TY  - GEN
N2  - The practical application of machine learning models in clinical medicine is limited by the substantial amount of time required to input the many variables that the models depend on. This study aimed to develop a good-performing prediction tool for outcomes after posterior cervical fusion procedures using a limited number of pre-operative variables.
DO  - 10.6083/bpxhc41648
DO  - doi
AB  - The practical application of machine learning models in clinical medicine is limited by the substantial amount of time required to input the many variables that the models depend on. This study aimed to develop a good-performing prediction tool for outcomes after posterior cervical fusion procedures using a limited number of pre-operative variables.
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
T1  - A machine learning model utilizing only six  variables can accurately predict outcomes  following posterior cervical fusion
DA  - 2023-08-25
AU  - Smith, Spencer
AU  - Yoo, Jung
L1  - https://digitalcollections.ohsu.edu/record/41648/files/ResearchWeek.2023.Smith.Spencer.pdf
PB  - Oregon Health and Science University
LA  - eng
PY  - 2023-08-25
ID  - 41648
L4  - https://digitalcollections.ohsu.edu/record/41648/files/ResearchWeek.2023.Smith.Spencer.pdf
KW  - Body Mass Index
KW  - Spinal Fusion
KW  - Spinal Diseases
KW  - Machine Learning
KW  - Ankylosis
KW  - Albumins
KW  - Humans
KW  - Artificial Intelligence
KW  - cervical fusion
KW  - posterior cervical fusion
KW  - spinal deformity
TI  - A machine learning model utilizing only six  variables can accurately predict outcomes  following posterior cervical fusion
Y1  - 2023-08-25
L2  - https://digitalcollections.ohsu.edu/record/41648/files/ResearchWeek.2023.Smith.Spencer.pdf
LK  - https://digitalcollections.ohsu.edu/record/41648/files/ResearchWeek.2023.Smith.Spencer.pdf
UR  - https://digitalcollections.ohsu.edu/record/41648/files/ResearchWeek.2023.Smith.Spencer.pdf
ER  -