Enriching queries with user preferences in healthcare

https://doi.org/10.1016/j.ipm.2014.03.004Get rights and content

Highlights

  • Enriching queries using user preferences increases Information Retrieval in healthcare domain.

  • Prioritizing user preferences improves service discovery.

  • Conditional preference network is employed to rank user preferences.

Abstract

Query enrichment is a process of dynamically enhancing a user query based on her preferences and context in order to provide a personalized answer. The central idea is that different users may find different services relevant due to different preferences and contexts. In this paper, we present a preference model that combines user preferences, user context, domain knowledge to enrich the initial user query. We use CP-nets to rank the preferences using implicit and explicit user preferences and domain knowledge. We present some algorithms for preferential matching. We have implemented the proposed model as a prototype. The initial results look promising.

Introduction

Technological advances have brought tremendous progress in healthcare. Examples are: electronic medical records, mobile health and eHealth, videoconferencing, medical decision making, remote monitoring and many more. Besides these, healthcare now can provide personalized health decisions for patients and empower patients to participate in the decision making. Recently, patient empowerment and engagement has gotten a big boost. Patient-centeredness helps patients and families and/or doctors to make informed healthcare decisions during diagnosis and treatment (Arnold, 2013).

However, these benefits of technology in healthcare are not equitably available for developing countries with their restricted infrastructure and limited resources. Illiteracy, little medical knowledge and poor or ambiguous healthcare queries are factors that hinder the realization of patient empowerment. Low infrastructure, unavailability of technologies, costs and shortage and high turnover of clinicians are some of the obstacles to realize patient-centeredness in particular and eHealth in general. These challenges and opportunities have triggered the work in this paper. Especially we have focused on the Ethiopian situation (see (Berhan, 2008, Serra et al., 2010 in general and Tegegne and Weide, 2011, Tegegne et al., 2010) for a more detailed description).

In this paper we focus on a basic architecture to support health workers. This architecture supports the health worker to make a diagnosis, and then tries to find the best treatment for the patient at hand adapted to the environment of this patient. The dialogue support principles have been discussed in Tegegne and Weide (2013) and are outside the scope of this paper. In this paper we focus on the enrichment of the initial (medical) query, before matching it against a body of medical knowledge and making a prioritization of the possible treatments.

Rather than setting up an advanced expert system to cover for any medical case, we use a basic Information Retrieval approach to match a diagnosis with patterns defined for typical diseases. Then we use personal and environmental information to rank the associated treatments. The diagnosis can be seen as the initial query, that is enriched by personal and environmental information.

Illiteracy and low level medical knowledge brings about poor query formulation. To this end, we incorporate personal profile and context to enrich the user initial query (which consists of signs and symptoms of the patient). This work also allows clinicians, specially community health workers (Health Extension Workers in Ethiopia context) with very limited training, to query diseases and treatments based on patients profile, symptoms and contexts.

The motive behind query enrichment is to enable low/semiliterate patients and clinicians (health workers) to create a medical query to search personalized healthcare information as well as to provide rich diagnosis and treatment options for both patients and clinicians. In general, enriching user queries using domain knowledge, personal profile and context can help to maximize cure and to minimize adverse effects, cost, and waiting time.

Queries may contain implicit preferences. This is for example the case in the query fragment Martha is 6th months pregnant. She is on HIV/AIDS regimen; currently she shows some signs of malaria. At this moment, the system should identify complications: antiretroviral may cause antimalarial resistance as well as antimalarial may affect pregnancy. Thus, while enriching the query, it is advisable to be cognizant of the impact of indirect preferences stated in the query. In the above query the woman might not have any knowledge about all the constraints resulting from her condition.

Other preferences may come from (if known) a user profile and a user context. The user profile contains interests and preferences, while the user context is considered as the actual state of the user current task. For the purpose of our paper, we see both user profile and user context as preferences for that user. In this paper we focus on handling user preferences in the context of healthcare application. Typically, the query (request) then is a consequence of a diagnosis obtained by the doctor or an automated diagnosis system. Consequently, we may assume that both user profile and user context are available upon entering the query (request).

The main purpose of this paper is to answer the following question: How to develop a general preference model for enriching queries in the context of healthcare application. This leads to the following sub-questions:

  • R-1

    What is a general preference model that can handle medical preferences (medical preferences in this context is patients treatment or medication preference)?

  • R-2

    How is preferential matching defined in this model?

The layout of this paper is as follows. Related work is presented in Section 2. In Section 3 we show the overall architecture of the proposed system. We shortly discuss the system components and discuss the overall quality of the system. The preference model is presented in Section 4. In Section 5 we discuss the preferential similarity model. Section 6 presents the application of preference prioritization in healthcare domain; and a medical case study is discussed to test our model in Section 7. Section 8 gives a brief description of the prototype. Finally, we conclude with some future research directions in Section 9.

Section snippets

Related work

The expansion of the Internet is providing a fertile ground to access a wealth of information. As the volume of heterogeneous web resources increases and the data become more varied, a massive response is issued to user queries. That makes it hard to distinguish relevant information from irrelevant or secondary information. Nowadays, various mechanisms are employed to enhance user queries, such as reformulation user queries, expanding query terms, adding a user profile and considering the user

Overall architecture

The overall architecture of our system is displayed in Fig. 1.

The architecture is set up to streamline this communication. To allow effective separation of concerns, our system has been split into the following three main sub-systems:

  • 1.

    The dialogue system D: This sub-system is like a receptionist, it handles the interrogation of the users in order to obtain a well-structured request. The dialogue system communicates with the user on the basis of text.

  • 2.

    The query interpreter QI: This sub-system

Preference expressions

Personalized service discovery is designed to provide services that match a user’s personal interest and thus provide more effective and efficient services discovery. A key feature in developing successful service discovery is to build a profile that accurately represents the preferences and interests of the user. User preferences are typically incomplete and, our knowledge of them is imperfect and partial (Koutrika & Ioannidis, 2010). We first will describe a preference modeling method, and

Preference and SQL

For the case of SQL the relevancy operator has a binary result: a tuple either satisfies the restriction (WHERE-clause), or otherwise (false or undefined result) the tuple is not selected. This is the initial result of the query. Then, according to Kießling et al. (2011), the effect of the preference operator is that only those tuples are chosen that are not preferred by another tuple from the initial result. Formally:TopsP(A){xA|¬x[P]A}where x[P]A abbreviates yA[x[A]y]. Later we will

Symptoms and diseases

In a medical diagnosis situation as encountered by the Matcher in Fig. 1, we have a diagnosis (a set of symptoms), a set of treatments and a set of risks. An enriched query is an pair id,q,P where id is the patient id, q is the set of symptoms that have been reported for this patient and P the preferential expression that combines the user profile and the contextual information. Each disease d is characterized by one or more sets of symptoms. For convenience we identify the set of symptoms

A case study

In this section we discuss the case of patient Martha as described in the introduction. The structure of this section is as follows (see Fig. 11):

  • 1.

    First we discuss in Section 7.1 a session with the Dialogue System (see Fig. 1).

  • 2.

    Then in Section 7.2 we describe the relevant Knowledge and Service Repository (see Fig. 1) as input from medical theory. (step A in Fig. 11). As described in Section 6 we will describe this repository as a CP-net.

  • 3.

    In Section 7.3 we show how Martha’s request is translated by

A prototype

To validate the preference model presented in the previous sections, we have developed a prototype that enriches user queries by adding personal information from the user profile, user context and domain knowledge. The interface asks the user their ID and the symptoms (as presented in Fig. 16) that describes their situation.

The prototype enriches a user query as follows: (1) the dialogue system (the user interface) sends a request to the query interpreter. The request contains user

Conclusions and future research

In this paper, we introduced the preference model to model user preferences. We discussed how these problems can be obtained from the user by interviewing or from the user history. Then we showed in Section 5 how the initial user query (user id and set of symptoms) can be enriched to involve the user preferences, the user context and domain knowledge. In Section 5, we showed also how preferential matching can be performed. We also presented a prototype to demonstrate our approach.

The medial

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