Clinical Outcome Prediction

Clinical outcome prediction uses machine learning to forecast patient outcomes based on diverse data sources, aiming to improve healthcare decision-making and personalize treatment. Current research emphasizes multimodal learning, integrating heterogeneous data like medical images, clinical notes, and genomics, often employing deep learning architectures such as graph neural networks, convolutional neural networks, and recurrent neural networks, sometimes enhanced by ensemble methods. These advancements hold significant promise for improving diagnostic accuracy, treatment planning, and resource allocation across various medical specialties, ultimately leading to better patient care.

Papers

November 5, 2024

Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models
Zizhang Chen, Peizhao Li, Xiaomeng Dong, Pengyu Hong
Language Model Uncertainty Quantification Model Uncertainty Clinical Prediction Task Clinical Outcome Prediction

July 28, 2024

Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai
LeArning Abstract Medical Information Mart for Intensive Patient Representation Forecasting Task Patient Trajectory Clinical Outcome Prediction

June 11, 2024

Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes
Asim Waqas, Aakash Tripathi, Paul Stewart, Mia Naeini, Ghulam Rasool
Graph Neural Network Outcome Prediction Patient Representation Multimodal Embeddings Survival Outcome Clinical Outcome Prediction Multi Modal Graph Neural Network

June 21, 2023

Precision psychiatry: predicting predictability
Edwin van Dellen
Predictability Problem Clinical Outcome Prediction Precision Psychiatry

August 17, 2022

May 15, 2022

Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning
Yikuan Li, Mohammad Mamouei, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, Kazem Rahimi, Gholamreza Salimi-Khorshidi
Counterfactual Explanation Concept Bottleneck Model Counterfactual Reasoning Causal Interpretation Representation Learning Framework Clinical Outcome Prediction

May 11, 2022

CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients
Nima Hatami, Tae-Hee Cho, Laura Mechtouff, Omer Faruk Eker, David Rousseau, Carole Frindel
Long Short Term Memory CNN LSTM Multi Modal MRI Stroke Patient Functional Outcome Clinical Outcome Prediction Stroke Assessment

March 2, 2022

Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
Aishik Konwer, Xuan Xu, Joseph Bae, Chao Chen, Prateek Prasanna
Medical Image Temporal Convolutional Network Temporal Context Disease Progression Image Prediction Temporal Image Clinical Outcome Prediction

November 16, 2021

Literature-Augmented Clinical Outcome Prediction
Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope
Clinical Note Clinical Text Patient Outcome Prediction Clinical Outcome Prediction

Clinical Outcome Prediction

Papers

Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models

Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction

Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes

Precision psychiatry: predicting predictability

Prediction of Oral Food Challenge Outcomes via Ensemble Learning

On the Role of Negative Precedent in Legal Outcome Prediction

Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning

CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients

Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations

Literature-Augmented Clinical Outcome Prediction