Geometric generalisation of surrogate model-based optimisation to combinatorial and program spaces

Open access journalSurrogate models (SMs) can profitably be employed, often in conjunction with evolutionary algorithms, in optimisation in which it is expensive to test candidate solutions. The spatial intuition behind SMs makes them naturally suited to continuous problems, and the only combinatorial problems that have been previously addressed are those with solutions that can be encoded as integer vectors. We show how radial basis functions can provide a generalised SM for combinatorial problems which have a geometric solution representation, through the conversion of that representation to a different metric space. This approach allows an SM to be cast in a natural way for the problem at hand, without ad hoc adaptation to a specific representation. We test this adaptation process on problems involving binary strings, permutations, and tree-based genetic programs. © 2014 Yong-Hyuk Kim et al

Geometric Generalisation of Surrogate Model-Based Optimisation to Combinatorial and Program Spaces

Surrogate models (SMs) can profitably be employed, often in conjunction with evolutionary algorithms, in optimisation in which it is expensive to test candidate solutions. The spatial intuition behind SMs makes them naturally suited to continuous problems, and the only combinatorial problems that have been previously addressed are those with solutions that can be encoded as integer vectors. We show how radial basis functions can provide a generalised SM for combinatorial problems which have a geometric solution representation, through the conversion of that representation to a different metric space. This approach allows an SM to be cast in a natural way for the problem at hand, without ad hoc adaptation to a specific representation. We test this adaptation process on problems involving binary strings, permutations, and tree-based genetic programs

Prevalencia de enfermedad arterial periférica en pacientes con diabetes mellitus tipo II de enero a agosto 2023 en Unidad de Salud Intermedia Nejapa

El siguiente estudio se realizó debido a la ausencia de información en nuestro país respecto a la enfermedad arterial periférica. Se estableció como objetivo principal la prevalencia de la enfermedad arterial periférica en pacientes con diabetes mellitus 2 que consultaron en la unidad de salud intermedia de Nejapa; a manera de consecución de los objetivos se definió un estudio descriptivo transversal y prospectivo. En base al total de pacientes que consultaron en el periodo definido en la investigación se obtuvo una muestra a la cual se le realizó la toma de índice tobillo-brazo y aplicación del cuestionario de fountaine obteniendo como resultado una prevalencia del 17% a partir de lo cual se concluye que la enfermedad arterial periférica continua siendo una patología infradiagnosticada, lo cual se debe al curso asintomático en la mayoría de casos, dentro de los principales factores de riesgo se determinó que el habito tabáquico y la hipertensión arterial tienen una gran predisposición para el desarrollo de esta enfermedad, respecto a la edad y los años de evolución de la diabetes tipo 2 se encontró una relación directa con esta enfermedad

Incorporation of postoperative CT data into clinical models to predict 5-year overall and recurrence free survival after primary cytoreductive surgery for advanced ovarian cancer

PURPOSE: The use of multivariable clinical models to assess postoperative prognosis in ovarian cancer increased. All published models incorporate surgical debulking. However, postoperative CT can detect residual disease (CT-RD) in 40% of optimally resected patients. The aim of our study was to investigate the added value of incorporating CT-RD evaluation into clinical models for assessment of overall survival (OS) and progression free survival (PFS) in patients after primary cytoreductive surgery (PCS). METHODS: 212 women with PCS for advanced ovarian cancer between 01/1997 and 12/2011, and a contrast enhanced abdominal CT 1-7weeks after surgery were included in this IRB approved retrospective study. Two radiologists blinded to clinical data, evaluated all CT for the presence of CT-RD, and Cohen's kappa assessed agreement. Cox proportional hazards regression with stepwise selection was used to develop OS and PFS models, with CT-RD incorporated afterwards. Model fit was assessed with bootstrapped Concordance Probability Estimates (CPE), accounting for over-fitting bias by correcting the initial estimate after repeated subsampling. RESULTS: Readers agreed on the majority of cases (179/212, k=0.68). For OS and PFS, CT-RD was significant after adjusting for clinical factors with a CPE 0.663 (p=0.0264) and 0.649 (p=0.0008). CT-RD was detected in 37% of patients assessed as optimally debulked (RD<1cm) and increased the risk of death (HR: 1.58, 95% CI: 1.06-2.37%). CONCLUSION: CT-RD is a significant predictor after adjusting for clinical factors for both OS and PFS. Incorporating CT-RD into the clinical model improved the prediction of OS and PFS in patients after PCS for advanced ovarian cancer