In this paper we present the retrieval and adaptation mechanisms used in our information system on travel medicine, docQuery. The retrieval method's main feature is an overall improved accuracy of retrieval results' similarities through a more diverse distribution of similarities over the retrieved result sets. Its underlying idea is the execution of several consecutive retrievals on one case base, where attributes from the cases resulting from the first query are used to refine a subsequent query in order to yield better results than the first retrieval. The refined result sets narrow down the search space for cases to be used in result adaptation, which improves adaptation quality. The mechanisms are implemented in the docQuery information system on travel medicine.
Intelligent information systems provide a technology for covering even complex
topics in a comprehensive but flexible way. Realising such systems requires high
quality data sources, knowledge models, and maintenance techniques. To achieve
this, knowledge has to be acquired, analysed, stored, and retrieved, which
challenges a knowledge-based system and is crucial for its continuous existence over
a longer period of time. Case-Based Reasoning (CBR) is a methodology that
has proven most effective for knowledge storage, retrieval and adaptation in all
kinds of intelligent information systems [
In this paper we present the 2-step retrieval mechanism, a retrieval mechanism for CBR systems that works in an iterative way, executing two consecutive retrieval steps on the same case base, using information gained from the results of the first retrieval step in order to refine the second one. This consecutive retrieval strategy leads to an overall improved accuracy of retrieval results’ similarities through a more diverse distribution of similarities over the retrieved result set.
Our first application of the 2-step retrieval mechanism is the docQuery
project [
The paper is structured as follows: Section 2 presents the docQuery project, the application domain travel medicine and its particular challenges with regard to knowledge-based systems. Section 3 presents the actual 2-step retrieval algorithm, with an illustrated example. Section 4 finally presents an evaluation of the 2-step retrieval algorithm based on its application in the docQuery project, describing the evaluation’s setup in subsection 4.1 and its results in subsection 4.2 followed by a description of related approaches in section 5. The paper closes with a conclusion and an outlook on future work in section 6. 2
Travel medicine is an interdisciplinary speciality concerned with the prevention, management and research of health problems associated with travel and covers all medical aspects a traveller has to take care of before, during and after a journey. For that reason it covers many medical areas and combines them. Furthermore, information about the destination, the activities planned and additional conditions have to be considered when giving medical advise to a traveller. Travel medicine starts when a person moves from one place to another by any kind of transportation and stops after returning home healthy. In case a traveller gets sick after a journey a travel medicine consultation might also be required.
The research project presented in this paper is supported by mediScon worldwide, a Germany based company with a team of physicians specialized on travel medicine and TEMOS1, a tele-medical project of the Institute of Aerospace Medicine at the German Aerospace Center DLR2. Together we will develop docQuery, an intelligent information system on travel medicine which provides relevant information for each traveller for their individual journey. First of all we will focus on prevention work, followed by information provision during a journey and information for diseased returnees.
Since common sources on the World Wide Web are not authorized by
physicians and/or experts, we aim at providing reliable information for everybody. In
preparation for a healthy journey it is important to get a high quality and
reliable answer on travel medicine issues which both laymen and experts should be
able to use. Based on the SEASALT architecture [
Modularising case bases into subdomains instead of simply partitioning them into smaller ones with the same case format, has several advantages. Firstly the individual case bases are easier to maintain with regard to the correctness of their contents, since they represent a more simple knowledge domain. Also, breaking up the rather complex domain of travel medical advisories into more simple subdomains that are than recombined as needed, gives the whole information system more flexibility. Providing the appropriate combination rules exist, the contents of the individual case bases can also be combined into cases that have not yet been presented to the system, as long as they adhere to the respective combination rules.
Further, not all knowledge domains that are included in an information system on travel medicine require the same type of maintenance and are subject to the same amount of change over time. While it is for instance no problem to keep a rather simple domain such as countries and regions as minimal and consistent as possible, the domain of travel related diseases is better served by including as many cases as possible, even if some of them are very similar. By splitting the knowledge domain into these smaller subdomains, each of them can be maintained in a way that is best suited for the respective subdomain.
Since the topic of this paper is the retrieval on one individual case base, we
only gave a short overview of the docQuery System and its modularised case
bases in this section. More on modularised case bases, their maintenance, and
the combination of their results can be found in [
When dealing with a topic like travel medicine we cannot assume that all users
ask complete and/or correct questions and like Weibelzahl [
Our initial retrieval is based on the geographic position of a country and because of the fact that the earth is divided in a manageable amount of countries, which are completely covered in our case base’s similarity measure in the form of a geographic taxonomy, we can rely that every requested country can be retrieved. However, we cannot be sure that we will have (complete) information on that country. Also, due to the nature of our domain, travel medicine, geographic proximity is not sufficient to find feasible adaptation candidates to complete the retrieved country’s information – also occurring diseases have to be noticed. In our retrieval mechanism we thus start by requesting the destination country, this step is followed by an enhanced query including additional information about the initial country’s diseases. The second retrieval’s results with the highest similarity will be the adaptation candidates we take into account. In the current approach we randomly pick one of the cases with the highest similarity as adaptation source. In the future we will add maintenance information to the cases to be able to compute the most updated or most recently maintained adaptation candidate.
The approach presented here concentrates on interdependent attributes that are not completely given for every single case. We will show how we can narrow the result set by retrieving reliable information snippets in order to adapt them to create a (complete) response.
Assuming that we have a traveller planning a journey, the retrieval will start based on the destination region. We know that our model contains all countries of the world, so the retrieval algorithm will be able to find the appropriate destination. But due to many changes of disease outbreaks we have to provide up to date country and regional information. Therefore we do not only retrieve the country we have searched for, we also include in our result set countries with a similar structure considering travel medical aspects. To realize this we also use information about vaccinations that can be divided in three categories: 1. Obligatory Vaccinations: Those vaccinations are required in order to be allowed enter a country. 2. Standard Vaccinations: Those vaccinations are required if one is travelling to a certain country - although they are not a regulation. 3. Risk Vaccinations: Those vaccinations are required for people with an enfeebled immune system such as pregnant women, children, elderly people, or those who suffer from different kinds of (chronic) diseases and require a higher protection provided by a vaccination.
Additionally our system will give information about diseases that can be
contracted during a journey. According to [
Currently we do have information about vaccination advices, but we do not have complete information about diseases contracted during a journey, because they rely until a certain point on up-to-date information. Nevertheless we will provide this kind of information to the users of docQuery and since we do have similarity models for each type of disease we will adapt the information from similar countries.
In order to ensure that our system adapts correct data we will use the 2Step-Retrieval to reduce the amount of cases we can adapt from. In the first step we will only do the retrieval based on our geographic taxonomy, then we narrow the set of retrieved cases by adding vaccination information to a second query. The taxonomy includes 228 countries and islands arranged by continents, subcontinents (e.g. Western Europe), regions (e.g. Iberian Peninsula) followed by the country. A generalization step leads to a value of 0.5 and specialization to a value of 0.8.
When performing a standard 1-step retrieval on Laos, a whole of 10 countries in it’s geographic proximity have a similarity of 40%. When performing a 2-step retrieval, the distribution of similarities is much more diverse, as illustrated in figure 1.
Laos does not yet have complete information on the diseases contracted there, so these attributes have to be filled using adaptation from another, similar case. To complete the disease information we now have to choose one country to adapt from. Using 1-step retrieval there are 10 adaptation candidates to chose from, some of which, such as e. g. the Philippines are in fact quite different from Laos with regard to travel medicine.
To ensure that the cases we adapt from are more similar to the requested country, we now also consider vaccination information in our request, using it in a second retrieval step as illustrated in figure 2. Since we need information on countries with a similar disease structure in order to be able to find a country profile with an appropriate amount of information, even if the destination originally given by the user does not offer those, we use 2-step retrieval. An example query (again using the country, this time plus the vaccination information of the originally retrieved country) for the second retrieval step would be: ”Laos, Yellow fever, Diphtheria, Hepatitis A, Measles, Tetanus, Cholera, Hepatitis B, Japanese Encephalitis, Rabies, Typhoid fever”.
After the second retrieval step, the countries situated around Laos now have more differentiated similarities and offer a higher amount of information considering their disease structure. As can be seen in figure 1, this time only the countries near Laos as well as Malaysia are returned with the same similarity to Laos, reducing the number of adaptation candidates to 5. If we are taking one of those countries into account the likelihood of retrieving a valid result set will be highly increased. 4
Following the example given in section 3 we will now present the evaluation of our approach in the travel medicine domain. Therefore we will present how the 2-Step-Retrieval affects the retrieved result sets and we illustrate its advantage in comparison to a straightforward 1-step retrieval approach. In the architecture of docQuery, the case bases Destination, Associated Information, and Activity are adequate to do 2-step retrieval, since our other case bases contain health critical information that require a more strict retrieval. 4.1
The Destination case base covers country characteristics that are used to prepare an information leaflet for a traveller. It contains vaccination requirements and vaccination-preventable infectious diseases, pre-travel information on different kinds of diseases that might occur in a certain country or region, as well as hygiene and prevention advise.
The experimental data contain a case base covering all countries in the world and the vaccination information abroad we have to consider preparing information leaflets for travellers. To carry out the experiment we took a controlled sample of 18 countries of East and South East Asia, representative with respect to country borders, coasts, islands and climatic conditions and manually filled in the data on transmittable diseases, so that we have 18 cases with complete information. The sample comprises all countries of East and South East Asia that ensures that we are able to find neighbouring countries, non-neighbouring countries, and countries associated to different nodes (e.g. China which is situated in Eastern Asia and Thailand which is in South East Asia).
We carried out our evaluation as a leave-one-out experiment. For each country in the case base we did the following steps: 1. Remove diseases information from country. 2. Do a one-step retrieval using the country name. 3. Do a two-step retrieval using the country name and vaccination information as additional information. 4. Do an adaptation with each respective result set. 5. Compare the set of diseases obtained from the two respective adaptation candidates to the original set of diseases.
For the experiment we use the following weights: sim = 6 × [Region] + 4 × [V acc Risk] + 3 × [V acc Std] + 2 × [V acc Obl] (1)
In our similarity measure ”Region” is weighted times 6 because it is the most diverse and reliable fact, and because we expect that travellers know their destination, but not the diseases. ”Risk people vaccination” information are weighted times 4 because vaccination advices depend on each traveller’s profile and in particular his or her disease history in combination with chronic illness(es). Also, this attribute differentiates countries from each other. ”Standard vaccination” advices are weighted times 3 because they describe the disease structure from the medical point of view and allow a general classification. ”Obligatory vaccination” is weighted times 2 because obligatory vaccination requirements depend on the geographic region as well as on official orders.
For each country we did 5 runs3and compared the result of the 1-step-retrieval and the 2-step-retrieval to the expected result. First we compared the number of adaptation candidates and then the resulting adaptation quality, checking if all expected disease were found, if one or more diseases were missed (false negatives), or if incorrect extra diseases were added to the case (false positives). 4.2
Figure 3 shows how the number of adaptation dropped significantly in 90% of our test cases. These numbers illustrate that result sets are indeed more diverse when enriching the queries with extra information. Moreover not only the number of adaptation candidates change, also the adaptation candidates differ between 1step and 2-step retrieval. For example the retrieved countries for Japan: In the 1-step-retrieval North Korea, Mongolia, Taiwan, China, as well as Macau are returned, but the adaptation candidates for the 2-step retrieval do not include Mongolia, but Hong Kong, because of the fact that the disease structure of Hong 3 We chose to perform several runs, since the randomising element in the final choice of the adaptation candidate can yield different results for the same query. Kong is much more similar to Japan than Mongolia. In all test cases at least one country has been dropped out of the adaptation candidates, but on average 3.3 countries were not taken into account in the 2-step retrieval. In 39% of the cases an adaptation candidate has been add.
In the next step we investigated whether the adaptation candidates remaining after the 2-step retrieval were in fact the better ones, that is, if their adaptation results were better than the adaptation results from the candidates resulting from one-step retrieval. The results of the adaptations can be seen in Figure 4.
Each column pair represents the aggregated results of one country – the first column shows the adaptation results of the 1-step-retrieval and the second of the 2-step-retrieval. The y-axis shows the number of correct diseases (true positives, positive scale, light-coloured) and extra (false positives, positive scale, darkcoloured) diseases found as well as missed diseases (false negatives, negative scale, dark-coloured). The result sets used for the evaluation result from requesting the following countries: Korea, Mongolia, Taiwan, China, Macau, Japan, Brunei, Indonesia, Myanmar, Malaysia, Singapore, Vietnam, Cambodia, Laos, Thailand, Timor-Leste, Hong Kong, and the Philippines.
In total we did 90 single-case requests and after the 1-step-retrieval 62% of the adapted cases contained all of the expected diseases. Applying the 2-step retrieval to the same cases 76% of the adapted cases contained all expected diseases. Although both retrieval variants also return false positives in most of the tests, the solutions of the 2-step retrieval are generally more reliable, especially according to false negatives. The 2-step retrieval performed significantly better than the 1-step retrieval with regard to false negatives as can be seen in Figure 5.
In summary the experiment shows that the 2-step retrieval provides more robust results and especially in the field of travel medicine a query can be significantly enhanced by adding more information on the destination, because, for example, disease and vaccination advice can only be provided by an expert, not by the traveller. Furthermore the information stored in the case bases can be used to create more refined queries. 5
The idea of using and combining information from different cases has also been
discussed in [
A similar approach has been presented in [
In [
In this paper we have presented a retrieval mechanism that enhances a query with information in order to receive a more diverse result set. Using 2-step retrieval provides us with robust results to dispatch the subsequent retrieval and result combination. The 2-step retrieval algorithm presented in this paper exemplifies how the retrieval strategy can be implemented in a CBR system. Further on we use this approach to combine and adapt parts of cases and attributes of different case bases, because we expect that our information obtained of the travel medicine community will be incomplete. Also we suppose that taking more attributes into account might help the algorithm to receive even more diverse result sets.
As a next step we will enable our system to combine the retrieval results of cases retrieved of modularised heterogeneous case bases in order to create a whole individual information leaflet for travellers containing information on activities, diseases, medication, etc. Hence, we will implement a multi-agent system centred around a coordination agent (or broker agent) combining retrieval results and ensuring complete information regarding given constraints.
Another aspect of our future work is generalising the 2-step retrieval algorithm and evaluating whether the algorithm can by applied to other case bases and domains as well, or if this only works for our specific domain. Also, we have to figure out if the algorithm of using retrieval results for refining queries can be applied to other case bases in the travel medicine application domain as well as in other application domains.
Integrating the 2-step retrieval algorithm in SEASALT puts forward our idea of using knowledge lines for building CoMES upon existing knowledge sources.