AIMC Topic: Databases, Factual

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CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and pati...

An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.

BioMed research international
BACKGROUND: Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of...

Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs.

PloS one
BACKGROUND: Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investig...

Wavelet decomposition facilitates training on small datasets for medical image classification by deep learning.

Histochemistry and cell biology
The adoption of low-dose computed tomography (LDCT) as the standard of care for lung cancer screening results in decreased mortality rates in high-risk population while increasing false-positive rate. Convolutional neural networks provide an ideal op...

Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria.

International journal of molecular sciences
ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. It includes genetic, biochemical, histopathologica...

AI Accelerated Human-in-the-loop Structuring of Radiology Reports.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Rule-based Natural Language Processing (NLP) pipelines depend on robust domain knowledge. Given the long tail of important terminology in radiology reports, it is not uncommon for standard approaches to miss items critical for understanding the image...

Neural Multi-Task Learning for Adverse Drug Reaction Extraction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A reliable and searchable knowledge database of adverse drug reactions (ADRs) is highly important and valuable for improving patient safety at the point of care. In this paper, we proposed a neural multi-task learning system, NeuroADR, to extract ADR...

Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems i...

A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effects of high absorbed doses are relatively well known, the effects of low absorbed doses are still deb...

Knowledge Extraction of Cohort Characteristics in Research Publications.

AMIA ... Annual Symposium proceedings. AMIA Symposium
When healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the patients they see. We have pr...