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DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction

AAAI 2018 · Label-efficient and robust learning › Semi-supervised learning · approach: Self-training theory · 142 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace hand-engineered biomarkers with the supervised LSTMDeepHeart · Diabetesc-statistic: 0.7906 -> 0.8451Helped
Replace hand-engineered biomarkers with the supervised LSTMDeepHeart · High Cholesterolc-statistic: 0.5996 -> 0.6736Helped
Replace hand-engineered biomarkers with the supervised LSTMDeepHeart · Sleep Apneac-statistic: 0.6583 -> 0.8041Helped
Replace hand-engineered biomarkers with the supervised LSTMDeepHeart · High Blood Pressurec-statistic: 0.7195 -> 0.7991Helped
Add heuristic heart-rate-variability pretrainingLSTM · High Cholesterolc-statistic: 0.6736 -> 0.7148Helped
Add heuristic heart-rate-variability pretrainingLSTM · High Blood Pressurec-statistic: 0.7991 -> 0.7427Hurt
Add heuristic heart-rate-variability pretrainingLSTM · Diabetesc-statistic: 0.8451 -> 0.8366No clear effect
Add heuristic heart-rate-variability pretrainingLSTM · Sleep Apneac-statistic: 0.8041 -> 0.7951No clear effect
Add sequence-autoencoder pretrainingLSTM · High Cholesterolc-statistic: 0.6736 -> 0.7441Helped
Add sequence-autoencoder pretrainingLSTM · Sleep Apneac-statistic: 0.8041 -> 0.8298Helped
Add sequence-autoencoder pretrainingLSTM · High Blood Pressurec-statistic: 0.7991 -> 0.8086Helped
Add sequence-autoencoder pretrainingLSTM · Diabetesc-statistic: 0.8451 -> 0.7998Hurt

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