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Modern healthcare is ripe for disruption by AI. A game changer would beautomatic understanding the latent processes from electronic medical records,which are being collected for billions of people worldwide. However, thesehealthcare processes are complicated by the interaction between at least threedynamic components: the illness which involves multiple diseases, the carewhich involves multiple treatments, and the recording practice which is biasedand erroneous. Existing methods are inadequate in capturing the dynamicstructure of care. We propose Resset, an end-to-end recurrent model that readsmedical record and predicts future risk. The model adopts the algebraic view inthat discrete medical objects are embedded into continuous vectors lying in thesame space. We formulate the problem as modeling sequences of sets, a novelsetting that have rarely, if not, been addressed. Within Resset, the bag ofdiseases recorded at each clinic visit is modeled as function of sets. The samehold for the bag of treatments. The interaction between the disease bag and thetreatment bag at a visit is modeled in several, one of which as residual ofdiseases minus the treatments. Finally, the health trajectory, which is asequence of visits, is modeled using a recurrent neural network. We reportresults on over a hundred thousand hospital visits by patients suffered fromtwo costly chronic diseases -- diabetes and mental health. Resset showspromises in multiple predictive tasks such as readmission prediction,treatments recommendation and diseases progression.

Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records