Abstract: This disclosure relates to method and system for automatically generating template-based patient notes. The method includes receiving patient data from Electronic Medical Records (EMR) of the patient and an audio input corresponding to a patient from a healthcare provider. The method further includes generating primary insights text data from the audio input through a speech-to-text conversion model to obtain a plurality of unique natural language sentences. The method further includes assigning primary category and at least one secondary category associated with the primary category to each of the unique natural language sentences through a set of Machine Learning (ML) models. The method further generates a final note corresponding to the patient based on a note template.

Abstract: This disclosure relates to method and system for creating Bayesian Knowledge Networks (BKNs) for medical conditions. The method includes retrieving an audit trail dataset from an Electronic Medical Record (EMR) corresponding to each of a plurality of patients for one or more medical conditions. The method includes determining a generalized hospital event timeline for each of the one or more medical conditions. The method includes generating a fully connected network of the plurality of nodes of one or more generalized hospital event timelines of the one or more medical conditions. The method includes determining a relation type corresponding to each two of the plurality of nodes of the fully connected network using a causal network learning algorithm. The method includes creating a BKN from the fully connected network based on the determined relation type.

Abstract: This disclosure relates to method and system determining a plurality of patient health indicators through a Machine Learning (ML) model. The method includes receiving numerical data from a monitoring device. The numerical data is based on patient input data. The patient input data includes predefined variables, discretely sampled data, and continuously sampled data. The method further includes identifying a set of patterns from the numerical data through the ML model. The ML model is based on at least one of a Long Short Term Memory (LSTM), Extreme Gradient Boost (XGBoost), and Transformers. The method further includes comparing the set of patterns with historical medical data of the patient. The method further includes determining the plurality of patient health indicators through the ML model based on the comparing.