From data patterns to mechanistic models in acute critical illness

摘要: The complexity of the physiologic and inflammatory response in acute critical illness has stymied accurate diagnosis development therapies. Society for Complex Acute Illness was formed a decade ago with goal leveraging multiple complex systems approaches to address this unmet need. Two main paths have characterized society's approach: (i) data pattern analysis, either defining diagnostic/prognostic utility metrics signals or multivariate analyses molecular genetic (ii) mechanistic mathematical computational modeling, all being performed an explicit translational goal. Here, we summarize progress date on each these approaches, along pitfalls inherent use approach alone. We suggest that next holds potential merge connecting patient treatment via mechanism-based dynamical system modeling feedback control allowing extrapolation from biomarkers novel drug candidates. As predicate example, focus role data-driven models neuroscience impact merging can general anesthesia.