Functional Outcome

Functional outcome research aims to predict and improve the effectiveness of interventions, focusing on quantifiable measures of patient ability or system performance after an intervention. Current research utilizes diverse machine learning approaches, including recurrent neural networks, support vector machines, and various ensemble methods, often integrating multimodal data such as imaging, clinical records, and physiological signals to enhance predictive accuracy. These advancements hold significant promise for personalized medicine, optimizing rehabilitation strategies, and improving the design of assistive technologies by providing objective measures of treatment efficacy and identifying key factors influencing outcomes.

Papers

November 5, 2024

[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
Maren Pielka, Tobias Schneider, Jan Terheyden, Rafet Sifa
Machine Learning Generative AI Chatbot Response Diabetic Retinopathy Multiple Outcome Functional Outcome

October 27, 2024

Predicting Mortality and Functional Status Scores of Traumatic Brain Injury Patients using Supervised Machine Learning
Lucas Steinmetz, Shivam Maheshwari, Garik Kazanjian, Abigail Loyson, Tyler Alexander, Venkat Margapuri, C. Nataraj
Logistic Regression Supervised Machine Learning Mortality Prediction Year Specific Mortality Surface Functional Outcome Traumatic Brain Injury

May 24, 2024

Automated Assessment and Adaptive Multimodal Formative Feedback Improves Psychomotor Skills Training Outcomes in Quadrotor Teleoperation
Emily Jensen, Sriram Sankaranarayanan, Bradley Hayes
Multimodal Feedback Functional Outcome

May 9, 2024

Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning
Fengyi Gao, Xingyu Zhang, Sonish Sivarajkumar, Parker Denny, Bayan Aldhahwani, Shyam Visweswaran, Ryan Shi, William Hogan, Allyn Bove, Yanshan Wang
Machine Learning Electronic Health Record Functional Outcome Post Stroke

February 16, 2024

Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning
Chia-Ling Tsai, Hui-Yun Su, Shen-Feng Sung, Wei-Yang Lin, Ying-Ying Su, Tzu-Hsien Yang, Man-Lin Mai
Contrastive Learning Clinical Data Stroke Detection Structured Clinical Diffusion Weighted Ischemic Stroke Diffusion Weighted MRI Functional Outcome

January 26, 2024

Predicting loss-of-function impact of genetic mutations: a machine learning approach
Arshmeet Kaur, Morteza Sarmadi
Machine Learning Approach Single Point Mutation Functional Outcome

November 22, 2023

Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIs
Soham Bafana
Long Short Term Memory Dimensionality Reduction Brain Computer Interface Visual Imagery Motor Imagery Classification Motor Impairment Functional Outcome Uniform Manifold Approximation and Projection

August 11, 2023

Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures
Anna-Grace Linton, Vania Dimitrova, Amy Downing, Richard Wagland, Adam Glaser
Text Classification Content Analysis Domain Corpus Weakly Supervised Text Classification Functional Outcome

March 8, 2023

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury
Shubhayu Bhattacharyay, Pier Francesco Caruso, Cecilia Åkerlund, Lindsay Wilson, Robert D Stevens, David K Menon, Ewout W Steyerberg, David W Nelson, Ari Ercole, the CENTER-TBI investigators/participants
Client Contribution Intensive Care Unit Mining Complex Clinical Setting Intensive Care Traumatic Brain Injury Functional Outcome

October 20, 2022

Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery
Joshua C. Chang, Carson C. Chow, Julia Porcino
Scientific Inference Calibration Performance Item Response Theory Functional Outcome

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

February 5, 2022

Machine Learning Method for Functional Assessment of Retinal Models
Nikolas Papadopoulos, Nikos Melanitis, Antonio Lozano, Cristina Soto-Sanchez, Eduardo Fernandez, Konstantina S Nikita
Machine Learning Visual Perception Visual Circuit Functional Outcome Retina Model Retinal Prosthesis

Functional Outcome

Papers

[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI

Predicting Mortality and Functional Status Scores of Traumatic Brain Injury Patients using Supervised Machine Learning

Automated Assessment and Adaptive Multimodal Formative Feedback Improves Psychomotor Skills Training Outcomes in Quadrotor Teleoperation

Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning

Predicting loss-of-function impact of genetic mutations: a machine learning approach

Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIs

Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury

Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery

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

Machine Learning Method for Functional Assessment of Retinal Models