3 recherche sur le mot-clé 'electronic health record'

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records / Mengge ZHAO in Journal of Neurodevelopmental Disorders, 14 (2022)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 14 (2022) Titre : Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records Type de document : texte imprimé Auteurs : Mengge ZHAO, Auteur ; James HAVRILLA, Auteur ; Jacqueline PENG, Auteur ; Madison DRYE, Auteur ; Maddie FECHER, Auteur ; Whitney GUTHRIE, Auteur ; Birkan TUNC, Auteur ; Robert SCHULTZ, Auteur ; Kai WANG, Auteur ; Yunyun ZHOU, Auteur Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis/genetics  Electronic Health Records  Humans  Natural Language Processing  Phenotype  Vocabulary  Autism  Autism spectrum disorder  Electronic health record  Natural language processing  Phenotype ontology  Terminology set Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives. METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making. En ligne : https://dx.doi.org/10.1186/s11689-022-09442-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records [texte imprimé] / Mengge ZHAO, Auteur ; James HAVRILLA, Auteur ; Jacqueline PENG, Auteur ; Madison DRYE, Auteur ; Maddie FECHER, Auteur ; Whitney GUTHRIE, Auteur ; Birkan TUNC, Auteur ; Robert SCHULTZ, Auteur ; Kai WANG, Auteur ; Yunyun ZHOU, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 14 (2022) Mots-clés : Autism Spectrum Disorder/diagnosis/genetics  Electronic Health Records  Humans  Natural Language Processing  Phenotype  Vocabulary  Autism  Autism spectrum disorder  Electronic health record  Natural language processing  Phenotype ontology  Terminology set Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives. METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making. En ligne : https://dx.doi.org/10.1186/s11689-022-09442-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

Prediction of early-onset bipolar using electronic health records / Bo WANG in Journal of Child Psychology and Psychiatry, 66-8 (August 2025)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 66-8 (August 2025) . - p.1141-1154 Titre : Prediction of early-onset bipolar using electronic health records Type de document : texte imprimé Auteurs : Bo WANG, Auteur ; Yi-Han SHEU, Auteur ; Hyunjoon LEE, Auteur ; Robert G. MEALER, Auteur ; Victor M. CASTRO, Auteur ; Jordan W. SMOLLER, Auteur Article en page(s) : p.1141-1154 Langues : Anglais (eng) Mots-clés : Early-onset bipolar disorder  mood disorders  risk prediction  electronic health record  machine learning Index. décimale : PER Périodiques Résumé : Background Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical notes for predicting early-onset BD. Methods Structured and unstructured data were extracted from the longitudinal EHR of the Mass General Brigham health system. We defined three cohorts aged 10 25 years: (1) the full youth cohort (N 300,398); (2) a subcohort defined by having a mental health visit (N 105,461); and (3) a subcohort defined by having a diagnosis of mood disorder or ADHD (N 35,213). By adopting a prospective landmark modeling approach that aligns with clinical practice, we developed and validated a range of machine learning models, across different cohorts and prediction windows. Results We found the two tree-based models, random forests (RF) and light gradient-boosting machine (LGBM), achieving good discriminative performance across different clinical settings (area under the receiver operating characteristic curve 0.76 0.88 for RF and 0.74 0.89 for LGBM). In addition, we showed comparable performance can be achieved with a greatly reduced set of features, demonstrating computational efficiency can be attained without significant compromise of model accuracy. Conclusions Good discriminative performance for models predicting early-onset BD can be achieved utilizing large-scale EHR data. Our study offers a scalable and accurate method for identifying youth at risk for BD that could help inform clinical decision-making and facilitate early intervention. Future work includes evaluating the portability of our approach to other healthcare systems and exploring considerations regarding possible implementation. En ligne : https://doi.org/10.1111/jcpp.14131 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=565 [article] Prediction of early-onset bipolar using electronic health records [texte imprimé] / Bo WANG, Auteur ; Yi-Han SHEU, Auteur ; Hyunjoon LEE, Auteur ; Robert G. MEALER, Auteur ; Victor M. CASTRO, Auteur ; Jordan W. SMOLLER, Auteur . - p.1141-1154. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 66-8 (August 2025) . - p.1141-1154 Mots-clés : Early-onset bipolar disorder  mood disorders  risk prediction  electronic health record  machine learning Index. décimale : PER Périodiques Résumé : Background Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical notes for predicting early-onset BD. Methods Structured and unstructured data were extracted from the longitudinal EHR of the Mass General Brigham health system. We defined three cohorts aged 10 25 years: (1) the full youth cohort (N 300,398); (2) a subcohort defined by having a mental health visit (N 105,461); and (3) a subcohort defined by having a diagnosis of mood disorder or ADHD (N 35,213). By adopting a prospective landmark modeling approach that aligns with clinical practice, we developed and validated a range of machine learning models, across different cohorts and prediction windows. Results We found the two tree-based models, random forests (RF) and light gradient-boosting machine (LGBM), achieving good discriminative performance across different clinical settings (area under the receiver operating characteristic curve 0.76 0.88 for RF and 0.74 0.89 for LGBM). In addition, we showed comparable performance can be achieved with a greatly reduced set of features, demonstrating computational efficiency can be attained without significant compromise of model accuracy. Conclusions Good discriminative performance for models predicting early-onset BD can be achieved utilizing large-scale EHR data. Our study offers a scalable and accurate method for identifying youth at risk for BD that could help inform clinical decision-making and facilitate early intervention. Future work includes evaluating the portability of our approach to other healthcare systems and exploring considerations regarding possible implementation. En ligne : https://doi.org/10.1111/jcpp.14131 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=565

Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field / Shyam Sundar RAJAGOPALAN in Journal of Neurodevelopmental Disorders, 16 (2024)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 16 (2024) Titre : Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field Type de document : texte imprimé Auteurs : Shyam Sundar RAJAGOPALAN, Auteur ; Kristiina TAMMIMIES, Auteur Langues : Anglais (eng) Mots-clés : Humans  Machine Learning  Electronic Health Records  Neurodevelopmental Disorders/diagnosis/epidemiology  Attention Deficit Disorder with Hyperactivity/diagnosis/epidemiology  Autism Spectrum Disorder/diagnosis/epidemiology  Electronic Health Record  Neurodevelopmental Disorder  Population Register  for publication Not applicable. Competing interests The authors declare that  there are no competing interests. Index. décimale : PER Périodiques Résumé : Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early. En ligne : https://dx.doi.org/10.1186/s11689-024-09579-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576 [article] Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field [texte imprimé] / Shyam Sundar RAJAGOPALAN, Auteur ; Kristiina TAMMIMIES, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 16 (2024) Mots-clés : Humans  Machine Learning  Electronic Health Records  Neurodevelopmental Disorders/diagnosis/epidemiology  Attention Deficit Disorder with Hyperactivity/diagnosis/epidemiology  Autism Spectrum Disorder/diagnosis/epidemiology  Electronic Health Record  Neurodevelopmental Disorder  Population Register  for publication Not applicable. Competing interests The authors declare that  there are no competing interests. Index. décimale : PER Périodiques Résumé : Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early. En ligne : https://dx.doi.org/10.1186/s11689-024-09579-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576