Decision support framework for supply chain planning with flexible demand

The most challenging issue of today’s production management is certainly to manage networked organisations under an uncertain demand so that to provide a good service to the customer at low cost. In this article, a model of the decision making parameters involved in this management process is suggested, on the base of case studies. A mixed integer linear planning model embedded in a framework simulating a rolling horizon planning process is described on the base of this analysis. The model takes into account the capabilities of reaction of the planned system and of its environment (suppliers, subcontractors and customers), as well as the corresponding costs. The suggested simulation framework may assist the decision maker for coping with an uncertain or flexible demand, using various planning strategies. Some possible applications of this simulation framework are given in order to illustrate how it can help to solve various types of practical planning problems

Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

BACKGROUND: Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings

Prenatal Prediction of Poor Maternal and Offspring Outcomes: Implications for Selection into Intensive Parent Support Programs

This study examined the predictive ability of mother’s age, antenatal depression, education, financial difficulties, partner status, and smoking for a range of poor maternal and offspring outcomes assessed up to 61 months postnatally. Outcomes obtained from the Avon Longitudinal Study of Parents and Children (ALSPAC) were maternal postnatal depression at 8 weeks (n = 10,070), never breastfeeding (n = 7,976), feelings of poor attachment (n = 8,253) and hostility (n = 8,159) at 47 months, and not in employment, education or training (NEET, n = 8,265) at 61 months. Only a small proportion of women with each outcome were aged less than 20 years when they were pregnant. At least half of the women experiencing these outcomes, and up to 74.7% of women with postnatal depression, could be identified if they had at least one of the predictors measured during pregnancy (age < 20, depression, education less than O level, financial difficulties, no partner, or smoking). Model discrimination was poor using maternal age only (area under the receiver operator characteristic (AUROC) curve approximately 0.52), except for never breastfeeding (0.63). Discrimination improved (AUROC: 0.80, 0.69, 0.62, 0.60, 0.66 for depression, never breastfeeding, poor attachment, hostility and NEET, respectively) when all six predictors were included in the model. Calibration improved for all outcomes with the model including all six predictors, except never breastfeeding where even age alone demonstrated good calibration. Factors other than young maternal age, including education, smoking and depression during pregnancy should be considered in identifying women and their offspring likely to benefit from parenting support interventions

Can the outcome of pelvic-floor rehabilitation in patients with fecal incontinence be predicted?

Purpose: Pelvic-floor rehabilitation does not provide the same degree of relief in all fecal incontinent patients. We aimed at studying prospectively the ability of tests to predict the outcome of pelvic-floor rehabilitation in patients with fecal incontinence. Materials and methods: Two hundred fifty consecutive patients (228 women) underwent medical history and a standardized series of tests, including physical examination, anal manometry, pudendal nerve latency testing, anal sensitivity testing, rectal capacity measurement, defecography, endoanal sonography, and endoanal magnetic resonance imaging. Subsequently, patients were referred for pelvic-floor rehabilitation. Outcome of pelvic-floor rehabilitation was quantified by the Vaizey incontinence score. Linear regression analyses were used to identify candidate predictors and to construct a multivariable prediction model for the posttreatment Vaizey score. Results: After pelvic-floor rehabilitation, the mean baseline Vaizey score (18, SD±3) was reduced with 3.2 points (p<0.001). In addition to the baseline Vaizey score, three elements from medical history were significantly associated with the posttreatment Vaizey score (presence of passive incontinence, thin stool consistency, primary repair of a rupture after vaginal delivery at childbed) (R2, 0.18). Th