Track Categories

The track category is the heading under which your abstract will be reviewed and later published in the conference printed matters if accepted. During the submission process, you will be asked to select one track category for your abstract.

Track 1 : Deep Learning for Medical Image Analysis

Exploring the use of deep learning algorithms, such as convolutional neural networks (CNNs), for automated analysis and interpretation of medical images.

Track 2 : Machine Learning in Electronic Health Records (EHR)

Exploring the application of machine learning algorithms to extract insights, identify patterns, and improve decision-making from large-scale electronic health record data.

Track 3 : Clinical Decision Support Systems using Machine Learning

Discussing the development and implementation of machine learning models to assist healthcare professionals in making accurate and timely clinical decisions.
 

Track 4 : Predictive Analytics for Disease Progression

Addressing the use of machine learning techniques to predict disease progression and patient outcomes based on clinical data, genetics, and other relevant factors.

Track 5 : Machine Learning for Personalized Medicine

Addressing the application of machine learning algorithms to analyze patient data and create personalized treatment plans based on individual characteristics, genomics, and medical history.
 

Track 6 : Explainable AI in Medicine

Discussing the interpretability and transparency of machine learning models in medicine to ensure trust, ethical considerations, and regulatory compliance.

 

Track 7 : Drug Discovery and Development using Machine Learning

Exploring the use of machine learning in drug discovery pipelines, virtual screening, and predicting drug-target interactions for accelerating the development of new medications.
 

Track 8 : Natural Language Processing (NLP) in Healthcare

Discussing the use of NLP techniques to extract information, classify medical documents, and enable semantic understanding for applications like clinical coding and adverse event detection.
 
 

Track 9 : Real-time Health Monitoring with Machine Learning

Exploring the integration of machine learning models with wearable devices and sensors for continuous monitoring of vital signs, early detection of anomalies, and personalized health feedback.
 
 
 

Track 10 : Transfer Learning in Medical Data Analysis

Addressing the utilization of transfer learning techniques to leverage pre-trained models and limited annotated medical datasets for various medical tasks, such as classification and segmentation.
 

Track 11 : Machine Learning for Cancer Diagnosis and Prognosis

Discussing the advancements in machine learning algorithms for accurate and early detection of cancer, prognosis prediction, and treatment optimization.
 

Track 12 : Ethical Considerations in Medical Machine Learning

Examining the ethical challenges, bias mitigation, privacy concerns, and regulatory frameworks associated with the deployment of machine learning in healthcare.
 
 
 
 
 
 

Track 13 : Machine Learning in Remote Patient Monitoring

Exploring the use of machine learning algorithms for analyzing data from remote patient monitoring systems, identifying health trends, and enabling proactive interventions.
 
 

Track 14 : Machine Learning for Precision Radiology

Discussing the integration of machine learning models in radiology workflows to enhance image interpretation, automate detection of abnormalities, and improve diagnostic accuracy.
 

Track 15 : Federated Learning in Healthcare

 Addressing the use of federated learning approaches to train machine learning models across multiple healthcare institutions while preserving data privacy and security.