Preprint (JMIR Preprints): https://preprints.jmir.org/preprint/27017
Authors' Response to Peer-Review Reports: https://med.jmirx.org/2021/3/e31568/
Published Article: https://med.jmirx.org/2021/3/e27017/
This is a peer-review report submitted for the paper “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
Round 1 Review
This concise manuscript  reports an exploratory study that seeks to detect adverse events from the words within electronic health records. By conducting a computational linguistic analysis, the authors aimed to identify patterns of words that can be used to classify such events. The methodology is novel and has potential use cases that could benefit the automation and scalability of applications in the future.
I have some minor comments for the authors to consider:
- At the end of the Introduction section, it would benefit the reader if the authors could provide some justification for why the Shakespeare method might be useful, rather than simply stating “We hoped.”
- The methods are well described and the results are straightforward.
- In the Discussion section, there are some missing details that should be added. In particular, it would be useful for researchers seeking to follow up on this work to know what lessons were learned during the course of conducting this research. This could take the form of a short limitations paragraph, and importantly, some recommendations to guide future research. Relatedly, some additional details concerning how this work could inform real-world applications would also be welcome.
Conflicts of Interest
Edited by E Meinert; This is a non–peer-reviewed article. submitted 24.06.21; accepted 24.06.21; published 11.08.21Copyright
©Mark Antoniou. Originally published in JMIRx Med (https://med.jmirx.org), 11.08.2021.
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