Article

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence

2023-10-12

Abstract excerpt

Despite advances in precision oncology, clinical decision-making still relies on limited parameters and expert knowledge. To address this limitation, we combined multimodal real- world data and explainable artificial intelligence (xAI) to introduce novel AI-derived (AID) markers for clinical decision support. We used deep learning to model the outcome of 15,726 patients across 38 solid cancer entities based on 350...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: cd75f525-c309-5f18-83f8-53668ea037e4
DOI: 10.1101/2023.10.12.23296873

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligenceDOI 10.1101/2023.10.12.23296873Read

I3LUNG: Clinical Validation of a Multimodal AI Tool to Support Immunotherapy Decisions in NSCLCSemantic proximity Read

Artificial Intelligence (AI) and Machine Learning (ML) in Diagnosing Cancer: Current TrendsSemantic proximity Read

PanoraOnc: A pan-cancer clinico-genomic AI model for transferable outcome predictionsSemantic proximity Read

Explainable Bayesian Artificial Intelligence for Precision Oncology: A Multimodal Framework for Personalized Cancer PrognosisSemantic proximity Read

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision OncologySemantic proximity Read

Feature Importance in Predicting Clinical Outcome: Statistics vs. Explainable Artificial IntelligenceSemantic proximity Read

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic ReviewSemantic proximity Read

AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosisSemantic proximity Read

Explainable AI for the Prediction of Anticancer Drug Resistance Mechanisms in Human CancersSemantic proximity Read

Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AISemantic proximity Read

Development of Biologically Interpretable Multimodal Deep Learning Model for Cancer Prognosis PredictionSemantic proximity Read

Explainable AI for Precision Oncology: A Task-Specific Approach Using Imaging, Multi-omics, and Clinical DataSemantic proximity Read