Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data
New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not compl...
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MDPI AG
2021
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oai:doaj.org-article:08ef6332ead24064ac63b1e0c41c0eb62021-11-25T16:38:10ZScreening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data10.3390/app1122107902076-3417https://doaj.org/article/08ef6332ead24064ac63b1e0c41c0eb62021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10790https://doaj.org/toc/2076-3417New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland.Joanna HenzelJoanna TobiaszMichał KozielskiMałgorzata BachPaweł FosznerAleksandra GrucaMateusz KaniaJustyna MikaAnna PapiezAleksandra WernerJoanna ZylaJerzy JaroszewiczJoanna PolanskaMarek SikoraMDPI AGarticledata processingdata visualisationclassificationexplainable artificial intelligenceCOVID-19TechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10790, p 10790 (2021) |
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DOAJ |
| collection |
DOAJ |
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EN |
| topic |
data processing data visualisation classification explainable artificial intelligence COVID-19 Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
| spellingShingle |
data processing data visualisation classification explainable artificial intelligence COVID-19 Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Joanna Henzel Joanna Tobiasz Michał Kozielski Małgorzata Bach Paweł Foszner Aleksandra Gruca Mateusz Kania Justyna Mika Anna Papiez Aleksandra Werner Joanna Zyla Jerzy Jaroszewicz Joanna Polanska Marek Sikora Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data |
| description |
New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland. |
| format |
article |
| author |
Joanna Henzel Joanna Tobiasz Michał Kozielski Małgorzata Bach Paweł Foszner Aleksandra Gruca Mateusz Kania Justyna Mika Anna Papiez Aleksandra Werner Joanna Zyla Jerzy Jaroszewicz Joanna Polanska Marek Sikora |
| author_facet |
Joanna Henzel Joanna Tobiasz Michał Kozielski Małgorzata Bach Paweł Foszner Aleksandra Gruca Mateusz Kania Justyna Mika Anna Papiez Aleksandra Werner Joanna Zyla Jerzy Jaroszewicz Joanna Polanska Marek Sikora |
| author_sort |
Joanna Henzel |
| title |
Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data |
| title_short |
Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data |
| title_full |
Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data |
| title_fullStr |
Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data |
| title_full_unstemmed |
Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data |
| title_sort |
screening support system based on patient survey data—case study on classification of initial, locally collected covid-19 data |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/08ef6332ead24064ac63b1e0c41c0eb6 |
| work_keys_str_mv |
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