Computational tools in the assistance of personalized healthcare

摘要: Abstract Process Systems Engineering has been many years in the forefront, advancing standards healthcare and beyond. Gradually, integrated methods that utilize both experimental and/or clinical data, as well silico tools are becoming popular among medical community. In have already demonstrated their great potential various sectors, assisting industry to produce experiments of significantly reduced cost allow thorough investigation system at hand. Similarly, biomedical systems, advancement current state art through development intelligent computational can lead personalized protocols. The first part this chapter serves a brief review commonly used healthcare, such big data analytics dynamic mathematical models. challenges characterizing availability patient variability, also discussed here. We present advantages limitations we suggest generic framework for design testing advanced platforms. PARametric Optimization Control (PAROC) presented here is based on high-fidelity, dynamic, models then validated using data. Such provide basis execution optimization control studies patient-specific treatment final dedicated application PAROC three different examples, namely: (i) acute myeloid leukemia, (ii) anesthesia process, (iii) diabetes mellitus. each case relevant steps demonstrated.