Optum Technology, Inc.

Rohan Khilnani

V Kishore Ayyadevara

Vineet Shukla

Raghav Bali

Sree Harsha Ankem

Ankit Varshney

Rajat Gupta

Ravi Kumar Raju Gottumukkala

Saikumar Chintareddy

Ranraj Rana Singh

There is a need for more effective and efficient anomalous text detection. This need can be addressed by, for example, solutions for anomalous text detection that include the steps of performing a group of exploration-exploitation keyword extraction iterations based at least in part on one or more training corpus data entries until a per-iteration keyword list for an ultimate exploration-exploitation keyword extraction iteration satisfies a keyword list threshold condition; and subsequent to performing the exploration-exploitation keyword extraction iterations: processing one or more input corpus data entries using the language-model-based binary classification model to generate one or more inferred anomaly probabilities, processing the one or more input corpus data entries using the keyword model to generate explanatory metadata for the one or more inferred anomaly probabilities, and performing one or more prediction-based actions based at least in part on the one or more inferred anomaly probabilities and the explanatory metadata.

Anomalous text detection and entity identification using exploration-exploitation and pre-trained language models

There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.

Weighted adaptive filtering based loss function to predict the first occurrence of multiple events in a single shot

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Hyderabad Telangana, India

Rohan Khilnani