TabTransformer-based In-hospital Classification of Osteoporosis in Adults Undergoing Spinal Neurosurgical Procedures

Summary

We developed and internally evaluated an attention-based TabTransformer model for in-hospital classification of osteoporosis among adults undergoing spinal neurosurgical procedures using routinely collected Medical Information Mart for Intensive Care IV clinical data.

Abstract

The objective was to develop and internally evaluate a deep learning model for in-hospital classification of osteoporosis among adults undergoing spinal neurosurgical procedures using routinely collected tabular clinical data. This retrospective study used the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to identify adult patients who underwent spinal neurosurgical procedures. Osteoporosis was defined by ICD-9/10 diagnosis codes recorded during the index admission. Demographic characteristics, medication records, laboratory measurements, and vital signs from the same hospitalization were used for model development. After stratified splitting into training, validation, and test sets, class imbalance was addressed only in the training set. A TabTransformer-based model was developed and compared with XGBoost, LSTM_Attention, and Temporal Convolutional Networks (TCN). Model interpretability was assessed using SHapley Additive exPlanations (SHAP). On the held-out test set, TabTransformer achieved the best overall performance, with an area under the receiver operating characteristic curve (AUC) of 0.953 and an average precision (AP) of 0.799. The corresponding values for TCN, XGBoost, and LSTM_Attention were 0.937/0.707, 0.857/0.303, and 0.755/0.179, respectively. SHAP analysis identified age, sex, creatinine, red cell distribution width, bicarbonate, phosphate, neutrophils, and several medication-related variables as influential contributors to model output. In this single-center retrospective cohort, the TabTransformer model showed strong discriminative performance for in-hospital classification of osteoporosis and provided interpretable feature-level associations through SHAP. Because predictors and outcome were defined within the same hospitalization, this framework should be interpreted as an internally validated exploratory classification model rather than a temporally explicit future-risk prediction tool.

Introduction

Osteoporosis (OP) is a widespread skeletal condition marked by decreased bone mineral density and structural degradation of bone tissue, leading to increased bone fragility and a higher likelihood of fractures¹. It continues to pose a significant public health concern, particularly among older individuals and postmenopausal women². In hospitalized patients undergoing spinal neurosurgical procedures, bone health may be influenced by reduced mobility, systemic stress responses, medication exposure, and disturbances in metabolic homeostasis during index admission. Prior evidence from neurologic conditions and spinal cord in....

Protocol

Data source
This study was a retrospective analysis based on the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, a publicly available critical care database hosted on PhysioNet (https://physionet.org/content/mimiciv/3.1/). The database was developed through a collaboration between the Massachusetts Institute of Technology Laboratory for Computational Physiology, Beth Israel Deaconess Medical Center, and Philips, and contains comprehensive de-identified clinical data, including demographic information, vital signs, laboratory measurements, and medication records.

All patient data in MIMIC....

Results

Model performance comparison
We compared the proposed TabTransformer model with three baseline models (XGBoost, LSTM with attention, and Temporal Convolutional Networks [TCN]) on the held-out test set. As shown in Figure 2, TabTransformer achieved the highest discriminative performance with an AUC of 0.953 (95% CI: 0.924–0.977), followed by TCN (AUC = 0.937, 95% CI: 0.903–0.967), XGBoost (AUC = 0.857, 95% CI: 0.821–0.890), and LSTM_Attention (AUC = 0.755, 95% CI: 0.707–0.......

Discussion

In this study, we developed an attention-based TabTransformer model for in-hospital classification of osteoporosis among adults undergoing spinal neurosurgical procedures and used SHapley Additive exPlanations (SHAP) to explore the main variables driving model output (Figure 5 and Figure 6). The SHAP results suggest that age was the dominant contributor, followed by recurring signals from renal and metabolic markers, hematologic indices, selected medication vari.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Adam optimizer	PyTorch	Included in PyTorch	Used for TabTransformer training
Jupyter Notebook	Project Jupyter	Latest available version	Used for model development and analysis workflow
MIMIC-IV database	PhysioNet	version 3.1; https://physionet.org/content/mimiciv/3.1/	Public de-identified critical care database
Python	Python Software Foundation	Latest available version	Main programming environment
PyTorch	PyTorch Foundation	Latest available version	Deep learning framework used to implement TabTransformer
Relational database management software	Navicat	version 17.0; https://www.navicat.com.cn/	Used for data extraction from MIMIC-IV
scikit-learn	scikit-learn developers	Latest available version	Used for preprocessing, data splitting, and baseline models
SHAP	SHAP developers	Latest available version	Used for model interpretability analysis
SMOTE	imbalanced-learn	Latest available version	Used for minority-class oversampling in the training set
XGBoost	XGBoost developers	Latest available version	Baseline comparison model