New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Implementing and evaluating a Web -based close call reporting system at an urban hospital

Medical errors and close calls are pervasive in health care. It is hypothesized that the causes of close calls are the same as for medical errors; therefore learning about close calls can help prevent errors and increase patient safety. Yet despite efforts to encourage close call reporting, close calls as well as medical errors are under-reported in health care. The purpose of this dissertation was to implement and evaluate a web-based anonymous close call reporting system in three units at an urban hospital. ^ The study participants were physicians, nurses and medical technicians (N = 187) who care for patients in the Medical Intermediate Care Unit, the Surgical Intermediate Care Unit, and the Coronary Catheterization Laboratory in the hospital. We provided educational information to the participants on how to use the system and e-mailed and delivered paper reminders to report to the participants throughout the 19-month project. We surveyed the participants at the beginning and at the end of the study to assess their attitudes and beliefs regarding incident reporting. We found that the majority of the health care providers in our study are supportive of incident reporting in general but in practice very few had actually reported an error or a close call, semi-structured interview 20 weeks after we made the close call reporting system available. The purpose of the interviews was to further assess the participants' attitudes regarding incident reporting and the reporting system. Our findings suggest that the health care providers are supportive of medical error reporting in general, but are not convinced of the benefit of reporting close calls. Barriers to close call reporting cited include lack of time, heavy workloads, preferring to take care of close calls "on the spot", and not seeing the benefits of close call reporting. Consequently only two = close calls were reported via the system by two separate caregivers during the project. ^ The findings suggest that future efforts to increase close call reporting must address barriers to reporting, especially the belief among care givers that it is not worth taking time from their already busy schedules to report close calls. ^

Key Aspects Affecting Students’ Perception Regarding the Instructional Quality of Online and Web Based Courses

This study assessed the perceptions of college students regarding the instructional quality of online and web based courses via a content management system. [See PDF for complete abstract]

A Web Services architecture for UMLS Knowledge Sources.

A web service is a collection of industry standards to enable reusability of services and interoperability of heterogeneous applications. The UMLS Knowledge Source (UMLSKS) Server provides remote access to the UMLSKS and related resources. We propose a Web Services Architecture that encapsulates UMLSKS-API and makes it available in distributed and heterogeneous environments. This is the first step towards intelligent and automatic UMLS services discovery and invocation by computer systems in distributed environments such as web.

Patient classification system: An integrated method for measuring nursing intensity and optimizing resource allocation

A patient classification system was developed integrating a patient acuity instrument with a computerized nursing distribution method based on a linear programming model. The system was designed for real-time measurement of patient acuity (workload) and allocation of nursing personnel to optimize the utilization of resources.^ The acuity instrument was a prototype tool with eight categories of patients defined by patient severity and nursing intensity parameters. From this tool, the demand for nursing care was defined in patient points with one point equal to one hour of RN time. Validity and reliability of the instrument was determined as follows: (1) Content validity by a panel of expert nurses; (2) predictive validity through a paired t-test analysis of preshift and postshift categorization of patients; (3) initial reliability by a one month pilot of the instrument in a practice setting; and (4) interrater reliability by the Kappa statistic.^ The nursing distribution system was a linear programming model using a branch and bound technique for obtaining integer solutions. The objective function was to minimize the total number of nursing personnel used by optimally assigning the staff to meet the acuity needs of the units. A penalty weight was used as a coefficient of the objective function variables to define priorities for allocation of staff.^ The demand constraints were requirements to meet the total acuity points needed for each unit and to have a minimum number of RNs on each unit. Supply constraints were: (1) total availability of each type of staff and the value of that staff member (value was determined relative to that type of staff's ability to perform the job function of an RN (i.e., value for eight hours RN = 8 points, LVN = 6 points); (2) number of personnel available for floating between units.^ The capability of the model to assign staff quantitatively and qualitatively equal to the manual method was established by a thirty day comparison. Sensitivity testing demonstrated appropriate adjustment of the optimal solution to changes in penalty coefficients in the objective function and to acuity totals in the demand constraints.^ Further investigation of the model documented: correct adjustment of assignments in response to staff value changes; and cost minimization by an addition of a dollar coefficient to the objective function. ^

Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

Federally regulated institutional review boards: The aspects of a "model" IRB---Is the current system in need of a tune up or complete overhaul?

Institutional Review Boards (IRBs) are the primary gatekeepers for the protection of ethical standards of federally regulated research on human subjects in this country. This paper focuses on what general, broad measures that may be instituted or enhanced to exemplify a "model IRB". This is done by examining the current regulatory standards of federally regulated IRBs, not private or commercial boards, and how many of those standards have been found either inadequate or not generally understood or followed. The analysis includes suggestions on how to bring about changes in order to make the IRB process more efficient, less subject to litigation, and create standardized educational protocols for members. The paper also considers how to include better oversight for multi-center research, increased centralization of IRBs, utilization of Data Safety Monitoring Boards when necessary, payment for research protocol review, voluntary accreditation, and the institution of evaluation/quality assurance programs. ^ This is a policy study utilizing secondary analysis of publicly available data. Therefore, the research for this paper focuses on scholarly medical/legal journals, web information from the Department of Health and Human Services, Federal Drug Administration, and the Office of the Inspector General, Accreditation Programs, law review articles, and current regulations applicable to the relevant portions of the paper. ^ Two issues are found to be consistently cited by the literature as major concerns. One is a need for basic, standardized educational requirements across all IRBs and its members, and secondly, much stricter and more informed management of continuing research. There is no federally regulated formal education system currently in place for IRB members, except for certain NIH-based trials. Also, IRBs are not keeping up with research once a study has begun, and although regulated to do so, it does not appear to be a great priority. This is the area most in danger of increased litigation. Other issues such as voluntary accreditation and outcomes evaluation are slowing gaining steam as the processes are becoming more available and more sought after, such as JCAHO accrediting of hospitals. ^ Adopting the principles discussed in this paper should promote better use of a local IRBs time, money, and expertise for protecting the vulnerable population in their care. Without further improvements to the system, there is concern that private and commercial IRBs will attempt to create a monopoly on much of the clinical research in the future as they are not as heavily regulated and can therefore offer companies quicker and more convenient reviews. IRBs need to consider the advantages of charging for their unique and important services as a cost of doing business. More importantly, there must be a minimum standard of education for all IRB members in the area of the ethical standards of human research and a greater emphasis placed on the follow-up of ongoing research as this is the most critical time for study participants and may soon lead to the largest area for litigation. Additionally, there should be a centralized IRB for multi-site trials or a study website with important information affecting the trial in real time. There needs to be development of standards and metrics to assess the performance of the IRBs for quality assurance and outcome evaluations. The boards should not be content to run the business of human subjects' research without determining how well that function is actually being carried out. It is important that federally regulated IRBs provide excellence in human research and promote those values most important to the public at large.^

Improved Techniques for Acquisition and Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Detecting Vascular Permeability in the Central Nervous System

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique for quantitative assessment of the integrity of blood-brain barrier and blood-spinal cord barrier (BSCB) in the presence of central nervous system pathologies. However, the results of DCE-MRI show substantial variability. The high variability can be caused by a number of factors including inaccurate T1 estimation, insufficient temporal resolution and poor contrast-to-noise ratio. My thesis work is to develop improved methods to reduce the variability of DCE-MRI results. To obtain fast and accurate T1 map, the Look-Locker acquisition technique was implemented with a novel and truly centric k-space segmentation scheme. In addition, an original multi-step curve fitting procedure was developed to increase the accuracy of T1 estimation. A view sharing acquisition method was implemented to increase temporal resolution, and a novel normalization method was introduced to reduce image artifacts. Finally, a new clustering algorithm was developed to reduce apparent noise in the DCE-MRI data. The performance of these proposed methods was verified by simulations and phantom studies. As part of this work, the proposed techniques were applied to an in vivo DCE-MRI study of experimental spinal cord injury (SCI). These methods have shown robust results and allow quantitative assessment of regions with very low vascular permeability. In conclusion, applications of the improved DCE-MRI acquisition and analysis methods developed in this thesis work can improve the accuracy of the DCE-MRI results.