Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.

Complex dynamical systems often exhibit formation of a pattern in observed variables in the steady state. An important special case is when the system consists of multiple subsystems (or "agents") subjected to local interactions to reach consensus or an arbitrary pattern specified by their relative positioning in the state space. This paper formulates a general pattern formation problem as the design of a feedback controller such that selected outputs of a linear plant exponentially converge to Re Λt ρo for some vector ρo, with prescribed matrices R and Λ. We show that the problem reduces equivalently to an eigenstructure assignment problem, and provide a necessary and sufficient condition for existence of a feasible controller as well as a parameterization of all such controllers. This general theory is further specialized to give a complete solution to a heterogeneous multi-agent synchronization problem. Two numerical examples are provided to demonstrate the efficacy of the proposed design method: one illustrates the importance of adaptive pattern formation through sensory feedback and another suggests an extension for achieving stable limit cycles by additional nonlinearities.

This article introduces a systematic method for designing a distributed nonlinear controller to achieve multiple distinct gaits, each of which is characterized by a prescribed oscillation profile and velocity, for a class of locomotion systems. We base the controller on the central pattern generator (CPG), a neural circuit which governs repetitive motions, such as walking and swimming, in most animals. First, we establish a general method for designing a nonlinear CPG-inspired controller to assign a single gait for a linear plant; we show that this problem reduces to an eigenstructure assignment problem for which a solution has recently become available. We then extend the design to an adaptive, structured controller that can adjust the gait in response to variations in the environment. The essential problem becomes a controller design to satisfy different eigenstructure conditions for different plants; a computationally tractable formulation is provided for this problem. We provide two numerical examples using a link-chain model as a plant representative of animals that move through undulatory motions, such as leeches or eels, to demonstrate the efficacy of this theory. In the first example, we employ an analytical condition for eigenstructure assignment to design an unstructured controller that assigns a single gait for the link-chain model. The second example searches for a structured controller to assign two different gaits that can be switched using a higher level command.

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