Drawing conclusions from free form texts with deep reinforcement learning

At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:

• segmenting a free form clinical note into a plurality of segments;
• for each segment of the plurality of segments:
• identifying a candidate diagnosis, comprising identifying a document in an evidence pool that most closely matches said segment, the evidence pool comprising clinical reference documents and associated diagnoses, and selecting a diagnosis associated with the identified document as the candidate diagnosis;
• applying a reinforcement learning trained agent to a state vector encoding said segment and said candidate diagnosis to make a determination of whether to accept or reject the candidate diagnosis, the reinforcement learning trained agent being trained to accept or reject candidate diagnoses based on a reward function measuring an accuracy of candidate diagnoses;
• and
• presenting the one or more accepted candidate diagnoses.

A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:

• segmenting a free form clinical note into a plurality of segments;
• for each segment of the plurality of segments:
• identifying a candidate diagnosis, comprising identifying a document in an evidence pool that most closely matches said segment, the evidence pool comprising clinical reference documents and associated diagnoses, and selecting a diagnosis associated with the identified document as the candidate diagnosis;
• applying a reinforcement learning trained agent to a state vector encoding said segment and said candidate diagnosis to make a determination of whether to accept or reject the candidate diagnosis, the reinforcement learning trained agent being trained to accept or reject candidate diagnoses based on a reward function measuring an accuracy of candidate diagnoses;
• and
• presenting the one or more accepted candidate diagnoses.