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Determining physiological significance of inertial gait features in multiple sclerosis.

, , , and . BSN, page 266-271. IEEE, (2016)

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SpanPredict: Extraction of Predictive Document Spans with Neural Attention., , , , , and . NAACL-HLT, page 5234-5258. Association for Computational Linguistics, (2021)Neural Conditional Event Time Models., , , and . MLHC, volume 126 of Proceedings of Machine Learning Research, page 223-244. PMLR, (2020)Enhancing early autism prediction based on electronic records using clinical narratives., , , , , , , and . J. Biomed. Informatics, (August 2023)Toward Detection and Monitoring of Gait Pathology using Inertial Sensors under Rotation, Scale, and Offset Invariant Dynamic Time Warping., , , , and . BODYNETS, ICST, (2015)Determining physiological significance of inertial gait features in multiple sclerosis., , , and . BSN, page 266-271. IEEE, (2016)Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model., , , , , and . MLHC, volume 85 of Proceedings of Machine Learning Research, page 312-331. PMLR, (2018)Correlations between Inertial Body Sensor Measures and Clinical Measures in Multiple Sclerosis., , , and . BODYNETS, ICST, (2015)Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data., and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 9571-9581. PMLR, (2022)Remotely engaged: Lessons from remote monitoring in multiple sclerosis., , , , and . Int. J. Medical Informatics, (2017)Relationship between kernel density function estimates of gait time series and clinical data., , , and . BHI, page 329-332. IEEE, (2017)