Abstract: A facility diagnoses AMD in a subject patient. The facility obtains one or more patient images for a subject patient, which depict at least one of the subject patient's eyes. The facility applies an image-based classifier to at least one of the patient images to obtain a first AMD risk score. The facility identifies the macular region of an eye depicted in the patient images, and applies a deep learning-based classifier to the identified macular region to obtain a second AMD risk score. The facility identifies lesions present in an eye depicted in the patient images, and applies a deep learning-based classifier to the identified lesions to obtain a third AMD risk score. The facility combines the first AMD risk score, second AMD risk score, and third AMD risk score to obtain a unified AMD risk score.

Abstract: A facility for predicting patient outcomes on the basis of clinical trials is described. The facility obtains information describing one or more completed clinical trials, and extracts features from the obtained clinical trial information. The facility uses the extracted features to train both a time-series data model for predicting clinical outcomes and a non-time-series data model for predicting clinical outcomes. The facility applies these trained models to information describing a subject patient to predict a clinical outcome for the subject patient.

Abstract: A facility providing a medical outcome prediction model data structure is described. The data structure constitutes a trained statistical model that can be applied to image data and electronic health record data for a patient to predict a cancer survival outcome for the patient.

Abstract: A facility predicts a weight of two or more independent variables used by a subject model trained to predict outcomes using a first dataset which includes values for the independent variables. The facility creates a second dataset by adding noise to the first dataset and trains an autoencoder to reconstruct the first dataset based on the second dataset. The facility access a subject instance, including an output of the subject model, and generates test data based on the subject instance. The facility obtains output from the subject model for each of the data points in the test data. The facility constructs a training observation from the test data and the subject model output, and determines a weight for each training observation by using the autoencoder. The facility trains a local interpretable model based on the determined weight for each training observation.

Abstract: An automatic heuristic deporting-based modeling system is described. The system constructs a state of a space graph data structure. The data structure contains a number of nodes, each corresponding to a different machine learning model instance. Each stores a model type of the model instance, model parameter values of the model instance, and data features of the model instance. The contents of the data structure can be used to discern a model evolution history and select a model instance suited to a new machine learning project.

Abstract: A vehicle damage assessment system is described. The system receives one or more photos in connection with a distinguished vehicle insurance claim. For each received photo, the system: uses a statistical model to identify a portion of the vehicle shown in the identified region; applies to the photo one of a number of content-based retrieval systems that is specific to the identified vehicle portion to retrieve one or more similar photos submitted with resolved claims that show the identified region of a vehicle that is the subject of the claim; and, for each retrieved photo, accesses a quantitative measure describing repair work performed under the resolved claim with which the retrieved photo was submitted. The system aggregates some or all of the accessed quantitative measures to obtain a quantitative measure predicted for the distinguished claim. The system outputs the obtained quantitative measure predicted for the distinguished claim.

Abstract: A facility for predicting patient outcomes on the basis of clinical trials is described. The facility obtains information describing one or more completed clinical trials, and extracts features from the obtained clinical trial information. The facility uses the extracted features to train both a time-series data model for predicting clinical outcomes and a non-time-series data model for predicting clinical outcomes. The facility applies these trained models to information describing a subject patient to predict a clinical outcome for the subject patient.

Abstract: A facility providing a medical outcome prediction model data structure is described. The data structure constitutes a trained statistical model that can be applied to image data and electronic health record data for a patient to predict a cancer survival outcome for the patient.