Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models

2022-12-22 · Zhichao Yang, Weisong Liu, Dan Berlowitz, Hong Yu

Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this prognostic study of 6.8 million patients, our denoising sequence-to-sequence prediction model of multiple outcomes outperformed state-of-the-art models scuh pretrained BERT on a broad range of patient outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep bidirectional and autoregressive representation improves patient outcome prediction.

📄 PDF Abstract BibTeX arXiv:2212.12067

Code (0)

등록된 구현이 없습니다.

Tasks

Decoder Denoising Prediction

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Weight Decay 설명 없음

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

WordPiece 설명 없음

Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records

2020-10-29 · Hua Zheng, Ilya O. Ryzhov, Wei Xie, Judy Zhong

Comorbid chronic conditions are common among people with type 2 diabetes. We developed an Artificial Intelligence algorithm, based on Reinforcement Learning (RL), for personalized diabetes and multi-morbidity management …

Management reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction

2025-06-16 · Mbithe Nzomo, Deshendran Moodley

Multimodal electronic health record (EHR) data is useful for disease risk prediction based on medical domain knowledge. However, general medical knowledge must be adapted to specific healthcare settings and patient popul…

Knowledge Graphs Prediction

Cluster-based Feature Importance Learning for Electronic Health Record Time-series

2021-09-29 · Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu

The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution. However, prediction of disease progression wit…

Feature Importance Respiratory Failure Time Series Time Series Analysis

DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System

2026-05-14 · Yunying Zhu, Andrew R Weckstein, Kueiyu Joshua Lin, Jie Yang arxiv

Accurate disease trajectory prediction is critical for early intervention, resource allocation, and improving long-term outcomes. While electronic health records (EHRs) provide a rich longitudinal view of patient health …

Trajectory Prediction

ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations

2022-07-05 · Asem Alaa, Erik Mayer, Mauricio Barahona

Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs. With the massive amount of information available in electronic health records (EHRs), there is g…