Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes
Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/1/79 |
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| author | Antonio Martinez-Millana Jose-Luis Bayo-Monton María Argente-Pla Carlos Fernandez-Llatas Juan Francisco Merino-Torres Vicente Traver-Salcedo |
| author_facet | Antonio Martinez-Millana Jose-Luis Bayo-Monton María Argente-Pla Carlos Fernandez-Llatas Juan Francisco Merino-Torres Vicente Traver-Salcedo |
| author_sort | Antonio Martinez-Millana |
| collection | DOAJ |
| description | Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects. |
| first_indexed | 2024-04-14T06:42:20Z |
| format | Article |
| id | doaj.art-c4f56a3cd565464584ef09701f0e21f8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:42:20Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c4f56a3cd565464584ef09701f0e21f82022-12-22T02:07:17ZengMDPI AGSensors1424-82202017-12-011817910.3390/s18010079s18010079Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 DiabetesAntonio Martinez-Millana0Jose-Luis Bayo-Monton1María Argente-Pla2Carlos Fernandez-Llatas3Juan Francisco Merino-Torres4Vicente Traver-Salcedo5Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, SpainInstituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, SpainServicio de Endocrinología y Nutrición del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, SpainInstituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, SpainServicio de Endocrinología y Nutrición del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, SpainInstituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, SpainLife expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.https://www.mdpi.com/1424-8220/18/1/79type 2 diabetesrisk modelsservice-oriented architecturesystem integrationsystem reliability pilotdecision makinghealth care |
| spellingShingle | Antonio Martinez-Millana Jose-Luis Bayo-Monton María Argente-Pla Carlos Fernandez-Llatas Juan Francisco Merino-Torres Vicente Traver-Salcedo Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes Sensors type 2 diabetes risk models service-oriented architecture system integration system reliability pilot decision making health care |
| title | Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes |
| title_full | Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes |
| title_fullStr | Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes |
| title_full_unstemmed | Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes |
| title_short | Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes |
| title_sort | integration of distributed services and hybrid models based on process choreography to predict and detect type 2 diabetes |
| topic | type 2 diabetes risk models service-oriented architecture system integration system reliability pilot decision making health care |
| url | https://www.mdpi.com/1424-8220/18/1/79 |
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