=Paper=
{{Paper
|id=Vol-2028/paper20
|storemode=property
|title=A Collaborative CBR Recommender System to Support Patients, its Relatives and Caregivers in Chronic and Palliative Care
|pdfUrl=https://ceur-ws.org/Vol-2028/paper20.pdf
|volume=Vol-2028
|authors=Sara Nasiri
|dblpUrl=https://dblp.org/rec/conf/iccbr/Nasiri17
}}
==A Collaborative CBR Recommender System to Support Patients, its Relatives and Caregivers in Chronic and Palliative Care==
https://ceur-ws.org/Vol-2028/paper20.pdf
194
A Collaborative CBR Recommender System to Support
Patients, its Relatives and Caregivers in
Chronic and Palliative Care
Sara Nasiri
Department of Electrical Engineering & Computer Science, University of Siegen
Institute of Knowledge Based Systems & Knowledge Management
Hölderlinstr. 3
57076 Siegen
sara.nasiri@uni-siegen.de
1. Introduction
In the last decades, many medical assistance systems have been developed, and the interest
in computer-aided problem-solving in medical and healthcare is constantly growing. Most
of the software systems in this domain focus on decision support and recommendation of
effective medication for patients. The combination of statistical analysis and case-based
reasoning can facilitate a better medical diagnosis [1] [2] [3]. Within the case comparison
mechanism of CBR, feature selection, similarity measurement, and adaptation methods
play an important role to retrieve and revise cases. In this research, DePicT (Detect and
Predict diseases using image classification and Text Information from patient health
records) uses image interpretation and word associations for feature selection and
recommendation of medical solutions [4]. All gathered patient records are stored in
relational databases as structured or closed-format (e.g. parameters and statistics), or
unstructured or open-format e.g. texts and images. For example, images of affected areas
of a melanoma skin cancer can contribute and support early stage diagnosis. Also, further
information on answering questions or writing a statement about the patient's health
condition is added to the knowledge base. Domain Experts can validate and verify the
collected information and also update the case-base to correct the data records of patients.
In the other hand, more over than assisting for detecting and predict the disease, the
Vocational Educational Training (VET) and Technology Enhanced Learning (TEL) [5] is
a research field which is investigated continuously. DePicT CLASS (Detect and Predict
diseases using image classification and Text information in Case-based Learning
Assistant System) is a CBR system by enrichment of cases with learning materials (e.g.
reference images and textbook) [6]. It is utilized smart (knowledge-based) and accessible
systems to provide vocational educational learning opportunities and achieving higher
education. CBR is applied in various problem-solving domains, and it is appropriate in
medicine to integrate the system and for explicit experience, cognitive adequateness, the
duality of objective/subjective knowledge, and to extract subjective knowledge [7]. Design
and development of the DePicT and DePicT CLASS are the main contributions of this
investigation. It is a case-based system which uses DePicT Profile Matrix of the
association strength between title phrase and identified keywords of cases. Making
experiments to validate the research and this recommender system lead us to do it in the
Copyright © 2017 for this paper by its authors. Copying permitted for private and
academic purpose. In Proceedings of the ICCBR 2017 Workshops. Trondheim, Norway
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skin and brain diseases as patient assistance system-DePicT CBMelanom and caregiver
learning assistant system-DePicT Dementia CLASS, respectively.
2. Research Questions and Aims
This section presents the research questions which have been reached by my doctoral
thesis:
1. How can we extract knowledge from healthcare processes and stakeholders to find
the gap in the current system to create the desired system with considering requested
a change?
2. How is it possible to establish an assistant system and utilizing data from the network
communication between patients, caregivers and doctors contributing to a better
understanding of their challenges?
3. How can we improve user experience with using Case-based systems?
4. How to evaluate the adaptation mechanism?
a. A new proposal that word association profile can follow adaptation on the
most similar cases. The practicalities of this will be discussed.
b. A system is learned which guides manual adaptation of similar references
retrieved from the case base. (e.g. learner can rank the best references)
c. In the context of adaptation, it is compared with the:
i. Information extraction using domain expert & stakeholders
ii. Information extraction using references.
iii. Statistical approaches (e.g. value),
iv. Statistical approaches enhanced by learned knowledge. (e.g. grade
of ranking)
The approaches to addressing the research questions and the explanation of how the
remaining aspects of these research questions are going to be investigated are described in
the next sections.
3. Proposed Plan of Research
This research divided to a preliminary concept which is DePicT and the educational
concept which is DePicT CLASS recommender procedure. In these concepts, we focus on
the combination of the textual and structural case based reasoning and utilizes word
association method which is called CIMAWA [8] to find the word association strength
between case title and case features which identified keywords.
DePicT [4] is a conception of a knowledge-based system for the identification and
diagnosis of diseases. It utilizes the graphical and textual similarity measurements of non-
image and image information which are Tverskey's similarity measure [9], Frucci et al.'s
image dissimilarity measure [10]. So, DePicT CLASS [6] is able to search for references
based on the comparison of word association profiles of identified keywords to find the
best similar ones with the request. DePicT CLASS is a Case-based Learning Assistant
System which is the application of the DePicT concept. To answer the research questions
which are explained in the previous section, two use cases are investigated. The prototype
of DePicT CBMelanom is developed which is briefly explained in the next section and
DePicT Dementia CLASS is fully implemented based on the retrieval and adaptation
mechanism of DePicT CLASS [6] and evaluated based on the test problems from
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Alzheimer and dementia forums and homepages [11]. This research proposes a new
adaptation method for dementia vocational educational training which uses the WHO
Framework for the International Classification of Functioning, Disability, and Health
(ICF) [12] word association strength of the DePicT Profile Matrix [6], [11]. It is utilized
abstraction adaptation to characterize each case by an identified keywords list which is
associated with each dementia disease and compositional adaptation to computing a value
for each reference from the most similar cases. Based on the definition of Kolodner [13],
DePicT CLASS adaptation is also categorized as a particular type, while it is considered
the collaborative recommendation of users by ranking the references, adding the images
tags, suggesting images impact factors, sending the feedback to contribute to the reference
collection. Therefore, the DePicT CLASS adaptation mechanism has a combination of
value comparison based on the requested word association profiles and manual adaptation
based on user collaborative recommendation e.g. learner can rank the best references and
learning materials based on their understanding and requirements. In future, the other
parameters of caregiving e.g. their challenges and task’s difficulties will be considered.
Moreover, for completing the evaluation phase, it will be tested with a domain of informal
caregivers. For further development of an application of the DePicT concept, DePicT
CBMealnom will be developed based on the 7-point checklist (7PCL) which has been
recommended by NICE (2005) and the case base is modifying based on the Melanoma
Datasets. The future work is addressing the fourth research question in this use case.
4. Description of Progress to Date
DePicT CBMelanom is utilized by patients who has skin problems. While a user can not
necessarily be able to formulate the question in a machine-readable form which is ready
for the CBR system, Conversational Case-Based Reasoning provides a question dialog to
guide users to describe their problem incrementally through an answering procedure [14,
15]. Its prototype utilizes myCBR tool [16] to create the CBR system for early detection
of skin cancer. Based on the report of American cancer society’s cancer facts and figures
2016 [10], “Melanoma accounts for only 1% of all skin cancer cases, but the vast majority
of skin cancer deaths. In 2016, an estimated 10,130 deaths from melanoma and 3,520
deaths from other types of skin cancer (not including KC) will occur.” Therefore, early
detection is crucial in this kind of cancers and “the best way to detect it early is to recognize
new or changing skin growths, particularly those that look different from other moles
[10].” Even after treatment, it is imperative that patients keep their medical history and
records [11]. Therefore, DePicT Case-based Melanom (DePicT CBMelanom) illustrates
how Melanoma is detected and predicted utilizing conversational case-based reasoning.
DePicT CLASS of Dementia is used and updated by caregivers and domain experts. It
enables caregivers and patients’ relatives to find their learning materials and references
which address the problems that they are looking for. Although the increasing prevalence
of dementia poses a major challenge for global health at multiple levels [17], CBR is
applied in the care of Alzheimer’s disease patients from 2001 [18]. “Dementia
encompasses a range of neurological disorders characterized by memory loss and
cognitive impairment. In 2015, almost 47 million people worldwide were estimated to be
affected by dementia, and the numbers are expected to reach 75 million by 2030, and 131
million by 2050, with the greatest increase expected in low-income and middle-income
countries [19]. Since the development of ICF [12], several projects for specific health
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conditions and disabilities are defined to develop core sets of ICF codes. DePicT CLASS
uses DePicT Profile Matrix weights of the association strength between title phrase and
identified keywords of cases (including references) which are dementia related diseases
and ICF parameters, respectively. In this analysis, the references and learning materials
with high valued keywords in word association profiles from the most similar cases are
recommended to the selected case. This research proposes a new abstraction,
compositional and collaborative adaptation method for medical vocational educational
training which uses the calculated word association strength of the DePicT Profile Matrix
[6]. DePicT Dementia CLASS is used and updated by caregivers and domain experts. It
enables caregivers and patient’s relatives to find their learning materials and references
which address the problems that they are looking for. Case formation identifies the
requested keywords and assigning their values based on DePicT Profile Matrix. These 111
ICF parameters are searched in 40 dementia and caregiving books and handbooks to create
the large document as a reference of DePicT Profile Matrix. DePicT Dementia CLASS
experimentally evaluated adapted references compared to the retrieval only references
using its similarity measurement [6]. We have used two rates to investigate the hypothesis
which is DePicT Dementia CLASS is able to select cases (three most similar ones) which
can be adapted more related to the user request in comparison with the retrieval only
references. The attract rate is defined based on the ratio of the value of references to their
rank. In this way, DePicT CLASS compares reference ranking which is enhanced by
learned knowledge of users. Besides the attract rate, for evaluating the adaptation results,
the adaptation rate (adapt rate) is defined based on the ratio of retrieval only references to
the total number of associated references. Thus, the recommendation of selected case is
arranged based on the combination of high-value and ranked references.
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