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Found 7 papers, 1 papers with code
A Novel Framework to Expedite Systematic Reviews by Automatically Building Information Extraction Training Corpora
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.
A Semi-supervised learning approach to enhance health care Community-based Question Answering: A case study in alcoholism
In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA).
CRTS: A type system for representing clinical recommendations
Conclusion: We showed that our proposed type system is precise and comprehensive in representing a large sample of recommendations available for various disorders.
Automatically extracting, ranking and visually summarizing the treatments for a disease
We analyzed the treatments of two disorders - Atrial Fibrillation and Congestive Heart Failure.
An Information Extraction Approach to Prescreen Heart Failure Patients for Clinical Trials
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.
A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision
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).
A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records
Rare diseases are very difficult to identify among large number of other possible diagnoses.
