A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management

López-Martínez, Fernando; Núñez‐Valdéz, Edward Rolando; García‐Díaz, Vicente; Bursac, Zoran

doi:10.3390/a13040102

Cited by 40 publications

(14 citation statements)

References 30 publications

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“…A study aimed to examine the usability of ML techniques to understand teamwork and behaviors related to healthcare management and patient safety and to contribute to the literature and research on collaboration in healthcare [ 25 ]. Another study discussed how developing an intelligent big data analytics platform based on ML and data integration principles is a new way to improve population healthcare management, value-based care, and upcoming challenges in healthcare [ 26 ]. Islam and Shamsuddin utilized the ML algorithms to develop a diagnostic system for patients with hypertension so that people can change their daily lifestyles to manage their condition [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

2022

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A healthcare resource allocation generally plays a vital role in the number of patients treated (pnt) and the patient waiting time (wt) in healthcare institutions. This study aimed to estimate pnt and wt as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δi) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δi of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for pnt; 0.9514, 0.9517, 0.9514, and 0.9514 for wt, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ0.2 (AB had a better accuracy at 0.8709 based on the value of δ0.2 for pnt) in the test stage. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for pnt; 0.8820, 0.8821, 0.8819, and 0.8818 for wt in the training phase, respectively. All scenarios created by the δi coefficient should be preferred for ED since the income provided by the pnt value to the hospital was more than the cost of healthcare resources. On the contrary, the wt estimation results of ML algorithms based on the δi coefficient differed. Although wt values in all ML algorithms with δ0.0 and δ0.1 coefficients reduced the cost of the hospital, wt values based on δ0.2 and δ0.3 increased the cost of the hospital.

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Section: Introductionmentioning

confidence: 99%

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

2022

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“…In a relatively narrow sense, health management (Heath Management) refers to the purpose of preventing and controlling the occurrence and development of diseases, reducing medical expenses, and improving the quality of life [10][11][12][13][14][15]. Chinese scholars have combined several representative de nitions of health management at home and abroad to de ne health management as a process of comprehensively managing the health needs of individuals and groups [16][17][18][19]. Here, health needs can be a health risk factor, such as hypertension and obesity, or a disease state, such as coronary heart disease or diabetes.…”

Section: Introductionmentioning

confidence: 99%

Management and Analysis of Sports Health Level of the Elderly Based on Deep Learning

Xiao

Li-min

Chang

et al. 2022

Computational Intelligence and Neuroscience

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With the accelerating rate of population aging in China, the health of the elderly has received more and more attention and has become one of the most important issues in the elderly care industry. Because of insufficient research on the personal health of the elderly, the value of medical examination data cannot be fully exploited, many physical indicators have a certain impact on overall health or heart health, and there are few studies on heart health assessment. This paper proposes a deep learning-based elderly management analysis method of human exercise health level, using the exercise health management model to evaluate the heart health level of the elderly. Firstly, the indicators to measure heart health are proposed through traditional expert knowledge and personal health index to analyze heart health. Through dynamic assessment, predict the heart health status at the next time point, analyze possible heart diseases, and provide corresponding methods for the health of the elderly, which helps improve the physical health of the elderly. Quality of life provides assistance to meet the needs of improving the health of older adults.

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“…Systems medicine is substantially dependent on innovative key technologies. These include, for example, high-throughput omics technologies, bioinformatics and medical informatics, artificial intelligence (AI), and big data analytics [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. The SARS-CoV-2 pandemic also triggered a digitalization offensive in the healthcare sector; innovations were implemented much faster under the impact of the pandemic [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

Lemmen

Simić

Stock

2021

IJERPH

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Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.

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A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management

Cited by 40 publications

References 30 publications

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

Management and Analysis of Sports Health Level of the Elderly Based on Deep Learning

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

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