On the Blameworthiness of Forgetting

It is a mistake to think that we cannot be morally responsible for forgetting because, as a matter of principle, forgetting is outside of our control. Sometimes we do have control over our forgetting. When forgetting is under our control there is no question that it is the proper object of praise and blame. But we can also be morally responsible for forgetting something when it is beyond our control that we forget that thing. The literature contains three accounts of the blameworthiness of forgetting over which the agent has no control—the tracing account, the liberalized awareness condition, and attributionism. Even though these are competing accounts of the blameworthiness of harmful forgetting they are compatible with one another. In particular, it is possible to come up with a position that endorses the tracing account for certain kinds of harmful forgetting and attributionism for other kinds of harmful forgetting

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.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach