Prescriptions

Predicting Inpatient Medication Orders From Electronic Health Record Data.

In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine-learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most f...

Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence.

Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional medication adherence program (IMAP). Sociodemographic and clinical variables were retrieved from the Sw...

Achieving affective human-virtual agent communication by enabling virtual agents to imitate positive expressions.

Affective communication, communicating with emotion, during face-to-face communication is critical for social interaction. Advances in artificial inte...

The Effect of Resampling on Data-imbalanced Conditions for Prediction towards Nuclear Receptor Profiling Using Deep Learning.

In toxicity evaluation based on the nuclear receptor signalling pathway, in silico prediction tools are used for the detection of the early stages of ...

Robot-Assisted Radical Prostatectomy Associated with Decreased Persistent Postoperative Opioid Use.

Minimally invasive surgery offers reduced pain and opioid use postoperatively compared with open surgery, but large-scale comparative studies are lac...

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis...

Design and verification of a human-robot interaction system for upper limb exoskeleton rehabilitation.

This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human-robot interaction perfo...

Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network.

The advent of computational methods for efficient prediction of the druglikeness of small molecules and their ever-burgeoning applications in the fiel...

A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.

BACKGROUND: The key to modern drug discovery is to find, identify and prepare drug molecular targets. However, due to the influence of throughput, pre...

SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network.

Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-dise...

A Review on Applications of Computational Methods in Drug Screening and Design.

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the ...

Laser-Induced Graphene for Electrothermally Controlled, Mechanically Guided, 3D Assembly and Human-Soft Actuators Interaction.

Mechanically guided, 3D assembly has attracted broad interests, owing to its compatibility with planar fabrication techniques and applicability to a d...

SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction.

Complex network is a general model to represent the interactions within technological, social, information, and biological interaction. Often, the dir...

HNet-DNN: Inferring New Drug-Disease Associations with Deep Neural Network Based on Heterogeneous Network Features.

Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, refer...

Machine learning models for drug-target interactions: current knowledge and future directions.

Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of bind...

Visual information flow in Wilson-Cowan networks.

In this paper, we study the communication efficiency of a psychophysically tuned cascade of Wilson-Cowan and divisive normalization layers that simula...

Towards IoT-Aided Human-Robot Interaction Using NEP and ROS: A Platform-Independent, Accessible and Distributed Approach.

This article presents the novel Python, C# and JavaScript libraries of Node Primitives (NEP), a high-level, open, distributed, and component-based fra...

Early prediction of circulatory failure in the intensive care unit using machine learning.

Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to proce...

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM.

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in ...

Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population.

Hypertension is the leading risk factor of cardiovascular disease and has profound effects on both the structure and function of the microvasculature....