Abstract: A system for retrieving patient information using large language models including a computing device configured to receive a natural language query as a function of a user input, input the natural language query into a large language model communicatively connected to the least a processor, receive a computer language query comprising a plurality of nodes from the large language model, map the plurality of nodes to one or more entries in a patient database, receive a database response from the patient database as a function of the mapping, generate a final database query as a function of the database response. query the patient database using the final database query, receive a user response as a function of the final database query, and transmit the user response to a graphical user interface as a function of the final database query.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a de-identification method including receiving a text sequence; providing the text sequence to a plurality of entity tagging models, each of the plurality of entity tagging models being trained to tag one or more portions of the text sequence having a corresponding entity type; tagging one or more entities in the text sequence using the plurality of entity tagging models; and obfuscating each entity among the one or more tagged entities by replacing the entity with a surrogate, the surrogate being selected based on one or more attributes of the entity and maintaining characteristics similar to the entity being replaced.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: A method and system is provided for mapping a term to a vector representation in a semantic space. Provided techniques allow for efficient and accurate determination of vector representations for query terms that are terms of emerging interest or are otherwise not included in a set of terms for which vector representations are pre-calculated.

Abstract: An apparatus for generating annotations for electronic records is disclosed. The apparatus includes a processor and a memory containing instructions configuring the processor to receive unstructured data for a plurality of electronic records and generate a plurality of machine learning models (MLMs), wherein each MLM of the plurality of MLMs is trained using a machine learning algorithm. The processor is further configured to generate at least one annotation for the unstructured data using each MLM of the plurality of MLM and structure the unstructured data as structured data as a function of the generated annotations.

Abstract: A method and system is provided for mapping a term to a vector representation in a semantic space. Provided techniques allow for efficient and accurate determination of vector representations for query terms that are terms of emerging interest or are otherwise not included in a set of terms for which vector representations are pre-calculated.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: An apparatus and method for expanded cohort application of machine-learning classifier methods based on a seed data set. The apparatus comprises at least a processor configured to receive at least a labeled seed set, train an embedding model based on the seed data, determine a plurality of vector representations for both the seed set and the target data, identify commonalities between the seed vector representation and the target vector, and provide the vector training data to a supervised machine-learning application for subsequent classifications.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: In one aspect, a method is disclosed that includes selecting a first plurality of input candidates from a corpus of data; mapping the first plurality of input candidates onto a pretrained vector space of a pretrained model; clustering the first plurality of input candidates in the pretrained vector space; adding the first plurality of input candidates to a plurality of queues for labelling; and labelling the first plurality of input candidates.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: Disclosed systems, methods, and computer readable media can retrieve clinical information based on clinical patient data. For example, a method for retrieving clinical information based on clinical patient data includes receiving a specification of a patient cohort, receiving a query, retrieving a list of search results based on the query and the specification of the patient cohort, computing one or more inferences for each item in the list of search results, providing an aggregate statistical analysis associated with the one or more inferences, and providing, by the one or more hardware processors, a response to the query that includes the aggregate statistical analysis. Each element in the list of search results comprises at least a portion of a clinical data record associated with a patient in the patient cohort.

Abstract: A method and system is provided for mapping a term to a vector representation in a semantic space. Provided techniques allow for efficient and accurate determination of vector representations for query terms that are terms of emerging interest or are otherwise not included in a set of terms for which vector representations are pre-calculated.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: A tunable cavity resonator includes a housing, a post, and a controllably variable capacitive coupling. The housing defines an interior and has at least one side wall, a first end, and a second end. The post is located within the interior and extends from the first end to the second end. The post and the housing define a resonating cavity. The controllably variable capacitive coupling is disposed in the housing.

Abstract: A tunable cavity resonator includes a housing, a post, and a controllably variable capacitive coupling. The housing defines an interior and has at least one side wall, a first end, and a second end. The post is located within the interior and extends from the first end to the second end. The post and the housing define a resonating cavity. The controllably variable capacitive coupling is disposed in the housing.