Accessing Patient Information for Probabilistic Patient Models Using Existing Standards (original) (raw)

User-centered Development of a Clinical Decision Support System

2022

Scientific progress is offering increasingly better ways to tailor a patient's treatment to the patient's needs, i.e., better support for optimal clinical decision-making can be offered. Choosing the appropriate treatment for a patient depends on numerous factors, including pathology results, tumor stage, genetic, and molecular characteristics. Bayesian networks are a type of probabilistic artificial intelligence, which in principle would be suitable to support complex clinical decision-making. However, most clinicians do not have experience with these networks. This paper describes an approach of developing a clinical decision support system based on Bayesian networks, that does not require insight knowledge about the underlying computational model for its use. It is developed as a therapy-oriented approach with a focus on usability and explainability. The approach features the computation and presentation of individualized treatment recommendations, comparison of treatments and patient cases, as well as explanations and visualizations providing additional information on the current patient case.

Architectures integrating case-based reasoning and bayesian networks for clinical decision support

Intelligent Information Processing V, 2010

In this paper we discuss different architectures for reasoning under uncertainty related to our ongoing research into building a medical decision support system. The uncertainty in the medical domain can be divided into a well understood part and a less understood part. This motivates the use of a hybrid decision support system, and in particular, we argue that a Bayesian network should be used for those parts of the domain that are well understood and can be explicitly modeled, whereas a case-based reasoning system should be employed to reason in parts of the domain where no such model is available. Four architectures that combine Bayesian networks and case-based reasoning are proposed, and our working hypothesis is that these hybrid systems each will perform better than either framework will do on its own.

Turning medical data into decision-support knowledge.

1994

Abstract Advances in information collection and analysis are reaching the point of providing physicians with the help of computer-based assistants. These systems will provide rapid second opinions to physicians in a clinical setting as well as assist them in the analysis of large sets of patient descriptions for research purposes. This paper presents INC2. 5 as such a decision-support system. INC2.

Annotated Bayesian networks: a tool to integrate textual and probabilistic medical knowledge

Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001, 2001

In previous publications we have reported on the development of Bayesian Network models for the preoperative discrimination between malignant and benign ovarian mass. The models incorporated both medical background knowledge and patient data, which required the traceability of the incorporated prior medical knowledge. For this purpose, we followed a particular annotation method for Bayesian Networks using a dedicated representation. In this paper we present the resulting Annotated Bayesian Network (ABN) representation that consists of a regular Bayesian Network with standard probabilistic semantics and a corresponding semantic network, to which the textual information sources are attached. We demonstrate the applicability of such dual model to represent both the rigorous probabilistic and the unconstrained textual medical knowledge. We describe methods on how these Annotated Bayesian Network models can be used: (1) as a domain model to arrange the personal textual information of the clinician according to the semantics of the domain , (2) in decision support to provide detailed, even personalized explanation, and (3) to enhance the information retrieval to find new textual information more efficiently.

Therapy Decision Support System using Bayesian Networks for Multidisciplinary Treatment Decisions

2017

Treatment decision-making in head and neck oncology is gaining complexity by the increasing evidence pointing towards more individualized and selective treatment options. Therefore, decision making in multidisciplinary teams is becoming the key point in the clinical pathways. Clinical decision-support systems based on Bayesian networks can support complex decision-making processes by providing mathematically correct and transparent advises. In the last three decades, different clinical applications of Bayesian networks have been proposed. Because appropriate data for model learning and testing is often unobtainable, expert modeling is required. To decrease the modeling and validation effort, networks usually represent small or highly simplified decision structures. However, especially systems for supporting multidisciplinary treatment decisions may only gain a user’s confidence if the systems’ results are comprehensive and comprehensible. Challenges in developing such systems relate...

Medicine expert system dynamic Bayesian Network and ontology based

Expert Systems With Applications, 2011

The paper proposes an application framework to be used for medicine assisted diagnosis based on ontology and Bayesian Network (DBNO). There are two goals: (1) to separate the domain knowledge from the probabilistic information and (2) to create an intuitive user interface. The framework architecture has three layers: knowledge, uncertainty model and user interface. The contributions of the domain experts are decoupled, the ontology builder will create the domain concepts and relationships focusing on the domain knowledge only. The uncertainty model is Bayesian Network and the probabilities of the variables states are stored in a profile repository. The diagnostician will use the user interface feeded with the domain ontology and one uncertainty profile. The application was tested on a sample medicine model for the diagnose of heart disease.

Bayesian Networks for Clinical Decision Support in Lung Cancer Care

PLoS ONE, 2013

Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Cancer Database (LUCADA), we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data. We show first that the BN structure elicited from clinicians achieves a disappointing area under the ROC curve of 0.75 (± 0.03), whereas a structure learned by the CAMML hybrid causal discovery algorithm, which adheres with the temporal restrictions, achieves 0.81 (± 0.03). Second, our causal intervention results reveal that BN treatment recommendations, based on prescribing the treatment plan that maximises survival, can only predict the recorded treatment plan 29% of the time. However, this percentage rises to 76% when partial matches are included.