AIMC Topic: Big Data

Clear Filters Showing 21 to 30 of 659 articles

Optimized machine learning mechanism for big data healthcare system to predict disease risk factor.

Scientific reports
Heart disease is becoming more and more common in modern society because of factors like stress, inadequate diets, etc. Early identification of heart disease risk factors is essential as it allows for treatment plans that may reduce the risk of sever...

Interplay of Spontaneous Reporting and Longitudinal Healthcare Databases for Signal Management: Position Statement from the Real-World Evidence and Big Data Special Interest Group of the International Society of Pharmacovigilance.

Drug safety
Signal management, defined as the set of activities from signal detection to recommendations for action, is conducted using different data sources and leveraging data from spontaneous reporting databases (SRDs), which represent the cornerstone of pha...

The satisfaction of ecological environment in sports public services by artificial intelligence and big data.

Scientific reports
In order to gain a more accurate understanding and enhance the relationship between the fitness ecological environment and artificial intelligence (AI)-driven sports public services, this study combines a Convolutional Neural Network (CNN) approach b...

Metagenomics studies in aquaculture systems: Big data analysis, bioinformatics, machine learning and quantum computing.

Computational biology and chemistry
The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populatio...

optRF: Optimising random forest stability by determining the optimal number of trees.

BMC bioinformatics
Machine learning is frequently used to make decisions based on big data. Among these techniques, random forest is particularly prominent. Although random forest is known to have many advantages, one aspect that is often overseen is that it is a non-d...

Personalising Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and big Data: The PETRUSHKA Tool: Personnalisation du traitement antidépresseur de la dépression unipolaire associant choix individuels, risques et mégadonnées: l'outil PETRUSHKA.

Canadian journal of psychiatry. Revue canadienne de psychiatrie
OBJECTIVE: We summarize the key steps to develop and assess an innovative online, evidence-based tool that supports shared decision-making in routine care to personalize antidepressant treatment in adults with depression. This PETRUSHKA tool is part ...

Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

Journal of medical Internet research
BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fa...

Planning and layout of tourism and leisure facilities based on POI big data and machine learning.

PloS one
The spatial arrangement of tourism cities and the strategic placement of tourism and leisure facilities are pivotal to the development of smart tourism cities. The integration of Point of Interest (POI) data, enriched with location-specific insights,...

Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Sensors (Basel, Switzerland)
Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and patient discomfort. Non-invasive biomarkers represent a transformative alternative, providing diagno...