12 recherche sur le mot-clé 'electronic health records'

Breastfeeding and autism: An electronic health records study of baby wellness visit records / Ayelet BEN-SASSON in Autism, 30-3 (March 2026)  

Public ISBD [article]  inAutism > 30-3 (March 2026) . - p.723-735 Titre : Breastfeeding and autism: An electronic health records study of baby wellness visit records Type de document : texte imprimé Auteurs : Ayelet BEN-SASSON, Auteur ; Aviva MIMOUNI-BLOCH, Auteur ; Sukaina SAMHAT-DARAWSHI, Auteur ; Keren ILANN, Auteur ; Lidia V. GABIS, Auteur Article en page(s) : p.723-735 Langues : Anglais (eng) Mots-clés : autism  baby wellness visits  breastfeeding  electronic health records  socioeconomic Index. décimale : PER Périodiques Résumé : Evidence supporting the association between breastfeeding patterns and autism is inconsistent. This study examined sociodemographic and birth factors related to breastfeeding duration and subsequent autism spectrum disorder (autism) diagnosis according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, compared to a neurotypically developing cohort, based on electronic health records. Demographics, feeding preferences, and breastfeeding duration as reported by parents during routine baby wellness visits were analyzed for a cohort of 11,766 (1.9%) children with autism spectrum disorder diagnosis and a random subsample of 12,000 (2.03%) neurotypically developing children. Autism spectrum disorder diagnosis was based on a national autism registry and assigned after electronic health records were completed. Preterm, very low birth weight, multiple births, and complex medical comorbidities were excluded. Infants subsequently diagnosed with autism were breastfed for an average of 5.0 months, 1.5 months shorter than neurotypically developing. Fewer autism spectrum disorder infants were exclusively breastfed in the first year of life. Two-way analysis of variance indicated a significant effect of socioeconomic status and autism spectrum disorder on breastfeeding duration, and a significant interaction of socioeconomic status with autism spectrum disorder. Shorter breastfeeding duration among infants with subsequent autism spectrum disorder was confirmed, calling for closer monitoring for autism traits in infants with breastfeeding difficulties. These challenges were independent of birth parameters; however, they were influenced by socioeconomic factors.Lay Abstract This study found that infants later diagnosed with autism were breastfed for a shorter duration than their typically developing peers, with differences influenced by socioeconomic status. These findings highlight the importance of monitoring breastfeeding challenges as a potential early indicator of autism, particularly in families of mid-range socioeconomic status. En ligne : https://dx.doi.org/10.1177/13623613251409334 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=582 [article] Breastfeeding and autism: An electronic health records study of baby wellness visit records [texte imprimé] / Ayelet BEN-SASSON, Auteur ; Aviva MIMOUNI-BLOCH, Auteur ; Sukaina SAMHAT-DARAWSHI, Auteur ; Keren ILANN, Auteur ; Lidia V. GABIS, Auteur . - p.723-735. Langues : Anglais (eng) in Autism > 30-3 (March 2026) . - p.723-735 Mots-clés : autism  baby wellness visits  breastfeeding  electronic health records  socioeconomic Index. décimale : PER Périodiques Résumé : Evidence supporting the association between breastfeeding patterns and autism is inconsistent. This study examined sociodemographic and birth factors related to breastfeeding duration and subsequent autism spectrum disorder (autism) diagnosis according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, compared to a neurotypically developing cohort, based on electronic health records. Demographics, feeding preferences, and breastfeeding duration as reported by parents during routine baby wellness visits were analyzed for a cohort of 11,766 (1.9%) children with autism spectrum disorder diagnosis and a random subsample of 12,000 (2.03%) neurotypically developing children. Autism spectrum disorder diagnosis was based on a national autism registry and assigned after electronic health records were completed. Preterm, very low birth weight, multiple births, and complex medical comorbidities were excluded. Infants subsequently diagnosed with autism were breastfed for an average of 5.0 months, 1.5 months shorter than neurotypically developing. Fewer autism spectrum disorder infants were exclusively breastfed in the first year of life. Two-way analysis of variance indicated a significant effect of socioeconomic status and autism spectrum disorder on breastfeeding duration, and a significant interaction of socioeconomic status with autism spectrum disorder. Shorter breastfeeding duration among infants with subsequent autism spectrum disorder was confirmed, calling for closer monitoring for autism traits in infants with breastfeeding difficulties. These challenges were independent of birth parameters; however, they were influenced by socioeconomic factors.Lay Abstract This study found that infants later diagnosed with autism were breastfed for a shorter duration than their typically developing peers, with differences influenced by socioeconomic status. These findings highlight the importance of monitoring breastfeeding challenges as a potential early indicator of autism, particularly in families of mid-range socioeconomic status. En ligne : https://dx.doi.org/10.1177/13623613251409334 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=582

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

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

The physical and psychiatric health conditions related to autism genetic scores, across genetic ancestries, sexes and age-groups in electronic health records / Maria NIARCHOU in Journal of Neurodevelopmental Disorders, 15 (2023)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 15 (2023) Titre : The physical and psychiatric health conditions related to autism genetic scores, across genetic ancestries, sexes and age-groups in electronic health records Type de document : texte imprimé Auteurs : Maria NIARCHOU, Auteur ; Tyne MILLER-FLEMING, Auteur ; Beth A. MALOW, Auteur ; Lea K. DAVIS, Auteur Langues : Anglais (eng) Mots-clés : Male  Female  Humans  Autistic Disorder/epidemiology/genetics  Electronic Health Records  Multifactorial Inheritance  Phenotype  Neoplasms  Autism  PheWAS  Polygenic score Index. décimale : PER Périodiques Résumé : BACKGROUND: Although polygenic scores (PGS) for autism have been related to various psychiatric and medical conditions, most studies to date have been conducted in research ascertained populations. We aimed to identify the psychiatric and physical conditions associated with autism PGS in a health care setting. METHODS: We computed PGS for 12,383 unrelated participants of African genetic ancestry (AF) and 65,363 unrelated participants of European genetic ancestry (EU) from Vanderbilt's de-identified biobank. Next, we performed phenome wide association studies of the autism PGS within these two genetic ancestries. RESULTS: Seven associations surpassed the Bonferroni adjusted threshold for statistical significance (p = 0.05/1374 = 3.6 × 10(-5)) in the EU participants, including mood disorders (OR (95%CI) = 1.08(1.05 to 1.10), p = 1.0 × 10(-10)), autism (OR (95%CI) = 1.34(1.24 to 1.43), p = 1.2 × 10(-9)), and breast cancer (OR (95%CI) = 1.09(1.05 to 1.14), 2.6 × 10(-5)). There was no statistical evidence for PGS-phenotype associations in the AF participants. Conditioning on the diagnosis of autism or on median body mass index (BMI) did not impact the strength of the reported associations. Although we observed some sex differences in the pattern of associations, there was no significant interaction between sex and autism PGS. Finally, the associations between autism PGS and autism diagnosis were stronger in childhood and adolescence, while the associations with mood disorders and breast cancer were stronger in adulthood. DISCUSSION: Our findings indicate that autism PGS is not only related to autism diagnosis but may also be related to adult-onset conditions, including mood disorders and some cancers. CONCLUSIONS: Our study raises the hypothesis that genes associated with autism may also increase the risk for cancers later in life. Future studies are necessary to replicate and extend our findings. En ligne : https://dx.doi.org/10.1186/s11689-023-09485-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=575 [article] The physical and psychiatric health conditions related to autism genetic scores, across genetic ancestries, sexes and age-groups in electronic health records [texte imprimé] / Maria NIARCHOU, Auteur ; Tyne MILLER-FLEMING, Auteur ; Beth A. MALOW, Auteur ; Lea K. DAVIS, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 15 (2023) Mots-clés : Male  Female  Humans  Autistic Disorder/epidemiology/genetics  Electronic Health Records  Multifactorial Inheritance  Phenotype  Neoplasms  Autism  PheWAS  Polygenic score Index. décimale : PER Périodiques Résumé : BACKGROUND: Although polygenic scores (PGS) for autism have been related to various psychiatric and medical conditions, most studies to date have been conducted in research ascertained populations. We aimed to identify the psychiatric and physical conditions associated with autism PGS in a health care setting. METHODS: We computed PGS for 12,383 unrelated participants of African genetic ancestry (AF) and 65,363 unrelated participants of European genetic ancestry (EU) from Vanderbilt's de-identified biobank. Next, we performed phenome wide association studies of the autism PGS within these two genetic ancestries. RESULTS: Seven associations surpassed the Bonferroni adjusted threshold for statistical significance (p = 0.05/1374 = 3.6 × 10(-5)) in the EU participants, including mood disorders (OR (95%CI) = 1.08(1.05 to 1.10), p = 1.0 × 10(-10)), autism (OR (95%CI) = 1.34(1.24 to 1.43), p = 1.2 × 10(-9)), and breast cancer (OR (95%CI) = 1.09(1.05 to 1.14), 2.6 × 10(-5)). There was no statistical evidence for PGS-phenotype associations in the AF participants. Conditioning on the diagnosis of autism or on median body mass index (BMI) did not impact the strength of the reported associations. Although we observed some sex differences in the pattern of associations, there was no significant interaction between sex and autism PGS. Finally, the associations between autism PGS and autism diagnosis were stronger in childhood and adolescence, while the associations with mood disorders and breast cancer were stronger in adulthood. DISCUSSION: Our findings indicate that autism PGS is not only related to autism diagnosis but may also be related to adult-onset conditions, including mood disorders and some cancers. CONCLUSIONS: Our study raises the hypothesis that genes associated with autism may also increase the risk for cancers later in life. Future studies are necessary to replicate and extend our findings. En ligne : https://dx.doi.org/10.1186/s11689-023-09485-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=575

Health profiles of adults with autism spectrum disorder: Differences between women and men / Leann S. DAWALT in Autism Research, 14-9 (September 2021)  

Public ISBD [article]  inAutism Research > 14-9 (September 2021) . - p.1896-1904 Titre : Health profiles of adults with autism spectrum disorder: Differences between women and men Type de document : texte imprimé Auteurs : Leann S. DAWALT, Auteur ; Julie LOUNDS TAYLOR, Auteur ; Arezoo MOVAGHAR, Auteur ; Jinkuk HONG, Auteur ; Bryan KIM, Auteur ; Murray H. BRILLIANT, Auteur ; Marsha R. MAILICK, Auteur Année de publication : 2021 Article en page(s) : p.1896-1904 Langues : Anglais (eng) Mots-clés : Adult  Autism Spectrum Disorder/complications/epidemiology  Autistic Disorder  Electronic Health Records  Female  Humans  Male  Sleep Wake Disorders  adults  electronic health records  health  health care utilization  sex differences Index. décimale : PER Périodiques Résumé : The purpose of the present study was to investigate the hypothesis that women with autism have poorer health compared with men with autism, and compared with women without autism. Utilizing electronic health records drawn from a single health care system serving over 2 million individuals, 2119 adults with diagnosed autism spectrum disorders were compared with age- and sex-matched controls. When considering health care utilization, we found evidence of multiplicative risk for conditions within some domains (i.e., nutrition conditions, neurologic disease, psychiatric conditions, and sleep disorders) such that women with autism spectrum disorder (ASD) experienced double jeopardy-meaning they had greater rates of health care utilization within a domain than what would separately be expected by virtue of being a woman and having ASD. For other domains (i.e., endocrine disorders, gastrointestinal disorders), the risk was additive such that being a female and having ASD were both associated with higher health care utilization, but there were no significant interaction effects. It was only with respect to one domain (cardiovascular) that rates of health care utilization were reflective of neither ASD diagnosis nor sex. Overall, our findings suggest that women with ASD are a vulnerable subgroup with high levels of health care utilization. LAY SUMMARY: This study asked whether women with autism have poorer health compared with men with autism, and compared with women without autism. To answer this question, we used data from electronic health records. We found that women with autism spectrum disorder (ASD) were at the greatest risk for health problems such as nutrition conditions, neurologic disease, psychiatric conditions, and sleep disorders. More research on health of women with ASD is needed. En ligne : http://dx.doi.org/10.1002/aur.2563 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=449 [article] Health profiles of adults with autism spectrum disorder: Differences between women and men [texte imprimé] / Leann S. DAWALT, Auteur ; Julie LOUNDS TAYLOR, Auteur ; Arezoo MOVAGHAR, Auteur ; Jinkuk HONG, Auteur ; Bryan KIM, Auteur ; Murray H. BRILLIANT, Auteur ; Marsha R. MAILICK, Auteur . - 2021 . - p.1896-1904. Langues : Anglais (eng) in Autism Research > 14-9 (September 2021) . - p.1896-1904 Mots-clés : Adult  Autism Spectrum Disorder/complications/epidemiology  Autistic Disorder  Electronic Health Records  Female  Humans  Male  Sleep Wake Disorders  adults  electronic health records  health  health care utilization  sex differences Index. décimale : PER Périodiques Résumé : The purpose of the present study was to investigate the hypothesis that women with autism have poorer health compared with men with autism, and compared with women without autism. Utilizing electronic health records drawn from a single health care system serving over 2 million individuals, 2119 adults with diagnosed autism spectrum disorders were compared with age- and sex-matched controls. When considering health care utilization, we found evidence of multiplicative risk for conditions within some domains (i.e., nutrition conditions, neurologic disease, psychiatric conditions, and sleep disorders) such that women with autism spectrum disorder (ASD) experienced double jeopardy-meaning they had greater rates of health care utilization within a domain than what would separately be expected by virtue of being a woman and having ASD. For other domains (i.e., endocrine disorders, gastrointestinal disorders), the risk was additive such that being a female and having ASD were both associated with higher health care utilization, but there were no significant interaction effects. It was only with respect to one domain (cardiovascular) that rates of health care utilization were reflective of neither ASD diagnosis nor sex. Overall, our findings suggest that women with ASD are a vulnerable subgroup with high levels of health care utilization. LAY SUMMARY: This study asked whether women with autism have poorer health compared with men with autism, and compared with women without autism. To answer this question, we used data from electronic health records. We found that women with autism spectrum disorder (ASD) were at the greatest risk for health problems such as nutrition conditions, neurologic disease, psychiatric conditions, and sleep disorders. More research on health of women with ASD is needed. En ligne : http://dx.doi.org/10.1002/aur.2563 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=449

An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities / Isabella SLABY in Journal of Neurodevelopmental Disorders, 14 (2022)  

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Predicting autism traits from baby wellness records: A machine learning approach / Joshua GUEDALIA ; Keren ILAN ; Meirav SHAHAM ; Galit SHEFER ; Roe COHEN ; Yuval TAMIR ; Lidia V. GABIS in Autism, 28-12 (December 2024)  

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Project GIVE: using a virtual genetics service platform to reduce health inequities and improve access to genomic care in an underserved region of Texas / Blake VUOCOLO in Journal of Neurodevelopmental Disorders, 16 (2024)  

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Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning / Colin G. WALSH in Journal of Child Psychology and Psychiatry, 59-12 (December 2018)  

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Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers / Cartik KOTHARI in Journal of Neurodevelopmental Disorders, 14 (2022)  

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