JMIR Publications

ABSTRACT

Background:

The quality of healthcare is continuously improving and is expected to improve further due to advancement of machine learning and knowledge-based techniques along with innovation and availability of wearable sensors. With these advancements, healthcare professionals are now becoming more interested and involved in order to seek scientific research evidence from external sources for decision-making relevant to medical diagnosis, treatments, and prognosis. Not much work has been done to develop methods for unobtrusive and seamless for curating data from the biomedical literature.

Objective:

We aim to design a framework, which can enable bringing quality publications intelligently to the users’ desk to assist practitioners in answering clinical questions and fulfilling their informational needs

Methods:

The proposed framework consists of methods for efficient biomedical literature curation, including the automatic construction of a well-built question, the recognition of evidence quality by proposing E-QRM (extended quality recognition model), and the ranking and summarization of the extracted evidences.

Results:

Unlike previous works, the proposed framework systematically integrates the echelons of biomedical literature curation by including methods for searching queries, content quality assessments, and ranking & summarization. Using ensemble approach, the performance of our high impact classifier E-QRM obtained significantly improved accuracy than the existing QRM model (90.97% vs 77.21%).

Conclusions:

Our proposed methods and evaluation demonstrate the validity and rigorousness of the results, which can be used in different applications, including evidence-based medicine, precision medicine, and medical education.