Predicting patient's long-term clinical status after hip arthroplasty using hierarchical decision modelling and data mining.
Construction of a prognostic model is presented for the long-term outcome after femoral neck fracture treatment with implantation of hip endoprosthesis. While the model is induced from the follow-up data, we show that the use of additional expert knowledge is absolutely crucial to obtain good predictive accuracy. A schema is proposed where domain knowledge is encoded as a hierarchical decision model of which only a part is induced from the data while the rest is specified by the expert. Although applied to hip endoprosthesis domain, the proposed schema is general and can be used for the construction of other prognostic models where both follow-up data and human expertise is available.
Zupan
B
B
Faculty of Computer and Information Sciences, University of Ljubljana, Slovenia. blaz.zupan@fri.uni-lj.si
Demsar
J
J
Smrke
D
D
Bozikov
K
K
Stankovski
V
V
Bratko
I
I
Beck
J R
JR
D000368:Aged; D000465:Algorithms; D019644:Arthroplasty, Replacement, Hip / Q000534:rehabilitation*; D003661:Decision Support Techniques; D005265:Femoral Neck Fractures / Q000175:diagnosis* / Q000534:rehabilitation / Q000601:surgery; D006801:Humans; D015233:Models, Statistical; D011379:Prognosis