Abstract: The present disclosure relates to a digital medicine companion for patients undergoing oncology treatments. A patient may enter symptoms on a prescription software. A wearable device may passively collect other healthcare data such as biological data and/or physical activity data. The patient may therefore be monitored using the prescription software and/or wearables. Furthermore, the prescription software may be integrated with biofluid testing systems. For example, an at-home biofluid monitoring kit and/or a laboratory system may communicate with the prescription software and/or its backend server. The healthcare data collected through the monitoring and the biofluid testing may be fed into a machine learning model, which may output whether the patient is likely to develop side effects such as cytopenia. One or more alert notifications, e.g., to a clinician dashboard and/or to the prescription software, may be triggered when the machine learning model determines a higher likelihood of such side effects.

Abstract: Embodiments disclosed herein may provide digital medicine support for predicting and mitigating side effects of bispecific antibody treatment (e.g., Pfizer's Elranatamab) for myeloma patients. The side effects may include cytokine release syndrome (CRS), infection (e.g., sepsis), neurotoxicity (e.g., peripheral neuropathy), cytopenia (e.g., neutropenia), etc. These side effects may be predicted based on passive collection of data from patient wearables, patient entered data on healthcare application, and other data such as bloodwork data. Trained machine learning models may be used for predicting the side effects. As the side effects may be predicted before their onsets, a proactive intervention may be feasible to improve the healthcare outcomes for myeloma patients.

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.