A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology
This study explores the generalization of heterogeneous medical data for monitoring anomalies and changes over time using fuzzy intervals. The most important feature of these intervals is saving the parameter value as a membership function from the interval [0, 1]. An example illustrating this metho...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523001016 |
| _version_ | 1797754831392210944 |
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| author | Antoni Wilinski Ryszard Tadeusiewicz Andrzej Piegat Grzegorz Bocewicz Adam Skorzak Krzysztof Dabkowski Andrzej Smereczynski Teresa Starzynska |
| author_facet | Antoni Wilinski Ryszard Tadeusiewicz Andrzej Piegat Grzegorz Bocewicz Adam Skorzak Krzysztof Dabkowski Andrzej Smereczynski Teresa Starzynska |
| author_sort | Antoni Wilinski |
| collection | DOAJ |
| description | This study explores the generalization of heterogeneous medical data for monitoring anomalies and changes over time using fuzzy intervals. The most important feature of these intervals is saving the parameter value as a membership function from the interval [0, 1]. An example illustrating this method is the blood count parameters of an oncological patient recorded for three years with a monthly frequency. Over 20 typical measurements of these features are considered, and eight with the highest variance are selected. The registration of the overall picture of changes, a synthesis of eight fuzzy intervals, allowed for observing a systematic improvement in health. This approach allows the doctor to take a holistic view of the patient’s health (based on blood tests), avoiding the dilemma of which parameters are less and which are more important. The Mamdani fuzzy inference system was used to assess the patient’s health status. The study presents the actual results of medical measurements, and the GitHub repository contains measurement data. |
| first_indexed | 2024-03-12T17:39:04Z |
| format | Article |
| id | doaj.art-ccb02a1a17284a588f96741becec18f4 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-12T17:39:04Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-ccb02a1a17284a588f96741becec18f42023-08-04T05:51:18ZengElsevierHealthcare Analytics2772-44252023-12-014100234A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphologyAntoni Wilinski0Ryszard Tadeusiewicz1Andrzej Piegat2Grzegorz Bocewicz3Adam Skorzak4Krzysztof Dabkowski5Andrzej Smereczynski6Teresa Starzynska7WSB Merito University, Gdansk, Poland; Correspondence to: Al. Grunwaldzka 238A, 80-266 Gdansk, Poland.AGH University of Science and Technology, Krakow, PolandWestpomeranian University of Technology, Szczecin, PolandKoszalin University of Technology, PolandPomeranian Hospitals, Gdynia, PolandDepartment of Gastroenterology Pomeranian Medical University in Szczecin, PolandGold Med., Szczecin, PolandDepartment of Gastroenterology Pomeranian Medical University in Szczecin, PolandThis study explores the generalization of heterogeneous medical data for monitoring anomalies and changes over time using fuzzy intervals. The most important feature of these intervals is saving the parameter value as a membership function from the interval [0, 1]. An example illustrating this method is the blood count parameters of an oncological patient recorded for three years with a monthly frequency. Over 20 typical measurements of these features are considered, and eight with the highest variance are selected. The registration of the overall picture of changes, a synthesis of eight fuzzy intervals, allowed for observing a systematic improvement in health. This approach allows the doctor to take a holistic view of the patient’s health (based on blood tests), avoiding the dilemma of which parameters are less and which are more important. The Mamdani fuzzy inference system was used to assess the patient’s health status. The study presents the actual results of medical measurements, and the GitHub repository contains measurement data.http://www.sciencedirect.com/science/article/pii/S2772442523001016Fuzzy intervalsFuzzy setsBlood morphology parametersHealth assessmentHealth monitoringFuzzy logic in medicine |
| spellingShingle | Antoni Wilinski Ryszard Tadeusiewicz Andrzej Piegat Grzegorz Bocewicz Adam Skorzak Krzysztof Dabkowski Andrzej Smereczynski Teresa Starzynska A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology Healthcare Analytics Fuzzy intervals Fuzzy sets Blood morphology parameters Health assessment Health monitoring Fuzzy logic in medicine |
| title | A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology |
| title_full | A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology |
| title_fullStr | A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology |
| title_full_unstemmed | A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology |
| title_short | A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology |
| title_sort | fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology |
| topic | Fuzzy intervals Fuzzy sets Blood morphology parameters Health assessment Health monitoring Fuzzy logic in medicine |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523001016 |
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