AIMC Topic: Humans

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An improved multiply robust estimator for the average treatment effect.

BMC medical research methodology
BACKGROUND: In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). How...

Causal inference and observational data.

BMC medical research methodology
Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from obser...

Optimal view detection for ultrasound-guided supraclavicular block using deep learning approaches.

Scientific reports
Successful ultrasound-guided supraclavicular block (SCB) requires the understanding of sonoanatomy and identification of the optimal view. Segmentation using a convolutional neural network (CNN) is limited in clearly determining the optimal view. The...

Unsupervised machine learning method for indirect estimation of reference intervals for chronic kidney disease in the Puerto Rican population.

Scientific reports
Reference intervals (RIs) for clinical laboratory values are extremely important for diagnostics and treatment of patients. However, the determination of these ranges is costly and time-consuming. As a result, often different unverified RIs are used ...

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Nature communications
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value f...

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.

Nature communications
Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the mult...

Measurement scales of mental health related to climate change: a scoping review protocol using artificial intelligence.

BMJ open
INTRODUCTION: Human actions have influenced climate changes around the globe, causing extreme weather phenomena and impacting communities worldwide. Climate change has caused, directly or indirectly, health effects such as injury and physical injurie...

disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.

BMC bioinformatics
Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs ...

Ultra-fast deep-learned CNS tumour classification during surgery.

Nature
Central nervous system tumours represent one of the most lethal cancer types, particularly among children. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of r...

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke.

PloS one
Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of advantages over manual therapies, but can suffer from slacking behavior, where the user lets the robo...