AIMC Topic: Humans

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A review of deep learning applications in human genomics using next-generation sequencing data.

Human genomics
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, arti...

A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID-19 patients.

BMC medical informatics and decision making
BACKGROUND: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep l...

Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study.

BMC gastroenterology
BACKGROUND AND AIMS: Chronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is ...

A miRNA Target Prediction Model Based on Distributed Representation Learning and Deep Learning.

Computational and mathematical methods in medicine
MicroRNAs (miRNAs) are a kind of noncoding RNA, which plays an essential role in gene regulation by binding to messenger RNAs (mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulat...

Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length.

Medical image analysis
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the a...

Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets.

Pharmaceutical medicine
BACKGROUND: The US Food and Drug Administration (FDA) collects and retains several data sets on post-market drugs and associated adverse events (AEs). The FDA Adverse Event Reporting System (FAERS) contains millions of AE reports submitted by the pub...

BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images.

Sensors (Basel, Switzerland)
Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current pract...

Predictors of accurate intrapedicular screw placement in single-level lumbar (L4-5) fusion: robot-assisted pedicle screw, traditional pedicle screw, and cortical bone trajectory screw insertion.

BMC surgery
BACKGROUND: The superiorities in proximal facet joint protection of robot-assisted (RA) pedicle screw placement and screw implantation via the cortical bone trajectory (CBT) have rarely been compared. Moreover, findings on the screw accuracy of both ...

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain.

NeuroImage
Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Re...

Development of a machine learning-based risk prediction model for cerebral infarction and comparison with nomogram model.

Journal of affective disorders
BACKGROUND: Development of a cerebral infarction (CI) risk prediction model by mining routine test big data with machine learning algorithms.