Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Matthew C Lenert"'
Autor:
Emily E Haroz, Fiona Grubin, Novalene Goklish, Shardai Pioche, Mary Cwik, Allison Barlow, Emma Waugh, Jason Usher, Matthew C Lenert, Colin G Walsh
Publikováno v:
JMIR Public Health and Surveillance, Vol 7, Iss 9, p e24377 (2021)
BackgroundMachine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. ObjectiveThis study aim
Externí odkaz:
https://doaj.org/article/eb15768bf0014de2b50a016c281ba123
Autor:
Melissa L McPheeters, Ben Tyndall, Allison Roberts, Matthew C. Lenert, Sarah C. Lotspeich, Michael Ripperger, Sanura M. Latham, Drew Wilimitis, Carrie E Fry, Charlotte Cherry, Colin G. Walsh, Katelyn Robinson, Qingxia Chen
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA
Objective To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8298218d99378f19666e7e934442eb94
https://doi.org/10.1093/jamia/ocab218
https://doi.org/10.1093/jamia/ocab218
Publikováno v:
Arthritis Care Res (Hoboken)
OBJECTIVE Patients with fibromyalgia (FM) are 10 times more likely to die by suicide than the general population. The purpose of this study was to externally validate published models predicting suicidal ideation and suicide attempts in patients with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad64cc3660e61c4ff75d51fd469c8673
https://doi.org/10.1002/acr.23748
https://doi.org/10.1002/acr.23748
Autor:
Emily E Haroz, Fiona Grubin, Novalene Goklish, Shardai Pioche, Mary Cwik, Allison Barlow, Emma Waugh, Jason Usher, Matthew C Lenert, Colin G Walsh
BACKGROUND Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. OBJECTIVE This study ai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7740f3131c5fa43e5a6f3dd21571551
https://doi.org/10.2196/preprints.24377
https://doi.org/10.2196/preprints.24377
Autor:
Allison Barlow, Novalene Goklish, Shardai Pioche, Colin G. Walsh, Fiona Grubin, Emily E. Haroz, Jason Usher, Mary F. Cwik, Matthew C. Lenert, Emma Waugh
Publikováno v:
JMIR Public Health and Surveillance
Background Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. Objective This study ai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a402055c66cb894f54e774d01aeb36b3
https://doi.org/10.2196/24377
https://doi.org/10.2196/24377
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA
J Am Med Inform Assoc
J Am Med Inform Assoc
Predictive analytics have begun to change the workflows of healthcare by giving insight into our future health. Deploying prognostic models into clinical workflows should change behavior and motivate interventions that affect outcomes. As users respo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59101af1189b20b413203cd5d674b5e9
https://pubmed.ncbi.nlm.nih.gov/31722385
https://pubmed.ncbi.nlm.nih.gov/31722385
Publikováno v:
AMIA ... Annual Symposium proceedings. AMIA Symposium. 2017
When patients and doctors collaborate to make healthcare decisions, they rely on clinical trial results to guide discussions. Trials are designed to recruit diverse participants. The question remains — how well do trial results apply to me or to pe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::4bb62d246517bb34010e8386d2b919b9
https://pubmed.ncbi.nlm.nih.gov/29854179
https://pubmed.ncbi.nlm.nih.gov/29854179
Publikováno v:
Journal of biomedical informatics. 84
Objective Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation. Materials and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a4616cd493e8af1a67412366dd855e1
https://pubmed.ncbi.nlm.nih.gov/29940263
https://pubmed.ncbi.nlm.nih.gov/29940263
Publikováno v:
Journal of the American Medical Informatics Association. 26:1677-1678
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7d1ca613cdffd929cda7f12a4d4ad51
https://doi.org/10.1093/jamia/ocz198
https://doi.org/10.1093/jamia/ocz198
Publikováno v:
Journal of Biomedical Informatics. 91:103111
Objective Administrators assess care variability through chart review or cost variability to inform care standardization efforts. Chart review is costly and cost variability is imprecise. This study explores the potential of physician orders as an al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40aa806c104e55c52bfbed58b3017110
https://doi.org/10.1016/j.jbi.2019.103111
https://doi.org/10.1016/j.jbi.2019.103111