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.
Rare diseases are very difficult to identify among large number of other possible diagnoses.
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).
Conclusion: We showed that our proposed type system is precise and comprehensive in representing a large sample of recommendations available for various disorders.
We analyzed the treatments of two disorders - Atrial Fibrillation and Congestive Heart Failure.
In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA).
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.