Core Concepts
A multimodal deep learning model combining pre-operative static patient data, hip and chest images, and per-operative vital signs and medication data can predict 30-day mortality in elderly hip fracture patients.
Abstract
The authors developed a multimodal deep learning model to predict 30-day mortality in elderly hip fracture patients. The model combines the following data modalities:
Pre-operative data:
Static patient data (demographics, daily living activities, nutrition, surgery information, lab results, medication, and comorbidities)
Hip and chest X-ray images
Per-operative data:
Vital signs (heart rate, pulse, oxygen saturation, blood pressure)
Medication data
The authors trained unimodal models for each data modality and then fused them into a multimodal model. They found that:
The pre-operative static patient data was the most important modality, contributing 70.3% to the mortality prediction on average.
Adding per-operative data (vitals and medication) did not significantly improve the performance compared to the pre-operative multimodal model.
The multimodal model achieved an average test set AUC of 0.78, which is on par with state-of-the-art models using only pre-operative data.
The authors applied Shapley value analysis to explain the multimodal model's predictions, showing that static features related to kidney function and self-reliance were most important.
The results suggest that pre-operative data, especially static patient data, is the most important for predicting 30-day mortality in elderly hip fracture patients. The authors propose future work to further improve the model by addressing overfitting and missing data issues.
Stats
The 30-day mortality rate in the dataset is 7.8% (131 out of 1669 patients).
The average age of patients is 83 years, with 71.2% females and 28.8% males.
Quotes
"An accurate risk score computed using data known before surgery (pre-operative data) can lead to better information about the patient and be of help in the treatment selection."
"Clinicians use multiple data sources in their decision-making, therefore multimodal models should in theory be better at making these complex predictions."