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

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 et/ou numérique Auteurs : Leann S. DAWALT, Auteur ; Julie LOUNDS TAYLOR, Auteur ; A. MOVAGHAR, Auteur ; J. HONG, Auteur ; B. KIM, Auteur ; M. H. BRILLIANT, Auteur ; M. R. MAILICK, Auteur 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 et/ou numérique] / Leann S. DAWALT, Auteur ; Julie LOUNDS TAYLOR, Auteur ; A. MOVAGHAR, Auteur ; J. HONG, Auteur ; B. KIM, Auteur ; M. H. BRILLIANT, Auteur ; M. R. MAILICK, Auteur . - 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

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)  

Public ISBD [article]  inAutism > 28-12 (December 2024) . - p.3063-3077 Titre : Predicting autism traits from baby wellness records: A machine learning approach Type de document : Texte imprimé et/ou numérique Auteurs : Joshua GUEDALIA, Auteur ; Keren ILAN, Auteur ; Meirav SHAHAM, Auteur ; Galit SHEFER, Auteur ; Roe COHEN, Auteur ; Yuval TAMIR, Auteur ; Lidia V. GABIS, Auteur Article en page(s) : p.3063-3077 Langues : Anglais (eng) Mots-clés : autism spectrum conditions  developmental milestones  electronic health records  machine learning  screening Index. décimale : PER Périodiques Résumé : Early detection of autism spectrum condition is crucial for children to maximally benefit from early intervention. The study examined a machine learning model predicting the increased likelihood for autism from wellness records from 0 to 24?months. The study included 591,989 non-autistic and 12,846 autistic children. A gradient boosting model with a threefold cross-validation and SHAPley additive explanation tool quantified feature importance. The model had an average area under the curve of 0.81 (SD = 0.004). The high-likelihood group detected by the model had a 0.073 autism spectrum condition incidence rate; 3.42-fold more than in the entire cohort (0.02). Sex-specific models had higher specificity (0.81 boys and 0.79 girls) than sensitivity (0.64 boys and 0.66 girls). The common predictors were more parental concerns, older mothers, never nursing, lower initial and higher last weight percentiles, and several delayed milestones. SHAPley additive explanation tool results show common, important predictors in the full sample and separate boys' and girls' models. These included birth, growth, familial, postnatal parameters and delayed language, fine motor, and social milestones from 12 to 24?months. Machine learning algorithms can help detect increased autism signs by relying on the multidimensional data routinely recorded during the first 2 years. Lay abstract Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12-24?months, mother?s age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life. En ligne : https://dx.doi.org/10.1177/13623613241253311 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=543 [article] Predicting autism traits from baby wellness records: A machine learning approach [Texte imprimé et/ou numérique] / Joshua GUEDALIA, Auteur ; Keren ILAN, Auteur ; Meirav SHAHAM, Auteur ; Galit SHEFER, Auteur ; Roe COHEN, Auteur ; Yuval TAMIR, Auteur ; Lidia V. GABIS, Auteur . - p.3063-3077. Langues : Anglais (eng) in Autism > 28-12 (December 2024) . - p.3063-3077 Mots-clés : autism spectrum conditions  developmental milestones  electronic health records  machine learning  screening Index. décimale : PER Périodiques Résumé : Early detection of autism spectrum condition is crucial for children to maximally benefit from early intervention. The study examined a machine learning model predicting the increased likelihood for autism from wellness records from 0 to 24?months. The study included 591,989 non-autistic and 12,846 autistic children. A gradient boosting model with a threefold cross-validation and SHAPley additive explanation tool quantified feature importance. The model had an average area under the curve of 0.81 (SD = 0.004). The high-likelihood group detected by the model had a 0.073 autism spectrum condition incidence rate; 3.42-fold more than in the entire cohort (0.02). Sex-specific models had higher specificity (0.81 boys and 0.79 girls) than sensitivity (0.64 boys and 0.66 girls). The common predictors were more parental concerns, older mothers, never nursing, lower initial and higher last weight percentiles, and several delayed milestones. SHAPley additive explanation tool results show common, important predictors in the full sample and separate boys' and girls' models. These included birth, growth, familial, postnatal parameters and delayed language, fine motor, and social milestones from 12 to 24?months. Machine learning algorithms can help detect increased autism signs by relying on the multidimensional data routinely recorded during the first 2 years. Lay abstract Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12-24?months, mother?s age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life. En ligne : https://dx.doi.org/10.1177/13623613241253311 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=543

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

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 59-12 (December 2018) . - p.1261-1270 Titre : Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning Type de document : Texte imprimé et/ou numérique Auteurs : C. G. WALSH, Auteur ; J. D. RIBEIRO, Auteur ; J. C. FRANKLIN, Auteur Article en page(s) : p.1261-1270 Langues : Anglais (eng) Mots-clés : Suicide  adolescent  attempted  decision support techniques  electronic health records  machine learning Index. décimale : PER Périodiques Résumé : BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suicide risk using only routinely collected clinical data. We used a machine learning approach validated on longitudinal clinical data in adults to address this challenge in adolescents. METHODS: This is a retrospective, longitudinal cohort study. Data were collected from the Vanderbilt Synthetic Derivative from January 1998 to December 2015 and included 974 adolescents with nonfatal suicide attempts and multiple control comparisons: 496 adolescents with other self-injury (OSI), 7,059 adolescents with depressive symptoms, and 25,081 adolescent general hospital controls. Candidate predictors included diagnostic, demographic, medication, and socioeconomic factors. Outcome was determined by multiexpert review of electronic health records. Random forests were validated with optimism adjustment at multiple time points (from 1 week to 2 years). Recalibration was done via isotonic regression. Evaluation metrics included discrimination (AUC, sensitivity/specificity, precision/recall) and calibration (calibration plots, slope/intercept, Brier score). RESULTS: Computational models performed well and did not require face-to-face screening. Performance improved as suicide attempts became more imminent. Discrimination was good in comparison with OSI controls (AUC = 0.83 [0.82-0.84] at 720 days; AUC = 0.85 [0.84-0.87] at 7 days) and depressed controls (AUC = 0.87 [95% CI 0.85-0.90] at 720 days; 0.90 [0.85-0.94] at 7 days) and best in comparison with general hospital controls (AUC 0.94 [0.92-0.96] at 720 days; 0.97 [0.95-0.98] at 7 days). Random forests significantly outperformed logistic regression in every comparison. Recalibration improved performance as much as ninefold - clinical recommendations with poorly calibrated predictions can lead to decision errors. CONCLUSIONS: Machine learning on longitudinal clinical data may provide a scalable approach to broaden screening for risk of nonfatal suicide attempts in adolescents. En ligne : http://dx.doi.org/10.1111/jcpp.12916 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=371 [article] Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning [Texte imprimé et/ou numérique] / C. G. WALSH, Auteur ; J. D. RIBEIRO, Auteur ; J. C. FRANKLIN, Auteur . - p.1261-1270. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 59-12 (December 2018) . - p.1261-1270 Mots-clés : Suicide  adolescent  attempted  decision support techniques  electronic health records  machine learning Index. décimale : PER Périodiques Résumé : BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suicide risk using only routinely collected clinical data. We used a machine learning approach validated on longitudinal clinical data in adults to address this challenge in adolescents. METHODS: This is a retrospective, longitudinal cohort study. Data were collected from the Vanderbilt Synthetic Derivative from January 1998 to December 2015 and included 974 adolescents with nonfatal suicide attempts and multiple control comparisons: 496 adolescents with other self-injury (OSI), 7,059 adolescents with depressive symptoms, and 25,081 adolescent general hospital controls. Candidate predictors included diagnostic, demographic, medication, and socioeconomic factors. Outcome was determined by multiexpert review of electronic health records. Random forests were validated with optimism adjustment at multiple time points (from 1 week to 2 years). Recalibration was done via isotonic regression. Evaluation metrics included discrimination (AUC, sensitivity/specificity, precision/recall) and calibration (calibration plots, slope/intercept, Brier score). RESULTS: Computational models performed well and did not require face-to-face screening. Performance improved as suicide attempts became more imminent. Discrimination was good in comparison with OSI controls (AUC = 0.83 [0.82-0.84] at 720 days; AUC = 0.85 [0.84-0.87] at 7 days) and depressed controls (AUC = 0.87 [95% CI 0.85-0.90] at 720 days; 0.90 [0.85-0.94] at 7 days) and best in comparison with general hospital controls (AUC 0.94 [0.92-0.96] at 720 days; 0.97 [0.95-0.98] at 7 days). Random forests significantly outperformed logistic regression in every comparison. Recalibration improved performance as much as ninefold - clinical recommendations with poorly calibrated predictions can lead to decision errors. CONCLUSIONS: Machine learning on longitudinal clinical data may provide a scalable approach to broaden screening for risk of nonfatal suicide attempts in adolescents. En ligne : http://dx.doi.org/10.1111/jcpp.12916 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=371