Automated sig code translation using machine learning
A pharmacy management system for automated sig code translation using machine learning includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the pharmacy management system to train a machine learning model to analyze sig codes, receive a sig code, analyze the sig code utterance and generate an output corresponding to the sig code utterance. A computer-implemented method includes training a machine learning model to analyze sig codes, receiving a sig code, analyzing the sig code utterance, and generating an output corresponding to the sig code utterance. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: train a machine learning model to analyze sig codes, receive a sig code, analyze the sig code utterance, and generate an output corresponding to the sig code utterance.
1. A pharmacy management system for automated sig code translation using machine learning, comprising:
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the pharmacy management system to
train, via the one or more processors, a machine learning model to analyze sig code utterances,
receive a sig code utterance,
analyze the sig code utterance using the trained machine learning model, wherein the analyzing includes identifying one or more entities within the sig code utterance, and wherein the one or more entities includes at least one quantity entity and at least one frequency entity; and
generate an output corresponding to the sig code utterance including one or more entity results, each entity result including an entity type, an entity name, and an entity normalized value corresponding to a respective one of the one or more entities within the sig code utterance.
2. The pharmacy management system of claim 1 , the memory storing further instructions that, when executed by the one or more processors, cause the pharmacy management system to
train the machine learning model to analyze sig code utterances by training a natural language machine learning model using a training data set of labeled sig code utterances.
3. The pharmacy management system of claim 1 , the memory storing further instructions that, when executed by the one or more processors, cause the pharmacy management system to
receive the sig code utterance from a physician computing system.
4. The pharmacy management system of claim 1 , the memory storing further instructions that, when executed by the one or more processors, cause the pharmacy management system to
analyze the output corresponding to the sig code utterance to generate an indication of verification.
5. The pharmacy management system of claim 1 , the memory storing further instructions that, when executed by the one or more processors, cause the pharmacy management system to
analyze the output corresponding to the sig code utterance to generate an electronic calendar object; and
transmit the electronic calendar object to a patient computing device.
6. The pharmacy management system of claim 1 , the memory storing further instructions that, when executed by the one or more processors, cause the pharmacy management system to
analyze the sig code utterance using a pre-trained machine learning model, to identify one or more general entities.
7. The pharmacy management system of claim 1 , the memory storing further instructions that, when executed by the one or more processors, cause the pharmacy management system to
receive the sig code utterance from an application programming interface client via an application programming interface, and
transmit the output corresponding to the sig code utterance to the application programming interface client.
8. A computer-implemented method for automated sig code translation using machine learning, the method comprising:
training, via one or more processors, a machine learning model to analyze sig code utterances,
receiving a sig code utterance,
analyzing the sig code utterance using the trained machine learning model,
wherein the analyzing includes identifying one or more entities within the sig code utterance, and
wherein the one or more entities includes at least one quantity entity and at least one frequency entity; and
generating an output corresponding to the sig code utterance
including one or more entity results, each entity result including an entity type, an entity name, and an entity normalized value corresponding to a respective one of the one or more entities within the sig code utterance.
9. The computer-implemented method of claim 8 , further comprising:
training the machine learning model to analyze sig code utterances by training a natural language machine learning model using a training data set of labeled sig code utterances.
10. The computer-implemented method of claim 8 , further comprising:
receiving the sig code utterance from a physician computing system.
11. The computer-implemented method of claim 8 , further comprising:
analyzing the output corresponding to the sig code utterance to generate an indication of verification.
12. The computer-implemented method of claim 8 , further comprising:
analyzing the output corresponding to the sig code utterance to generate an electronic calendar object; and
transmitting the electronic calendar object to a patient computing device.
13. The computer-implemented method of claim 8 , further comprising:
analyzing the sig code utterance using a pre-trained machine learning model, to identify one or more general entities.
14. The computer-implemented method of claim 8 , further comprising:
receive the sig code utterance from an application programming interface client via an application programming interface, and
transmit the output corresponding to the sig code utterance to the application programming interface client.
15. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to:
train a machine learning model to analyze sig code utterances,
receive a sig code utterance,
analyze the sig code utterance using the trained machine learning model,
wherein the analyzing includes identifying one or more entities within the sig code utterance, and
wherein the one or more entities includes at least one quantity entity and at least one frequency entity; and
generate an output corresponding to the sig code utterance
including one or more entity results, each entity result including
an entity type, an entity name, and an entity normalized value corresponding to a respective one of the one or more entities within the sig code utterance.
16. The non-transitory computer readable medium of claim 15 containing further program instructions that when executed, cause a computer to:
train the machine learning model to analyze sig code utterances by training a natural language machine learning model using a training data set of labeled sig code utterances.
17. The non-transitory computer readable medium of claim 15 containing further program instructions that when executed, cause a computer to:
analyze the output corresponding to the sig code utterance to generate an indication of verification.
18. The non-transitory computer readable medium of claim 15 containing further program instructions that when executed, cause a computer to:
analyze the output corresponding to the sig code utterance to generate an electronic calendar object; and
transmit the electronic calendar object to a patient computing device.
19. The non-transitory computer readable medium of claim 15 containing further program instructions that when executed, cause a computer to:
analyze the sig code utterance using a pre-trained machine learning model, to identify one or more general entities.
20. The non-transitory computer readable medium of claim 15 containing further program instructions that when executed, cause a computer to:
receive the sig code utterance from an application programming interface client via an application programming interface, and
transmit the output corresponding to the sig code utterance to the application programming interface client.