CSEO - the Cigarette Smoke Exposure Ontology

BACKGROUND

In the past years, significant progress has been made to develop and use experimental settings for extensive data collection on tobacco smoke exposure and tobacco smoke exposure-associated diseases. Due to the growing number of such data, there is a need for domain-specific standard ontologies to facilitate the integration of tobacco exposure data.

RESULTS

The CSEO (version 1.0) is composed of 20091 concepts. The ontology in its current form is able to capture a wide range of cigarette smoke exposure concepts within the knowledge domain of exposure science with a reasonable sensitivity and specificity. Moreover, it showed a promising performance when used to answer domain expert questions. The CSEO complies with standard upper-level ontologies and is freely accessible to the scientific community through a dedicated wiki at https://publicwiki-01.fraunhofer.de/CSEO-Wiki/index.php/Main_Page.

CONCLUSIONS

The CSEO has potential to become a widely used standard within the academic and industrial community. Mainly because of the emerging need of systems toxicology to controlled vocabularies and also the lack of suitable ontologies for this domain, the CSEO prepares the ground for integrative systems-based research in the exposure science.

Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks

Background

Using knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper’s primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?

Results

Our existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.

Conclusions

With some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels.

Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support

This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models' performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics' suggestion for UTI.

Evaluation of an Enhanced Role-Based Access Control model to manage information access in collaborative processes for a statewide clinical education program

BACKGROUND

Managing information access in collaborative processes is a critical requirement to team-based biomedical research, clinical education, and patient care. We have previously developed a computation model, Enhanced Role-Based Access Control (EnhancedRBAC), and applied it to coordinate information access in the combined context of team collaboration and workflow for the New York State HIV Clinical Education Initiative (CEI) program. We report in this paper an evaluation study to assess the effectiveness of the EnhancedRBAC model for information access management in collaborative processes when applied to CEI.

METHODS

We designed a cross-sectional study and performed two sets of measurement: (1) degree of agreement between EnhancedRBAC and a control system CEIAdmin based on 9152 study cases, and (2) effectiveness of EnhancedRBAC in terms of sensitivity, specificity, and accuracy based on a gold-standard with 512 sample cases developed by a human expert panel. We applied stratified random sampling, partial factorial design, and blocked randomization to ensure a representative case sample and a high-quality gold-standard.

RESULTS

With the kappa statistics of four comparisons in the range of 0.80-0.89, EnhancedRBAC has demonstrated a high level of agreement with CEIAdmin. When evaluated against the gold-standard, EnhancedRBAC has achieved sensitivities in the range of 97-100%, specificities at the level of 100%, and accuracies in the range of 98-100%.

CONCLUSIONS

The initial results have shown that the EnhancedRBAC model can be effectively used to manage information access in the combined context of team collaboration and workflow for coordination of clinical education programs. Future research is required to perform longitudinal evaluation studies and to assess the effectiveness of EnhancedRBAC in other applications.

On quantum models of the human mind

Recent years have witnessed rapidly increasing interests in developing quantum theoretical models of human cognition. Quantum mechanisms have been taken seriously to describe how the mind reasons and decides. Papers in this special issue report the newest results in the field. Here we discuss why the two levels of commitment, treating the human brain as a quantum computer and merely adopting abstract quantum probability principles to model human cognition, should be integrated. We speculate that quantum cognition models gain greater modeling power due to a richer representation scheme.

Expert system for skin problem consultation in Thai traditional medicine

BACKGROUND

This paper aimed to demonstrate the research and development of a rule-based expert system for skin problem consulting in the areas of acne, melasma, freckle, wrinkle, and uneven skin tone, with recommended treatments from Thai traditional medicine knowledge.

MATERIALS AND METHODS

The tool selected for developing the expert system is a software program written in the PHP language. MySQL database is used to work together with PHP for building database of the expert system. The system is web-based and can be reached from anywhere with Internet access.

RESULTS

The developed expert system gave recommendations on the skin problem treatment with Thai herbal recipes and Thai herbal cosmetics based on 416 rules derived from primary and secondary sources. The system had been tested by 50 users consisting of dermatologists, Thai traditional medicine doctors, and general users. The developed system was considered good for learning and consultation.

CONCLUSION

The present work showed how such a scattered body of traditional knowledge as Thai traditional medicine and herbal recipes could be collected, organised and made accessible to users and interested parties. The expert system developed herein should contribute in a meaningful way towards preserving the knowledge and helping promote the use of Thai traditional medicine as a practical alternative medicine for the treatment of illnesses.

Describing disease processes using a probabilistic logic of qualitative time

BACKGROUND

Clinical knowledge about progress of diseases is characterised by temporal information as well as uncertainty. However, precise timing information is often unavailable in medicine. In previous research this problem has been tackled using Allen's qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty.

OBJECTIVES

It is investigated whether and how Allen's temporal algebra can be extended to handle uncertainty to better fit available knowledge and data of disease processes.

METHODS

To bridge the gap between probability theory and qualitative time reasoning, methods from probabilistic logic are explored. The relation between the probabilistic logic representation and dynamic Bayesian networks is analysed. By studying a typical, and clinically relevant problem, the detection of exacerbations of chronic obstructive pulmonary disease (COPD), it is determined whether the developed probabilistic logic of qualitative time is medically useful.

RESULTS

The probabilistic logic extension of Allen's temporal algebra, called Qualitative Time CP-logic provides a tool to model disease processes at a natural level of abstraction and is sufficiently powerful to reason with imprecise, uncertain knowledge. The representation of the COPD disease process gives evidence that the framework can be applied functionally to a clinical problem.

CONCLUSION

The combination of qualitative time and probabilistic logic offers a useful framework for modelling knowledge and data to describe disease processes in clinical medicine.

Clinical modeling--a critical analysis

BACKGROUND

Modeling clinical processes (and their informational representation) is a prerequisite for optimally enabling and supporting high quality and safe care through information and communication technology and meaningful use of gathered information.

OBJECTIVES

The paper investigates existing approaches to clinical modeling, thereby systematically analyzing the underlying principles, the consistency with and the integration opportunity to other existing or emerging projects, as well as the correctness of representing the reality of health and health services.

METHODS

The analysis is performed using an architectural framework for modeling real-world systems. In addition, fundamental work on the representation of facts, relations, and processes in the clinical domain by ontologies is applied, thereby including the integration of advanced methodologies such as translational and system medicine.

RESULTS

The paper demonstrates fundamental weaknesses and different maturity as well as evolutionary potential in the approaches considered. It offers a development process starting with the business domain and its ontologies, continuing with the Reference Model-Open Distributed Processing (RM-ODP) related conceptual models in the ICT ontology space, the information and the computational view, and concluding with the implementation details represented as engineering and technology view, respectively.

CONCLUSION

The existing approaches reflect at different levels the clinical domain, put the main focus on different phases of the development process instead of first establishing the real business process representation and therefore enable quite differently and partially limitedly the domain experts' involvement.

Diagnosis support system based on clinical guidelines: comparison between case-based fuzzy cognitive maps and Bayesian networks

Several studies have described the prevalence and severity of diagnostic errors. Diagnostic errors can arise from cognitive, training, educational and other issues. Examples of cognitive issues include flawed reasoning, incomplete knowledge, faulty information gathering or interpretation, and inappropriate use of decision-making heuristics. We describe a new approach, case-based fuzzy cognitive maps, for medical diagnosis and evaluate it by comparison with Bayesian belief networks. We created a semantic web framework that supports the two reasoning methods. We used database of 174 anonymous patients from several European hospitals: 80 of the patients were female and 94 male with an average age 45±16 (average±stdev). Thirty of the 80 female patients were pregnant. For each patient, signs/symptoms/observables/age/sex were taken into account by the system. We used a statistical approach to compare the two methods.

Computer-interpretable clinical guidelines: a methodological review

Clinical practice guidelines (CPGs) aim to improve the quality of care, reduce unjustified practice variations and reduce healthcare costs. In order for them to be effective, clinical guidelines need to be integrated with the care flow and provide patient-specific advice when and where needed. Hence, their formalization as computer-interpretable guidelines (CIGs) makes it possible to develop CIG-based decision-support systems (DSSs), which have a better chance of impacting clinician behavior than narrative guidelines. This paper reviews the literature on CIG-related methodologies since the inception of CIGs, while focusing and drawing themes for classifying CIG research from CIG-related publications in the Journal of Biomedical Informatics (JBI). The themes span the entire life-cycle of CIG development and include: knowledge acquisition and specification for improved CIG design, including (1) CIG modeling languages and (2) CIG acquisition and specification methodologies, (3) integration of CIGs with electronic health records (EHRs) and organizational workflow, (4) CIG validation and verification, (5) CIG execution engines and supportive tools, (6) exception handling in CIGs, (7) CIG maintenance, including analyzing clinician's compliance to CIG recommendations and CIG versioning and evolution, and finally (8) CIG sharing. I examine the temporal trends in CIG-related research and discuss additional themes that were not identified in JBI papers, including existing themes such as overcoming implementation barriers, modeling clinical goals, and temporal expressions, as well as futuristic themes, such as patient-centric CIGs and distributed CIGs.

Implementation and management of a biomedical observation dictionary in a large healthcare information system

OBJECTIVE

This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system.

METHODS

AP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary.

RESULTS

This new management platform is now active in AP-HP. The AnaBio dictionary is shared by 120 laboratories and currently includes 50 000 codes. The mapping implementation to LOINC reaches 40% of the AnaBio entries and uses 26% of LOINC records. The results of our work validate the choice made to develop a local dictionary aligned with LOINC.

DISCUSSION AND CONCLUSIONS

This work constitutes a first step towards a wider use of the platform. The next step will support the entire biomedical production chain, from the clinician prescription, through laboratory tests tracking in the laboratory information system to the communication of results and the use for decision support and biomedical research. In addition, the increase in the mapping implementation to LOINC ensures the interoperability allowing communication with other international health institutions.

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner’s knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner’s knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

A proposal for augmenting biological model construction with a semi-intelligent computational modeling assistant

The translational challenge in biomedical research lies in the effective and efficient transfer of mechanistic knowledge from one biological context to another. Implicit in this process is the establishment of causality from correlation in the form of mechanistic hypotheses. Effectively addressing the translational challenge requires the use of automated methods, including the ability to computationally capture the dynamic aspect of putative hypotheses such that they can be evaluated in a high throughput fashion. Ontologies provide structure and organization to biomedical knowledge; converting these representations into executable models/simulations is the next necessary step. Researchers need the ability to map their conceptual models into a model specification that can be transformed into an executable simulation program. We suggest this mapping process, which approximates certain steps in the development of a computational model, can be expressed as a set of logical rules, and a semi-intelligent computational agent, the Computational Modeling Assistant (CMA), can perform reasoning to develop a plan to achieve the construction of an executable model. Presented herein is a description and implementation for a model construction reasoning process between biomedical and simulation ontologies that is performed by the CMA to produce the specification of an executable model that can be used for dynamic knowledge representation.

Two kinds of knowledge in scientific discovery

Research on computational models of scientific discovery investigates both the induction of descriptive laws and the construction of explanatory models. Although the work in law discovery centers on knowledge-lean approaches to searching a problem space, research on deeper modeling tasks emphasizes the pivotal role of domain knowledge. As an example, our own research on inductive process modeling uses information about candidate processes to explain why variables change over time. However, our experience with IPM, an artificial intelligence system that implements this approach, suggests that process knowledge is insufficient to avoid consideration of implausible models. To this end, the discovery system needs additional knowledge that constrains the model structures. We report on an extended system, SC-IPM, that uses such information to reduce its search through the space of candidates and to produce models that human scientists find more plausible. We also argue that although people carry out less extensive search than SC-IPM, they rely on the same forms of knowledge--processes and constraints--when constructing explanatory models.

The implementation and evaluation of a theory for high level cognitive skill acquisition through expert systems modelling techniques

From previous studies by Koubek and Salvendy (1988), it has been established that differences exist in the high level controlled processes between expert (E) and super-expert (SE) subjects on computer program modification tasks. This study examines the implications of this finding by applying modelling techniques via expert systems technology. To examine the hypothesis that the knowledge representation is critical to SE performance, and to indicate how these results might have practical application, two small prototype expert systems were developed using the E and SE knowledge representations respectively. A qualitative analysis reveals significant performance differences between systems attributable to the knowledge representation and suggests a combination of E and SE knowledge be used for construction of a hybrid expert system. The SE knowledge base is a hierarchical structure and is organized under abstract categories, suggesting a breadth first approach. This structure allows for interactions within the program itself and between the program and user environment. The E knowledge base is narrow and task specific, indicating a depth first approach. In conclusion, theoretical and practical implications of this research into SE performance and modelling techniques are discussed.