Vishal Panchal


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Annotation of a Large Clinical Entity Corpus
Pinal Patel | Disha Davey | Vishal Panchal | Parth Pathak
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Having an entity annotated corpus of the clinical domain is one of the basic requirements for detection of clinical entities using machine learning (ML) approaches. Past researches have shown the superiority of statistical/ML approaches over the rule based approaches. But in order to take full advantage of the ML approaches, an accurately annotated corpus becomes an essential requirement. Though there are a few annotated corpora available either on a small data set, or covering a narrower domain (like cancer patients records, lab reports), annotation of a large data set representing the entire clinical domain has not been created yet. In this paper, we have described in detail the annotation guidelines, annotation process and our approaches in creating a CER (clinical entity recognition) corpus of 5,160 clinical documents from forty different clinical specialities. The clinical entities range across various types such as diseases, procedures, medications, medical devices and so on. We have classified them into eleven categories for annotation. Our annotation also reflects the relations among the group of entities that constitute larger concepts altogether.


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ezDI: A Supervised NLP System for Clinical Narrative Analysis
Parth Pathak | Pinal Patel | Vishal Panchal | Sagar Soni | Kinjal Dani | Amrish Patel | Narayan Choudhary
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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Annotating a Large Representative Corpus of Clinical Notes for Parts of Speech
Narayan Choudhary | Parth Pathak | Pinal Patel | Vishal Panchal
Proceedings of LAW VIII - The 8th Linguistic Annotation Workshop

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ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes
Parth Pathak | Pinal Patel | Vishal Panchal | Narayan Choudhary | Amrish Patel | Gautam Joshi
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)