Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Doctor of Philosophy

dissertationTemporal reasoning denotes the modeling of causal relationships between different variables across different instances of time, and the prediction of future events or the explanation of past events. Temporal reasoning helps in modeling and understanding interactions between human pathophysiological processes, and in predicting future outcomes such as response to treatment or complications. Dynamic Bayesian Networks (DBN) support modeling changes in patients' condition over time due to both diseases and treatments, using probabilistic relationships between different clinical variables, both within and across different points in time. We describe temporal reasoning and representation in general and DBN in particular, with special attention to DBN parameter learning and inference. We also describe temporal data preparation (aggregation, consolidation, and abstraction) techniques that are applicable to medical data that were used in our research. We describe and evaluate various data discretization methods that are applicable to medical data. Projeny, an opensource probabilistic temporal reasoning toolkit developed as part of this research, is also described. We apply these methods, techniques, and algorithms to two disease processes modeled as Dynamic Bayesian Networks. The first test case is hyperglycemia due to severe illness in patients treated in the Intensive Care Unit (ICU). We model the patients' serum glucose and insulin drip rates using Dynamic Bayesian Networks, and recommend insulin drip rates to maintain the patients' serum glucose within a normal range. The model's safety and efficacy are proven by comparing it to the current gold standard. The second test case is the early prediction of sepsis in the emergency department. Sepsis is an acute life threatening condition that requires timely diagnosis and treatment. We present various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients' admission to the emergency department. We also discuss factors affecting the computational tractability of the models and appropriate optimization techniques. In this dissertation, we present a guide to temporal reasoning, evaluation of various data preparation, discretization, learning and inference methods, proofs using two test cases using real clinical data, an open-source toolkit, and recommend methods and techniques for temporal reasoning in medicine

A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data

A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosis, patient-specific decisions or help in planning the utilization of resources for patient groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling and inference process itself. The challenges from the clinical domain include insufficient knowledge of temporal interactions of processes in the medical literature, the sparse nature and variability of medical data collection, and the difficulty in preparing and abstracting clinical data in a suitable format without losing valuable information in the process. Challenges about the DBN methodology and implementation include the lack of tools that allow easy modelling of temporal processes. Overcoming this challenge will help to solve various clinical temporal reasoning problems. In this thesis, we addressed these challenges while building a temporal network with explanations of the effects of predisposing factors, such as age and gender, and the progression information of all diagnoses using claims data from an insurance company in Kenya. We showed that our network could differentiate the possible probability exposure to a diagnosis given the age and gender and possible paths given a patient's history. We also presented evidence that the more patient history is provided, the better the prediction of future diagnosis

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Thinking in, with, across, and beyond cases with John Forrester

We consider the influence that John Forrester’s work has had on thinking in, with, and from cases in multiple disciplines. Forrester’s essay ‘If p, Then What? Thinking in Cases’ was published in History of the Human Sciences in 1996 and transformed understandings of what a case was, and how case-based thinking worked in numerous human sciences (including, centrally, psychoanalysis). Forrester’s collection of essays Thinking in Cases was published posthumously, after his untimely death in 2015, and is the inspiration for the special issue we introduce. This comprises new research from authors working in and across the history of science and medicine, gender and sexuality studies, philosophy of science, semiotics, film studies, literary studies and comparative literature, psychoanalytic studies, medical humanities, and sociology. This research addresses what it means to reason in cases in particular temporal, spatial, or genre-focused contexts; introduces new figures (e.g. Eugène Azam, C. S. Peirce, Michael Balint) into lineages of case-based reasoning; emphasizes the unfinished and unfinishable character of some case reading and autobiographical accounts; and shows the frequency with which certain kinds of reasoning attempted with cases fail (often in instructive ways). The special issue opens up new directions for thinking and working with cases and case-based reasoning in the humanities and human sciences