AIMC Topic: Humans

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Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor.

International journal of environmental research and public health
Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market...

Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments.

Sensors (Basel, Switzerland)
GOAL: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model.

Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification.

Scientific reports
In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where ...

Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing.

Genome biology
We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore's 1D and related sequencing protocols. Our software PoreOver ( https://github.com/jordisr/poreover ) finds the consensus of two neural networks...

MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors.

Genome biology
Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present Methyla...

DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects.

BioMed research international
PURPOSE: A recurrent neural network (RNN) and its variants such as gated recurrent unit-based RNN (GRU-RNN) were found to be very suitable for dose-volume histogram (DVH) prediction in our previously published work. Using the dosimetric information g...

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

PloS one
INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important f...

Customer Centricity in Medical Affairs Needs Human-centric Artificial Intelligence.

Pharmaceutical medicine
The evolution of healthcare, together with the changing behaviour of healthcare professionals, means that medical affairs functions of pharmaceutical organisations are constantly reinventing themselves. The emergence of digital ways of working, exped...

Development and validation of a deep-learning-based pediatric early warning system: A single-center study.

Biomedical journal
BACKGROUND: Early detection and prompt intervention for clinically deteriorating events are needed to improve clinical outcomes. There have been several attempts at this, including the introduction of rapid response teams (RRTs) with early warning sc...

Tacrolimus Exposure Prediction Using Machine Learning.

Clinical pharmacology and therapeutics
The aim of this work is to estimate the area-under the blood concentration curve of tacrolimus (TAC) following b.i.d. or q.d. dosing in organ transplant patients, using Xgboost machine learning (ML) models. A total of 4,997 and 1,452 TAC interdose ar...