Mechanical properties of porous Ti-26NB alloy for regenerative medicine

Porous titanium-26at.%niobium (hereafter, Ti-26Nb) alloys with different porosities were prepared by space-holder sintering. The porous structure of the alloys was characterized by scanning electron microscopy (SEM). Mechanical properties of the porous alloys were investigated using compression test. Results indicate that the porous alloys with 60, 70 and 80% porosities exhibit interconnected porous structure with pore sizes of 100-300 µm. The porous structure has the potential to provide new bone tissue ingrowth ability. The mechanical properties of these porous alloys decrease with the increase of porosity. The mechanical properties of the porous Ti-26Nb alloys can be tailored to match those of human bone.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.