Daniel C. Barth-Jones

Nosocomial acquisition of Candida parapsilosis: An epidemiologic study.

OBJECTIVE The purpose of this study was to determine aspects of the epidemiology of nosocomial infection due to Candida parapsilosis. Candida species are important nosocomial pathogens; however, little epidemiologic information is available. PATIENTS AND METHODS We prospectively cultured specimens from 98 patients admitted to the bone marrow transplant unit and a medicine intensive care unit (ICU) of a tertiary care hospital. Specimens from hands of personnel and environmental surfaces were also cultured. Environmental cultures were done before patients were admitted to a studied unit. Restriction enzyme analysis (REA) of chromosomal DNA was used as a typing system to determine the relatedness of strains. RESULTS C. parapsilosis was identified from five patients, six hand cultures from four hospital staff, and two environmental surfaces. All five patients had negative initial cultures and acquired C. parapsilosis after admission to the study unit. There were no significant differences between patients and control subjects in age, underlying disease, immunosuppressive therapy, and instrumentation. The duration of antibiotic therapy (median: 32.8 versus 11.8 days, p = 0.05) and the duration in the unit (means: 30.1 versus 16.1 days, p = 0.048) was longer in patients than in controls. No common source was identified. REA revealed three strain types; however, one strain type was identical in four patients, three staff members, and two environmental surfaces. CONCLUSION These results suggest exogenous acquisition of C. parapsilosis. Based upon isolation of identical patient strains of C. parapsilosis from inanimate surfaces before patients were admitted to a study unit, there is evidence that the organism may have been acquired from the hospital environment. The principal mechanism of transmission was probably indirect contact via the hands of hospital personnel.

Decision making in fuzzy discrete event systems

The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on experts knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.

A Fuzzy Discrete Event System Approach to Determining Optimal HIV/AIDS Treatment Regimens

Treatment decision-making is complex and involves many factors. A systematic decision-making and optimization technology capable of handling variations and uncertainties of patient characteristics and physicians subjectivity is currently unavailable. We recently developed a novel general-purpose fuzzy discrete event systems theory for optimal decision-making. We now apply it to develop an innovative system for medical treatment, specifically for the first round of highly active antiretroviral therapy of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) patients involving three historically widely used regimens. The objective is to develop such a system whose regimen choice for any given patient will exactly match expert AIDS physicians selection to produce the (anticipated) optimal treatment outcome. Our regimen selection system consists of a treatment objectives classifier, fuzzy finite state machine models for treatment regimens, and a genetic-algorithm-based optimizer. The optimizer enables the system to either emulate an individual doctors decision-making or generate a regimen that simultaneously satisfies diverse treatment preferences of multiple physicians to the maximum extent. We used the optimizer to automatically learn the values of 26 parameters of the models. The learning was based on the consensus of AIDS specialists A and B on this project, whose exact agreement was only 35%. The performance of the resulting models was first assessed. We then carried out a retrospective study of the entire system using all the qualifying patients treated in our institutions AIDS Clinical Center in 2001. A total of 35 patients were treated by 13 specialists using the regimens (four and eight patients were treated by specialists A and B, respectively). We compared the actually prescribed regimens with those selected by the system using the same available information. The overall exact agreement was 82.9% (29 out of 35), with the exact agreement with specialists A and B both at 100%. The exact agreement for the remaining 11 physicians not involved in the system training was 73.9% (17 out of 23), an impressive result given the fact that expert opinion can be quite divergent for treatment decisions of such complexity. Our specialists also carefully examined the six mismatched cases and deemed that the system actually chose a more appropriate regimen for four of them. In the other two cases, either would be reasonable choices. Our approach has the capabilities of generalizing, learning, and representing knowledge even in the face of weak consensus, and being readily upgradeable to new medical knowledge. These are practically important features to medical applications in general, and HIV/AIDS treatment in particular, as national HIV/AIDS treatment guidelines are modified several times per year

Control of fuzzy discrete event systems and its applications to clinical treatment planning

In this paper, we further develop a modeling and control approach to fuzzy discrete event systems that we initially proposed in Lin, F. and H. Ying, (2001), (2002). We first investigate an optimal control problem in fuzzy discrete event systems. The problem is abstracted from real applications in biomedical fields. The control objective is to maximize a treatment effectiveness measure while keeping some cost below a given level. This problem is difficult because both the effectiveness function and the cost function are state dependent and hence are not monotonic. Furthermore, the state space of a fuzzy discrete event system is infinite in general. We develop an online approach that can solve this problem. We then apply this approach to HIV/AIDS treatment planning, because it is one of the most difficult treatments in medicine. We also develop a novel computerized treatment decision-making system based on the optimal control approach. The preliminary statistic evaluation of our system shows a strong agreement between the physicians and our system in terms of which treatment regimens to be used for patients of various conditions.

Disclosure risk measures for microdata

We define several disclosure risk measures for microdata. We analyze disclosure risk based on the disclosure control techniques applied to initial microdata. Disclosure Control is the discipline concerned with the modification of data containing confidential information about individual entities, such as persons, households, businesses, etc. in order to prevent third parties working with these data from recognizing entities in the data and thereby disclosing information about these entities. In very broad terms, disclosure risk is the risk that a given form of disclosure will occur if a masked microdataset is released. Microdata represents a series of records, each record containing information on an individual unit. The disclosure risk measures presented in the paper are validated in our experiments.

Theory for a Control Architecture of Fuzzy Discrete Event Systems for Decision Making

Since we introduced a method for control of fuzzy discrete event systems (FDES) in 2001, we have been focusing our attention on medical applications. In this paper, we propose a new control architecture of FDES that includes a fuzzy objective generator for generating optimal control objectives online and an online optimal control scheme using both disablement and enforcement. The optimal control problem is nontrivial because its performance index is state dependent and hence not monotonic. Furthermore, the state space of a FDES is infinite in general. We show that our online approach can solve this problem efficiently. The architecture is general and can be used for decision making in many complex systems. We demonstrate the usefulness of the architecture by applying it to HIV/AIDS treatment planning, because it poses some of the most difficult treatment challenges in medicine. We build a FDES decision model from experts knowledge, treatment guidelines, clinic trials, patient database statistics, and other information available in the medical literature. The system generates optimal control objectives for real patients from our database and applies our online approach to decide a regimen for each patient.

A fuzzy discrete event system for HIV/AIDS treatment planning

Treatment decision-making for most diseases is currently partial art and partial science. To a large extent, this is due to the fact that every patient is unique, and many symptoms and diagnoses are inherently imprecise and difficult to measure. A systematic decision-making and optimization technology capable of handling all the clinical difficulties is still unavailable despite significant efforts documented in the literature, including the use of artificial intelligence and statistical methods. One promising approach is the novel fuzzy discrete event systems whose theoretical framework was recently developed by us. We apply it to treatment planning for HIV/AIDS patients who have never received antiretroviral therapy. We show how to design such a system. We have also statistically evaluated the preliminary results produced by the system in comparison with two AIDS specialists on our team. The results indicate strong agreement between the physicians and the fuzzy discrete event system. This is the first application of fuzzy discrete event systems in the literature.

Assessing global disclosure risk in masked microdata

In this paper, we introduce a general framework for microdata and three disclosure risk measures (minimal, maximal and weighted). We classify the attributes from a given microdata in two different ways: based on their potential identification utility and based on the order relation that exists in their domain of value. We define inversion and change factors that allow data users to quantify the magnitude of masking modification incurred for values of a key attribute. The disclosure risk measures are based on these inversion and change factors, and can be computed for any specific disclosure control method, or any combination of methods applied in succession to a given microdata. Using simulated medical data in our experiments, we show that the proposed disclosure risk measures perform as expected in real-life situations.

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition-primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs.

A Fuzzy Discrete Event System for HIV/AIDS Treatment

The United Nations estimates that 38 million people worldwide are infected with HIV/AIDS, and that more than 22 million have died. Like most diseases, treatment decision-making for this disease is currently more an art than science. This is partially because every patient is unique, with his/her own history, set of genetic traits, predisposition to side effects, and prognosis. We reported previously how we had developed a theoretical framework of novel fuzzy discrete event systems, which are knowledge-based (Lin and Ying, 2002). We showed how to apply it to develop a part of HIV/AIDS regimen selection system for treating antiretroviral-naive patients (Lin et al., 2004). In the present paper, we describe our recent development - we have furthered the system design by adding a Genetic-Algorithm-Based Regimen Selection Optimizer and a Treatment Objectives Classifier to the system. The full system is capable of prescribing a regimen for any given patient. The Optimizer enables the system to either emulate an individual doctors decision-making or generate a regimen that simultaneously satisfies diverse treatment preferences of multiple physicians to the maximum extent. We show the promising preliminary results of retrospective evaluation of the system using 48 treatment-naive patients who started antiretroviral treatment in our AIDS Clinic in 2001. Our fuzzy DES approach possesses a number of unique features and advantages that are especially important to medical applications: (1) higher flexibility and scalability, and (2) easier knowledge upgrade for accommodating fast treatment strategy evolution with minimal system modification. These are particularly important to HIV/AIDS treatment as the U.S. Public Health Service updates its treatment guidelines at least once a year