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

On the General Analytical Solution of the Kinematic Cosserat Equations

Based on a Lie symmetry analysis, we construct a closed form solution to the kinematic part of the (partial differential) Cosserat equations describing the mechanical behavior of elastic rods. The solution depends on two arbitrary analytical vector functions and is analytical everywhere except a certain domain of the independent variables in which one of the arbitrary vector functions satisfies a simple explicitly given algebraic relation. As our main theoretical result, in addition to the construction of the solution, we proof its generality. Based on this observation, a hybrid semi-analytical solver for highly viscous two-way coupled fluid-rod problems is developed which allows for the interactive high-fidelity simulations of flagellated microswimmers as a result of a substantial reduction of the numerical stiffness.Comment: 14 pages, 3 figure

Demand forecasting: a case study in the food industry

The use of forecasting methods is nowadays regarded as a business ally since it supports both the operational and the strategic decision-making processes. This paper is based on a research project aiming the development of demand forecasting models for a company (designated here by PR) that operates in the food business, more specifically in the delicatessen segment. In particular, we focused on demand forecasting models that can serve as a tool to support production planning and inventory management at the company. The analysis of the company’s operations led to the development of a new demand forecasting tool based on a combination of forecasts, which is now being used and tested by the company.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Development of an international standard set of outcome measures for patients with atrial fibrillation: a report of the International Consortium for Health Outcomes Measurement (ICHOM) atrial fibrillation working group.

AIMS: As health systems around the world increasingly look to measure and improve the value of care that they provide to patients, being able to measure the outcomes that matter most to patients is vital. To support the shift towards value-based health care in atrial fibrillation (AF), the International Consortium for Health Outcomes Measurement (ICHOM) assembled an international Working Group (WG) of 30 volunteers, including health professionals and patient representatives to develop a standardized minimum set of outcomes for benchmarking care delivery in clinical settings. METHODS AND RESULTS: Using an online-modified Delphi process, outcomes important to patients and health professionals were selected and categorized into (i) long-term consequences of disease outcomes, (ii) complications of treatment outcomes, and (iii) patient-reported outcomes. The WG identified demographic and clinical variables for use as case-mix risk adjusters. These included baseline demographics, comorbidities, cognitive function, date of diagnosis, disease duration, medications prescribed and AF procedures, as well as smoking, body mass index (BMI), alcohol intake, and physical activity. Where appropriate, and for ease of implementation, standardization of outcomes and case-mix variables was achieved using ICD codes. The standard set underwent an open review process in which over 80% of patients surveyed agreed with the outcomes captured by the standard set. CONCLUSION: Implementation of these consensus recommendations could help institutions to monitor, compare and improve the quality and delivery of chronic AF care. Their consistent definition and collection, using ICD codes where applicable, could also broaden the implementation of more patient-centric clinical outcomes research in AF

Heart Rhythm Monitoring Strategies for Cryptogenic Stroke: 2015 Diagnostics and Monitoring Stroke Focus Group Report.

No abstract available

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction

Transplantation of canine olfactory ensheathing cells producing chondroitinase ABC promotes chondroitin sulphate proteoglycan digestion and axonal sprouting following spinal cord injury

Olfactory ensheathing cell (OEC) transplantation is a promising strategy for treating spinal cord injury (SCI), as has been demonstrated in experimental SCI models and naturally occurring SCI in dogs. However, the presence of chondroitin sulphate proteoglycans within the extracellular matrix of the glial scar can inhibit efficient axonal repair and limit the therapeutic potential of OECs. Here we have used lentiviral vectors to genetically modify canine OECs to continuously deliver mammalian chondroitinase ABC at the lesion site in order to degrade the inhibitory chondroitin sulphate proteoglycans in a rodent model of spinal cord injury. We demonstrate that these chondroitinase producing canine OECs survived at 4 weeks following transplantation into the spinal cord lesion and effectively digested chondroitin sulphate proteoglycans at the site of injury. There was evidence of sprouting within the corticospinal tract rostral to the lesion and an increase in the number of corticospinal axons caudal to the lesion, suggestive of axonal regeneration. Our results indicate that delivery of the chondroitinase enzyme can be achieved with the genetically modified OECs to increase axon growth following SCI. The combination of these two promising approaches is a potential strategy for promoting neural regeneration following SCI in veterinary practice and human patients

Coherent control of three-spin states in a triple quantum dot

Spin qubits involving individual spins in single quantum dots or coupled spins in double quantum dots have emerged as potential building blocks for quantum information processing applications. It has been suggested that triple quantum dots may provide additional tools and functionalities. These include the encoding of information to either obtain protection from decoherence or to permit all-electrical operation, efficient spin busing across a quantum circuit, and to enable quantum error correction utilizing the three-spin Greenberger-Horn-Zeilinger quantum state. Towards these goals we demonstrate for the first time coherent manipulation between two interacting three-spin states. We employ the Landau-Zener-St\"uckelberg approach for creating and manipulating coherent superpositions of quantum states. We confirm that we are able to maintain coherence when decreasing the exchange coupling of one spin with another while simultaneously increasing its coupling with the third. Such control of pairwise exchange is a requirement of most spin qubit architectures but has not been previously demonstrated.Comment: 12 pages, 13 figures, and 2 table