Prescriptions

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images.

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages w...

An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations.

We developed a method to apply artificial neural networks (ANNs) for predicting time-series pharmacokinetics (PKs), and an interpretable the ANN-PK model, which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). A previous population PK (PopPK) model of cyclosporin A was used as the comparison model. The patients' data were used for the ANN-PK model input, and...

Cell morphology-based machine learning models for human cell state classification.

Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively f...

Economic and utilization outcomes of medication management at a large Medicaid plan with disease management pharmacists using a novel artificial intelligence platform from 2018 to 2019: a retrospective observational study using regression methods.

Medication therapy management (MTM) and comprehensive medication management (CMM) have been practiced by clinical pharmacists as a predominantly manu...

5FU encapsulated polyglycerol sebacate nanoparticles as anti-cancer drug carriers.

The majority of anti-cancer drugs fail to reach clinical trials due to their low water solubility. A biocompatible drug delivery system that encapsula...

Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition.

As many as 40% to 50% of patients do not adhere to long-term medications for managing chronic conditions, such as diabetes or hypertension. Limited op...

Hidden Fluids in Plain Sight: Identifying Intravenous Medication Classes as Contributors to Intensive Care Unit Fluid Intake.

Fluid stewardship targets optimal fluid management to improve patient outcomes. Intravenous (IV) medications, flushes, and blood products, collective...

Machine learning directed drug formulation development.

Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the...

Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists.

The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive ass...

Rationality over fashion and hype in drug design.

The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or ina...

Extracting Adverse Drug Events from Clinical Notes.

Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we req...

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.

Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consis...

MultiPredGO: Deep Multi-Modal Protein Function Prediction by Amalgamating Protein Structure, Sequence, and Interaction Information.

Protein is an essential macro-nutrient for perceiving a wide range of biochemical activities and biological regulations in living cells. In this work,...

A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores.

We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johns...

Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions.

Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage...

Promises and trust in human-robot interaction.

Understanding human trust in machine partners has become imperative due to the widespread use of intelligent machines in a variety of applications and...

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation.

Background The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-...

A minimal model of the interaction of social and individual learning.

Learning is thought to be achieved by the selective, activity dependent, adjustment of synaptic connections. Individual learning can also be very hard...

Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.

Despite numerous advances in cancer radiotherapy, tumor radioresistance remain one of the major challenges limiting treatment efficacy of radiotherapy...

Computational medication regimen for Parkinson's disease using reinforcement learning.

Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning...