Results 151 to 160 of about 1,095,580 (188)

An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records. [PDF]

BMC Med Inform Decis Mak
Wang H, Zhang M, Mai L, Li X, Bellou A, Wu L.   +5 more
europepmc   +1 more source

Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States. [PDF]

Diabetes Obes Metab
Choong C, Xavier N, Falcon B, Kan H, Lipkovich I, Nowak C, Hoyt M, Houle C, Kahan S.   +8 more
europepmc   +1 more source

Correction: Systematic review and meta-analysis of workload among medical records coders in China. [PDF]

BMC Med Inform Decis Mak
Liu Y, Wu C, Cao M, Lei C, Zhou Z, Ou W.
europepmc   +1 more source

Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records. [PDF]

J Cachexia Sarcopenia Muscle
Kim DW, Seo J, Kwon S, Park CM, Han C, Kim Y, Kim J, Kim CS, Park SW, Yoon D, Kim KM.   +10 more
europepmc   +1 more source

Supervised machine learning compared to large language models for identifying functional seizures from medical records. [PDF]

Epilepsia
Kerr WT, McFarlane KN, Pucci GF, Carns DR, Israel A, Vighetti L, Pennell PB, Stern JM, Xia Z, Wang Y.   +9 more
europepmc   +1 more source

Suicidal Communication Prior to Suicide in Children and Young Adults-A Medical Records Review in Health Care Services in Sweden. [PDF]

Int J Environ Res Public Health
Hansson AL, Johnsson P, Eberhard S, Ehnvall A, Lindström S, Waern M, Westrin Å.   +6 more
europepmc   +1 more source

Predicting preterm birth using electronic medical records from multiple prenatal visits. [PDF]

BMC Pregnancy Childbirth
Huang C, Long X, van der Ven M, Kaptein M, Oei SG, van den Heuvel E.   +5 more
europepmc   +1 more source

Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting. [PDF]

BMC Med Inform Decis Mak
Endebu T, Taye G, Deressa W.
europepmc   +1 more source

Exploring the Recurrence Risk Factors and Development of a Nomogram Prediction Model for Uterine Fibroid Patients Post-Myomectomy Based on Patient Medical Records. [PDF]

Int J Womens Health
Li C, Li L, Hu Y.
europepmc   +1 more source