Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions

Shravya; Manish; M. Ramakrishna

doi:10.1007/978-981-96-4008-9_2

Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions

Shravya
, Manish
, M. Ramakrishna^*

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This study aims to enhance society by detecting early antimicrobial resistance in Candida species and improving healthcare outcomes. They involve the use of AMRSN data, a Python environment with required libraries, and machine learning models to predict resistance. The main goal is to detect their possible resistance in time to help doctors make better decisions and treatment choices. It highlights two medicines: Anidulafungin reliable against Candida and Caspofungin. Additionally, a predictive model such as K-nearest neighbors (KNNs) is used to identify trends in drug resistance, which is useful in planning strategies for treatment.

Original language	English
Title of host publication	Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024
Editors	Andrew Jeyabose, Andrew Jeyabose, Valentina Emilia Balas, Valentina Emilia Balas, Steven L. Fernandes
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	17-28
Number of pages	12
ISBN (Print)	9789819640072
DOIs	https://doi.org/10.1007/978-981-96-4008-9_2
Publication status	Published - 2025
Event	International Conference on Health Informatics, Intelligent Systems, and Networking Technologies, HINT 2024 - Manipal, India Duration: 13-03-2024 → 14-03-2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1286 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Health Informatics, Intelligent Systems, and Networking Technologies, HINT 2024
Country/Territory	India
City	Manipal
Period	13-03-24 → 14-03-24

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-96-4008-9_2

Cite this

Shravya, Manish, & Ramakrishna, M. (2025). Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions. In A. Jeyabose, A. Jeyabose, V. E. Balas, V. E. Balas, & S. L. Fernandes (Eds.), Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024 (pp. 17-28). (Lecture Notes in Networks and Systems; Vol. 1286 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-4008-9_2

Shravya ; Manish, ; Ramakrishna, M. / Revolutionizing Antifungal Treatment : Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions. Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. editor / Andrew Jeyabose ; Andrew Jeyabose ; Valentina Emilia Balas ; Valentina Emilia Balas ; Steven L. Fernandes. Springer Science and Business Media Deutschland GmbH, 2025. pp. 17-28 (Lecture Notes in Networks and Systems).

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title = "Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions",

abstract = "This study aims to enhance society by detecting early antimicrobial resistance in Candida species and improving healthcare outcomes. They involve the use of AMRSN data, a Python environment with required libraries, and machine learning models to predict resistance. The main goal is to detect their possible resistance in time to help doctors make better decisions and treatment choices. It highlights two medicines: Anidulafungin reliable against Candida and Caspofungin. Additionally, a predictive model such as K-nearest neighbors (KNNs) is used to identify trends in drug resistance, which is useful in planning strategies for treatment.",

author = "Shravya and Manish and M. Ramakrishna",

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Shravya, Manish, & Ramakrishna, M 2025, Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions. in A Jeyabose, A Jeyabose, VE Balas, VE Balas & SL Fernandes (eds), Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. Lecture Notes in Networks and Systems, vol. 1286 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 17-28, International Conference on Health Informatics, Intelligent Systems, and Networking Technologies, HINT 2024, Manipal, India, 13-03-24. https://doi.org/10.1007/978-981-96-4008-9_2

Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions. / Shravya; Manish, ; Ramakrishna, M.
Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. ed. / Andrew Jeyabose; Andrew Jeyabose; Valentina Emilia Balas; Valentina Emilia Balas; Steven L. Fernandes. Springer Science and Business Media Deutschland GmbH, 2025. p. 17-28 (Lecture Notes in Networks and Systems; Vol. 1286 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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PY - 2025

Y1 - 2025

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AB - This study aims to enhance society by detecting early antimicrobial resistance in Candida species and improving healthcare outcomes. They involve the use of AMRSN data, a Python environment with required libraries, and machine learning models to predict resistance. The main goal is to detect their possible resistance in time to help doctors make better decisions and treatment choices. It highlights two medicines: Anidulafungin reliable against Candida and Caspofungin. Additionally, a predictive model such as K-nearest neighbors (KNNs) is used to identify trends in drug resistance, which is useful in planning strategies for treatment.

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Shravya, Manish , Ramakrishna M. Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions. In Jeyabose A, Jeyabose A, Balas VE, Balas VE, Fernandes SL, editors, Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 17-28. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-4008-9_2

Revolutionizing Antifungal Treatment: Machine Learning Insights into Candida Species Antimicrobial Resistance Patterns for Informed Clinical Decisions

Abstract

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All Science Journal Classification (ASJC) codes

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