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Background
Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics.
Methods
We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features’ clinical relevance and technical feasibility.
Results
In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was “surgical skill and quality of performance” for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was “Instrument” (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were “intraoperative adverse events”, “action performed with instruments”, “vital sign monitoring”, and “difficulty of surgery”.
Conclusion
Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.
This white paper builds a new financial theory of euro area sovereign bond markets under stress. The theory explains the abnormal bond pricing and increasing spreads during the recent market turmoil. We find that the strong disconnect of bond spreads from the respective bonds’ underlying fundamental values in 2010 was triggered by an increase in asymmetric information and weak reputation of government policies. Both factors cause a normal bond market to switch into a crisis mode. Finally, those markets are prone to self-fulfilling bubbles in which the economic effects are amplified by herding behaviour arising from animal spirits. Altogether, this produces contagious effects and multiple equilibria. Thus, we argue that government bond markets in a monetary union are more fragile and vulnerable to liquidity and solvency crises. Consequently, the systemic mispricing of sovereign debt creates more macroeconomic instability and bubbles in the euro area than in a single country. In other words, financial markets are partly blind to national default risks in a currency union. Therefore, the current European institutional framework puts the wrong incentives in place and needs structural changes soon. To tackle the root causes we suggest more market incentives via consistent rules, pre-emptive austerity measures in good economic times, and a resolution scheme for heavily indebted countries. In summary, our paper enhances the bond market theory and provides new insights into the recent bond market turmoil in Europe.