Explainable Artificial Intelligence in medical decision support systems:
Medical decision support systems (MDSS) are computer-based programs that analyse data within a patient's healthcare records to provide questions, prompts, or reminders to assist clinicians at the point of care. Inputting a patient's data, symptoms, or current treatment regimens into an MDS...
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Stevenage
The Institution of Engineering and Technology
2022
|
| Schriftenreihe: | Healthcare technologies series
50 |
| Online-Zugang: | TUM01 UBY01 Volltext |
| Zusammenfassung: | Medical decision support systems (MDSS) are computer-based programs that analyse data within a patient's healthcare records to provide questions, prompts, or reminders to assist clinicians at the point of care. Inputting a patient's data, symptoms, or current treatment regimens into an MDSS, clinicians are assisted with the identification or elimination of the most likely potential medical causes, which can enable faster discovery of a set of appropriate diagnoses or treatment plans. Explainable AI (XAI) is a "white box" model of artificial intelligence in which the results of the solution can be understood by the users, who can see an estimate of the weighted importance of each feature on the model's predictions, and understand how the different features interact to arrive at a specific decision. This book discusses XAI-based analytics for patient-specific MDSS as well as related security and privacy issues associated with processing patient data. It provides insights into real-world scenarios of the deployment, application, management, and associated benefits of XAI in MDSS. The book outlines the frameworks for MDSS and explores the applicability, prospects, and legal implications of XAI for MDSS. Applications of XAI in MDSS such as XAI for robot-assisted surgeries, medical image segmentation, cancer diagnostics, and diabetes mellitus and heart disease prediction are explored. |
| Beschreibung: | 1 Online-Ressource (xx, 521 Seiten) Diagramme |
| ISBN: | 9781839536212 |
| DOI: | 10.1049/PBHE050E |
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| 520 | |a Medical decision support systems (MDSS) are computer-based programs that analyse data within a patient's healthcare records to provide questions, prompts, or reminders to assist clinicians at the point of care. Inputting a patient's data, symptoms, or current treatment regimens into an MDSS, clinicians are assisted with the identification or elimination of the most likely potential medical causes, which can enable faster discovery of a set of appropriate diagnoses or treatment plans. Explainable AI (XAI) is a "white box" model of artificial intelligence in which the results of the solution can be understood by the users, who can see an estimate of the weighted importance of each feature on the model's predictions, and understand how the different features interact to arrive at a specific decision. This book discusses XAI-based analytics for patient-specific MDSS as well as related security and privacy issues associated with processing patient data. It provides insights into real-world scenarios of the deployment, application, management, and associated benefits of XAI in MDSS. The book outlines the frameworks for MDSS and explores the applicability, prospects, and legal implications of XAI for MDSS. Applications of XAI in MDSS such as XAI for robot-assisted surgeries, medical image segmentation, cancer diagnostics, and diabetes mellitus and heart disease prediction are explored. | ||
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| id | DE-604.BV048609705 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:11:42Z |
| indexdate | 2024-07-10T09:42:55Z |
| institution | BVB |
| isbn | 9781839536212 |
| language | English |
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| series2 | Healthcare technologies series |
| spelling | Explainable Artificial Intelligence in medical decision support systems edited by Agbotiname Lucky Imoize, Jude Hemanth, Dinh-Thuan Do and Samarendra Nath Sur Stevenage The Institution of Engineering and Technology 2022 1 Online-Ressource (xx, 521 Seiten) Diagramme txt rdacontent c rdamedia cr rdacarrier Healthcare technologies series 50 Medical decision support systems (MDSS) are computer-based programs that analyse data within a patient's healthcare records to provide questions, prompts, or reminders to assist clinicians at the point of care. Inputting a patient's data, symptoms, or current treatment regimens into an MDSS, clinicians are assisted with the identification or elimination of the most likely potential medical causes, which can enable faster discovery of a set of appropriate diagnoses or treatment plans. Explainable AI (XAI) is a "white box" model of artificial intelligence in which the results of the solution can be understood by the users, who can see an estimate of the weighted importance of each feature on the model's predictions, and understand how the different features interact to arrive at a specific decision. This book discusses XAI-based analytics for patient-specific MDSS as well as related security and privacy issues associated with processing patient data. It provides insights into real-world scenarios of the deployment, application, management, and associated benefits of XAI in MDSS. The book outlines the frameworks for MDSS and explores the applicability, prospects, and legal implications of XAI for MDSS. Applications of XAI in MDSS such as XAI for robot-assisted surgeries, medical image segmentation, cancer diagnostics, and diabetes mellitus and heart disease prediction are explored. Imoize, Agbotiname Lucky (DE-588)1240342586 edt Hemanth, D. Jude 1981- (DE-588)1202956807 edt Do, Dinh-Thuan 1980- (DE-588)1304396150 edt Sur, Samarendra Nath (DE-588)1283021706 edt Erscheint auch als Druck-Ausgabe 9781839536205 https://doi.org/10.1049/PBHE050E Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Explainable Artificial Intelligence in medical decision support systems |
| title | Explainable Artificial Intelligence in medical decision support systems |
| title_auth | Explainable Artificial Intelligence in medical decision support systems |
| title_exact_search | Explainable Artificial Intelligence in medical decision support systems |
| title_exact_search_txtP | Explainable Artificial Intelligence in medical decision support systems |
| title_full | Explainable Artificial Intelligence in medical decision support systems edited by Agbotiname Lucky Imoize, Jude Hemanth, Dinh-Thuan Do and Samarendra Nath Sur |
| title_fullStr | Explainable Artificial Intelligence in medical decision support systems edited by Agbotiname Lucky Imoize, Jude Hemanth, Dinh-Thuan Do and Samarendra Nath Sur |
| title_full_unstemmed | Explainable Artificial Intelligence in medical decision support systems edited by Agbotiname Lucky Imoize, Jude Hemanth, Dinh-Thuan Do and Samarendra Nath Sur |
| title_short | Explainable Artificial Intelligence in medical decision support systems |
| title_sort | explainable artificial intelligence in medical decision support systems |
| url | https://doi.org/10.1049/PBHE050E |
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