# Learning to Diagnose with LSTM Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1511.03677 · HTML: https://mlsanity.com/paper/1511.03677/

## Key facts

- Cited by 99 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2015.

## Built on by (most cited first, 30 of 99)

- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [Heterogeneous Similarity Graph Neural Network on Electronic Health Records](https://mlsanity.com/paper/2101.06800/) (2021)
- [MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records](https://mlsanity.com/paper/2102.02340/) (2021)
- [Towards Generating Real-World Time Series Data](https://mlsanity.com/paper/2111.08386/) (2021)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2108.03625/) (2021)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis](https://mlsanity.com/paper/2203.14469/) (2022)
- [Clairvoyance: A Pipeline Toolkit for Medical Time Series](https://mlsanity.com/paper/2310.18688/) (2023)
- [Foundation models for electronic health records: representation dynamics and transferability](https://mlsanity.com/paper/2504.10422/) (2025)
- [Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model](https://mlsanity.com/paper/2410.17545/) (2024)
- [GraSSNet: Graph Soft Sensing Neural Networks](https://mlsanity.com/paper/2111.06980/) (2021)
- [Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems](https://mlsanity.com/paper/2204.09183/) (2022)
- [Memory-free Online Change-point Detection: A Novel Neural Network Approach](https://mlsanity.com/paper/2207.03932/) (2022)
- [SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation](https://mlsanity.com/paper/2312.05790/) (2023)
- [Learning under Temporal Label Noise](https://mlsanity.com/paper/2402.04398/) (2024)
- [EMIT- Event-Based Masked Auto Encoding for Irregular Time Series](https://mlsanity.com/paper/2409.16554/) (2024)
- [Medical data wrangling with sequential variational autoencoders](https://mlsanity.com/paper/2103.07206/) (2021)
- [Supervised multi-specialist topic model with applications on large-scale electronic health record data](https://mlsanity.com/paper/2105.01238/) (2021)
- [Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks](https://mlsanity.com/paper/2106.07925/) (2021)
- [Leveraging Image-based Generative Adversarial Networks for Time Series Generation](https://mlsanity.com/paper/2112.08060/) (2021)
- [UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge](https://mlsanity.com/paper/2211.08082/) (2022)
- [Short term prediction of demand for ride hailing services: A deep learning approach](https://mlsanity.com/paper/2212.03956/) (2022)
- [Evaluating Model Performance in Medical Datasets Over Time](https://mlsanity.com/paper/2305.13426/) (2023)
- [Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning](https://mlsanity.com/paper/2309.03219/) (2023)
- [Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models](https://mlsanity.com/paper/2601.01321/) (2026)
- [Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints](https://mlsanity.com/paper/2604.01841/) (2026)
- [Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformers](https://mlsanity.com/paper/2111.05431/) (2021)
- [Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model](https://mlsanity.com/paper/2305.19997/) (2023)
- [Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit](https://mlsanity.com/paper/2306.01970/) (2023)

## Abstract (arXiv)

Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data. Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data. They effectively model varying length sequences and capture long range dependencies. We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. First, we establish the effectiveness of a simple LSTM network for modeling clinical data. Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step. Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features.