Medical-NER
Maintainer: blaze999
117
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The **deberta-med-ner-2** model is a fine-tuned version of the DeBERTa model on the PubMED Dataset. It is a Medical NER Model that has been fine-tuned on BERT to recognize 41 Medical entities. This model was created by Saketh Mattupalli, who has also developed other medical NER models like Medical-NER. While the bert-base-NER and bert-large-NER models are focused on general named entity recognition, this model is specialized for the medical domain.
Model inputs and outputs
Inputs
- Text: The model takes in natural language text as input, such as medical case reports or clinical notes.
Outputs
- Named Entities: The model outputs recognized medical named entities from the input text, including entities like diseases, medications, symptoms, etc.
Capabilities
The deberta-med-ner-2 model is capable of accurately identifying a wide range of medical named entities within text. This can be useful for tasks like extracting relevant information from medical records, monitoring patient conditions, or automating medical documentation processes.
What can I use it for?
This model could be used in a variety of healthcare and life sciences applications, such as:
- Automating the extraction of relevant medical information from clinical notes or case reports
- Enabling more robust medical text mining and analysis
- Improving the accuracy and efficiency of medical coding and billing workflows
- Supporting clinical decision support systems by providing structured data about patient conditions
Things to try
Some ideas to explore with this model include:
- Evaluating its performance on your specific medical text data or use case, to understand how it generalizes beyond the PubMED dataset
- Combining it with other NLP models or techniques to build more comprehensive medical language understanding systems
- Investigating ways to fine-tune or adapt the model further for your particular domain or requirements
By leveraging the specialized medical knowledge captured in this model, you may be able to unlock new opportunities to improve healthcare processes and deliver better patient outcomes.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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Medical-NER
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Medical-NER is a fine-tuned version of the DeBERTa model developed by the Clinical-AI-Apollo team. This model was trained on the PubMed dataset to recognize 41 medical entities, making it a specialized tool for natural language processing tasks in the healthcare and biomedical domains. Model inputs and outputs Inputs Text data, such as clinical notes, research papers, or other biomedical literature Outputs Identified named entities within the input text, categorized into 41 different medical classes, including diseases, symptoms, medications, and more. Capabilities The Medical-NER model excels at extracting relevant medical concepts and entities from unstructured text. This can be particularly useful for tasks like clinical information retrieval, adverse event monitoring, and knowledge extraction from large biomedical corpora. By leveraging the model's specialized training on medical data, users can achieve more accurate and reliable results compared to general-purpose NER models. What can I use it for? The Medical-NER model can be utilized in a variety of healthcare and biomedical applications. For example, it could be integrated into clinical decision support systems to automatically identify key medical information from patient records, or used to extract relevant entities from research literature to aid in systematic reviews and meta-analyses. The model's capabilities can also be valuable for pharmaceutical companies monitoring drug safety, or for public health organizations tracking disease outbreaks and trends. Things to try One interesting aspect of the Medical-NER model is its ability to recognize a wide range of specialized medical terminology. Users might experiment with feeding the model complex, domain-specific text, such as clinical trial protocols or grant proposals, to see how it performs at identifying relevant concepts and entities. Additionally, the model could be fine-tuned on more targeted datasets or combined with other NLP techniques, such as relation extraction, to unlock even more advanced biomedical text processing capabilities.
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biomedical-ner-all
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The biomedical-ner-all model is an English Named Entity Recognition (NER) model trained on the Maccrobat dataset to recognize 107 biomedical entities from text. It is built on top of the distilbert-base-uncased model. Compared to similar models like bert-base-NER, this model is specifically focused on identifying a wider range of biomedical concepts. Another related model is Bio_ClinicalBERT, which was pre-trained on clinical notes from the MIMIC III dataset. Model inputs and outputs Inputs Free-form text in English, such as clinical case reports or biomedical literature. Outputs A list of recognized biomedical entities, with the entity type, start and end position, and confidence score for each entity. Capabilities The biomedical-ner-all model can accurately identify a diverse set of 107 biomedical entities, including medical conditions, treatments, procedures, anatomical structures, and more. This makes it well-suited for tasks like extracting structured data from unstructured medical text, powering biomedical search and information retrieval, and supporting downstream applications in clinical decision support and biomedical research. What can I use it for? The biomedical-ner-all model could be leveraged in a variety of biomedical and healthcare applications. For example, it could be used to automatically annotate electronic health records or research papers, enabling better search, analysis, and knowledge discovery. It could also be integrated into clinical decision support systems to help identify key medical concepts. Additionally, the model's capabilities could be further fine-tuned or combined with other models to tackle more specialized tasks, such as adverse drug event detection or clinical trial eligibility screening. Things to try One interesting thing to try with the biomedical-ner-all model is to compare its performance to other biomedical NER models like bert-base-NER or Bio_ClinicalBERT on a range of biomedical text sources. This could help identify the model's strengths, weaknesses, and optimal use cases. Additionally, exploring ways to integrate the model's entity recognition capabilities into larger healthcare systems or biomedical research workflows could uncover valuable applications and lead to impactful real-world deployments.
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bert-large-NER
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bert-large-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a bert-large-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset. If you'd like to use a smaller BERT model fine-tuned on the same dataset, a bert-base-NER version is also available from the same maintainer, dslim. Model inputs and outputs Inputs A text sequence to analyze for named entities Outputs A list of recognized entities, their type (LOC, ORG, PER, MISC), and their position in the input text Capabilities bert-large-NER can accurately identify and classify named entities in English text, such as people, organizations, locations, and miscellaneous entities. It outperforms previous state-of-the-art models on the CoNLL-2003 NER benchmark. What can I use it for? You can use bert-large-NER for a variety of applications that involve named entity recognition, such as: Information extraction from text documents Knowledge base population by identifying key entities Chatbots and virtual assistants to understand user queries Content analysis and categorization The high performance of this model makes it a great starting point for building NER-based applications. Things to try One interesting thing to try with bert-large-NER is analyzing text from different domains beyond news articles, which was the primary focus of the CoNLL-2003 dataset. The model may perform differently on text from social media, scientific publications, or other genres. Experimenting with fine-tuning or ensembling the model for specialized domains could lead to further performance improvements.
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bert-base-NER
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The bert-base-NER model is a fine-tuned BERT model that is ready to use for Named Entity Recognition (NER) and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset. If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available. The maintainer, dslim, has also provided several other NER models including distilbert-NER, bert-large-NER, and both cased and uncased versions of bert-base-NER. Model inputs and outputs Inputs Text**: The model takes a text sequence as input and predicts the named entities within that text. Outputs Named entities**: The model outputs the recognized named entities, along with their type (LOC, ORG, PER, MISC) and the start/end position within the input text. Capabilities The bert-base-NER model is capable of accurately identifying a variety of named entities within text, including locations, organizations, persons, and miscellaneous entities. This can be useful for applications such as information extraction, content analysis, and knowledge graph construction. What can I use it for? The bert-base-NER model can be used for a variety of text processing tasks that involve identifying and extracting named entities. For example, you could use it to build a search engine that allows users to find information about specific people, organizations, or locations mentioned in a large corpus of text. You could also use it to automatically extract key entities from customer service logs or social media posts, which could be valuable for market research or customer sentiment analysis. Things to try One interesting thing to try with the bert-base-NER model is to experiment with incorporating it into a larger natural language processing pipeline. For example, you could use it to first identify the named entities in a piece of text, and then use a different model to classify the sentiment or topic of the text, focusing on the identified entities. This could lead to more accurate and nuanced text analysis. Another idea is to fine-tune the model further on a domain-specific dataset, which could help it perform better on specialized text. For instance, if you're working with legal documents, you could fine-tune the model on a corpus of legal text to improve its ability to recognize legal entities and terminology.
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