# Neural networks versus Logistic regression for 30 days all-cause readmission prediction

arXiv: https://arxiv.org/abs/1812.09549 · HTML: https://mlsanity.com/paper/1812.09549/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission](https://mlsanity.com/paper/2204.06766/) (2022)
- [Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes](https://mlsanity.com/paper/2304.05986/) (2023)
- [MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction](https://mlsanity.com/paper/2311.07608/) (2023)
- [Predicting machine failures from multivariate time series: an industrial case study](https://mlsanity.com/paper/2402.17804/) (2024)
- [Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge](https://mlsanity.com/paper/2104.04377/) (2021)
- [Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death](https://mlsanity.com/paper/2208.12814/) (2022)
- [Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions](https://mlsanity.com/paper/2304.09981/) (2023)
- [Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.18270/) (2026)

## Abstract (arXiv)

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1)explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2)to examine the additive value of patients' hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95%CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets.