Edidiong Ekaette

A framework for distributed fault management using intelligent software agents

This paper proposes a framework for distributed management of network faults by software agents. Intelligent network agents with advanced reasoning capabilities address many of the issues for the distribution of processing and control in network management. The agents detect, correlate and selectively seek to derive a clear explanation of alarms generated in their domain. The causal relationship between faults and their effects is presented as a Bayesian network. As evidence (alarms) is gathered, the probability of the presence of any particular fault is strengthened or weakened. Agents having a narrower view of the network forward their findings to another with a much broader view of the network. Depending on the networks degree of automation, the agent can carry out local recovery actions. A prototype reflecting the ideas discussed in this paper is under implementation.

Implications of Cancer Staging Uncertainties in Radiation Therapy Decisions

Introduction . Radiation therapy (RT) for cancer is a critical medical procedure that occurs in a complex environment involving numerous health professionals, hardware, software, and equipment. Uncertainties and potential incidents can lead to inappropriate administration of radiation to patients, with sometimes catastrophic consequences such as premature death or appreciably impaired quality of life. The authors evaluate the impact of incorrectly staging (i.e., estimation of extent of cancer) breast cancer patients and resulting inappropriate treatment decisions. Methods . The authors employ analytic and simulation methods in an influence-diagram framework to estimate the probability of incorrect staging and treatment decisions. As inputs, they use a combination of literature information on the accuracy and precision of pathology and tests as well as expert judgment. Sensitivity and value-of-information analyses are conducted to identify important uncertainties. Results and conclusions . The authors find a small but nontrivial probability that breast cancer patients will be incorrectly staged and thus may be subjected to inappropriate treatment. Results are sensitive to a number of variables, and some routinely used tests for metastasis have very limited information value. This work has implications for the methods used in cancer staging, and the methods are generalizable for quantitative risk assessment of treatment errors.

Taxonometric Applications in Radiotherapy Incident Analysis

Recent publications in both the scientific and the popular press have highlighted the risks to which patients expose themselves when entering a healthcare system. Patient safety issues are forcing us to, not only acknowledge that incidents do occur, but also actively develop the means for assessing and managing the risks of such incidents. To do this, we ideally need to know the probability of an incidents occurrence, the consequences or severity for the patient should it occur, and the basic causes of the incident. A structured approach to the description of failure modes is helpful in terms of communication, avoidance of ambiguity, and, ultimately, decision making for resource allocation. In this report, several classification schemes or taxonomies for use in risk assessment and management are discussed. In particular, a recently developed approach that reflects the activity domains through which the patient passes and that can be used as a basis for quantifying incident severity is described. The estimation of incident severity, which is based on the concept of the equivalent uniform dose, is presented in some detail. We conclude with a brief discussion on the use of a defined basic-causes table and how adding such a table to the reports of incidents can facilitate the allocation of resources.

A Monte Carlo simulation approach to the characterization of uncertainties in cancer staging and radiation treatment decisions

Radiation treatment (RT) for cancer is a critical medical procedure that occurs in a complex environment that is subject to uncertainties and errors. We employed a simulation (a variant of Monte Carlo) model that followed a cohort of hypothetical breast cancer patients to estimate the probability of incorrect staging and treatment decisions. As inputs, we used a combination of literature information and expert judgement. Input variables were defined as probability distributions within the model. Uncertainties were propagated via simulation. Sensitivity and value-of-information analyses were then conducted to quantify the effect of variable uncertainty on the model outputs. We found a small but non-trivial probability that patients would be incorrectly staged and thus be subjected to inappropriate treatment. Some routinely used tests for staging and metastasis detection have very limited informational value. This work has implications for the methods used in cancer staging and subsequent risk assessment of treatment errors.