Data-driven methodology to predict the ICU length of stay: A multicentre study of 99,492 admissions in 109 Brazilian units.

PURPOSE: The length of stay (LoS) is one of the most used metrics for resource use in Intensive Care Units (ICU). We propose a structured data-driven methodology to predict the ICU length of stay and the risk of prolonged stay, and its application in a large multicentre Brazilian ICU database. METHODS: Demographic data, comorbidities, complications, laboratory data, and primary and secondary diagnosis were prospectively collected and retrospectively analysed by a data-driven methodology, which includes eight different machine learning models and a stacking model. The study setting included 109 mixed-type ICUs from 38 Brazilian hospitals and the external validation was performed by 93 medical-surgical ICUs of 55 hospitals in Brazil. RESULTS: A cohort of 99,492 adult ICU admissions were included from the 1st of January to the 31st of December 2019. The stacking model combining Random Forests and Linear Regression presented the best results to predict ICU length of stay (RMSE = 3.82; MAE = 2.52; R² = 0.36). The prediction model for the risk of long stay were accurate to early identify prolonged stay patients (Brier Score = 0.04, AUC = 0.87, PPV = 0.83, NPV = 0.95). CONCLUSION: The data-driven methodology to predict ICU length of stay and the risk of long-stay proved accurate in a large multicentre cohort of general ICU patients. The proposed models are helpful to predict the individual length of stay and to early identify patients with high risk of prolonged stay.

Variable step-size evolving participatory learning with kernel recursive least squares applied to gas prices forecasting in Brazil.

A prediction model is an indispensable tool in business, helping to make decisions, whether in the short, medium, or long term. In this context, the implementation of machine learning techniques in time series forecasting models has a notorious relevance, as information processing and efficient and dynamic knowledge uncovering are increasingly demanded. This paper develops a model called Variable step-size evolving Participatory Learning with Kernel Recursive Least Squares, VS-ePL-KRLS, applied to the forecast of weekly prices for S500 and S10 diesel oil, at the Brazilian level, for biweekly and monthly horizons. The presented model demonstrates a better accuracy compared with analogous models in the literature, without loss of computational performance for all time series analyzed.

Simulation of Appointment Scheduling Policies: a Study in a Bariatric Clinic.

PURPOSE: Appointment scheduling systems traditionally book patients at fixed intervals, without taking into account the complexity factors of the health system. This paper analyzes several appointment scheduling policies of the literature and proposes the most suitable to a bariatric surgery clinic, considering the following complexity factors: (i) stochastic service times, (ii) patient unpunctuality, (iii) service interruptions, and (iv) patient no-shows. MATERIALS AND METHODS: We conducted the study using data collected in a bariatric surgery clinic located in Rio de Janeiro, Brazil. The dataset presented 1468 appointments from June 29, 2015, to June 29, 2016. We comparatively evaluate the main literature policies through a discrete event simulation (DES). RESULTS: The proposed policy (IICR) provides a 30% increase in attendance and allows a decrease in the total cost, maintaining the level of service in terms of average waiting time. CONCLUSION: IICR was successfully implemented, and the practical results were very close to the simulated ones.