Modeling Depression Progression Dynamics from Electronic Health Record

Thesis (Master's)--University of Washington, 2016-06

Depression severity levels and cognitive functions in institutionalized elderly patients

Depression is a highly prevalent illness among institutionalized aged and assumes peculiar characteristics such as the risk for progressing to dementia. The aims of this study was to assess the cognitive functions of institutionalized elderly with clinical diagnosis of depression and compare the severity of depressive symptoms with cognitive performance. From 120 residents at a nursing home in Rio Claro, Brazil, we study 23 individuals (mean age: 74.3 years; mean schooling: 4.0 years) with diagnosis of depression. At first, a clinical diagnosis of depression and measurement of its symptoms using the Geriatric Depression Scale were performed. The patient then underwent a neuropsychological assessment based on the following tests: Mini-Mental Examination, Verbal Fluency, Visual Perception, Immediate Memory, Recent Memory, Recognition, Clock Drawing Test. The patients were divided into two groups: those with less severe depression symptoms (Group 1: N=9) and more severe symptoms (Group 2: N=14). The significant difference between symptom severity of the two groups was p=0.0001. Patients with more severe symptoms revealed a slightly inferior cognitive performance in most of the tests when compared to those with less severe symptoms (p>0.05). In relation to Verbal Fluency, patients with more severe depression symptoms presented a significantly inferior cognitive performance when compared to those with less severe symptoms (p=0.0082). Verbal Fluency revealed to be a more sensitive test for measuring early cognitive alterations in institutionalized aged with depression, and appears to be a useful resource in monitoring the cognitive functions of patients faced with the risk of dementia. © Copyright Moreira Jr. Editora.

Motor Activity and Depression Severity in Adolescent Outpatients

Objectives: The present study investigated the association between motor activity and severity of depression in 6 depressed adolescent outpatients. Method: Motor activity was assessed by actigraphy and the severity of depression was assessed weekly using the CDRS-R. The levels of motor activity were analyzed by considering activity parameters. Results: Among the parameters of motor activity studied, the mean total activity, the mean 24-hour activity levels, the mean waking activity, and the mean activity level between 12:00 and 18:00 h were inversely correlated with severity of depression. The means of the 10 most active hours tended toward a negative correlation with the depressive severity score. Conclusion: The results seem to suggest an association between motor activity level and severity of depression in adolescents. Nevertheless, in order to reach a more conclusive understanding, it would be necessary to replicate this study using a larger number of individuals as well as a longer observation period. Copyright (C) 2009 S. Karger AG, Basel

Talking bodies: nonverbal behavior in the assessment of depression severity

BACKGROUND: Evaluations of clinical depression are traditionally based on verbal information. Nonverbal expressive behavior, however, being associated with a person's reflexive responses, may reveal negative emotional or social processes that are not under complete control of the patients. However, investigations of nonverbal behavior in the evaluation of depressed patients are still scarce. This study examines the nonverbal behaviors of a group of Brazilian patients, associating their nonverbal behavior with severity of depression. METHODS: Forty depressed patients were evaluated at baseline (T0) and after a two-week transcranial direct current stimulation treatment (T1), according to rating scales and through a 21-category Ethogram for assessment of the frequency of nonverbal behaviors displayed during an interview. RESULTS: Behaviors that were related to negative feelings and social disinterest decreased with corresponding clinical improvement and were associated with increased severity of symptoms at T0 and greater negative affect and dissatisfaction at T1. Pro-social behaviors were associated with milder symptoms at T0 and increased after treatment. Facial, head and hand expressive movements stood out as important indicators because of their associations with severity of depression. LIMITATIONS: Duration of behaviors was not assessed and there was not a healthy control group with which to compare the findings. CONCLUSIONS: These results support the usefulness of nonverbal behavior as an evaluation technique in the assessment of clinical depression.

Relative transition probability and pressure broadening parameters of copper atomic lines /

Inserted Report documentation page designates D. W. Boyer ... [et al.] as "authors."

Measured transition probability for the first-positive band system of nitrogen.

"This report covers work performed under Contract no. DA-30-069-ORD-3443, ARPA order number 253-62 [and] is a part of Project Defender."

Pavement management: Capturing surface treatment effectiveness

Acquiring detailed knowledge of surface treatments effectiveness is required to improve performance-based decisions for allocating resources to preserve and maintain pavements on any road network. Measurement of treatment effectiveness is a complex task that requires historical records of treatments with observations of before and after performance trends. Lack of data is often an obstacle that impedes development and incorporation of surface maintenance treatments into pavement management. This paper analyzes the effect of surface treatments on asphalt paved arterial roads for several control sections of New Brunswick. The method uses a Transition Probability Matrix to capture main effects by mapping mean trends of surface improvement and pavement structure decay. It was found that surface treatments have an immediate effect reducing the rate of loss of structural capacity. Pavements with international roughness index (IRI) smaller than 1.4 m/km did not seem to benefit from surface treatments. Those with IRI higher than 1.66 m/km gained from 6 to 8 years of additional life. Reset value for surface treatments fall between 1.18 and 1.29 m/km. This paper aims to serve to practitioners seeking to capture and incorporate effectiveness of surface treatments (i.e., crack-sealing) into Pavement Management.

Factors affecting partitioning of private forest holdings in Finland.

Questions of the small size of non-industrial private forest (NIPF) holdings in Finland are considered and factors affecting their partitioning are analyzed. This work arises out of Finnish forest policy statements in which the small average size of holdings has been seen to have a negative influence on the economics of forestry. A survey of the literature indicates that the size of holdings is an important factor determining the costs of logging and silvicultural operations, while its influence on the timber supply is slight. The empirical data are based on a sample of 314 holdings collected by interviewing forest owners in the years 1980-86. In 1990-91 the same holdings were resurveyed by means of a postal inquiry and partly by interviewing forest owners. The principal objective in compiling the data is to assist in quantifying ownership factors that influence partitioning among different kinds of NIPF holdings. Thus the mechanism of partitioning were described and a maximum likelihood logistic regression model was constructed using seven independent holding and ownership variables. One out of four holdings had undergone partitioning in conjunction with a change in ownership, one fifth among family owned holdings and nearly a half among jointly owned holdings. The results of the logistic regression model indicate, for instance, that the odds on partitioning is about three times greater for jointly owned holdings than for family owned ones. Also, the probabilities of partitioning were estimated and the impact of independent dichotomous variables on the probability of partitioning ranged between 0.02 and 0.10. The low value of the Hosmer-Lemeshow test statistic indicates a good fit of the model and the rate of correct classification was estimated to be 88 per cent with a cutoff point of 0.5. The average size of holdings undergoing ownership changes decreased from 29.9 ha to 28.7 ha over the approximate interval 1983-90. In addition, the transition probability matrix showed that the trends towards smaller size categories mostly involved in the small size categories, less than 20 ha. The results of the study can be used in considering the effects of the small size of holdings for forestry and if the purpose is to influence partitioning through forest or rural policy.

Expected Fair Allocation in Farsighted Network Formation

I consider cooperation situations where players have network relations. Networks evolve according to a stationary transition probability matrix and at each moment in time players receive payoffs from a stationary allocation rule. Players discount the future by a common factor. The pair formed by an allocation rule and a transition probability matrix is called expected fair if for every link in the network both participants gain, marginally, and in discounted, expected terms, the same from it; and it is called a pairwise network formation procedure if the probability that a link is created (or eliminated) is positive if the discounted, expected gains to its two participants are positive too. The main result is the existence, for the discount factor small enough, of an expected fair and pairwise network formation procedure where the allocation rule is component balanced, meaning it distributes the total value of any maximal connected subnetwork among its participants. This existence result holds for all discount factors when the pairwise network formation procedure is restricted. I finally provide some comparison with previous models of farsighted network formation.

Forward-looking Pairwise Stability in Networks with Externalities

We consider cooperation situations where players have network relations. Networks evolve according to a stationary transition probability matrix and at each moment in time players receive payoffs from a stationary allocation rule. Players discount the future by a common factor. The pair formed by an allocation rule and a transition probability matrix is called a forward-looking network formation scheme if, first, the probability that a link is created is positive if the discounted, expected gains to its two participants are positive, and if, second, the probability that a link is eliminated is positive if the discounted, expected gains to at least one of its two participants are positive. The main result is the existence, for all discount factors and all value functions, of a forward-looking network formation scheme. Furthermore, we can always nd a forward-looking network formation scheme such that (i) the allocation rule is component balanced and (ii) the transition probabilities increase in the di erence in payo s for the corresponding players responsible for the transition. We use this dynamic solution concept to explore the tension between e ciency and stability.

基于MRF模型的油气储层属性随机建模方法研究

Stochastic reservoir modeling is a technique used in reservoir describing. Through this technique, multiple data sources with different scales can be integrated into the reservoir model and its uncertainty can be conveyed to researchers and supervisors. Stochastic reservoir modeling, for its digital models, its changeable scales, its honoring known information and data and its conveying uncertainty in models, provides a mathematical framework or platform for researchers to integrate multiple data sources and information with different scales into their prediction models. As a fresher method, stochastic reservoir modeling is on the upswing. Based on related works, this paper, starting with Markov property in reservoir, illustrates how to constitute spatial models for catalogued variables and continuum variables by use of Markov random fields. In order to explore reservoir properties, researchers should study the properties of rocks embedded in reservoirs. Apart from methods used in laboratories, geophysical means and subsequent interpretations may be the main sources for information and data used in petroleum exploration and exploitation. How to build a model for flow simulations based on incomplete information is to predict the spatial distributions of different reservoir variables. Considering data source, digital extent and methods, reservoir modeling can be catalogued into four sorts: reservoir sedimentology based method, reservoir seismic prediction, kriging and stochastic reservoir modeling. The application of Markov chain models in the analogue of sedimentary strata is introduced in the third of the paper. The concept of Markov chain model, N-step transition probability matrix, stationary distribution, the estimation of transition probability matrix, the testing of Markov property, 2 means for organizing sections-method based on equal intervals and based on rock facies, embedded Markov matrix, semi-Markov chain model, hidden Markov chain model, etc, are presented in this part. Based on 1-D Markov chain model, conditional 1-D Markov chain model is discussed in the fourth part. By extending 1-D Markov chain model to 2-D, 3-D situations, conditional 2-D, 3-D Markov chain models are presented. This part also discusses the estimation of vertical transition probability, lateral transition probability and the initialization of the top boundary. Corresponding digital models are used to specify, or testify related discussions. The fifth part, based on the fourth part and the application of MRF in image analysis, discusses MRF based method to simulate the spatial distribution of catalogued reservoir variables. In the part, the probability of a special catalogued variable mass, the definition of energy function for catalogued variable mass as a Markov random field, Strauss model, estimation of components in energy function are presented. Corresponding digital models are used to specify, or testify, related discussions. As for the simulation of the spatial distribution of continuum reservoir variables, the sixth part mainly explores 2 methods. The first is pure GMRF based method. Related contents include GMRF model and its neighborhood, parameters estimation, and MCMC iteration method. A digital example illustrates the corresponding method. The second is two-stage models method. Based on the results of catalogued variables distribution simulation, this method, taking GMRF as the prior distribution for continuum variables, taking the relationship between catalogued variables such as rock facies, continuum variables such as porosity, permeability, fluid saturation, can bring a series of stochastic images for the spatial distribution of continuum variables. Integrating multiple data sources into the reservoir model is one of the merits of stochastic reservoir modeling. After discussing how to model spatial distributions of catalogued reservoir variables, continuum reservoir variables, the paper explores how to combine conceptual depositional models, well logs, cores, seismic attributes production history.

A Methodological Framework for Modeling Pavement Maintenance Costs for Projects with Performance-based Contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

Escape times for rigid Brownian rotators in a bistable potential from the time evolution of the Green function and the characteristic time of the probability evolution

The greatest relaxation time for an assembly of three- dimensional rigid rotators in an axially symmetric bistable potential is obtained exactly in terms of continued fractions as a sum of the zero frequency decay functions (averages of the Legendre polynomials) of the system. This is accomplished by studying the entire time evolution of the Green function (transition probability) by expanding the time dependent distribution as a Fourier series and proceeding to the zero frequency limit of the Laplace transform of that distribution. The procedure is entirely analogous to the calculation of the characteristic time of the probability evolution (the integral of the configuration space probability density function with respect to the position co-ordinate) for a particle undergoing translational diffusion in a potential; a concept originally used by Malakhov and Pankratov (Physica A 229 (1996) 109). This procedure allowed them to obtain exact solutions of the Kramers one-dimensional translational escape rate problem for piecewise parabolic potentials. The solution was accomplished by posing the problem in terms of the appropriate Sturm-Liouville equation which could be solved in terms of the parabolic cylinder functions. The method (as applied to rotational problems and posed in terms of recurrence relations for the decay functions, i.e., the Brinkman approach c.f. Blomberg, Physica A 86 (1977) 49, as opposed to the Sturm-Liouville one) demonstrates clearly that the greatest relaxation time unlike the integral relaxation time which is governed by a single decay function (albeit coupled to all the others in non-linear fashion via the underlying recurrence relation) is governed by a sum of decay functions. The method is easily generalized to multidimensional state spaces by matrix continued fraction methods allowing one to treat non-axially symmetric potentials, where the distribution function is governed by two state variables. (C) 2001 Elsevier Science B.V. All rights reserved.

Increases in prefrontal cortex activity when regulating negative emotion predicts symptom severity trajectory over six months in depression

Context: Emotion regulation is critically disrupted in depression and use of paradigms tapping these processes may uncover essential changes in neurobiology during treatment. In addition, as neuroimaging outcome studies of depression commonly utilize solely baseline and endpoint data – which is more prone to week-to week noise in symptomatology – we sought to use all data points over the course of a six month trial. Objective: To examine changes in neurobiology resulting from successful treatment. Design: Double-blind trial examining changes in the neural circuits involved in emotion regulation resulting from one of two antidepressant treatments over a six month trial. Participants were scanned pretreatment, at 2 months and 6 months posttreatment. Setting: University functional magnetic resonance imaging facility. Participants: 21 patients with Major Depressive Disorder and without other Axis I or Axis II diagnoses and 14 healthy controls. Interventions: Venlafaxine XR (doses up to 300mg) or Fluoxetine (doses up to 80mg). Main Outcome Measure: Neural activity, as measured using functional magnetic resonance imaging during performance of an emotion regulation paradigm as well as regular assessments of symptom severity by the Hamilton Rating Scale for Depression. To utilize all data points, slope trajectories were calculated for rate of change in depression severity as well as rate of change of neural engagement. Results: Those depressed individuals showing the steepest decrease in depression severity over the six months were those individuals showing the most rapid increases in BA10 and right DLPFC activity when regulating negative affect over the same time frame. This relationship was more robust than when using solely the baseline and endpoint data. Conclusions: Changes in PFC engagement when regulating negative affect correlate with changes in depression severity over six months. These results are buttressed by calculating these statistics which are more reliable and robust to week-to-week variation than difference scores.