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. ^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Introduction to Bayesian Data Analysis

A workshop providing an introduction to Bayesian data analysis and hypothesis testing using R, Jags and the BayesFactor package.

Afdeyu Research Substation: Hydrometeorological Data Analysis 1984-2007

This report presents a basic analyis of the data collected on agroclimatology, erosion, and soil and water conservation at Afdeyu Station in the central highlands of Eritrea between 1984 and 2007. Datasets and graphs include rainfall, air and soil surface temperatures, soil loss, surface runoff, river discharge, and land use including cropping patterns of the measured catchment.

Detection of Malnutrition in Patients Undergoing Maintenance Haemodialysis: A Quantitative Data Analysis on 12 Parameters

Background Protein-energy-malnutrition (PEM) is common in people with end stage kidney disease (ESKD) undergoing maintenance haemodialysis (MHD) and correlates strongly with mortality. To this day, there is no gold standard for detecting PEM in patients on MHD. Aim of Study The aim of this study was to evaluate if Nutritional Risk Screening 2002 (NRS-2002), handgrip strength measurement, mid-upper arm muscle area (MUAMA), triceps skin fold measurement (TSF), serum albumin, normalised protein catabolic rate (nPCR), Kt/V and eKt/V, dry body weight, body mass index (BMI), age and time since start on MHD are relevant for assessing PEM in patients on MHD. Methods The predictive value of the selected parameters on mortality and mortality or weight loss of more than 5% was assessed. Quantitative data analysis of the 12 parameters in the same patients on MHD in autumn 2009 (n = 64) and spring 2011 (n = 40) with paired statistical analysis and multivariate logistic regression analysis was performed. Results Paired data analysis showed significant reduction of dry body weight, BMI and nPCR. Kt/Vtot did not change, eKt/v and hand grip strength measurements were significantly higher in spring 2011. No changes were detected in TSF, serum albumin, NRS-2002 and MUAMA. Serum albumin was shown to be the only predictor of death and of the combined endpoint “death or weight loss of more than 5%”. Conclusion We now screen patients biannually for serum albumin, nPCR, Kt/V, handgrip measurement of the shunt-free arm, dry body weight, age and time since initiation of MHD.

Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands

Accurate rainfall data are the key input parameter for modelling river discharge and soil loss. Remote areas of Ethiopia often lack adequate precipitation data and where these data are available, there might be substantial temporal or spatial gaps. To counter this challenge, the Climate Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction (NCEP) readily provides weather data for any geographic location on earth between 1979 and 2014. This study assesses the applicability of CFSR weather data to three watersheds in the Blue Nile Basin in Ethiopia. To this end, the Soil and Water Assessment Tool (SWAT) was set up to simulate discharge and soil loss, using CFSR and conventional weather data, in three small-scale watersheds ranging from 112 to 477 ha. Calibrated simulation results were compared to observed river discharge and observed soil loss over a period of 32 years. The conventional weather data resulted in very good discharge outputs for all three watersheds, while the CFSR weather data resulted in unsatisfactory discharge outputs for all of the three gauging stations. Soil loss simulation with conventional weather inputs yielded satisfactory outputs for two of three watersheds, while the CFSR weather input resulted in three unsatisfactory results. Overall, the simulations with the conventional data resulted in far better results for discharge and soil loss than simulations with CFSR data. The simulations with CFSR data were unable to adequately represent the specific regional climate for the three watersheds, performing even worse in climatic areas with two rainy seasons. Hence, CFSR data should not be used lightly in remote areas with no conventional weather data where no prior analysis is possible.

Design and implementation of a clinical dashboard

In a large health care system, the importance of accurate information as feedback mechanisms about its performance is necessary on many levels from the senior level management to service level managers for valid decision-making purposes. The implementation of dashboards is one way to remedy the problem of data overload by providing up-to-date, accurate, and concise information. As this health care system seeks to have an organized, systematic review mechanism in place, dashboards are being created in a variety of the hospital service departments to monitor performance indicators. The Infection Control Administration of this health care system is one that does not currently utilize a dashboard but seeks to implement one. ^ The purpose of this project is to research and design a clinical dashboard for the Infection Control Administration. The intent is that the implementation and usefulness of the clinical dashboard translates into improvement in the measurement of health care quality.^

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

Secondary data analysis of a pilot study of use of an instructional video for dietary assessment

Introduction. Food frequency questionnaires (FFQ) are used study the association between dietary intake and disease. An instructional video may potentially offer a low cost, practical method of dietary assessment training for participants thereby reducing recall bias in FFQs. There is little evidence in the literature of the effect of using instructional videos on FFQ-based intake. Objective. This analysis compared the reported energy and macronutrient intake of two groups that were randomized either to watch an instructional video before completing an FFQ or to view the same instructional video after completing the same FFQ. Methods. In the parent study, a diverse group of students, faculty and staff from Houston Community College were randomized to two groups, stratified by ethnicity, and completed an FFQ. The "video before" group watched an instructional video about completing the FFQ prior to answering the FFQ. The "video after" group watched the instructional video after completing the FFQ. The two groups were compared on mean daily energy (Kcal/day), fat (g/day), protein (g/day), carbohydrate (g/day) and fiber (g/day) intakes using descriptive statistics and one-way ANOVA. Demographic, height, and weight information was collected. Dietary intakes were adjusted for total energy intake before the comparative analysis. BMI and age were ruled out as potential confounders. Results. There were no significant differences between the two groups in mean daily dietary intakes of energy, total fat, protein, carbohydrates and fiber. However, a pattern of higher energy intake and lower fiber intake was reported in the group that viewed the instructional video before completing the FFQ compared to those who viewed the video after. Discussion. Analysis of the difference between reported intake of energy and macronutrients showed an overall pattern, albeit not statistically significant, of higher intake in the video before versus the video after group. Application of instructional videos for dietary assessment may require further research to address the validity of reported dietary intakes in those who are randomized to watch an instructional video before reporting diet compared to a control groups that does not view a video.^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

Secondary data analysis of lead screening compliance in high-risk children

Introduction. Despite the ban of lead-containing gasoline and paint, childhood lead poisoning remains a public health issue. Furthermore, a Medicaid-eligible child is 8 times more likely to have an elevated blood lead level (EBLL) than a non-Medicaid child, which is the primary reason for the early detection lead screening mandate for ages 12 and 24 months among the Medicaid population. Based on field observations, there was evidence that suggested a screening compliance issue. Objective. The purpose of this study was to analyze blood lead screening compliance in previously lead poisoned Medicaid children and test for an association between timely lead screening and timely childhood immunizations. The mean months between follow-up tests were also examined for a significant difference between the non-compliant and compliant lead screened children. Methods. Access to the surveillance data of all childhood lead poisoned cases in Bexar County was granted by the San Antonio Metropolitan Health District. A database was constructed and analyzed using descriptive statistics, logistic regression methods and non-parametric tests. Lead screening at 12 months of age was analyzed separately from lead screening at 24 months. The small portion of the population who were also related were included in one analysis and removed from a second analysis to check for significance. Gender, ethnicity, age of home, and having a sibling with an EBLL were ruled out as confounders for the association tests but ethnicity and age of home were adjusted in the nonparametric tests. Results. There was a strong significant association between lead screening compliance at 12 months and childhood immunization compliance, with or without including related children (p<0.00). However, there was no significant association between the two variables at the age of 24 months. Furthermore, there was no significant difference between the median of the mean months of follow-up blood tests among the non-compliant and compliant lead screened population for at the 12 month screening group but there was a significant difference at the 24 month screening group (p<0.01). Discussion. Descriptive statistics showed that 61% and 56% of the previously lead poisoned Medicaid population did not receive their 12 and 24 month mandated lead screening on time, respectively. This suggests that their elevated blood lead level may have been diagnosed earlier in their childhood. Furthermore, a child who is compliant with their lead screening at 12 months of age is 2.36 times more likely to also receive their childhood immunizations on time compared to a child who was not compliant with their 12 month screening. Even though there was no statistical significant association found for the 24 month group, the public health significance of a screening compliance issue is no less important. The Texas Medicaid program needs to enforce lead screening compliance because it is evident that there has been no monitoring system in place. Further recommendations include a need for an increased focus on parental education and the importance of taking their children for wellness exams on time.^

A categorical data analysis approach to estimation of demographic characteristics for Texas county populations

The need for timely population data for health planning and Indicators of need has Increased the demand for population estimates. The data required to produce estimates is difficult to obtain and the process is time consuming. Estimation methods that require less effort and fewer data are needed. The structure preserving estimator (SPREE) is a promising technique not previously used to estimate county population characteristics. This study first uses traditional regression estimation techniques to produce estimates of county population totals. Then the structure preserving estimator, using the results produced in the first phase as constraints, is evaluated.^ Regression methods are among the most frequently used demographic methods for estimating populations. These methods use symptomatic indicators to predict population change. This research evaluates three regression methods to determine which will produce the best estimates based on the 1970 to 1980 indicators of population change. Strategies for stratifying data to improve the ability of the methods to predict change were tested. Difference-correlation using PMSA strata produced the equation which fit the data the best. Regression diagnostics were used to evaluate the residuals.^ The second phase of this study is to evaluate use of the structure preserving estimator in making estimates of population characteristics. The SPREE estimation approach uses existing data (the association structure) to establish the relationship between the variable of interest and the associated variable(s) at the county level. Marginals at the state level (the allocation structure) supply the current relationship between the variables. The full allocation structure model uses current estimates of county population totals to limit the magnitude of county estimates. The limited full allocation structure model has no constraints on county size. The 1970 county census age - gender population provides the association structure, the allocation structure is the 1980 state age - gender distribution.^ The full allocation model produces good estimates of the 1980 county age - gender populations. An unanticipated finding of this research is that the limited full allocation model produces estimates of county population totals that are superior to those produced by the regression methods. The full allocation model is used to produce estimates of 1986 county population characteristics. ^

Planning and implementation efficacy as a determinant of health risk communication outcomes: Case studies of the U.S. Public Health Service (PHS)

This study was conducted under the auspices of the Subcommittee on Risk Communication and Education of the Committee to Coordinate Environmental Health and Related Programs (CCEHRP) to determine how Public Health Service (PHS) agencies are communicating information about health risk, what factors contributed to effective communication efforts, and what specific principles, strategies, and practices best promote more effective health risk communication outcomes.^ Member agencies of the Subcommittee submitted examples of health risk communication activities or decisions they perceived to be effective and some examples of cases they thought had not been as effective as desired. Of the 10 case studies received, 7 were submitted as examples of effective health risk communication, and 3, as examples of less effective communication.^ Information contained in the 10 case studies describing the respective agencies' health risk communication strategies and practices was compared with EPA's Seven Cardinal Rules of Risk Communication, since similar rules were not found in any PHS agency. EPA's rules are: (1) Accept and involve the public as a legitimate partner. (2) Plan carefully and evaluate your efforts. (3) Listen to the public's specific concerns. (4) Be honest, frank, and open. (5) Coordinate and collaborate with other credible sources. (6) Meet the needs of the media. (7) Speak clearly and with compassion.^ On the basis of case studies analysis, the Subcommittee, in their attempts to design and implement effective health risk communication campaigns, identified a number of areas for improvement among the agencies. First, PHS agencies should consider developing a focus specific to health risk communication (i.e., office or specialty resource). Second, create a set of generally accepted practices and guidelines for effective implementation and evaluation of PHS health risk communication activities and products. Third, organize interagency initiatives aimed at increasing awareness and visibility of health risk communication issues and trends within and between PHS agencies.^ PHS agencies identified some specific implementation strategies the CCEHRP might consider pursuing to address the major recommendations. Implementation strategies common to PHS agencies emerged in the following five areas: (1) program development, (2) building partnerships, (3) developing training, (4) expanding information technologies, and (5) conducting research and evaluation. ^

COMPREHENSIVE CALCULATION-BASED IMRT QA USING R&V DATA, TREATMENT RECORDS, AND A SECOND TREATMENT PLANNING SYSTEM

Purpose: Traditional patient-specific IMRT QA measurements are labor intensive and consume machine time. Calculation-based IMRT QA methods typically are not comprehensive. We have developed a comprehensive calculation-based IMRT QA method to detect uncertainties introduced by the initial dose calculation, the data transfer through the Record-and-Verify (R&V) system, and various aspects of the physical delivery. Methods: We recomputed the treatment plans in the patient geometry for 48 cases using data from the R&V, and from the delivery unit to calculate the “as-transferred” and “as-delivered” doses respectively. These data were sent to the original TPS to verify transfer and delivery or to a second TPS to verify the original calculation. For each dataset we examined the dose computed from the R&V record (RV) and from the delivery records (Tx), and the dose computed with a second verification TPS (vTPS). Each verification dose was compared to the clinical dose distribution using 3D gamma analysis and by comparison of mean dose and ROI-specific dose levels to target volumes. Plans were also compared to IMRT QA absolute and relative dose measurements. Results: The average 3D gamma passing percentages using 3%-3mm, 2%-2mm, and 1%-1mm criteria for the RV plan were 100.0 (σ=0.0), 100.0 (σ=0.0), and 100.0 (σ=0.1); for the Tx plan they were 100.0 (σ=0.0), 100.0 (σ=0.0), and 99.0 (σ=1.4); and for the vTPS plan they were 99.3 (σ=0.6), 97.2 (σ=1.5), and 79.0 (σ=8.6). When comparing target volume doses in the RV, Tx, and vTPS plans to the clinical plans, the average ratios of ROI mean doses were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.990 (σ=0.009) and ROI-specific dose levels were 0.999 (σ=0.001), 1.001 (σ=0.002), and 0.980 (σ=0.043), respectively. Comparing the clinical, RV, TR, and vTPS calculated doses to the IMRT QA measurements for all 48 patients, the average ratios for absolute doses were 0.999 (σ=0.013), 0.998 (σ=0.013), 0.999 σ=0.015), and 0.990 (σ=0.012), respectively, and the average 2D gamma(5%-3mm) passing percentages for relative doses for 9 patients was were 99.36 (σ=0.68), 99.50 (σ=0.49), 99.13 (σ=0.84), and 98.76 (σ=1.66), respectively. Conclusions: Together with mechanical and dosimetric QA, our calculation-based IMRT QA method promises to minimize the need for patient-specific QA measurements by identifying outliers in need of further review.

Development and Implementation of the Use of Optically Stimulated Luminescent Detectors in the Radiological Physics Center Anthropomorphic Quality Assurance Phantoms

The Radiological Physics Center (RPC) uses both on-site and remote reviews to credential institutions for participation in clinical trials. Anthropomorphic quality assurance (QA) phantoms are one tool the RPC uses to remotely audit institutions, which include thermoluminescent dosimeters (TLDs) and radiochromic film. The RPC desires to switch from TLD as the absolute dosimeter in the phantoms, to optically stimulated luminescent dosimeters (OSLDs), but a problem lies in the angular dependence exhibited by the OSLD. The purpose of this study was to characterize the angular dependence of OSLD and establish a correction factor if necessary, to provide accurate dosimetric measurements as a replacement for TLD in the QA phantoms. A 10 cm diameter high-impact polystyrene spherical phantom was designed and constructed to hold an OSLD to study the angular response of the dosimeter under the simplest of circumstances for both coplanar and non-coplanar treatment deliveries. OSLD were irradiated in the spherical phantom, and the responses of the dosimeter from edge-on angles were normalized to the response when irradiated with the beam incident normally on the surface of the dosimeter. The average normalized response was used to establish an angular correction factor for 6 MV and 18 coplanar treatments, and for 6 MV non-coplanar treatments specific to CyberKnife. The RPC pelvic phantom dosimetry insert was modified to hold OSLD, in addition to the TLD, adjacent to the planes of film. Treatment plans of increasing angular beam delivery were developed, three in Pinnacle v9.0 (4-field box, IMRT, and VMAT) and one in Accuray’s MultiPlan v3.5.3 (CyberKnife). The plans were delivered to the pelvic phantom containing both TLD and OSLD in the target volume. The pelvic phantom was also sent to two institutions to be irradiated as trials, one delivering IMRT, and the other a CyberKnife treatment. For the IMRT deliveries and the two institution trials, the phantom also included film in the sagittal and coronal planes. The doses measured from the TLD and OSLD were calculated for each irradiation, and the angular correction factors established from the spherical phantom irradiations were applied to the OSLD dose. The ratio of the TLD dose to the angular corrected OSLD dose was calculated for each irradiation. The corrected OSLD dose was found to be within 1% of the TLD measured dose for all irradiations, with the exception of the in-house CyberKnife deliveries. The films were normalized to both TLD measured dose and the corrected OSLD dose. Dose profiles were obtained and gamma analysis was performed using a 7%/4 mm criteria, to compare the ability of the OSLD, when corrected for the angular dependence, to provide equivalent results to TLD. The results of this study indicate that the OSLD can effectively be used as a replacement for TLD in the RPC’s anthropomorphic QA phantoms for coplanar treatment deliveries when a correction is applied for the dosimeter’s angular dependence.