Project Background:
Healthcare data analytics using federated learning centers on addressing the challenges of harnessing the vast potential of medical data while safeguarding patient privacy. Healthcare data, encompassing electronic health records, medical imaging, genomic information, and wearable sensor data, holds immense value for improving patient care, advancing medical research, and optimizing healthcare systems. The traditional data-sharing approaches often encounter barriers due to privacy regulations, security concerns, and data governance issues. Federated learning offers a promising solution by enabling collaborative model training across multiple decentralized data sources without the need for raw data exchange. This distributed learning paradigm allows healthcare institutions to leverage insights from their local datasets while preserving patient privacy and complying with regulatory requirements. This work uses federated learning techniques to develop robust predictive models, clinical decision support systems, and population health analytics tools to extract meaningful insights from distributed healthcare data sources. By establishing a secure and privacy-preserving framework for healthcare data analytics, this work endeavors to facilitate knowledge discovery, improve patient outcomes, and drive innovation in the healthcare domain.
Problem Statement
- Healthcare data analytics faces challenges due to privacy regulations and security concerns.
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Federated learning offers a solution by enabling collaborative model training across decentralized data sources.
- The problem lies in developing efficient federated learning algorithms tailored to healthcare settings.
- Ensuring patient privacy and compliance with regulatory requirements is crucial.
- Optimizing federated learning frameworks for heterogeneous, sparse, and imbalanced medical data is necessary.
- There is a need to investigate the efficacy of federated learning in predictive modeling for disease diagnosis, prognosis, and treatment recommendation.
- Handling longitudinal patient data, electronic health records, medical imaging, and genomic information poses additional challenges.
- Balancing the trade-off between model performance and privacy preservation is a key concern.
- The ultimate goal is to unlock insights from distributed healthcare data while upholding patient privacy and data security.
Aim and Objectives
- Enhance healthcare data analytics while preserving patient privacy through federated learning.
- Develop efficient federated learning algorithms tailored to healthcare settings.
- Ensure compliance with privacy regulations and security requirements.
- Optimize federated learning frameworks for heterogeneous medical data.
- Investigate the efficacy of federated learning in predictive modeling for disease diagnosis, prognosis, and treatment recommendation.
- Address challenges in handling longitudinal patient data, electronic health records, medical imaging, and genomic information.
- Balance model performance with privacy preservation.
Contributions to Healthcare Data Analytics using Federated Learning
- Preserving patient privacy while enabling collaborative model training across decentralized healthcare data sources.
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Developing efficient federated learning algorithms tailored to the unique challenges of healthcare data.
- Enhancing predictive modeling for disease diagnosis, prognosis, and treatment recommendation through federated learning.
- Optimizing federated learning frameworks for heterogeneous, sparse, and imbalanced medical data.
- Facilitating knowledge discovery and innovation in healthcare while complying with regulatory requirements.
- Balancing the trade-off between model performance and privacy preservation in healthcare data analytics.
Deep Learning Algorithms for Healthcare Data Analytics using Federated Learning
- Federated Learning with Differential Privacy (FLDP)
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Secure Aggregation for Federated Learning
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Federated Learning with Homomorphic Encryption
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Federated Transfer Learning
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Federated Meta-Learning
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Federated Reinforcement Learning
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Federated Generative Models
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Federated Bayesian Inference
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Federated Variational Inference
Datasets for Healthcare Data Analytics using Federated Learning
- MIMIC-III
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UK Biobank
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SEER (Surveillance, Epidemiology, and End Results)
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NHANES (National Health and Nutrition Examination Survey)
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PASCAL (Pattern Analysis, Statistical Modelling, and Computational Learning)
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PhysioNet
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TCGA (The Cancer Genome Atlas)
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ADNI (Alzheimers Disease Neuroimaging Initiative)
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MIDUS (Midlife in the United States)
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Framingham Heart Study
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
