Apixio, Inc.

Beekeeperai, Inc.

University of California

Ellipsis Health, Inc.

Userzoom Technologies, Inc.

Recovery Exploration Technologies Inc.

Oii, Inc.

Robert Derward Rogers

Vishnuvyas Sethumadhavan

Shahram Shawn Dastmalchi

John O Schneider

Darren Matthew Schulte

Imran N Chaudhri

Mary Elizabeth Chalk

Mary Ellen Campana

Shamshad Alam Ansari

Elizabeth E Shriberg

Tomasz Rutowski

Michael Aratow

Scott Hammond

Michael Blum

Rachael A Callcut

Mainul Islam

Amir Hossein Harati Nejad Torbati

David Lin

Farshid Haque

Alan Donald Czeszynski

Yang Lu

Joe Hesse

Xavier Mestres

Francesc Del Castillo

David A Epstein

Jennifer May Lee

Wian Stipp

David Robinson

Matthew McSorley

Carl Bate Tulley

David Sanchez

Xavier Pujol

David Michael Evans

Anthony Michael LaRocca

Michelle S Keller

Erin Michelle Faverty

Austin Robert Rogers

Imran N Chaudri

Nikolai N Kalnine

Joseph H Hesse

The present disclosure relates to techniques for developing artificial intelligence algorithms by distributing analytics to multiple sources of privacy protected, harmonized data. Particularly, aspects are directed to a computer implemented method that includes receiving an algorithm and input data requirements associated with the algorithm, identifying data assets as being available from a data host based on the input data requirements, curating the data assets within a data storage structure that is within infrastructure of the data host, and integrating the algorithm into a secure capsule computing framework. The secure capsule computing framework serves the algorithm to the data assets within the data storage structure in a secure manner that preserves privacy of the data assets and the algorithm. The computer implemented method further includes running the data assets through the algorithm to obtain an inference.

Distributed privacy-preserving computing on protected data

Systems and methods for the verification of cohort sample sets is provided. In some embodiments, a sample dataset is received, and used to generate a sample vector set. The sample vector is computed by encoding the dataset according to a set of classes, generating a matrix of the encoded dataset (where the rows of the matrix correspond to patients and the columns to a class or subclass), and converting the matrix into a series of vector spaces. An example vector set is received and the difference between the sample vector set and the example vector set. Calculating the difference is by framing the distance as a p-value in a hypothesis test, compared against a threshold. When the p-value is above the threshold the sample dataset is rejected.

Systems and methods for dataset verification in a zero-trust computing environment

Systems and methods for data obfuscation are provided. Data obfuscation is needed when protecting an algorithm from reverse engineering attempts. The data is obfuscated by requesting more data from the data steward than is needed by the algorithm. If there are not enough types of data available from the data steward, 'low intensity' data types can be requested to fill out the data types requested. These 'low intensity' data types are ones that are easily obtained or even regularly collected anyway. The algorithms libraries are altered to call for all the data fields available, thereby rendering reverse engineering extremely difficult, if not impossible.

Systems and methods for data obfuscation in a zero-trust environment

The present invention relates to systems and methods for intelligently optimizing supply chain is provided. In particular, the systems and methods provide the capability to configure supply chain systems so as to: balance between cost and service is optimised and profitability maximised; configure system parameters to respond to both current and future risks; ensure that variability is built into plans enabling maximised efficiency; and human error and bias are eliminated from the planning process such that pro-active rather than reactive behaviour becomes the norm.

Orchestrated intelligent supply chain optimizer

Confirmation of data selection in a zero-trust environment is provided. In some embodiments, a synthetic data steward and/or a traditional data steward can receive the dataset(s). Additionally, a script is received from the algorithm developer. The dataset(s) and script(s) reside within a secure computing node and are therefore inaccessible by any party. The script(s) are executed, resulting in at least one confirmation about the data within the dataset(s). The script(s) complete any of confirming a format for data in the at least one dataset, the expected class values for data within the at least one dataset, an overall characterization and completeness of the at least one dataset, and/or an expected class membership for different data attributes within the at least one dataset.

Synthetic and traditional data stewards for selecting, optimizing, verifying and recommending one or more datasets

The present disclosure provides systems and methods for assessing a mental state of a subject in a single session or over multiple different sessions, using for example an automated module to present and/or formulate at least one query based in part on one or more target mental states to be assessed. The query may be configured to elicit at least one response from the subject. The query may be transmitted in an audio, visual, and/or textual format to the subject to elicit the response. Data comprising the response from the subject can be received. The data can be processed using one or more individual, joint, or fused models. One or more assessments of the mental state associated with the subject can be generated for the single session, for each of the multiple different sessions, or upon completion of one or more sessions of the multiple different sessions.

Systems and methods for mental health assessment

An algorithm is trained on a dataset to facilitate dynamic data exfiltration protection in a zero-trust environment. An inversion threat model using the original training dataset (a 'gold standard' inversion model) may also be generated. This inversion model can be characterized to determine its performance/accuracy of properly identifying a given input as being within the original training dataset or not (a data exfiltration event). It is possible to reduce this risk of data exfiltration to a desired level, without unduly impacting the algorithm's performance using the inversion model for the generation of noise that is targeted (as opposed to Gaussian noise). Noise added to the original training dataset causes the inversion model to perform poorer (meaning data steward data is more secure) but has a corresponding impact on the algorithm accuracy and performance. By adding noise generated by the inversion model in an iterative manner, and measuring the inversion model's performance, a balance can be reached where the data steward's data is considered secure, while minimizing the negative impact on the algorithm performance.

Systems and methods for training set obfuscation utilizing an inverted threat model in a zero-trust computing environment

Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user.

Systems and methods for medical information data warehouse management

A medical information navigation engine ('MINE') is capable of inferring referral activity not reported into a referral workflow system by utilizing intent-based clustering of medical information. The intent based clustering reconciles received medical data, from a variety of sources, and then clusters the data by applying one or more clustering rules. After the referrals not otherwise reported are inferred, they may be utilized to generate metrics that can be utilized to enhance patient care, and reduce costs. Metrics may be generated for both in-network and out-of-network referrals in order to distinguish differences in reporting activity.

Systems and methods for patient retention in network through referral analytics

Systems and methods for the quantification of sample set quality is provided. In some embodiments, a sample dataset and a sample vector set are received. A rule-based screening of the sample dataset is applied to generate a heuristic quality score. Additionally, a sample vector set is generated from the sample dataset. The difference between the sample vector set and the example vector set is calculated to generate a degree of difference quality score. The heuristic quality score and the degree of difference quality score are normalized and then combined into a quality metric. Calculating the difference between the sample vector set and the example vector set is by framing the distance as a p-value in a hypothesis test, compared against a threshold.

Systems and methods for dataset quality quantification in a zero-trust computing environment

Systems and methods for recommendation of cohort sample sets is provided. In some embodiments, a set of dataset requirements is received as a required vector set. The historical vector sets are queried. Each vector set corresponds to a known dataset. The difference between the required vector set and each of the historical vector sets is calculated by framing the distance as a p-value in a hypothesis test, compared against a threshold. The historical vector set with the least difference to the required vector set is identified. The least difference is calculated as a count of differing classes or as a numerically weighted summation of differing classes.

Systems and methods for dataset recommendation in a zero-trust computing environment

Systems and methods for the deployment and operation of an algorithm in a zero-trust environment are provided. In some embodiments, an algorithm is encrypted by an algorithm developer within a zero-trust computing node, using a public key. This generates a payload that is transferred to a core management system which in turn distributes the payload to one or more sequestered computing nodes located within the infrastructure of one or more data stewards. The sequestered computing nodes are designed to preserve privacy of data assets and the algorithm. Next the payloads are decrypted, using a private key, within the sequestered computing nodes. This yields the algorithm that can be run against the data assets of the data steward. A report is generated that can be shared with the appropriate parties.

Systems and methods for zero-trust algorithm deployment and operation on a protected dataset

Systems and methods for the processing of diverse datasets by divergent algorithms within a zero-trust environment is provided. In some embodiments, a single data steward may receive multiple algorithms in a zero-trust environment. Alternatively, algorithm output may be obfuscated for sharing with the algorithm developer for validation, or to compare against the output of a different data steward's processed protected information, for example PHI. In such situations the hashed identifying information may be matched using AI models. In yet other embodiments, the output of one data steward's protected information may be provided in a zero-trust manner to the sequestered enclave of a second data steward in order to impact the processing of this second data steward's protected information by a second algorithm.

Systems and methods for multi-algorithm processing of datasets within a zero-trust environment

A medical information navigation engine ('MINE') is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Method of optimizing patient-related outcomes

Growing community of inventors

Pleasanton, CA, United States of America

Robert Derward Rogers