Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of São Paulo

Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning

BACKGROUND

Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cost structure rationally, so as to control the hospitalization costs of patients better.

METHODS

This research analyzed data (2020-2022) from a Kashgar pulmonary hospital's information system, involving 9570 eligible PTB patients. SPSS 26.0 was used for multiple regression analysis, while Python 3.7 was used for random forest regression (RFR) and MLP. The training set included data from 2020 and 2021, while the test set included data from 2022. The models predicted seven various costs related to PTB patients, including diagnostic cost, medical service cost, material cost, treatment cost, drug cost, other cost, and total hospitalization cost. The model's predictive performance was evaluated using R-square (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) metrics.

RESULTS

Among the 9570 PTB patients included in the study, the median and quartile of total hospitalization cost were 13,150.45 (9891.34, 19,648.48) yuan. Nine factors, including age, marital status, admission condition, length of hospital stay, initial treatment, presence of other diseases, transfer, drug resistance, and admission department, significantly influenced hospitalization costs for PTB patients. Overall, MLP demonstrated superior performance in most cost predictions, outperforming RFR and multiple regression; The performance of RFR is between MLP and multiple regression; The predictive performance of multiple regression is the lowest, but it shows the best results for Other costs.

CONCLUSION

The MLP can effectively leverage patient information and accurately predict various hospitalization costs, achieving a rationalized structure of hospitalization costs by adjusting higher-cost inpatient items and balancing different cost categories. The insights of this predictive model also hold relevance for research in other medical conditions.

Short-term relation between air pollutants and hospitalizations for respiratory diseases: analysis by temporal association rules

This study investigates the relation between exposure to critical air pollution events with multipollutant (CO, PM₁₀, PM_2.5, NO₂, O₃, and SO₂) and hospitalizations for respiratory diseases in the metropolitan area of São Paulo (RMSP) and in the countryside and coastline, from 2017 to 2021. Data mining analysis by temporal association rules searched for frequent patterns of respiratory diseases and multipollutants associated with time intervals. In the results, pollutants PM₁₀, PM_2.5, and O₃ showed high concentration values in the three regions, SO₂ on the coast, and NO₂ in the RMSP. Seasonality was similar between pollutants and between cities and concentrations significantly higher in winter, except for O₃, which was present in warm seasons. Hospitalizations were recurrent during the transition from summer to colder periods. In approximately 35% of the total days with hospitalization greater than the annual average, one or more pollutants had a high concentration. The rules showed that PM_2.5, PM₁₀, and O₃ pollutants are strongly associated with increased hospitalizations in the RMSP (PM_2.5 and PM₁₀ with 38.5% support and 77% confidence) and in Campinas (PM_2.5 with 66.1% support and 94% confidence) and the pollutant O₃ with maximum support of 17.5%. On the coast, SO₂ was related to high hospitalizations (43.85% support and 80% confidence). The pollutants CO and NO₂ were not associated with the increase in hospitalizations. The ratio delay indicates the pollutants that were associated with hospitalizations, having concentration remained above the limit for three days, oscillating in smaller hospitalizations on the 1st day and again higher on the 2^nd and 3^rd days of delay, in a decreasing way. In conclusion, high pollutant exposure is significantly associated with daily hospitalization for respiratory problems. The cumulative effect of air pollutants increased hospitalization in the following days, in addition to identifying the pollutants and which pollutant combinations are most harmful to health in each region.

A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil-An Artificial Neural Networks Approach

Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM₁₀, NO₂, and SO₂ concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM₁₀ and NO₂ concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.

Particulate Matter Concentration in Selected Facilities as an Indicator of Exposure to Their Service Activities

In recent years, the correlation between the concentration of pollutants in the atmosphere and inside buildings has been reported as high. The air inside living quarters and public utility buildings or the interiors of public transport vehicles, as well as the relationship between the internal and external sources of particulate matter (PM) and gaseous pollutants, have underwent sufficient research. On the other hand, non-production rooms, i.e., offices, restaurants, beauty salons, etc. remain very poorly recognized in this respect. For the above reasons, the aim of this work is to determine the difference in the total dust (TSP) and respirable PM (PM4) concentrations in selected rooms, i.e., offices and beauty centers, in relation to their outdoor concentrations. They were measured at six locations in accordance with the standard for the conditions at workplaces by means of PM aspirators. Indoor concentrations of TSP and PM4 were much higher than those in the external surroundings of the facilities. There were no significant relationships between the TSP and PM4 concentrations inside and outside tested rooms. Although the characteristic of the internal PM essentially depends on the characteristics of the external PM migrating to the interior of the premises, considering some types of non-production premises, internal emissions fundamentally changed the characteristics of PM.