Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning

This study presents a hybrid interactive machine learning approach to develop an early warning system for monitoring cancer patients. By integrating patient-reported outcomes with clinical data, the system aims to predict unplanned medical events, thereby enhancing patient care and reducing hospital readmissions. The methodology combines machine learning algorithms with expert knowledge to create a predictive model that is both accurate and interpretable. The results demonstrate the feasibility of such a system in a real-world clinical setting, highlighting its potential to improve patient outcomes through proactive monitoring. ([frontiersin.org](https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2024.1443987/full?utm_source=openai))

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Subject (DDC)

330 - Wirtschaft

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Event

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Conference start date

Conference end date

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ISBN

ISSN

2673-253X

Language

English

Created during FHNW affiliation

Yes

Strategic action fields FHNW

Publication status

Published

Review

peer-reviewed

Open access category

Gold

License

Citation

Trojan, A., Laurenzi, E., Jüngling, S., Roth, S., Kiessling, M., Atassi, Z., Kadavny, Y., Mannhart, M., Jackisch, C., Kullak-Ublick, G., & Witschel, H. F. (2024). Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning. Frontiers in Digital Health, 6. https://doi.org/10.3389/fdgth.2024.1443987

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