DOCTORAL ABSTRACT

A Framework for Enhancing the Query andMedical Record Representations for PatientSearch
Nut Limsopatham
School of Computing Science, University of Glasgow, Glasgow, UK
nut.limsopatham@glasgow.ac.uk
Abstract
This thesis focuses on enhancing the search of electronic medical records (EMRs), withthe aim of identifying patients with medical histories relevant to the medical conditions statedin a text query. During retrieval, a healthcare practitioner indicates a number of inclusion
criteria describing the medical conditions of the patients of interest. However, finding patientswith particular medical conditions is challenging, due to the implicit knowledge inherent
within the patients’ medical records and queries - such knowledge may be known by medical
practitioners, but may be hidden from an information retrieval (IR) system. For instance,
the mention of a treatment such as a drug may indicate to a practitioner that a particulardiagnosis has been made for the patient, but this diagnosis may not be explicitly mentioned
in the patient’s medical records. Moreover, the use of negated language (e.g. ‘without’, ‘no’)to describe a medical condition of a patient (e.g. the patient has no fever) may cause asearch system to erroneously retrieve that patient for a query when searching for patientswith that medical condition (e.g. find patients with fever). To attain effective retrieval
performance, we hypothesise that, in a patient search system, both the information needs
and patients’ histories should be represented based upon the medical decision process. In
particular, this thesis argues that since the medical decision process typically encompasses
four aspects (symptom, diagnostic test, diagnosis and treatment), a patient search systemshould take into account these aspects and apply inferences to recover the possible implicit
knowledge. We postulate that considering these aspects and their derived implicit knowledge
at three different levels of the retrieval process (namely, sentence, medical record and interrecord levels) enhances the retrieval performance. Indeed, we propose a novel frameworkthat can gain insights from EMRs and queries, by modelling and reasoning upon information
during retrieval in terms of the four aforementioned aspects at the three levels of the retrieval
process, and can use these insights to enhance patient search.
Firstly, at the sentence level, we extract the medical conditions in the medical records
and queries. In particular, we propose to represent only the medical conditions related to thefour medical aspects in order to improve the accuracy of our search system. In addition, weidentify the context (negative/positive) of terms, which leads to an accurate representationof the medical conditions both in the EMRs and queries. In particular, we aim to prevent
patients whose EMRs state the medical conditions in the contexts different from the query

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from being ranked highly. For example, preventing patients whose EMRs state “no historyof dementia” from being retrieved for a query searching for patients with dementia.
Secondly, at the medical record level, using external knowledge-based resources (e.g.
ontologies and health-related websites), we leverage the relationships between medical terms
to infer the wider medical history of the patient in terms of the four medical aspects. In
particular, we estimate the relevance of a patient to the query by exploiting association rulesthat we extract from the semantic relationships between medical terms using the four aspectsof the medical process. For example, patients with a medical history involving a CABG
surgery (treatment) can be inferred as relevant to a query searching for a patient sufferingfrom heart disease (diagnosis), since a CABG surgery is a treatment of heart disease.
Thirdly, at the inter-record level, we enhance the retrieval of patients in two different
manners. First, we exploit knowledge about how the four medical aspects are handled bydifferent hospital departments to gain a better understanding about the appropriateness ofEMRs created by different departments for a given query. We propose to aggregate EMRs at
the department level (i.e. inter-record level) to extract implicit knowledge (i.e. the expertiseof each department) and model this department’s expertise, while ranking patients. For
instance, patients having EMRs from the cardiology department are likely to be relevant toa query searching for patients who suffered from a heart attack. Second, as a medical query
typically contains several medical conditions that the relevant patients should satisfy, wepropose to explicitly model the relevance towards multiple query medical conditions in theEMRs related to a particular patient during retrieval. In particular, we rank highly thosepatients that match all the stated medical conditions in the query by adapting coverage-based
diversification approaches originally proposed for the web search domain.
Finally, we examine the combination of our aforementioned approaches that exploit theimplicit knowledge at the three levels of the retrieval process to further improve the retrieval
performance by adapting techniques from the fields of data fusion and machine learning. In
particular, data fusion techniques, such as CombSUM and CombMNZ, are used to combine
the relevance scores computed by the different approaches of the proposed framework. On
the other hand, we deploy state-of-the-art learning to rank approaches (e.g. LambdaMARTand AdaRank) to learn from a set of training data an effective combination of the relevance
scores computed by the approaches of the framework. In addition, we introduce a novel
selective ranking approach that uses a classifier to effectively apply one of the approaches ofthe framework on a per-query basis.
This thesis draws insights from a thorough evaluation and analysis of the proposed framework using a standard test collection provided by the TREC Medical Records track. The experimental results show the effectiveness of the framework. In particular, the results demonstrate the importance of dealing with the implicit knowledge in patient search by focusing
on the medical decision criteria aspects at the three levels of the retrieval process.
Supervisors: Iadh Ounis and Craig Macdonald
Available: http://theses.gla.ac.uk/5983/1/2014LimsopathamPhD.pdf

ACM SIGIR Forum69

Vol. 49 No. 1 June 2015