Search Results for author: Siddhartha R. Jonnalagadda

Found 7 papers, 1 papers with code

CRTS: A type system for representing clinical recommendations

no code implementations6 Sep 2016 Ravi P Garg, Kalpana Raja, Siddhartha R. Jonnalagadda

Conclusion: We showed that our proposed type system is precise and comprehensive in representing a large sample of recommendations available for various disorders.

Clinical Knowledge Retrieval +1

An Information Extraction Approach to Prescreen Heart Failure Patients for Clinical Trials

no code implementations6 Sep 2016 Abhishek Kalyan Adupa, Ravi Prakash Garg, Jessica Corona-Cox, Sanjiv. J. Shah, Siddhartha R. Jonnalagadda

To reduce the large amount of time spent screening, identifying, and recruiting patients into clinical trials, we need prescreening systems that are able to automate the data extraction and decision-making tasks that are typically relegated to clinical research study coordinators.

Decision Making

A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision

no code implementations6 Sep 2016 Kalpana Raja, Andrew J Sauer, Ravi P Garg, Melanie R Klerer, Siddhartha R. Jonnalagadda

This is significantly high when compared to a query-expansion based baseline (F-score of 1. 3% on HF and 2. 2% on AFib) and a system that uses query expansion with disease hyponyms and journal names, concept-based screening, and term-based vector similarity system (F-score of 37. 5% on HF and 39. 5% on AFib).

Clinical Knowledge Information Retrieval +1

A Novel Framework to Expedite Systematic Reviews by Automatically Building Information Extraction Training Corpora

3 code implementations21 Jun 2016 Tanmay Basu, Shraman Kumar, Abhishek Kalyan, Priyanka Jayaswal, Pawan Goyal, Stephen Pettifer, Siddhartha R. Jonnalagadda

Initially, it uses information contained in existing systematic reviews to identify the sentences from the PDF files of the included references that contain specific data elements of interest using a modified Jaccard similarity measure.

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