Abstract: Described herein is an apparatus and method for training a machine learning model. An apparatus may include a computing device configured to receive a corpus of data containing entries corresponding to a plurality of subjects; identify a plurality of entries within the corpus, corresponding to a first subject of the plurality of subjects and representing medical history of the first subject; determine a plurality of temporal attributes of the plurality of entries; generate a plurality of tokens as a function of the plurality of entries; generate a chronological data structure segment ordering one or more of the plurality of entries and the plurality of tokens, as a function of the plurality of temporal attributes; and train a multimodal machine learning model on a training dataset including the chronological data structure segment.

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: An apparatus method for leveraging a repository of images containing implant devices in a human body are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a plurality of sets of historical subject data, classify the plurality of sets of historical subject data into one or more implant cohorts, generate implant training data using the plurality of sets of classified historical subject data in the one or more implant cohorts, train an implant machine-learning model using the implant training data, receive an inquiry datum from a user, wherein the inquiry datum includes current subject data and generate an output datum as a function of the inquiry datum using the trained implant machine-learning model.

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: An apparatus and method for de-identification of medical images including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a series of images, comprising metadata, and a plurality of image slices; select a sampling strategy as a function of the metadata, wherein the sampling strategy identifies a subset of image slices of the plurality of image slices to sample, apply a text classifier to the subset of image slices, wherein the text classifier is configured to identify a presence of textual information on the subset of image slices; determine at least one relationship between the subset of images slices including providing an output as a function of the presence of the textual information, the metadata, and a position of each image slice.

Abstract: An apparatus and method for de-identification of medical images including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a series of images, comprising metadata, and a plurality of image slices; select a sampling strategy as a function of the metadata, wherein the sampling strategy identifies a subset of image slices of the plurality of image slices to sample; apply a text classifier to the subset of image slices, wherein the text classifier is configured to identify a presence of textual information on the subset of image slices; determine at least one relationship between the subset of images slices including providing an output as a function of the presence of the textual information, the metadata, and a position of each image slice.

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: An apparatus and method for locating a position of an electrode on an organ model. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to receive an organ model configured to digitally represent an organ, receive a set of sensor data from at least a sensor including an ultrasound sensor, determine an electrode position within the organ model as a function of the set of sensor data using a position machine-learning module, wherein determining the electrode position includes determining a model position within the organ model as a function of the set of sensor data and determining the electrode position within the model position of the organ model as a function of the set of sensor data and add a visual marker onto the electrode position in the model position of the organ model.

Abstract: Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input; generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model; and using a conversational interface displayed using the user interface, display the first generative model output to the user.

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 de-identification of medical images including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a series of images, comprising metadata, and a plurality of image slices; select a sampling strategy as a function of the metadata, wherein the sampling strategy identifies a subset of image slices of the plurality of image slices to sample; apply a text classifier to the subset of image slices, wherein the text classifier is configured to identify a presence of textual information on the subset of image slices; determine at least one relationship between the subset of images slices including providing an output as a function of the presence of the textual information, the metadata, and a position of each image slice.

Abstract: Methods for treating or reducing viral infection in a human subject, comprising administering to the subject a pharmaceutical composition comprising a protease inhibitor are described.

Abstract: An apparatus for generating a dictionary data filter for data deidentification is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a plurality of user data. The memory instructs the processor to generate contextual data as a function of the plurality of user data. The memory instructs the processor to identify a plurality of patient identifiers and a plurality of localized terms within the plurality of user data. The memory instructs the processor to generate a dictionary data filter as a function of the plurality of localized terms. The memory instructs the processor to identify one or more misidentified patient identifiers within the plurality of patient identifiers using the dictionary data filter. The memory instructs the processor to modify the plurality of patient identifiers as a function of the one or more misidentified patient identifiers.

Abstract: An apparatus for generating obfuscated data within a computing environment, comprising a processor and a memory containing instructions configuring the processor to access a database containing a plurality of private data elements belonging to at least a private record, generate a set of obfuscated data elements, representative of the at least a private record, as a function of the plurality of private data elements using an generative model, determine a first distance measure between at least an obfuscated data element within the set of obfuscated data elements and at least a private data element of the plurality of private data elements within the database, and verify the first distance measure is within a distance range, wherein a minimum threshold of the distance range is determined as a function of a deidentification parameter and a maximum threshold of the distance range is determined as a function of an obfuscation parameter.

Abstract: An apparatus for the anonymization of user data is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a plurality of user data. The memory contains instructions configuring the processor to identify a plurality of patient identifiers within the plurality of user data. The memory contains instructions configuring the processor to generate contextual data associated with each patient identifier of the plurality of patient identifier. The memory contains instructions configuring the processor to identify one or more false positive patient identifiers within the plurality of patient identifiers as a function of the contextual data.

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 identifying clusters based on augmented data sets, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive one or more vitality data sets, retrieve auxiliary information for the one or more vitality data sets, generate one or more augmented data sets as a function of the auxiliary information and the one or more vitality data sets, identify at least one cluster based on the one or more augmented data sets and provide the at least one cluster to a cluster analysis platform, wherein the cluster analysis platform is configured to generate a similarity datum as a function of the at least one cluster and the one or more augmented data sets.

Abstract: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.

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.