بدائل البحث:
largest decrease » larger decrease (توسيع البحث)
marked decrease » marked increase (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
e decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), _ decreased (توسيع البحث)
largest decrease » larger decrease (توسيع البحث)
marked decrease » marked increase (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
e decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), _ decreased (توسيع البحث)
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Overview of the WeARTolerance program.
منشور في 2024"…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …"
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Using Environmental Mixture Exposure-Triggered Biological Knowledge-Driven Machine Learning to Predict Early Pregnancy Loss
منشور في 2025"…The GO-integrated model, with an area under the curve (AUC) of 0.876, outperformed others (AUC = 0.819), even when the sample size decreased to 60% of the total. Additionally, this framework deciphered critical exposures (e.g., serum selenium and chromium) and biological perturbations (e.g., cell population proliferation and apoptotic nuclear changes), linking mixture exposure to EPL. …"
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Passive sensing data.
منشور في 2025"…The CrossCheck dataset includes 6,364 mental state surveys using 4-point ordinal rating scales and 23,551 days of smartphone sensor data contributed by patients with schizophrenia. We trained 120 machine learning models to forecast 10 mental states (e.g., Calm, Depressed, Seeing things) from passive sensor data on 2 predictive tasks (ordinal regression, binary classification) with 2 learning algorithms (XGBoost, LSTM) over 3 forecast horizons (same day, next day, next week). …"
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Surveys.
منشور في 2025"…The CrossCheck dataset includes 6,364 mental state surveys using 4-point ordinal rating scales and 23,551 days of smartphone sensor data contributed by patients with schizophrenia. We trained 120 machine learning models to forecast 10 mental states (e.g., Calm, Depressed, Seeing things) from passive sensor data on 2 predictive tasks (ordinal regression, binary classification) with 2 learning algorithms (XGBoost, LSTM) over 3 forecast horizons (same day, next day, next week). …"
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