NLPbased medical abstract classifier, incorporate it into a semiautomatic meta-analysis procedure, and validate the effectiveness of this procedure. Other implementations of NLP technology in breast cancer research, such as parsing pathology reports and mining electronic healthcare records, are also discussed. We hope this review will help breast cancer physicians and researchers to recognize, understand, and apply this technology to meet their own clinical or research needs. K E Y W O R D Sbreast cancer, genetics, medical literature, natural language processing

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“…In some cases, additional works are cited to provide context. A total of 15 articles were finally selected for inclusion [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: About the Paper Selectionmentioning

confidence: 99%

“…More recent efforts have demonstrated a high accuracy in identifying HPO terms in EHRs. ClinPhen [6] breaks EHR-derived free text into sentences and words, and uses heuristics to identify HPO terms in commonly encountered clinical phraseology. For instance, the phrase "Hands are large" will match the HPO term "Large hands (HP:0001176)", and excluded phenotypic abnormalities or those that were found in other family members are recognized as such.…”

Section: Identifying Phenotypic Abnormalities In Ehr Datamentioning

confidence: 99%

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

Robinson

Haendel

2020

Yearb Med Inform

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Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning. Methods: A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning. Results: Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology ; (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs. Conclusion: Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research.

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ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis

Cited by 84 publications

References 35 publications

Validation of a Semiautomated Natural Language Processing–Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance

Validation of a Semiautomated Natural Language Processing–Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance

Natural language processing to facilitate breast cancer research and management

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

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