Abstract: Techniques performed by a data processing system for operating a personalized oncology system herein include accessing a first histopathological image of a histopathological slide of a sample taken from a first patient; analyzing the first histopathological image using a first machine learning model configured to extract first features from the first histopathological image; searching a histological database that includes a plurality of second histopathological images and corresponding clinical data for a plurality of second patients to generate search results; analyzing the plurality of third histopathological images and the corresponding clinical data associated with the plurality of third histopathological images using statistical analysis techniques to generate associated statistics and metrics associated with mortality, morbidity, time-to-event, or a combination thereof for the plurality of third patients associated with the third histopathological images; and presenting an interactive visual representat

Abstract: Background noise from relevant data sets, including for example over-the-counter sales data, absenteeism data, etc., is subtracted using a background estimation algorithm that outputs residual data. The effects of hypothetical anomalous events, such as a bio-terrorist attack, on the relevant data sets are modeled to create replica data. The replica data may be based on input from epidemiologists and various scenario templates including information on disease manifestation and other intelligence. The residual data and the replica data are then matched using a detector. Types of detectors include for example adaptive matched-filter detectors, change detectors and Bayesian Inference Networks. An alarm is triggered if a real anomalous event similar to a hypothetical anomalous event is detected. A Geographical Information System (GIS) may be used to display data from individual zip codes.

Abstract: Background noise from relevant data sets, including for example over-the-counter sales data, absenteeism data, etc., is subtracted using a background estimation algorithm that outputs residual data The effects of hypothetical anomalous events, such as a bio-terrorist attack, on the relevant data sets are modeled to create replica data. The replica data may be based on input from epidemiologists and various scenario templates including information on disease manifestation and other intelligence. The residual data and the replica data are then matched using a detector. Types of detectors include for example adaptive matched-filter detectors, change detectors and Bayesian Inference Networks. An alarm is triggered if a real anomalous event similar to a hypothetical anomalous event is detected. A Geographical Information System (GIS) may be used to display data from individual zip codes.