Identification of promising research directions using machine learning aided medical literature analysis

TL;DR

This paper proposes a machine learning-based methodology to analyze large medical literature corpora, enabling the extraction of meaningful information and tracking of temporal research trends to identify promising future directions.

Contribution

It introduces a novel machine learning approach for analyzing extensive medical research literature and tracking temporal changes to identify promising research directions.

Findings

Effective extraction of meaningful information from large corpora

Capability to track complex temporal changes in research trends

Potential to assist researchers in identifying promising directions

Abstract

The rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora, and of tracking complex temporal changes within it.