Results 91 to 100 of about 1,307,990 (175)

A theoretical bayesian decision tree model for prioritizing RA-TKA under constrained resources robotic-assisted versus conventional total knee arthroplasty. [PDF]

J Robot Surg
Urresta FE, Peñaherrera-Carrillo C, Barros Castro A, Helito C.   +3 more
europepmc   +1 more source

Interpretable machine learning based decision tree model for predicting obstructive airway disease in a large non-smoking health screening population. [PDF]

Sci Rep
Chang CY, Shen HS, Kuo YL, Lee LN, Kao KC, Liu TC, Lu CJ.   +6 more
europepmc   +1 more source

Grafted Composite Decision Tree: Adaptive Online Fault Diagnosis with Automated Robot Measurements. [PDF]

Sensors (Basel)
Kim S, Do Y, Zhang F.
europepmc   +1 more source

The right fit: A decision tree to select a protocol for assessing the welfare of laying hens. [PDF]

Poult Sci
Hanif MF, Van Damme LGW, Vanden Hole C, Rodenburg TB, Bavinck V, Averós X, Estevez I, Agus A, Tuyttens FAM.   +8 more
europepmc   +1 more source

Cost-Effectiveness of Tailored vs. Empirical Therapy for H. pylori: A Decision-Tree Analysis. [PDF]

Helicobacter
Kim JS, Lee H, Jeong Y, Kim BW.
europepmc   +1 more source

From complex algorithms to clinical practice: a multicenter machine learning model and simplified decision tree for predicting cachexia risk in gastric cancer. [PDF]

Front Oncol
Zhao J, Deng Y, Guo Y, Wu Y, Yang X, Zeng T, Qiao Y, Zhao H, Song J, Hou B, Yang Q.   +10 more
europepmc   +1 more source

Response assessment in rectal cancer undergoing total neoadjuvant therapy: decision tree analysis integrating contrast enhanced T1-weighted MRI. [PDF]

Cancer Imaging
Chen AH, Shen EY, Deng CY, You JF, Chen TD, Chen CM, Lin G, Lai YC.   +7 more
europepmc   +1 more source

Predicting turnover intention among newly graduated nurses in South Korea: a decision tree analysis. [PDF]

BMC Nurs
Moon M, Kang H.
europepmc   +1 more source

Making multiple comparisons easy: a decision tree and visual statistics guide for data with more than two groups. [PDF]

Immunohorizons
Gurski CJ, Dittel BN.
europepmc   +1 more source

Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest. [PDF]

BMC Res Notes
Dehghani M, Ghobadi MZ, Mohammadi M, Dehkordy DT.   +3 more
europepmc   +1 more source