# Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models

arXiv: https://arxiv.org/abs/2112.15442 · HTML: https://mlsanity.com/paper/2112.15442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 76 references that are in the map.
- First posted on arXiv 28 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training](https://mlsanity.com/paper/1711.06606/) (2017)
- [Boosting Handwriting Text Recognition in Small Databases with Transfer Learning](https://mlsanity.com/paper/1804.01527/) (2018)
- [The Future AI in Healthcare: A Tsunami of False Alarms or a Product of Experts?](https://mlsanity.com/paper/2007.10502/) (2020)

## Abstract (arXiv)

Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morphologies and abnormalities specific to a given condition - T-wave Alternans (TWA) as a result of Post-Traumatic Stress Disorder, or PTSD - and significantly boost performance on a small database of rare individuals. Approach: Using a previously validated artificial ECG model, we generated 180,000 artificial ECGs with or without significant TWA, with varying heart rate, breathing rate, TWA amplitude, and ECG morphology. A DNN, trained on over 70,000 patients to classify 25 different rhythms, was modified the output layer to a binary class (TWA or no-TWA, or equivalently, PTSD or no-PTSD), and transfer learning was performed on the artificial ECG. In a final transfer learning step, the DNN was trained and cross-validated on ECG from 12 PTSD and 24 controls for all combinations of using the three databases. Main results: The best performing approach (AUROC = 0.77, Accuracy = 0.72, F1-score = 0.64) was found by performing both transfer learning steps, using the pre-trained arrhythmia DNN, the artificial data and the real PTSD-related ECG data. Removing the artificial data from training led to the largest drop in performance. Removing the arrhythmia data from training provided a modest, but significant, drop in performance. The final model showed no significant drop in performance on the artificial data, indicating no overfitting. Significance: In healthcare, it is common to only have a small collection of high-quality data and labels, or a larger database with much lower quality (and less relevant) labels. The paradigm presented here, involving model-based performance boosting, provides a solution through transfer learning on a large realistic artificial database, and a partially relevant real database.