Easy Twitter API Data Collection By python : https://github.com/moasgh/BumbleBee/tree/master/DataCollections/Twitter
Named Entity Recognition : https://github.com/moasgh/BumbleBee/tree/master/NER
Linking social media, medical literature, and clinical notes using deep learning.
Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format in cludes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational pow er causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units more accessible. Deep learnin (TPUs), processing text and images is becoming g algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the healthcare industry and collect data not only from electronic m edical records (EMRs) but also medical literature and social media. We propose a framework for linking social media, medical literature, and EMRs clinical notes using deep learning algorithms. Connecting data sources requires defining a link between them, (N and our key is finding concepts in the medical text. The National Library LM ) introduces a Unified Medical Language of Medicine System (UMLS) and we use this system as the foundation of our own system. We recognize social media’s dynamic nature and appl y supervised and semisupervised methodologies to generate concepts. Named entity recognition (NER) allows efficient extraction of information, or entities, from medical literature, and we extend the model to process the EMRs’ clinical notes via transfer l endtoend, webearning. The results include an integrated, based system solution that unifies social media, literature, and clinical notes, and improves access to medical knowledge for the public and experts.
Trends on Health in Social Media: Analysis using Twitter Topic Modeling
There is a growing interest on social networks for topics related to Healthcare. In particular, on Twitter, millions of tweets related to healthcare can be found. These posts contain public opinions on health, and allow to understand how is the popular perception on topics such as medical diagnosis, medicines, facilities, and claims. In this paper we present an adaptive system designed using 5 layers. The system contains a combination of unsupervised and supervised algorithms to track the trends of health social media. As it is based on a word2vec model, it also captures the correlation of words based on the context, improving over time, enhancing the accuracy of predictions and tweet tracking. In this work we focused on United States data and use it to detect the trending topics of each state. These topics are followed including new social network contributions. The supervised algorithm implemented is a Convolutional Neural Network (CNN) in conjunction with the Word2Vect model to classify and label new tweets, assigning a feedback to the topic models. The results of this algorithm present an accuracy of 83.34%, precision of 83%, recall 84% and F-Score of 83.8% when evaluated. Our results are compared with two state of the art techniques demonstrating an advantage that can be leveraged for further improvements.
A topic modeling framework for spatio-temporal information management
Highlights • Propose a robust procedure to take a decision for selecting the best topic model. We design an adaptive framework to use gained knowledge for improving the result over time. For our case study we used four topic modeling techniques and report the result of the evaluation techniques.
• Propose a neural network using transfer learning techniques to enhance the framework ability to detect unrelated messages over data streams existing in twitter. We focus our attention in healthcare to present examples.
• Create automatic deep cleaning method to enhance the quality of data to perform better classification in outlier and topic detection.
BINER: A low-cost biomedical named entity recognition
A primary focus of the healthcare industry is to improve patient experience and quality of service. Practitioners and health workers are generating large volumes of text that are captured in Electronic Medical Records, clinical reports, and publications. Additionally, patients post millions of comments on social media related to healthcare, on diverse topics such as hospital services, disease symptoms, and drugs effects. Unifying various data sources can guide physicians and healthcare workers to avoid unnecessary, irrelevant information and expedite access to helpful information. The main challenge to creating Biomedical Natural Language Understanding is the lack of standard datasets and the extensive computational resources needed to develop different models. This paper proposes a model trained on low-tier GPU computers, producing comparable results to larger models like BioBERT. We propose BINER, a Biomedical Named Entity Recognition architecture using limited data and computational resources.
@article{asghari2021linking,
title={Linking social media, medical literature, and clinical notes using deep learning.},
author={Asghari, Mohsen},
year={2021}
}
@inproceedings{asghari2018trends,
title={Trends on Health in Social Media: Analysis using Twitter Topic Modeling},
author={Asghari, Mohsen and Sierra-Sosa, Daniel and Elmaghraby, Adel},
booktitle={2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)},
pages={558--563},
year={2018},
organization={IEEE}
}
@article{asghari2020topic,
title={A topic modeling framework for spatio-temporal information management},
author={Asghari, Mohsen and Sierra-Sosa, Daniel and Elmaghraby, Adel S},
journal={Information Processing \& Management},
pages={102340},
year={2020},
publisher={Elsevier}
}
@article{asghari2022biner,
title={BINER: A low-cost biomedical named entity recognition},
author={Asghari, Mohsen and Sierra-Sosa, Daniel and Elmaghraby, Adel S},
journal={Information Sciences},
volume={602},
pages={184--200},
year={2022},
publisher={Elsevier}
}
Named Entity Regnition Datasets : https://github.com/moasgh/BumbleBee/tree/master/NER/datasets
Named Entity Regonition Models : https://github.com/moasgh/BumbleBee/tree/master/NER