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))

Schlagwörter

Fachgebiet (DDC)

330 - Wirtschaft

Projekt

Veranstaltung

Startdatum der Ausstellung

Enddatum der Ausstellung

Startdatum der Konferenz

Enddatum der Konferenz

Datum der letzten Prüfung

ISBN

ISSN

2673-253X

Sprache

Englisch

Während FHNW Zugehörigkeit erstellt

Ja

Zukunftsfelder FHNW

Publikationsstatus

Veröffentlicht

Begutachtung

publication.page.reviewtype.Peer-Reviewed

Open Access-Status

Gold

Lizenz

Zitation

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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