Abstract: Systems and methods for implementing machine-learning models for optimizing medical workflow are described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation workflow for a medical establishment having a group of patients. The methods may include making per-patient predictions for the group of patients, such as individual egg outcome predictions or predicting individual trigger day probability predictions, and, based on the per-patient predictions and one or more predictive models, making group predictions for the group of patients, such as a total number of eggs retrieved prediction or a total egg retrieval day probability prediction. The predictions may be made over a future timeframe such as a future day or set of future days. Also described herein are methods for optimizing ovarian stimulation for a patient, including predicting an optimal dose of ovarian stimulation to administer to the patient.

Abstract: Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.

Abstract: Systems and methods for rapid screening of signs and symptoms associated with disorders are disclosed. A processor or processing element in operable communication with one or more sensors is configured to detect physiological and movement changes associated with a disorder based on signals derived from sensor data generated by the one or more sensors as a user performs a set of predetermined scripted activities.

Abstract: Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.

Abstract: Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.