Abstract: In variants, a system for automatically prioritizing content provided to a user can include and/or interface with any or all of: a set of content, a set of models, a set of processing and/or computing subsystems, and a set of messaging platforms and/or messaging interfaces. In variants, a method for automatically prioritizing content provided to a user can include receiving inputs from a set of users and/or processing the set of inputs to determine a set of content recommendations. The method can optionally further include providing content recommendations to a user and/or training and/or updating a set of models.

Abstract: A system and method for developing a treatment plan using multi-stage machine learning. A method includes determining a treatment plan for a patient based on at least one mental health disorder of a patient, wherein the treatment plan includes a plurality of digital therapeutics exercise tasks, wherein each digital therapeutics exercise task is selected from among a category of digital therapeutics exercise tasks corresponding to a type of mental health disorder of the at least one mental health disorder of the patient; and administering treatment to the patient by prescribing the treatment plan to the patient and causing data for administering the treatment plan to a user device of the patient.

Abstract: A system and method for developing a treatment plan using multi-stage machine learning. A method includes identifying at least one cognitive distortion of a user by applying a first machine learning model to a first portion of features extracted from data related to the user, wherein the first machine learning model is a cognitive distortions model trained using training user-created content; determining a plurality of digital therapeutics exercise tasks for the user based on the at least one cognitive distortion by applying a second machine learning model to a second portion of the features extracted from the data related to the user and to the output of the first machine learning model, wherein the second machine learning model is a task recommender model trained using training cognitive distortions and the training user-created content; and generating a treatment plan including the plurality of digital therapeutics exercise tasks for the user.

Abstract: A method of treating a subject comprises administering a treatment to a subject identified as having a high probability of distant metastatic recurrence, wherein the probability of distant metastatic recurrence was determined by a process, comprising acquiring at least one image of a tissue sample comprising a plurality of cells, taken from a subject, classifying each of the plurality of cells into categories, dividing the at least one image into a plurality of patches, calculating values for a plurality of morphological features based on the patches, and calculating a distant metastatic recurrence probability based on the values. A computer-implemented method of training a neural network and a system for characterizing a cancer in a subject are also described.