Generating Clinical Queries from Patient Narratives: A Comparison between Machines and Humans

Bevan Koopman, Liam Cripwell, Guido Zuccon

Abstract: This paper investigates how automated query generation methods can be used to derive effective ad-hoc queries from verbose patient narratives. In a clinical setting, automatic query generation provides a means of retrieving information relevant to a clinician, based on a patient record, but without the need for the clinician to manually author a query. Given verbose patient narratives, we evaluated a number of query reduction methods, both generic and domain specific. Comparison was made against human generated queries, both in terms of retrieval effectiveness and characteristics of human queries. Query reduction was an effective means of generating ad-hoc queries from narratives. However, human generated queries were still significantly more effective than automatically generated queries. Further improvements were possible if parameters of the query reduction methods were set on a per-query basis and a means of predicting this was developed. Under ideal conditions, automated methods can exceed humans. Effective human queries were found to contain many novel keywords not found in the narrative. Automated reduction methods may be handicapped in that they only use terms from narrative. Future work, therefore, may be directed toward better understanding effective human queries and automated query rewriting methods that attempt to model the inference of novel terms by exploiting semantic inference processes.