2.9k citations · 4.1k across the 30 of their papers we have counts for
7 papers · 1 filter
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Shaoxiong Ji, Tianlin Zhang, Luna Ansari +3
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment. Early detection of mental disorders…
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning
Shaoxiong Ji, Pekka Marttinen
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of mul…
Multitask Balanced and Recalibrated Network for Medical Code Prediction
Wei Sun, Shaoxiong Ji, Erik Cambria +1
Human coders assign standardized medical codes to clinical documents generated during patients' hospitalization, which is error-prone and labor-intensive. Automated medical coding…
Fine-Tuning Pretrained Language Models With Label Attention for Biomedical Text Classification
Bruce Nguyen, Shaoxiong Ji
The massive scale and growth of textual biomedical data have made its indexing and classification increasingly important. However, existing research on this topic mainly utilized c…
Multitask Recalibrated Aggregation Network for Medical Code Prediction
Wei Sun, Shaoxiong Ji, Erik Cambria +1
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimburseme…
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study
Shaoxiong Ji, Matti Hölttä, Pekka Marttinen
Unsupervised pretraining is an integral part of many natural language processing systems, and transfer learning with language models has achieved remarkable results in many downstr…
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Shaoxiong Ji, Yue Tan, Teemu Saravirta +6
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, f…