Data-Driven Generation of Medical-Research Hypotheses in Cancer Patients

Hypotheses are the most important part of medical research. If we have a good hypothesis, we can design experiments and verify it. Therefore, we use the associations generated by association rule as hypotheses in clinical medicine research. We hope this method can help physicians quickly and correctly find research hypotheses. This experiment was divided into two parts. In the first part, we used the Apriori algorithm to find associations between cancer and other catastrophic illnesses. In the second part, we used these associations as medical-research hypotheses and designed cohort studies to verify them. In this study, we proved that the association-rules method could help clinical physicians quickly and correctly obtain clinical-medicine hypotheses.