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Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies - npj Digital Medicine

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Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies - npj Digital Medicine

Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable.

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Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies - npj Digital Medicine

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Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies - npj Digital Medicine

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