An important research enterprise for the Artificial Intelligence community since the 1970s has been the design of expert or “knowledge-based” systems. These programs used explicitly encoded human knowledge, often in the form of a production rule system, to solve problems in the areas of diagnostics and prognostics. The earliest research/development program in expert systems was created by Profe...

Objective: Evaluate the accuracy of the detailed diagnostic reasoning of the Heart Failure Program incorporating a new mechanism to handle temporal relationships and severity constraints. Design: Tools were developed to summarize diagnoses and automatically generate evaluation forms. Five expert cardiologists were asked to review the reasoning of the program, with two analyzing each case. Cases...

OBJECTIVE Evaluate the accuracy of the detailed diagnostic reasoning of the Heart Failure Program incorporating a new mechanism to handle temporal relationships and severity constraints. DESIGN Tools were developed to summarize diagnoses and automatically generate evaluation forms. Five expert cardiologists were asked to review the reasoning of the program, with two analyzing each case. Cases...

The process of the acquisition of an agreed, shared task model as a means to structure interaction between expert users and knowledge engineers is described. The role existing (generic) task models play in this process is illustrated for two domains of application, both domains requiring diagnostic reasoning. In both domains different levels of interaction between an expert user and a diagnosti...

In many diagnosis-and-repair domains, diagnostic reasoning cannot be abstracted from repair actions, nor from actions necessary to obtain diagnostic information. In general, in exploratory-corrective domains an agent has to interleave exploratory activity with activity aimed at achieving its goals. In TraumAID 2.0, a consultation system for multiple trauma management, we implement a reasoning f...

This paper describes some general diagnostic reasoning techniques which exploit recent advances in the field of Artificial Intelligence. These techniques were developed over a period of several years of research in this area at the Naval Research Laboratory. One of the products of this research is a fully implemented diagnostic reasoning system called FIS which embodies these techniques and is ...

BACKGROUND Better understanding of clinical reasoning could reduce diagnostic error linked to 8% of adverse medical events and 30% of malpractice cases. To a greater extent than the evidence-based movement, the clinical reasoning literature asserts the importance of practitioner intuition—unconscious elements of diagnostic reasoning. The study aimed to analyse the content of case report summari...

Diagnostic errors can result in tangible harm to patients. Despite our advances in medicine, the mental processes required to make a diagnosis exhibits shortcomings, causing diagnostic errors. Cognitive factors are found to be an important cause of diagnostic errors. With new understanding from psychology and social sciences, clinical medicine is now beginning to appreciate that our clinical re...

The purpose in diagnostic reasoning is to find the cause of observed effects by applying knowledge about the effects and their potential causes. In the causal structure linking causes and effects, effects can share causes or be linked more indirectly. The causal diversity effect reflects the increased support of a cause by a more widespread distribution of effects within the underlying causal s...