## 7 Discussions
### Important Research Topics Identified by STM  
Within the 16 identified research topics, there were several critical topics worthy of being highlighted, all of which were frequently discussed among authors regarding the NLP-enhanced clinical trial research.  
First of all, _Interoperability_ accounted for the largest proportion (13.21%), and it had also received increasing interest and attention from the research community, indicating its important role in the research area. Currently, there were generally several major problems of medical documentation, including a lack of transparency, an overwhelming amount of medical contents to be documented, and missing interoperability [98]. As indicated by [PERSON] et al. [99], semantic interoperability was important while conducting post-genomic clinical trials in which different affiliations cooperate. There were a great number of scholars attempting to solve such a challenge. For example, [PERSON] et al. [100] proposed a standards-driven method to guarantee semantic integration to facilitate clinico-genomic clinical trials analysis, by developing semantic interoperability based on various well-defined biomedical standards. [PERSON] et al. [101] adopted a service-centered architecture paradigm to facilitate technical interoperability.  
The second important topic was _Information extraction_, accounting for a proportion of 9.36%. The reuse of EHRs can facilitate scientific research and patient recruitment for clinical trials.  
Unfortunately, much information is hidden in unstructured texts such as discharge letters. Thus, it is required to be processed and transformed into structured data through the use of information extraction, which can be a time-consuming and labor-intensive task [102]. There were scholars working on such issues to facilitate clinical trial research. For example, [PERSON] et al. [103] proposed an NLP system with the use of dictionaries, rules, and patterns to extract measures of heart function recorded in echocardiogram reports. Further, by using a Curated semantic bootstrapping, a novel disambiguation approach based on semantic constraints, and a scalable framework, they enabled the proposed system to process large-scale clinical data. [PERSON] et al. [104] investigated the feasibility of information extraction from tables that contained essential information such as characteristics of clinical trials within academic literature. They proposed a rule-driven approach to decompose tables into cell-level structures and to further extract information from them.  
In addition, _Eligibility criteria_ were also an essential issue in the NLP-enhanced clinical trial research. An essential step for discovering novel treatments for diseases is to match subjects with potential clinical trials. Eligibility criteria for clinical trials are inclusion and exclusion criteria for clinical trials and are written in the format of free text. Standardization of the eligibility criteria is able to improve both the efficiency and accuracy of the recruitment process [105]. As indicated by [PERSON] et al. [106], the automated interpretation and the evaluation of patients' eligibility were challenging as the eligibility criteria of clinical trials is typically expressed as free text. There were many scholars trying to seek effective ways to facilitate the process.