Modelling a decision support system for oncology using rule-based and case-based reasoning methodologies Delphine Rossillea,b, Jean-François Laurentc, Anita Burguna a b
Laboratoire d’Informatique Médicale, Université de Rennes 1, France
Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada c
Centre Eugène Marquis, Rennes, France
Address for correspondence: Laboratoire d’Informatique Médicale - Faculté de Médecine - Université de Rennes 1 2, avenue du Professeur Léon Bernard 35043 Rennes Cedex, France Tel.: + (33) 2.99.28.42.15 Fax: + (33) 2.99.28.41.60 Email:
[email protected] Keywords Information Storage and retrieval; Models, Decision Support; Decision Support Systems, Clinical; Data Warehouse; Medical Informatics Applications; Medical Oncology.
Summary In most hospital medical units, multidisciplinary committees meet weekly to discuss their patients’ cases. The medical experts base their decisions on three sources of information. First, they check if their patient complies with existing guidelines. Failing these, the medical experts will base their therapeutic decisions on the cases of similar patients that they have treated in the past. We propose a multi-modal reasoning decision-support system based on both guideline and case series, which will automatically compare the patient’s case to the corresponding guideline, then to other cases, and retrieve similar cases. The general structure of the system is presented here, the domain of application being oncology. As the patients’ records are not currently stored in a database in a format which is directly accessible , an objectoriented model is proposed, which includes prognosis factors currently tested in clinical trials, wellestablished ones, and a description of the illness episodes. The system is designed to be a data warehouse. Such a system does not exist in the literature. Future work will be needed to define the similarity measures, and to connect the system to the current database.
1. Introduction In most hospital medical units, multidisciplinary committees (including surgeons, radiologists) meet weekly to discuss patients’ cases. The medical experts base their decisions on three sources of information: guidelines, clinical trials (either recent papers discussing results, or ongoing clinical trials), and case series. Evidence-based medicine relies on the guidelines issued and updated from the results of clinical trials. While most patients’ cases can be analysed following these guidelines, some patients are nonstandard (i.e. “atypical”) because they do not conform to any guideline (e.g. rare illnesses), or they do not comply with the entire guideline (e.g. the proposed treatment cannot be prescribed or it must be aborted). For some of these cases, “close” enough to a guideline, the medical experts can adapt the guideline production rules to the cases. For some others, they will rely on clinical trials. For the remainder, any decision can only be based on similar cases the medical experts have encountered in their career. Nowadays, most of the research is on computable rule-based reasoning decision support systems (RBR DSS) [e.g. 1-3]. Systems considering individual cases as a unique source of knowledge are becoming frequent in the medical domain [e.g. 4-5]. These case-based reasoning systems (CBR DSS) [6, 7] follow four processes : retrieval of similar cases, reuse of previous solutions applied to new cases, evaluation of the proposed solution, and case retaining. The system presented in this paper is meant to be a data warehouse in oncology, storing valuable information for treating, for instance, patients with rare tumours, or not reacting normally to a treatment. It will automatically retrieve similar cases, with a view to supporting medical experts when making decisions for non-standard cases, and to evaluate interpractice variations by checking the consistency of decisions made for similar cases. Nowadays, patients’ files are stored in large databases in formats not suitable for automatic analysis. The first development phase of this research is to design the system architecture and modelling. The second phase will be concerned with the integration of the functionalities for searching and comparing similar cases. The first phase is presented in this paper. UML (Unified Modelling Language) [8] is used as the modelling language. Because the system is based on both rule-based reasoning (with guidelines) and case-based reasoning (with individual cases), it is a multi-modal decision support system. The system is presented for breast cancer, and aims to be adaptable to any cancer. Its architecture was chosen so that the system can evolve at the same pace as medical knowledge. 2. Materials and Methods 2.1. Materials
The system prototype was analysed on breast cancer, more specifically stage I invasive non metastatic breast cancer, and then the system was generalized to any tumour. The patients’ data come from the Centre Eugène Marquis (CEM), Rennes, France. The CEM stores around 2,000 new cases per year, one-fourth being breast cancers. The Centre is one of the 20 centres of the Fédération Nationale des Centres de Lutte Contre
le Cancer (FNCLCC) [9] and deals with one-fourth of all national cancers (∼50,000 new cases per year). At the CEM, each patient’s medical record is stored both as a paper file and an electronic file. The electronic file is stored in the CEM proprietary database partly as database fields and partly as documents with no standardised format. Most patients’ images will shortly be stored in a separate Picture Archiving and Communication System (PACS). The guidelines used by multidisciplinary committees are described in the Standards, Options and Recommendations (SOR) [9]. Domain-specific vocabularies and thesauri are used such as the ICD-O1 or the TNM2 classifications. The multidisciplinary committee for breast cancer currently discusses patients’ cases once a week. To identify the patient, the medical experts rely on a specific document called UCPS document generated especially for the meeting. This document summarizes some of the most relevant medical data as well as the history of the pathology for the patient, and is used to record the unanimous decision taken by the committee. Whenever more medical details are needed, the medical experts will consult the paper file of that patient. Indeed, the paper file is comprehensive as it includes documents and images not yet digitalized (such as items of correspondence or mammograms), and as such it is the most suitable file on which the medical experts can base their decision. On the other hand, the electronic file already contains part of the relevant data in structured format suitable for automatic reasoning. Today’s decision making process for breast cancer at the CEM is illustrated in Figure 1. It shows who plays a part in the committee meeting : the medical experts, their secretaries as well as the secretary in charge of recording UCPS documents in the CEM database. 2.2. Methods
The first step was to model today’s decision making process, which, even though based on breast cancer cases, can be generalized to any type of tumours, and then to identify relevant data and knowledge. This was done in collaboration with the breast cancer committee and based on the medical records of the CEM. Because the decision support system serves different purposes from the CEM Hospital Information System, we had to select the data relevant to our objectives.
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ICD-O classification stands for International Classification of Diseases for Oncology TNM classification stands for Tumour-Node-Metastases
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Figure 1: Today's decision making process for breast cancer at CEM
For the purpose of this research, we selected (as indicated in Section 2.1) the stage I invasive non metastatic breast cancers, classified T1 N0 M0 [10], diagnosed in 1997, having had or not a relapse since. This subset corresponds to pathologies of normally easily-treated cancers (tumour’ size less than 2 cm, no metastases and no nodes invasion). However, a significant number of these cancers may evolve and some may have a fatal outcome. The diagnosis date was chosen as far back as possible so that any relapse could be recorded, whilst still providing data recorded in an electronic format. As the proposed system relies both on evidence-based medicine and experience-based medicine, a subset of all the patients’ data was chosen that would allow the comparison with the guidelines and similar cases, while containing suitable granularity for supporting therapeutic decisions. The DSS was then designed, and a class diagram of the medical cases was drawn using UML (Figure 4). The choice of guidelines representation was determined by the fact that guidelines are structured and must be computer-interpretable, as well as humanreadable, in order to allow for automatic comparison and viewing by experts.
3. Results 3.1. DSS Architecture and Reasoning Process
The system consists of: - a base of individual cases, containing patients’ relevant medical data - a knowledge base containing: o domain specific thesauri, including international coding standards (ICDO, TNM), national coding standards, protocols for treatments, medications o a base of guidelines o a classification table used by the reasoning method in order to retrieve similar cases o data used by the system’ administrator (related to users’ access rights for instance) - a processing engine - a human-computer interface. The system is aimed to be fed by the Hospital Information System (HIS) after appropriate formatting of relevant information. Note however, that in most French HIS today this step won’t be straightforward as medical data are not collected for the same purpose in the HIS as in our proposed system (for instance consultation reports written in free text contain relevant data that will have to be singled out to be fed to the proposed system). The architecture separating the knowledge base from the programming code provides suitable flexibility in the system for any information update that might be made necessary by the rapid evolution of medical knowledge. The human-computer interface will be easy and straightforward for noncomputer experts such as doctors or secretaries to use. The computerized reasoning process goes as follows : It is hybrid being based on two reasoning methodologies - evidence-based and experience-based medicine, and it is sequential. Once a new case is stored in the cases base, the system will automatically select the appropriate guideline and compare the new case with this guideline. The selection of the guideline will be made by comparing key medical terms (and their values) characterizing both medical cases and guidelines, these terms corresponding to common restricted vocabulary. For instance, for breast cancer, based on the SOR for infiltrating non metastatic breast cancer, such data as the TNM classification and the tumour size will be used. At the end of the comparison of the new case with the guideline, the last guideline step with which the case complies will be saved in a classification table. Thanks to the classification table it will be possible to identify directly which stored cases are classified under the same guideline step. Note that the comparison is only possible if the same vocabulary is used for both cases and guidelines and if the values of the medical data are normalized, that is, if cases and guidelines “speak the same language with the same semantic rules”. This information would normally be kept in the knowledge base.
When cases similar to a stored given case are searched, the system will use the classification table to retrieve the identification numbers of all similar cases according to the guideline (Figure 2). For each retrieved case, the system will then operate a more precise comparison on other predefined criteria in order to better select the closest similar cases to the given case. 3.2. Guidelines Representation
The GLIF3 object model [11] was selected to represent the structured guidelines. Unlike the Arden syntax, with GLIF a guideline is described as a flowchart of temporally ordered steps, allowing the system to keep track of the step at which a case is no more compliant with the guideline. It also stores information on the authors of the guidelines, the edition date, version, and eligibility criteria, which will make the selection of the appropriate guideline in the guidelines base easier.
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:classification indicate the granularity degree used for the comparison between cases
:user choose "search similar cases to a case"
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indicate the proximity level allowed in the guideline
choose granularity criterium
choose proximity criterium ask for selected case retrieve selected case
ask identifiers of similar cases according to guideline repeat for each case retrieved
retrieve identifiers ask for case n°X retrieve case n°X
compare case n°X to selected case output results of comparison
Figure 2: Sequence Diagram of the use case "Searching for similar cases"
3.3. Patient Data Representation 3.3.1. Patient’s relevant data set
A patient’s case becomes eligible to be stored in the system only once the primary tumour diagnosis has been made. This means that the database reflects only the completed diagnosis, treatments and examinations. Furthermore, a stored patient’s case is active (that is, retrievable as a similar case) only if the patient’s state is known, either because he/she has given recent news3 (attribute recentNewsDate in Figure 4) or he/she has died (attribute date_death). Relevant patients’ data are of two types : prognosis factors and episodes of the disease history, an episode being a primary tumour diagnosis, or a recurrence, metastatic or none. Prognosis factors are: -
the well-established factors referenced in guidelines such as hormonal receptor levels (ER and PR) for breast cancer [9]
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factors being currently validated in clinical trials (and not included yet in guidelines), for instance the Her2/Neu marker [10]
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factors that have modified the plan of care, such as a treatment’ abortion and its reasons (the patient’s refusal to continue the treatment or abortion due to harmful secondary effects)
These factors are either time-invariant and episode-independent (as the mutation of genes BRCA1 and BRCA2), time-invariant but episode-dependent (as ICD-OT Topography and ICD-OM Morphology codes for the primary tumour diagnosis), or time-variant and episode-dependent (as qualitative (qualitvalue) or quantitative (quantvalue) results of exams). They are attached to the patient or to an episode according to their dependencies. An episode is described by tumour-specific characteristics, associated treatments, and examinations. It includes temporal and factual data. Tumour-specific characteristics are of two kinds: either for primary tumour diagnosis, local and opposite mammary recurrences (as the ICD-O and TNM codes), or for metastatic recurrence (as location). Treatments include Surgery, RT (radiotherapy), CT (chemotherapy), HT (hormonotherapy). The Characteristics of each treatment are stored, including surgical procedure (e.g. mastectomy for Surgery), protocols, duration or date of the treatment, response to treatment and all alarms occurring during the treatment. The order (rank) of the treatments is also stored. Medical images are included through their qualitative results in the Exams class. The duration of an Episode (startDate being the date of the diagnosis and finalDate either the date of the recurrence or the final date of the last treatment) as well as a summary of the responses to treatments are provided. The required granularity level of relevant data is, as a minimum, provided by the level used in the guidelines. In order to classify cases in relation to themselves, additionnal details on relevant data will be stored, such as protocols for chemotherapy and radiotherapy. 3
“Recent news” means “within the last year” as, once a year, the list of national yearly deaths is used to update the medical records at the CEM.
Patients’ data are stored essentially in structured fields, in order to speed up cases retrieval. The field’s format may be numerical values, dates, or restricted vocabulary. Restricted vocabulary includes cancer-specific vocabulary (e.g. episode_type in Figure 4 can be “primary tumour”, “local recurrence”, “opposite mammary recurrence”, or “metastasis”), and coding standards such as ICD-O. Commentary fields (such as com_episode) have been included to allow for experts’ comments not included in other fields to be saved. Free text may be used in these fields. 3.3.2. UML modelling
The actors of the system (Figure 3) are the medical experts (i.e. surgeons and radiotherapists), the administrator and the CEM database. The administrator manages the system, including the guidelines (creation, suppression, update, activation) and the classification process of the patients’ cases. The medical experts consult a guideline or a patient’s case, search for similar patients’ cases or check the consistency of decisions for similar cases. The system cases base is fed by the CEM database. The Patient Case class is represented in Figure 4, where the hierarchical structure can be seen. The latter comprises two branches: the patient-specific characteristics (the patient class being its root), and the disease-specific characteristics (the Episode class being its root). Note that only part of the attributes is shown in Figure 4. In addition to the patient’s relevant data listed in section 3.3.1, each case is related to the Classification class that stores the step (leaf or node of the associated guideline or decision tree) at which the case does not comply with the guideline anymore, and henceforth, links together similar cases found after the first classification according to the guideline. In order for the system to be suitable to analyse any tumour, the case has an objectoriented architecture composed of generic classes (including patient, Alarms, FamilyHistory, Episode, CharacteristicsCancer, CharacteristicsMetastasis, Treatments (CT, RT, HT, Surgery), Exams), to which cancer-dependent classes are associated or inherited (such as BreastCancer characterizing the primary breast tumour, or BreastFactors characterizing the episode-independent breast cancer-dependent factors). In addition, for some generic classes (such as RT, CT, HT, Surgery), the content is cancer-dependent - that is, an attribute can only take values specific to a given cancer (the protocol for chemotherapy for breast cancer is different from that for liver cancer). 4. Discussion Most of the medical decision-support systems (DSS) rely nowadays on a unique reasoning methodology such as case-based reasoning (CBR) or rule-based reasoning (RBR). Yet, a few DSS base their reasoning process on a multi-modal approach. The system proposed in this paper is a multi-modal DSS based on both CBR and RBR. Four different approaches combining cases and rules may be found in the literature. The hybrid RBR-first CBR-last approach uses the RBR as its main reasoning process, CBR being applied when RBR is not found suitable that is for exceptions to the rules and/or non standard situations [12]. With the CBR-first RBR-last approach, the CBR is the main reasoning process and RBR is used to improve part of the reasoning process [13]. Other systems apply CBR and RBR in parallel. Either both outcomes are simply displayed [14], or the best outcome is proposed according to some given criteria. The
CARE-PARTNER system on bone-marrow and stem-cell post-transplant long-term follow-up [15] implements a closer cooperation between the two methodologies, through the iteration of partial reasoning steps, the outcome of each of these steps being the best outcome of the two methodologies. Yet, all of the above-mentioned multimodal systems apply CBR and RBR in mutually exclusive ways. The T-IDDM project [16] on diabetes management appears to be a multi-modal system realizing a real integration of both CBR and RBR. Indeed, CBR results are used to refine the rules, hence allowing the tailoring the final outcome on the specific patient’s needs. Kasimir [17], applied to cancer cases, is similar as it uses case-based reasoning to adapt the production rules of the guideline to atypical cases. In Oncology, the trend is for DSS systems to rely on the CBR approach [4-5]. The proposed system is a hybrid RBR-first CBR-last system, aiming at supporting the medical experts in their decisions for non standard (atypical) cases. In the literature, only Kasimir [17] is a similar multi-modal system applied to cancer, either for standard cases or non-standard cases close to standard ones. However the proposed system has a different and complementary approach as it can be useful even when no guideline exists for a specific rare disease.
UM Lcancer2
managing security
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managing cases administrator consulting a guideline or a case
searching similar cases expert consulting variability of decisions
completing cases HIS
Figure 3: Use Cases Diagram of the proposed system
Classification idguideline : string locationInGuideline : string idpatient : integer
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Episode episode_type : string 1 startDate : integer finalDate : integer com_episode : string 1 response : string
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protocol : string
RT protocol : string HT
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medication : string
procedure : string
Figure 4: Class Diagram of the Patient Case
The paper presents a patient data model for the multi-modal reasoning system. Several organisations (including HL7, CEN/TC251, OpenEHR Foundation) have been working on defining an object-oriented structure of hospital Electronic Patient Records (EPR) [2]. They provide representation models of patient data (including clinical data), and domain-specific concepts, and include data exchange capability (within the hospital as well as with outside partners). They are both intended for any health domain. The HL7 RIM appears too general to be directly usable in decision-support systems [2]. The CEN/TC251 EHRC and openEHR GEHR, as well as, for guideline-based systems, the EON and PRODIGY architectures [1-2], contain data which are not relevant to the proposed system as they correspond to non completed events (e.g. goals, planned interventions). The proposed architecture is close to the openEHR GEHR: the persistent transactions corresponding to the patient branch, and process-selected event-driven transactions to the Episode branch. However, the openEHR GEHR is designed to include all the aspects of the patient medical record within the Hospital Information System (as HL7 RIM and CEN/TC251 EHRC), while the proposed system relies on an independent cases base. In CBR DSS applied to oncology, Kasimir [17] bases its decisions only on the clinical data required by the guidelines, while the proposed system includes potential factors as well. Other CBR systems for breast cancer represent very specific information on the patient (e.g. histo-pathological data in [4]), whereas the proposed system is applicable to any tumour. UML was shown to be efficient to model
the system and confirms its applicability to CBR systems as was demonstrated in other studies, e.g. [18]. The proposed patient case may model any type of cancers. Not all cancers, however, have guidelines as structured as breast cancer and their automation can become more delicate. This is a very common difficulty encountered when designing computable guidelines. No real solution can be found from reading the literature, apart from the suggestion of interactively asking the expert to validate a choice [3]. This solution is not applicable to our system. For the future system to become entirely automated, patient’s data should be automatically acquired from the CEM database. However, most of the required data are still in electronic documents at the CEM, even though part of them is stored in structured fields (such as the ICD-O). Relevant data will thus have to be first identified in electronic documents and formatted appropriately before being stored in the proposed system. Finally, the proposed system is a data warehouse which is flexible enough to be able to integrate images in the future (including data from functional imagery of tumours) as well as new data described by emerging disciplines (factors such as genes expressed in cancers, or raw data such as patients’ micro-array results). 5. Conclusion This paper presents the modelling of a case-based retrieval which is designed to support therapeutic decisions in cases that do not or cannot comply with recommended guidelines. No such system exists as yet in the literature. This paper detailed the general architecture of the system and the modelling of patients’ cases and guidelines. The next research stage will be to define the similarity measures to be applied to patients’ data in order to allow the retrieval of similar cases and also to design a user-friendly human/machine interface. The proposed system is flexible enough for new data to be incorporated (for instance from emerging disciplines such as molecular biology) and should be able to take into account the rapid evolution of medical knowledge (e.g. [19]). 6. References [1] Tu S W, and Musen A M, Modeling data and knowledge in the EON guideline architecture. MEDINFO 2001, pp. 280-284. [2] Johnson P, Tu S W, Musen A M, and Purves I, A virtual medical record for guideline-based decision support. Proc AMIA Symp 2001, pp. 294-300. [3] Séroussi B, Bouaud J, and Antoine E, ONCODOC: a successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer. Artif Intell Med , 22 (1):pp. 43-64, April 2001. [4] Jaulent M-C., Le Bozec C., Zapletal E., and Deguoulet P., Case based diagnosis in histopathology of breast cancer. MEDINFO 1998, pp. 544-548. [5] Schmidt R, Gierl L, Case-based reasoning for antibiotics therapy advice: an investigation of retrieval algorithms and prototypes. Art Intelligence in Medicine, 2001, 23(2), pp. 171-186. [6] Aamodt A, and Plaza E, Case-based reasoning: Foundational issues, methodological variations, and
system approaches. AI Communications, IOS Press, 1994, 7 (1): pp. 39-59. [7] Kolodner J., Case-based reasoning, Morgan Kaufmann, San Francisco, CA, 1993. [8] Muller P-A, and Gaertner N, Modélisation objet avec UML. Eyrolles, eds. 2000, 2nd edition. [9] Fédération Nationale des Centres de Lutte Contre le Cancer, SOR: Cancers du sein infiltrants non métastatiques. John Libbey Eurotext, eds. 2001 (2nd edition). www.fnclcc.fr [10] Singletary S, Allred C, Ashley P, Bassett L, Berry D, Bland K, Borgen P, Clark G, Edge P, Hayes D, Hughes L, Hutter R, Morrow M, Page D, Recht A, Theriault R, Thor A, Weaver D, Wieand S, and Greene F, Revision of the American Joint Committee on cancer staging system for breast cancer. J Clin Oncol, 20 (17): pp. 3628-3636, 2002. [11] Peleg M, Boxwala A, Ogunyemi O, Zeng Q, Tu S, Lacson R, Bernstam E, Ash N, Mork P, OhnoMachado L, Shortliffe E, and Greenes R, GLIF3: The evolution of Guideline Representation Format. Proc AMIA Symp 2000, pp. 645-649. [12] Park H-J., Oh J-S., Jeong D-U., Park K-S., Automated sleep stage scoring using hybrid rule- and case-based reasoning, Computer and Biomedical Research (33), 2000, pp. 330-349. [13] Petot G.J., Marling C., Sterling L., An artificial intelligence system for computer-assisted menu planning, Journal of the American Dietetic Association, 1998, pp. 1009-1014. [14] Evans-Romaine K., Marling C., Prescribing exercice regimens for cardiac and pulmonary disease patients with CBR, ICCBR 2003. [15] Bichindaritz I, Siadak M, Jocom J, Moinpour C, Kansu E, Donaldson G, Bush N, Chapko M, Bradshaw J, Sullivan K, CARE-PARTNER: a computerized knowledge-support system for stem-cell post-transplant long-term follow-up on the world wide web. Proc AMIA Symp 1998, pp. 386-390. [16] Montani S., Bellazzi R., Supporting decisions in medical applications: the knowledge management perspective, International Journal of Medical Informatics 68, 2002, pp. 79-90. [17] Lieber J, D’Aquin M, Bey P, Bresson B, Croissant O, Falzon P, Lesur A, Lévêque J, Mollo V, Napoli A, Rios M, and Sauvagnac C, The Kasimir Project: Knowledge Management in Cancerology. Proc 4th Intern Workshop on Enterprise Networking and Computing in Health Care Industry (HealthComm 2002), pp. 125-127. [18] LeBozec C, Jaulent M-C, Zapletal E, and Degoulet P, Unified modeling language and design of a case-based retrieval system in medical imaging. Proc AMIA Symp 1998, pp. 887-891. [19] Van de Vijver MJ., He YD., van’t Veer LJ., Dai H., Hart AA., Voskuil DW., Schreiber GJ., Peterse JL., Roberts C., Marton MJ., Parrish M., Atsma D., Witteveen A., Glas A., Delahaye L., van der Velde T., Bartelink H., Rodenhuis S., Rutgers ET., Friend SH., Bernards R., A gene-expression signature as a predictor of survival in breast cancer, N. Engl. J. Med, 2002 Dec 19; 347(25):pp. 1999-2009.
