Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks

2015/10/26 by Zachary C. Lipton, Lipton, Zachary C., David C. Kale +3 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Time Series Analysis and Forecasting #Topic Modeling #cs.LG

paper · pdf · doi:10.48550/arxiv.1510.07641

openalex publication_date 2015/10/26 · arxiv created 2017/03/21 · arxiv updated 2017/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements. Our method outperforms a strong baseline on a variety of metrics.

Citations

• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Long Short-Term Memory
• A Novel Connectionist System for Unconstrained Handwriting Recognition
• Sequence to Sequence Learning with Neural Networks
• Learning to Forget: Continual Prediction with LSTM

Cited by

• Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence

Related

• Learning to Diagnose with LSTM Recurrent Neural Networks
• MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis
• Bayesian LSTMs in medicine
• Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests
• Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks