Tata Consultancy Services Limited

Sanjeev Manchanda

Mahesh M Kshirsagar

Ashish Omprakash Indani

Kamlesh Mhashilkar

Sonam Yashpal Sharma

Shriram Pillai

Nihar Ranjan Sahoo

Mahesh Kshirsagar

Ajeet Phansalkar

This disclosure relates generally to system and method for determining explainability of machine predicted decisions. Typical explainable AI (XAI) solutions are limited by type of data processed, such as structured, semi-structured and unstructured text. In addition, due to limited automation of the process of explainability, typical systems are cumbersome and time-consuming. The system and method provide an end to end solution for automating the determination of explainability of machine predicted decisions. The XAI process output an absolute relevance score indicative of relevance of the features associated with the prediction which is indicative of percentage relevance/contribution of individual feature. The system further computes relative relevance score of the features by adding up all the features and calculating how much each individual feature is contributing to the total score. The relative relevance scores are utilized for determining explainability of decisions of the prediction.

Systems and methods for determining explainability of machine predicted decisions

This disclosure relates generally to method and system for extracting contextual information from a knowledge base. The method receives a user query comprising a request to extract contextual information from the user query. Further, the user query is analyzed based on a plurality of predefined parameters to determine sufficiency of information comprised in the user query. The received user query identifies relevant sources of the structured data, the unstructured data or the semi-structured data storage repositories. The user query is processed using a fine grain approach, where a dictionary of one or more keywords with weights are created through the domain ontology builder from the one or more knowledge articles. Furthermore, an appropriate contextual information related to the user query is extracted using the fine grain approach, based on the knowledge articles associated with the trained knowledge base comprising information required by the user query extracted from the knowledge articles.

Method and system for extracting contextual information from a knowledge base

In medical writing, manual process of creating, updating and maintaining documents is expensive, time consuming. This disclosure provides a method of an automatic medical document writing by receiving, a plurality of input documents as an input; processing, the inputted plurality of documents by extracting into an at least one section to generate a list of sections; classifying, at least one category corresponding to the at least one section to generate a summary set; generating, at least one of a local context and a global context based on the summary set; parsing, at least one sentence based on the generated local context and global context to generate at least one sequence of the plurality of sentences; processing, the at least one sequence of the plurality of sentence to generate a queue of the at least one sequence; validating, the queue of the at least one sequence to obtain a combined summary set.

System and method for machine assisted documentation in medical writing

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Mumbai, India

Sanjeev Manchanda