PubMed · 4850281
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A R Buss. 1974. A recursive-nonrecursive factor model and developmental causal networks.. https://doi.org/10.1159/000271338
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We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables-a factor analysis model. This model can be seen as a linear version of the Helmholtz machine, and its parameters can be learned using the wake-sleep method, in which learning of the primary generative model is assisted by a recognition model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in wake and sleep phases, using just the delta rule. This learning procedure is comparable in simplicity to Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plausible alternative to Hebbian learning as a model of activity-dependent cortical plasticity.
BACKGROUND: The disfigurement and dysfunction associated with head and neck cancer affect emotional well-being and some of the most basic functions of life. Most cancer-specific quality-of-life assessments give a single composite score for head and neck cancer-related quality of life. OBJECTIVE: To develop and evaluate an improved multidimensional instrument to assess head and neck cancer-related functional status and well-being. METHODS: The item selection process included literature review, interviews with health care workers, and patient surveys. A survey with 37 disease-specific questions and the SF-12 survey were administered to 253 patients in 3 large medical centers. Factor analysis was performed to identify disease-specific domains. Domain scores were calculated as the standardized score of the component items. These domains were assessed for construct validity based on clinical hypotheses and test-retest reliability. RESULTS: Four relevant domains were identified: Eating (6 items), Communication (4 items), Pain (4 items), and Emotion (6 items). Each had an internal consistency (Cronbach alpha value) of greater than 0.80. Construct validity was demonstrated by moderate correlations with the SF-12 Physical and Mental component scores (r=0.43-0.60). Test-retest reliability for each domain demonstrated strong reliability between the 2 time points. Correlations were strong for each individual question, ranging from 0.53 to 0.93. Construct validity testing demonstrated that the direction of differences for each domain were as hypothesized. CONCLUSION: The Head and Neck Quality of Life questionnaire is a promising multidimensional tool with which to assess head and neck cancer-specific quality of life.
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