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Machine Learning for Digital Shadow Design in Health Insurance Sector
(Springer Nature, 2024)
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Marmolejo Saucedo, José Antonio
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Rodríguez-Aguilar, Miriam
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Marmolejo-Saucedo, Liliana
The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care as well as the integration of multiple sectors that allow the provision of health services. A first approach towards the construction of a Digital Twin in health organizations is a Digital Shadow that allows an orderly transition towards digital operation in real time. This paper presents a first approach to the design of a Digital Shadow for the health insurance sector and specifically for the care of patients diagnosed with COVID-19 through the implementation of an analytical intelligence system based on machine learning models to forecast and monitor to patients who represent catastrophic cases for the insurer. © 2024 Springer Nature
Scopus© Citations 1 23 1
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A machine learning-based analytical intelligence system for forecasting demand of new products based on chlorophyll : a hybrid approach
(Springer, 2024)
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Marmolejo Saucedo, José Antonio
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Garcia-Llamas, Eduardo
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Rodríguez-Aguilar, Miriam
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Marmolejo-Saucedo, Liliana
This manuscript addresses the problem of forecasting the demand for innovative products with limited and inhomogeneous sales data over time. The main objective of the study is to use the information available from a group of innovative chlorophyll-based food products to build a coherent demand forecasting system. From a transactional database, time series were constructed for each group of products, analyzing the stationarity and seasonality of the time series through the Dickey–Fuller and Canova–Hansen tests. Likewise, an ARIMA model, a long short-term memory (LSTM) recurrent deep neural network, and a support vector machine (SVM) were trained to select the best model for each product based on a forecast performance metric. A comparison between classical forecasting techniques and machine learning models is shown. The LSTM neural network was the best model for most products because the internal architecture of the network allows not only to capture non-linear relationships between variables but is also capable of controlling the flow of information to preserve characteristics over time that are relevant for forecasts. The second-best model was the SVM, which allows capturing non-linear behaviors through kernel functions and uses a smaller amount of data for its estimation. Finally, the ARIMA model presented the lowest performance for all products. The objective of having various methodologies is that the system allows the best forecast to be selected according to the type of product, availability of information and methodology used, which will allow the company to integrate new products into the system over time. ©Springer
23 1
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Machine Learning Applied to the Measurement of Quality in Health Services in Mexico: The Case of the Social Protection in Health System
(2018)
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Marmolejo Saucedo, José Antonio
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Vasant, Pandian
To propose a satisfaction indicator of users of health services affiliated to the Social Protection System in Health (SPSS). Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A machine-learning model based on Logistic Models and Principal Components was developed to estimate a satisfaction index. The survey data collected for the “SPSS 2014 User’s Satisfaction Study” was used, considering a sample of 28,290 users. The proposed model shows, in general, the positive perception of quality of health services (national average 0.0756). There are factors statistically significant that influence these results, the good perception of infrastructure (OR:2.12; CI 95%:1.9–2.36); the gratuity of the service provided (OR:1.98; CI 95%: 1.42–2.76); and full medicines supply (OR:1.81; CI 95%:1.91–2.36). The proposed index can be used as an indicator for improving health care quality of the population covered by the SPSS. © 2019, Springer Nature Switzerland AG.
Scopus© Citations 1 12 1
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Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning
(2019)
Tayal, Akash
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Kose, Utku
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Solanki, Arun
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Nayyar, Anand
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Marmolejo Saucedo, José Antonio
The facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three-stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta-heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology. © 2019 Wiley Periodicals, Inc.
Scopus© Citations 25 6 2
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Identification of Trading Strategies Using Markov Chains and Statistical Learning Tools
(2021)
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Marmolejo Saucedo, José Antonio
Technological advances have modified many operational and strategic areas in companies, the financial sector has been one of the sectors highly influenced by the methods of artificial intelligence and machine learning. The operation in the stock exchanges have used more technological tools to process information and be able to make investment decisions. The main objective is to be able to detect buying and selling opportunities at the right time. Stock markets have traditionally based their decisions on two major approaches, technical analysis and fundamental analysis, with new machine learning and artificial intelligence technologies, these paradigms have been updated making use of additional tools for their analysis. The present work is a proposal for the detection of trading signals in the markets through the use of Markov models and generalized additive models. In order to identify investment opportunities in the stock markets. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
37 2

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