TY - GEN
T1 - Enhancing Patient Care in Rare Genetic Diseases
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
AU - Xiao, Yao
AU - Enayati, Moein
AU - Schaeferle, Gavin M.
AU - Lanpher, Brendan C.
AU - Klee, Eric W.
AU - Ngufor, Che
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Identifying rare genetic diseases poses challenges, often resulting in delayed referrals to genetic specialty care. Consequently, a large number of rare diseases remain undiagnosed for years, with many of these patients often dying without an accurate diagnosis. To help address these diagnostic odysseys, we propose a two-step pipeline using natural language processing (NLP) and machine learning (ML) techniques leveraging raw clinical text narratives of patients from longitudinal clinical data in electronic health records (EHR). Our approach employs two state-of-the-art concept recognition algorithms, ClinPhen and PhenoTagger, to extract human phenotype ontology (HPO) terms from the clinical notes and ML algorithms to identify patients who may benefit from referral. By identifying key phrases indicative of rare diseases, our system can prompt healthcare providers to seek clinical genomics consultations, improving diagnosis and patient outcomes. We evaluated the predictive performance of extracted HPO terms using different ML models. Our strategy and methodological approach are intended to serve as complementary steps in aiding clinicians and researchers to improve the diagnosis efficiency of rare genetic diseases.
AB - Identifying rare genetic diseases poses challenges, often resulting in delayed referrals to genetic specialty care. Consequently, a large number of rare diseases remain undiagnosed for years, with many of these patients often dying without an accurate diagnosis. To help address these diagnostic odysseys, we propose a two-step pipeline using natural language processing (NLP) and machine learning (ML) techniques leveraging raw clinical text narratives of patients from longitudinal clinical data in electronic health records (EHR). Our approach employs two state-of-the-art concept recognition algorithms, ClinPhen and PhenoTagger, to extract human phenotype ontology (HPO) terms from the clinical notes and ML algorithms to identify patients who may benefit from referral. By identifying key phrases indicative of rare diseases, our system can prompt healthcare providers to seek clinical genomics consultations, improving diagnosis and patient outcomes. We evaluated the predictive performance of extracted HPO terms using different ML models. Our strategy and methodological approach are intended to serve as complementary steps in aiding clinicians and researchers to improve the diagnosis efficiency of rare genetic diseases.
KW - Clinical Notes
KW - Human Phenotype Extraction
KW - Machine Learning
KW - Natural Language Processing
KW - Rare Genetic Diseases
KW - Referral Prediction
UR - http://www.scopus.com/inward/record.url?scp=85184884709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184884709&partnerID=8YFLogxK
U2 - 10.1109/BIBM58861.2023.10385978
DO - 10.1109/BIBM58861.2023.10385978
M3 - Conference contribution
AN - SCOPUS:85184884709
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 2754
EP - 2760
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2023 through 8 December 2023
ER -