AIMC Topic: Machine Learning

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Data-level Linkage of Multiple Surveys for Improved Understanding of Global Health Challenges.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Data-driven approaches can provide more enhanced insights for domain experts in addressing critical global health challenges, such as newborn and child health, using surveys (e.g., Demographic Health Survey). Though there are multiple surveys on the ...

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

PloS one
We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI Ar...

Pebrine diagnosis using quantitative phase imaging and machine learning.

Journal of biophotonics
Pebrine is the most dreaded infectious disease of the silkworm and has devastated sericulture in Europe during the 18th century. Thereafter, if it is detected, the crop is burned to prevent further dissemination. The conventional microscopic examinat...

Fast Label-Free Nanoscale Composition Mapping of Eukaryotic Cells Via Scanning Dielectric Force Volume Microscopy and Machine Learning.

Small methods
Mapping the biochemical composition of eukaryotic cells without the use of exogenous labels is a long-sought objective in cell biology. Recently, it has been shown that composition maps on dry single bacterial cells with nanoscale spatial resolution ...

Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.

BMC urology
BACKGROUND: Machine learning has many attractive theoretic properties, specifically, the ability to handle non predefined relations. Additionally, studies have validated the clinical utility of mpMRI for the detection and localization of CSPCa (Gleas...

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
During the last decade, computer vision and machine learning have revolutionized the world in every way possible. Deep Learning is a sub field of machine learning that has shown remarkable results in every field especially biomedical field due to its...

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold o...

Breast cancer risk prediction in African women using Random Forest Classifier.

Cancer treatment and research communications
INTRODUCTION: One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage ...

Learning to recognize while learning to speak: Self-supervision and developing a speaking motor.

Neural networks : the official journal of the International Neural Network Society
Traditionally, learning speech synthesis and speech recognition were investigated as two separate tasks. This separation hinders incremental development for concurrent synthesis and recognition, where partially-learned synthesis and partially-learned...

Systematic review of machine learning models for personalised dosing of heparin.

British journal of clinical pharmacology
AIM: To identify and critically appraise studies of prediction models, developed using machine learning (ML) methods, for determining the optimal dosing of unfractionated heparin (UFH).