Expanding the DD-SIMCA concept: A case study of precision medicine.

It is proposed to use DD-SIMCA method, and, particularly, Full Distancse (FD) as an analytical signal that characterizes each sample in the frame of a classification task. The approach is demonstrated using medical data. FD values help to assess the proximity of each patient to the target class of the control (healthy) subjects. Furthermore, the FD values are used as a response in the PLS model, which predicts the distance of the subject (object) to the target class after a certain treatment and, therefore, the probability of recovery for each person. This enables the application of the personalized medicine. The proposed approach can be used not only in medicine, but also in other fields, e.g., restoration work to preserve and restore cultural heritage sites.