Medicine and Health Sciences Commons^™

Deep Learning Algorithm Reveals Probabilities Of Stage-Specific Time To Conversion In Individuals With Neurodegenerative Disease Late, Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng

Sanders-Brown Center on Aging Faculty Publications

Introduction: Limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage-specific future conversions at an individual level.

Methods: After using the Kaplan–Meier estimation and log-rank test to confirm the heterogeneity of LATE progression, we developed a deep learning–based approach to assess the stage-specific probabilities of time to LATE conversions for different subjects.

Results: Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score …

Editorial: Individual Differences In Cognition And Affects In The Era Of Pandemic And Machine Learning, Andrea Vranic, Yang Jiang, Xiaopeng Zhao

Behavioral Science Faculty Publications

No abstract provided.

Tracking Sentiments Toward Fat Acceptance Over A Decade On Twitter, Sadie Bograd, Benjamin Chen, Ramakanth Kavuluru

Internal Medicine Faculty Publications

The fat acceptance (FA) movement aims to counteract weight stigma and discrimination against individuals who are overweight/obese. We developed a supervised neural network model to classify sentiment toward the FA movement in tweets and identify links between FA sentiment and various Twitter user characteristics. We collected any tweet containing either “fat acceptance” or “#fatacceptance” from 2010–2019 and obtained 48,974 unique tweets. We independently labeled 2000 of them and implemented/trained an Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) neural network that incorporates transfer learning from language modeling to automatically identify each tweet’s stance toward the FA movement. Our model …