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.