Systems and methods for identifying clinical conditions from health records

A system for conducting feature importance analysis, the system comprising a processor and a non-transitory computer readable medium storing instructions such that when the instructions are executed by the processor, the system is configured to:

• provide a deep learning model;
• analyze data records using word embeddings to produce vectors indicative of similarity scores for words or phrases in the data records relative to electronically stored reference data;
• compute shapeley additive explanation (SHAP) values to analyze and rank contributions of the words or phrases to the similarity scores from the word embeddings;
• use a language model trained on a vocabulary used in the data records to identify contextual insights that are not explicitly disclosed in the data to improve accuracy of the word embeddings;
• and
• iteratively and dynamically adjust or update the model using human-in-the-loop based reinforcement learning from human feedback (RLHF) by:
• receiving from a human via an electronic human-machine interface (HMI) an input representative of a modification to at least one of the contributions determined by the SHAP computation;
• and
• responsive to receiving the input, adjusting at least one of the ranks of the word embeddings in the model.