Innovative Predictive Approach towards a Personalized Oxygen Dosing System
Despite the large impact chronic obstructive pulmonary disease (COPD) that has on the population, the implementation of new technologies for diagnosis and treatment remains limited. Current practices in ambulatory oxygen therapy used in COPD rely on fixed doses overlooking the diverse activities whi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/3/764 |
| _version_ | 1797318246868713472 |
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| author | Heribert Pascual-Saldaña Xavi Masip-Bruin Adrián Asensio Albert Alonso Isabel Blanco |
| author_facet | Heribert Pascual-Saldaña Xavi Masip-Bruin Adrián Asensio Albert Alonso Isabel Blanco |
| author_sort | Heribert Pascual-Saldaña |
| collection | DOAJ |
| description | Despite the large impact chronic obstructive pulmonary disease (COPD) that has on the population, the implementation of new technologies for diagnosis and treatment remains limited. Current practices in ambulatory oxygen therapy used in COPD rely on fixed doses overlooking the diverse activities which patients engage in. To address this challenge, we propose a software architecture aimed at delivering patient-personalized edge-based artificial intelligence (AI)-assisted models that are built upon data collected from patients’ previous experiences along with an evaluation function. The main objectives reside in proactively administering precise oxygen dosages in real time to the patient (the edge), leveraging individual patient data, previous experiences, and actual activity levels, thereby representing a substantial advancement over conventional oxygen dosing. Through a pilot test using vital sign data from a cohort of five patients, the limitations of a one-size-fits-all approach are demonstrated, thus highlighting the need for personalized treatment strategies. This study underscores the importance of adopting advanced technological approaches for ambulatory oxygen therapy. |
| first_indexed | 2024-03-08T03:49:38Z |
| format | Article |
| id | doaj.art-3fa2320d4adf426e8c4eb801f700bdb4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:49:38Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3fa2320d4adf426e8c4eb801f700bdb42024-02-09T15:21:46ZengMDPI AGSensors1424-82202024-01-0124376410.3390/s24030764Innovative Predictive Approach towards a Personalized Oxygen Dosing SystemHeribert Pascual-Saldaña0Xavi Masip-Bruin1Adrián Asensio2Albert Alonso3Isabel Blanco4Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, SpainAdvanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, SpainAdvanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, SpainFundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, SpainDepartment of Pulmonary Medicine, Hospital Clínic, University of Barcelona, 08036 Barcelona, SpainDespite the large impact chronic obstructive pulmonary disease (COPD) that has on the population, the implementation of new technologies for diagnosis and treatment remains limited. Current practices in ambulatory oxygen therapy used in COPD rely on fixed doses overlooking the diverse activities which patients engage in. To address this challenge, we propose a software architecture aimed at delivering patient-personalized edge-based artificial intelligence (AI)-assisted models that are built upon data collected from patients’ previous experiences along with an evaluation function. The main objectives reside in proactively administering precise oxygen dosages in real time to the patient (the edge), leveraging individual patient data, previous experiences, and actual activity levels, thereby representing a substantial advancement over conventional oxygen dosing. Through a pilot test using vital sign data from a cohort of five patients, the limitations of a one-size-fits-all approach are demonstrated, thus highlighting the need for personalized treatment strategies. This study underscores the importance of adopting advanced technological approaches for ambulatory oxygen therapy.https://www.mdpi.com/1424-8220/24/3/764chronic obstructive pulmonary disease COPDartificial intelligencemachine learningedge computingblood oxygen saturationpersonalized modeling |
| spellingShingle | Heribert Pascual-Saldaña Xavi Masip-Bruin Adrián Asensio Albert Alonso Isabel Blanco Innovative Predictive Approach towards a Personalized Oxygen Dosing System Sensors chronic obstructive pulmonary disease COPD artificial intelligence machine learning edge computing blood oxygen saturation personalized modeling |
| title | Innovative Predictive Approach towards a Personalized Oxygen Dosing System |
| title_full | Innovative Predictive Approach towards a Personalized Oxygen Dosing System |
| title_fullStr | Innovative Predictive Approach towards a Personalized Oxygen Dosing System |
| title_full_unstemmed | Innovative Predictive Approach towards a Personalized Oxygen Dosing System |
| title_short | Innovative Predictive Approach towards a Personalized Oxygen Dosing System |
| title_sort | innovative predictive approach towards a personalized oxygen dosing system |
| topic | chronic obstructive pulmonary disease COPD artificial intelligence machine learning edge computing blood oxygen saturation personalized modeling |
| url | https://www.mdpi.com/1424-8220/24/3/764 |
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