Health Policy

Confidence in uncertainty: Error cost and commitment in early speech hypotheses.

Interactions with artificial agents often lack immediacy because agents respond slower than their users expect. Automatic speech recognisers introduce this delay by analysing a user's utterance only after it has been completed. Early, uncertain hypotheses of incremental speech recognisers can enable artificial agents to respond more timely. However, these hypotheses may change significantly with e...

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning.

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the ...

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.

Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations...

A machine-learning framework for automatic reference-free quality assessment in MRI.

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be check...

Machine learning for energy cost modelling in wastewater treatment plants.

Understanding the energy cost structure of wastewater treatment plants is a relevant topic for plant managers due to the high energy costs and signifi...

Vasopressin antagonist-like effect of acetazolamide in a heart failure patient: a case report.

BACKGROUND: Hyponatraemia is easily corrected by treatment with an oral vasopressin antagonist, but these medications are costly and their use at outp...

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital.

BACKGROUND: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the sig...

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, mana...

Detection of genetic cardiac diseases by Ca transient profiles using machine learning methods.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have revolutionized cardiovascular research. Abnormalities in Ca transients hav...

Rough sets and Laplacian score based cost-sensitive feature selection.

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensiti...

Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care.

The role of sensing technologies, such as wearables, in delivering precision care is becoming widely acceptable. Given the very large quantities of se...

Reshaping Movement Distributions With Limit-Push Robotic Training.

High-cost situations need to be avoided. However, occasionally, cost may only be learned by experience. Here, we tested whether an artificially induce...

Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning.

Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent ye...

An intelligent algorithm for identification of optimum mix of demographic features for trust in medical centers in Iran.

Healthcare quality is affected by various factors including trust. Patients' trust to healthcare providers is one of the most important factors for tr...

Predicting Hospital Readmission via Cost-Sensitive Deep Learning.

With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment ...

Extracting Healthcare Quality Information from Unstructured Data.

Healthcare quality research is a fundamental task that involves assessing treatment patterns and measuring the associated patient outcomes to identify...

Supervised Learning Methods for Predicting Healthcare Costs: Systematic Literature Review and Empirical Evaluation.

An important informatics tool for controlling healthcare costs is accurately predicting the likely future healthcare costs of individuals. To address ...

Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections.

Historical maps are unique sources of retrospective geographical information. Recently, several map archives containing map series covering large spat...

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope.

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computat...

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems.

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at...