Summary
Objectives:
The objective of this study is to advocate a methodology for medical research that,
in contrast to traditional medical methodology, exploits the flexibility of machine
learning and retains the kind of statistical tests that are generally accepted in
the medical field for the confirmation of hypotheses.
Methods:
First, the medical problem is defined and data for an observed population are collected;
then a machine learning tool is used to generate hypotheses regarding the problem;
finally, statistical methods are used to determine the validity of the generated hypotheses.
Results:
To illustrate this approach, the problem of defining indications for hip arthroplasty
after an acute medial femoral neck fracture is investigated as a case study.
Conclusions:
The methodology is similar to the usual style of applying machine learning, but insists
on a link to the techniques of statistical tests that are normally used in medicine.
It aims at a more flexible and economical use of experimental data than in the usual
medical research, which is enabled by techniques of machine learning. At the same
time, by reference to traditional statistical tests, it is hoped that this approach
will lead to improved acceptance of machine learning in the medical field.
Keywords
Research Methodology - Medical Data Analysis - Machine Learning - Regression Trees
- Hip Arthroplasty
