Tata Consultancy Services Limited

Abir Naskar

Lipika Dey

Tirthankar Dasgupta

Ishan Verma

Mohammad Shakir

Tushar Goel

Sudeshna Jana

Anjan Dutta

Suyash Sangwan

Rupsa Saha

Akshara Rai

Priyankar Ghosh

Aninda Sukla

Currently systems for Length of Stay (LoS) and clinical interventions for patients work independent of each other. However, they are highly interdependent decisions for overall medical predictions for patients. Embodiments disclosed provide a method and system for transformer-based Neural Network (NN) for jointly predicting LoS and critical interventions for patients admitted to medical facilities. A joint NN model, comprising Bidirectional Encoder Representations from Transformers (BERT) model as one of the layers, processes first day clinical notes, available in an unstructured data format, and a plurality of medical attributes of the patient available in a structured data. Further, the joint NN model jointly predicts s (a) the LoS of the patient into one of the classes comprising LONG and SHORT and (b) a Type of Intervention (ToI) for the patient into one among a plurality of classes with each class of the ToI comprising a list of critical clinical interventions.

Transformer-based neural network for jointly predicting length of stay and critical interventions for patients

Conventionally, text summarization has been rule-based method and neural network based which required large dataset for training and the summary delivered had to be assessed by user in terms of relevancy. System and method are provided by present disclosure that generate causal insight summaries wherein event of importance is detected, and it is determined why event is relevant to a user. Text description is processed for named entities recognition, polarities of sentences identified, extraction of causal effects sentences (CES) and causal relationship identification in text segments which correspond to impacting events. Named entities are then role labeled. A score is computed for named entities, polarities of sentences, causal effects sentences, causal relationships, and the impacting events. A causal insight summary is generated with overall polarity being computed/determined. A customized causal insight summary is delivered to target users based on user preferences associated with specific named entities and impacting events.

Systems and methods for generating causal insight summary

System and method for method and system for event profiling is described that processes large volume of data gathered from a plurality of digital sources to automatically profile and continuously update an event. The system utilizes, an ensemble of probabilistic classifiers for automated extraction of finer details of the event, which use linguistic features for profiling information about the event, wherein the information is spread across various data sources. Further, disambiguation is performed to augment the accuracy of the event profiling. The system enables semantically linking of related events curated in the knowledge base and thereby performs semantic search over it. The system takes user-feedback and improves upon the information extraction process through reinforcement learning.

System and method for event profiling

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Kolkatta, India

Abir Naskar