Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context

Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements

Purpose

Given the information overload of scientific literature, there is an increasing need for computable biomedical knowledge buried in free text. This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements.

Design/methodology/approach

Taking cardiovascular research publications in China as a sample, we extracted subject–predicate–object triples (SPO triples) as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context. We introduced information entropy (IE) as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs (SO pairs) levels.

Findings

The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples. After evaluating the uncertainty of biomedical knowledge with IE, we identified the Top 10 SO pairs with highest IE, which implied the epistemic status pluralism. Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research.

Research limitations

The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words. The number of sentences surrounding a given triple may also influence the value of IE.

Practical implications

Our approach identified major uncertain knowledge areas such as diagnostic biomarkers, genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China. These areas are suggested to be prioritized; new hypotheses need to be verified, while disputes, conflicts, and contradictions need to be settled.

Originality/value

We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.

The confirmation of scientific theories using Bayesian causal networks and citation sentiments

The confirmation of scientific theories is approached by combining Bayesian probabilistic methods, in particular Bayesian causal networks, and the analysis of citing sentences for highly cited papers. It is assumed that causes and their effects can be identified by linguistic methods from the citing sentences and that the cause-and-effect pairs can be equated with theories and their evidence. Further, it is proposed that citation context sentiments for “evidence” and “uncertainty” can be used to supply the required conditional probabilities for Bayesian analysis where data is drawn from citing sentences for highly cited papers from various fields. Hence, the approach combines citation and linguistic methods in a probabilistic framework and, given the small sample of papers, should be considered a feasibility study. Special attention is given to the case of nociception in medicine, and analogies are drawn with various episodes from the history of science such as the Watson and Crick discovery of the structure of DNA and other discoveries where a striking and improbable fit between theory and evidence leads to a sense of confirmation.

Peer Review

https://publons.com/publon/10.1162/qss_a_00189