Abstract: A digital medicine companion for managing skin diseases (e.g., atopic dermatitis, psoriasis) may include patient wearable devices passively collecting patient data, patient user devices with healthcare applications for the patient to enter health related data, central analytics for flare prediction and disease progress tracking, and a clinician dashboard. For flare prediction, a prediction tool may be trained using the ground truth of recorded flare occurrences for the trained model to predict whether observed scratch events may result in a flare. Upon predicting a likely flare, alert notifications may be generated, e.g., for a clinician and/or the patient. Furthermore, a baseline may be established based on the data passively gathered from the wearables and actively gathered from the patient user devices. The continuously collected data may be compared against the established baseline. Upon detecting a significant deviation, alerts may be sent to the patient and/or the clinician.

Abstract: Technology is disclosed for detecting scratch events and predicting flares of pruritus, utilizing motion data sensed from a wearable sensor. Detecting scratch may be done with a two-tier approach by first detecting a hand motion from motion sensed data and then classifying that hand motion as a scratch event using one or more computerized classification models. Embodiments may focus on detecting nighttime scratch by utilizing motion sensed data captured during a user's detected sleep opportunity. Additionally, historical scratch event data may be used to predict a user's itch and flare risk for a future time interval. Decision support tools in the form of computer applications or services may utilize the detected scratch events or predicted itch or flare risk to initiate an action for reducing current itch and/or mitigating future risk, including initiating a treatment protocol that includes therapeutic agent.