Abstract: The invention generally relates to computer-based systems to evaluate and market clinical trial research centers. In certain aspects, the invention provides computer-based systems to collect information about clinical research centers. Systems include a tangible, non-transitory memory coupled to a processor operable to retrieve, based on a user's input, an identity of a clinical research center and prompt the user for information relating generally to the center. The system can collect disease-specific information by prompting the user for a selection of a disease and then collecting from the user information identifying an ability of the center to perform one or more tests relating to the disease.

Abstract: A graph-based clinical concept mapping algorithm maps ICD-9 (International Classification of Disease, Revision 9) and ICD-10 (International Classification of Disease, Revision 10) codes to unified Systematized Nomenclature of Medicine (SNOMED) clinical concepts to normalize longitudinal healthcare data to thereby improve tracking and the use of such data for research and commercial purposes. The graph-based clinical concept mapping algorithm advantageously combines a novel graph-based search algorithm and natural language processing to map orphan ICD codes (those without equivalents across codebases) by finding optimally relevant shared SNOMED concepts. The graph-based clinical concept mapping algorithm is further advantageously utilized to group ICD-9/10 codes into higher order, more prevalent SNOMED concepts to support clinical interpretation.

Abstract: One example method for predictive clinical planning and design includes instantiating a plurality of data objects, each data object of the plurality of data objects comprising clinical trial information; displaying a graphical user interface on one or more display screens, the graphical user interface providing a graphical representation of at least a portion of a clinical trial and comprising a plurality of graphical nodes; receiving a selection of the second graphical node; receiving, via an editor associated with the second graphical node, a modification of the second data object; propagating an indication of the modification to the first data object, the propagation modifying a clinical trial data item of the first data object; and displaying, within the first graphical node, the modified clinical trial data item of the first data object.

Abstract: Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for responding to a query. In some implementations, a computer obtains a query. The computer determines a meaning for each term in the query. The computer determines user data for the user that submitted the query. The computer identifies one or more ontologies based on the meanings for at least some of the terms. The computer identifies a knowledge graph based on the identified ontologies and the user data. The computer generates a response to the query by traversing a path of the identified knowledge graph to identify items in the knowledge graph based on the determined meaning for each of the terms. The computer generates path data that represents the path taken by the computer through the identified knowledge graph. The computer provides the generated response and the path data to the client device.

Abstract: A classification code parser and method can include: reading a classification code having a description; reading a required keyword, and a total number of keywords associated with the classification code; reading text of a note; tokenizing the text of the note to create a note token stream, the note token stream having a note token and a position of the note token within the note token stream; creating a keyword map including a total number of matched keywords; determining a match ratio from the total number of the matched keywords and the total number of the keywords; determining a proximity factor based on a shortest span of tokens within the note token stream containing all the matched keywords; and determining a strength of a match between the classification code and the note based on the match ratio being multiplied by the proximity factor.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for accessing volumes of data records structured to include sets dimensions, each dimension labelled in a manner specific to respective entities; identifying candidates data records keyed by managed keys that span a subset of dimensions even though at least one dimension from the subset of dimensions is labelled differently between the different volumes; comparing the candidate data records from the different volumes to determine whether a particular managed key is valid based on contents of the candidate data records from the different volumes; in response to determining that the particular managed key is valid, combining the candidate data records keyed by the valid managed key to be merged and accessible as one continuous entry; and in response to determining that the particular managed key is invalid, combining the candidate data records from the different volumes as separate entries.

Abstract: A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools.

Abstract: A method includes accessing genomic data from a first cohort and a second cohort of patients that are encrypted to comprise a probabilistic and irreversible hash of each patient's genomic sequence data; based on the probabilistic and irreversible hashes, determining one or more variants residing in a particular locale indicating where the one or more variants reside; comparing a first number of variants determined to reside in the particular locale for the first cohort of patients with a second number of variants determined to reside in the particular locale for the second cohort of patients; and in response to determining that the first number of variants determined to reside in the particular locale for the first cohort of patients and the second number of variants determined to reside in the particular locale for the second cohort of patients differ by more than a threshold value, identifying the particular locale.

Abstract: A computer-implemented method includes a first module of a computing system receiving application-specific data for determining utilization attributes of multiple application programs. A second module of the system receives data for determining patient archetypes. A third module of the system determines a clinical benefit of each application program based on analysis of the application-specific data. The system matches an application program with a particular patient archetype based on analysis of each patient archetype and the determined clinical benefit of the application program. The system selects a particular application program for use by a patient of a first patient group based on: i) a characteristic of the first patient group that is included in a first patient archetype; and ii) the clinical benefit of the particular application exceeding a threshold clinical benefit.

Abstract: A requirements to test (R2T) system is implemented, which provides an automated system by which a user interface (UI)-test automation script package is generated and the generated test scripts therein are executed against software. A visualized workflow is translated into some machine-consumable formatted file. The translated workflow is utilized by an artificial intelligence driven automated R2T engine to discover paths through the workflow, a series of executable steps for the paths that detail how the software will be used, and ultimately test scripts that are generated using pre-defined validation templates. An automation platform executes the test scripts through the software associated with the workflow, which automatically captures evidence of the executed test scripts to fulfill computer system validation requirements. The R2T system provides an automated solution for test script creation and system validation to expedite the validation process and thereby streamline a software's time to market.

Abstract: Documents in source natural languages are translated into target natural languages using a computer-implemented translation that is configured to operate within the domain of the subject matter of the documents that imposes specialized requirements for translation and readability. Subject matter specific documents typically include domain-specific terminology, are subject to various regulatory guidelines, and have different readability requirements depending on the intended reader. The computer-implemented translation applies machine-learning techniques that deconstruct elements of the subject matter specific document into a standard data structure and perform pre-processing steps to tokenize digitized document text to identify the correct sentence structure and syntax for the target natural language to optimize translation by, e.g., a neural machine translation engine.

Abstract: Some implementations provide a computer-implemented method for identifying, from on-line postings, reports of potential adverse effects resulting from consuming a healthcare product, the method including: receiving a log of on-line postings regarding consuming the healthcare product; receiving a database comprising a healthcare taxonomy and a set of linguistic rules; analyzing, based on the healthcare taxonomy, the log of on-line postings to identify a report of at least one adverse effect resulting from consuming the healthcare product; generating a score for the identified report according to the healthcare taxonomy and the set of linguistic rules; comparing the generated score with a threshold; and in response to determining that the generated score is above the threshold, flagging the identified report as a report of a potential adverse effect.

Abstract: Methods, system, and apparatus for pre-computing data metrics. A method can include receiving, by one or more computers, (i) data from a user including a plurality of data metrics, and (ii) historical data associated with the user; generating, by the one or more computers and based at least on the historical data associated with the user, a forecast for the user using an artificial neural network, the forecast indicating a prediction of future user behaviors; determining, by the one or more computers and based at least on the forecast for the user, a subset of data metrics, from among the plurality of data metrics, that are likely to be used by the user; performing, by the one or more computers, one or more automatic data operations on the subset of data metrics; and providing, by the one or more computers, the processed subset of data metrics for output.

Abstract: A computer-implemented includes a computing system receiving one or more queries. The computing system includes one or more compute nodes that perform computations for determining a response to at least one query. The system stores, in a storage device, domain data that includes at least one of: a dataset, a metric associated with the domain data, a query time, or a usage pattern that is based, in part, on the one or more queries. The method includes the system generating a distribution model based on analysis of the domain data. The distribution model is generated using machine learning logic executed by the system. The method further includes the system using the distribution model to distribute data to the one or more compute nodes. The distributed data is used to determine, within a threshold response time, the response to the at least one query.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining data for a set of patients that each have a certain condition. A first and second sequence of data is determined based on the obtained data. A scoring model is generated by processing the first and second sequence of data to train a neural network. The scoring model determines a confidence that an individual has the particular healthcare condition. Patient scoring data is provided to the scoring model to determine the confidence that the individual has the healthcare condition. A confidence score is received as an output of the scoring model in response to providing the patient scoring data. The confidence score represents a determined confidence that the individual has the healthcare condition. An indication that represents the confidence that the individual has the healthcare condition is provided based on the received confidence score.

Abstract: Embodiments of the present disclosure provide a method for monitoring/tracking the lifecycle of a drug from build (e.g., as part of clinical trial development), to approval (e.g., regulatory), to in-market (e.g., distribution and safety information). The use of artificial intelligence (AI) and blockchain technology may enable the system to track the drug down to the prescription level and may support a digital label that can be updated as necessary based on such monitoring (e.g., that can be amended based on safety information detected while the drug is in market and warnings sent out upon amendment).

Abstract: A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, receiving notification data, identifying anonymized patients that received the treatment, identifying notifications for the treatment that were received by the recipients, determining, for each of the identified notifications, whether the recipient is an anonymized patient identified as having received the treatment, determining, for each of the identified notifications for the treatment determined to be received by a recipient that is an anonymized patient identified as having received the treatment, a time relationship between the time when the treatment was received by the anonymized patient and the time that the notification was received by the recipient that is the anonymized patient, and determining, for each of the anonymized patients that received the treatment, associations between one or more time relationships for notifications received by the anonymiz

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for linking a first electronic data set to a second set of data fields in a second electronic data set. Automatically identifying a prescribing physician identifier based on the linked first and second electronic data sets. Determining a relationship between a physician associated with the prescribing physician identifier and at least one of the approved entities based on comparing the prescribing physician identifier and identifiers of the one or more approved entities to a fourth set of data fields from a fourth electronic data set. Automatically generating an electronic notification indicating that a product sold by the merchant is eligible for the discount in response to determining a relationship between a physician and the at least one of the approved entities.

Abstract: A computer-implemented method for providing a user with a performance indicator score includes receiving a first transaction message that includes historical clinical-trial performance data from one or more processors at a clinical research organization and receiving a second transaction message with health records data with parameters indicative of insurance claims data. The received historical clinical-trial performance data and the prescription data is translated into an updated database. Related records within the updated database are identified and one or more key performance indicators included in the data at the updated database for a first physician are identified. A score for each of the one or more key performance indicators are calculated and a performance indicator score record for the first physician is generated based on the calculated scores for each of the one or more key performance indicators. A multi-dimensional chart for organizing and evaluating investigators is generated.