Brandon Ballinger
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View article: DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction Open
We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high chole…
View article: Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch Open
This proof-of-concept study found that smartwatch photoplethysmography coupled with a deep neural network can passively detect AF but with some loss of sensitivity and specificity against a criterion-standard ECG. Further studies will help…
View article: DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk\n Prediction
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk\n Prediction Open
We train and validate a semi-supervised, multi-task LSTM on 57,675\nperson-weeks of data from off-the-shelf wearable heart rate sensors, showing\nhigh accuracy at detecting multiple medical conditions, including diabetes\n(0.8451), high ch…
View article: DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction Open
We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high chole…