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medical-dataset

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Arjun-08 / Sequence-to-sequence-networks-for-multi-text-document-summarization

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This repository explores the use of advanced sequence-to-sequence networks and transformer models, such as BERT, BART, PEGASUS, and T5, for summarizing multi-text documents in the medical domain. It leverages extensive datasets like CORD-19 and a Biomedical Abstracts dataset from Hugging Face to fine-tune these models.

python nlp bart summarization seq2seq pegasus rouge-metric bert fine-tuning medical-dataset t5
  • Updated May 17, 2024
  • Jupyter Notebook

arjunprakash027 / Fake-Heart-Sensor-Data-Using-Python-and-Kafka

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Fake-Heart-Sensor-Data-Using-Python-and-Kafka is a GitHub project that provides a simple and easy-to-use way to generate simulated heart sensor data using Python and Kafka. This project is ideal for developers who want to test their applications with realistic heart sensor data or simulate a data stream for research purposes.

python kafka datastream kafka-python heartrate-analysis medical-dataset heart-rate-data fake-data-generator
  • Updated Mar 23, 2023
  • Python

amadou-6e / pymimic3

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Pymimic3 is a scalable experimentation platform for MIMIC-III, featuring ready-to-run models, fully tested utilities for concept drift research, and a parallelized, configurable data pipeline.

machine-learning neural-networks data-preprocessing mimic-iii parallel-processing concept-drift machine-learning-models medical-data machine-learning-projects medical-dataset neural-networks-and-deep-learning clinical-informatics medical-datasets healthcare-ai data-preprocessing-pipelines medical-data-analysis concept-drift-detection data-preprocessing-and-augmentation
  • Updated Oct 30, 2024
  • Jupyter Notebook

NEHAKIRANNAYAK / Cancer-Classification

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This repository focuses on classifying cancer types using ML algorithms. It includes data preprocessing, feature selection, model training, and evaluation using Logistic Regression, Decision Trees, Random Forest, SVC, KNN, and Naive Bayes. Explainable AI techniques like SHAP and LIME are implemented for transparency.

machine-learning cancer healthcare data-analytics xai medical-dataset xai-analytics
  • Updated Feb 25, 2025
  • Jupyter Notebook

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