Depression is the major cause of years lived in disability world-wide; however, its diagnosis and tracking methods still rely mainly on assessing self-reported depressive symptoms, methods that originated more than fifty years ago. These methods, …
Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to …
Clinical trials serve an important role in oncology, not only advancing medical science but also offering patients promising therapy before it is widely available. Memorial Sloan Kettering Cancer Center (MSK) conducts over 500 therapeutic trials at …
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record. The method does not rely on the availability of gold-standard labels, but rather …
Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. …
Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patientâs electronic medical record phenotype …
This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co-occurring medical conditions, also referred to as comorbidities, using clinical notes from electronic health records (EHRs). A latent factor estimation …
We show how to learn low-dimensional representations (embeddings) of a wide range of concepts in medicine, including diseases (e.g., ICD9 codes), medications, procedures, and laboratory tests. We expect that these embeddings will be useful across …
Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in …
We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without …