AI Medical Compendium Topic

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Models, Theoretical

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Coupled liquid crystalline oscillators in Huygens' synchrony.

Nature materials
In the flourishing field of soft robotics, strategies to embody communication and collective motion are scarce. Here we report the synchronized oscillations of thin plastic actuators by an approach reminiscent of the synchronized motion of pendula an...

Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we ap...

Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments.

PloS one
We describe a novel approach to estimating the predictability of utterances given extralinguistic context in psycholinguistic research. Predictability effects on language production and comprehension are widely attested, but so far predictability has...

Multifidelity computing for coupling full and reduced order models.

PloS one
Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a mult...

Forecasting influenza activity using machine-learned mobility map.

Nature communications
Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials a...

A pre-training and self-training approach for biomedical named entity recognition.

PloS one
Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training dat...

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

Scientific reports
In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise li...

Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference.

PloS one
In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can r...

Machine learning predictive model for severe COVID-19.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji...

Machine learning for buildings' characterization and power-law recovery of urban metrics.

PloS one
In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us ...