نتائج البحث - "Timothy L. Lash"

1

Changes in Size and Demographic Composition of Transgender and Gender Non-Binary Population Receiving Care at Integrated Health Systems

المؤلفون: Vin Tangpricha, Michael Goodman, Courtney McCracken, W. Dana Flanders, Qi Zhang, Andrew Bradlyn, Suma Vupputuri, Leonidas Panagiotakopoulos, Douglas W. Roblin, Willi Rechler, David E. Sandberg, Rebecca Nash, Darios Getahun, Timothy L. Lash, Michael J. Silverberg

المصدر: Endocrine Practice. 27:390-395

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39dc60cf34cee2f475c7a0f3964a9420Test
https://doi.org/10.1016/j.eprac.2020.11.016Test

2

Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries

المؤلفون: Anthony J. Rosellini, Timothy L. Lash, Jaimie L. Gradus, Isaac R. Galatzer-Levy, Erzsébet Horváth-Puhó, Tammy Jiang, Henrik Toft Sørensen, Amy E. Street

المصدر: Am J Epidemiol
Gradus, J L, Rosellini, A J, Horváth-Puhó, E, Jiang, T, Street, A E, Galatzer-Levy, I, Lash, T L & Sørensen, H T 2021, ' Predicting Sex-Specific Non-Fatal Suicide Attempt Risk Using Machine Learning and Data from Danish National Registries ', American Journal of Epidemiology, vol. 190, no. 12, pp. 2517–2527 . https://doi.org/10.1093/aje/kwab112Test

مصطلحات موضوعية: Adult, Male, Sociodemographic Factors, Adolescent, Epidemiology, Denmark, Health Status, Response to Invited Commentary, Population, Emigrants and Immigrants, Suicide, Attempted, Machine learning, computer.software_genre, Risk profile, Machine Learning, Danish, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Humans, Medicine, suicide attempts, Registries, 030212 general & internal medicine, Medical diagnosis, education, education.field_of_study, Suicide attempt, National Registry, business.industry, Mental Disorders, prediction, Original Contribution, Middle Aged, Sex specific, language.human_language, machine learning, Mental Health, 030220 oncology & carcinogenesis, language, Female, Registry data, Artificial intelligence, Substance use, business, computer

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e23794b213b5c2c8e2b162097c176b8aTest
https://doi.org/10.1093/aje/kwab112Test

3

Bias Analysis Gone Bad

المؤلفون: Timothy L. Lash, Matthew P. Fox, Thomas P. Ahern, Lindsay J Collin, Richard F. MacLehose

المصدر: American Journal of Epidemiology. 190:1604-1612

مصطلحات موضوعية: Systematic error, Quality management, Epidemiology, Computer science, media_common.quotation_subject, 030204 cardiovascular system & hematology, Research findings, Original research, 03 medical and health sciences, 0302 clinical medicine, Econometrics, Quality (business), 030212 general & internal medicine, Epidemiologic research, Bias (Epidemiology), media_common

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::07da290aebb786cafd7db122a9bf7235Test
https://doi.org/10.1093/aje/kwab072Test

4

Sex‐Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark

المؤلفون: Rachel Sayko Adams, Erzsébet Horváth-Puhó, Jaimie L. Gradus, Amy E. Street, Anthony J. Rosellini, Tammy Jiang, Timothy L. Lash, Henrik Toft Sørensen, Katherine M. Keyes, Magdalena Cerdá

المصدر: Adams, R S, Jiang, T, Rosellini, A J, Horváth-Puhó, E, Street, A E, Keyes, K M, Cerdá, M, Lash, T L, Sørensen, H T & Gradus, J L 2021, ' Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark ', Addiction, vol. 116, no. 10, pp. 2882-2892 . https://doi.org/10.1111/add.15455Test
Addiction

مصطلحات موضوعية: Male, ARMY SOLDIERS, Denmark, 030508 substance abuse, Medicine (miscellaneous), ALCOHOL, Risk profile, Cohort Studies, 0302 clinical medicine, Risk Factors, Substance-Related Disorders/epidemiology, 030212 general & internal medicine, media_common, Cause of death, Alcohol-related disorders, substance use disorder, ASSOCIATION, Sex specific, Substance abuse, Suicide, Psychiatry and Mental health, machine learning, language, Female, 0305 other medical science, Adult, medicine.medical_specialty, Substance-Related Disorders, media_common.quotation_subject, Alcohol-related disorders, Denmark, machine learning, poisoning, substance use disorder, suicide, Adjustment disorders, CIVIL REGISTRATION SYSTEM, Article, DANISH, Danish, 03 medical and health sciences, medicine, Humans, COHORT, IDEATION, Psychiatry, business.industry, MORTALITY, Addiction, medicine.disease, Denmark/epidemiology, language.human_language, poisoning, Substance use, business

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9232d052d6f678ba9e8051ee08746b7Test
https://doi.org/10.1111/add.15455Test

5

Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark

المؤلفون: Henrik Toft Sørensen, Timothy L. Lash, Tammy Jiang, Anthony J. Rosellini, Jaimie L. Gradus, Amy E. Street, Erzsébet Horváth-Puhó, Brian Shiner

المصدر: Jiang, T, Rosellini, A J, Horváth-Puhó, E, Shiner, B, Street, A E, Lash, T L, Sorensen, H T & Gradus, J L 2021, ' Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark ', British Journal of Psychiatry, vol. 219, no. 2, pp. 440-447 . https://doi.org/10.1192/bjp.2021.19Test
Br J Psychiatry

مصطلحات موضوعية: Hospitals, Psychiatric, Male, postdischarge suicide, Denmark, media_common.quotation_subject, Aftercare, Machine learning, computer.software_genre, OBSTRUCTIVE PULMONARY-DISEASE, Article, DANISH, Cohort Studies, Machine Learning, Danish, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, suicide prediction, Humans, Medicine, Psychiatric hospital, Registries, 030212 general & internal medicine, Medical prescription, psychiatric hospital, Cause of death, media_common, business.industry, Mental Disorders, MORTALITY, Addiction, WOMEN, After discharge, Patient Discharge, language.human_language, 030227 psychiatry, Recurrent major depression, Suicide, Psychiatry and Mental health, machine learning, Increased risk, RISK-FACTORS, language, Female, Artificial intelligence, SMOKING, business, Alcohol-Related Disorders, computer