AIMC Topic: Data Accuracy

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Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses i...

Cheetah: A Computational Toolkit for Cybergenetic Control.

ACS synthetic biology
Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups ...

A primer on applying AI synergistically with domain expertise to oncology.

Biochimica et biophysica acta. Reviews on cancer
BACKGROUND: The concurrent growth of large-scale oncology data alongside the computational methods with which to analyze and model it has created a promising environment for revolutionizing cancer diagnosis, treatment, prevention, and drug discovery....

Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.

PloS one
Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presen...

KG2Vec: A node2vec-based vectorization model for knowledge graph.

PloS one
Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks,...

A novel method for clinical risk prediction with low-quality data.

Artificial intelligence in medicine
In real-world data, predictive models for clinical risks (such as adverse drug reactions, hospital readmission, and chronic disease onset) are constantly struggling with low-quality issues, namely redundant and highly correlated features, extreme cat...

Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To investigate the effect of data quality and quantity on the performance of deep learning (DL) models, for dose prediction of intensity-modulated radiotherapy (IMRT) of esophageal cancer.

Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.

Scientific reports
Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between path...

High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis.

Scientific reports
Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relations...

Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.

BMC medical informatics and decision making
BACKGROUND: Next-generation sequencing provides comprehensive information about individuals' genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in the clinical decision-making process has not ...