Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Towards an Explainable Framework for Personalized Treatment Recommendations
0
Zitationen
3
Autoren
2025
Jahr
Abstract
Most Clinical Decision Support Systems (CDSS) focus on diagnosis, offering limited help with personalized treatment. Although large language models (LLMs) show promise in clinical reasoning, integrating Clinical Practice Guidelines (CPGs) with patient-specific Electronic Health Records (EHRs) remains a challenge. Many systems also lack transparency, limiting clinician trust. This work proposes a lightweight, modular CDSS that fuses insights from CPGs and EHRs to generate treatment recommendations tailored to individual patients, with a clear emphasis on explainability.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.286 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.651 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.177 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.575 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.404 Zit.