  Non-negative tensor factorization has been shown a practical solution to
automatically discover phenotypes from the electronic health records (EHR) with
minimal human supervision. Such methods generally require an input tensor
describing the inter-modal interactions to be pre-established; however, the
correspondence between different modalities (e.g., correspondence between
medications and diagnoses) can often be missing in practice. Although heuristic
methods can be applied to estimate them, they inevitably introduce errors, and
leads to sub-optimal phenotype quality. This is particularly important for
patients with complex health conditions (e.g., in critical care) as multiple
diagnoses and medications are simultaneously present in the records. To
alleviate this problem and discover phenotypes from EHR with unobserved
inter-modal correspondence, we propose the collective hidden interaction tensor
factorization (cHITF) to infer the correspondence between multiple modalities
jointly with the phenotype discovery. We assume that the observed matrix for
each modality is marginalization of the unobserved inter-modal correspondence,
which are reconstructed by maximizing the likelihood of the observed matrices.
Extensive experiments conducted on the real-world MIMIC-III dataset demonstrate
that cHITF effectively infers clinically meaningful inter-modal correspondence,
discovers phenotypes that are more clinically relevant and diverse, and
achieves better predictive performance compared with a number of
state-of-the-art computational phenotyping models.
