Iqvia Inc.

Yilian Yuan

Yong Cai

Emily Zhao

Yunlong Wang

Fan Zhang

William McClellan

Yanping Liu

Ruoxin Li

Lucas Glass

Kristy Morgan

Dong Dai

Li Zhou

Steve Eichert

William Mcclellan

D Bruce West

Lynn Lu

Frank Jing

Lishan Feng

Guanhao Wei

Kezi Yu

John H Eichert, Jr

Michelle O'Keefe

Yuliya Nigmatullina

Chaitanya Alamuri

Robert Doyle

Anthony Michael Wojeck

Olivier Bouchard

Methods and systems to identify collaborative communities of individuals from graphs of multiple types of relationships amongst the individuals, including to mine data related to multiple types of relationships amongst individuals, construct graphs to represent the respective types of relationships amongst individuals, and perform a multiplex graph convolutional network (MGCN) artificial intelligence machine learning (AIML) analysis across the multiple graphs to identify the collaborative communities. A mathematical representation of the graphs may be learned/tuned to optimize clustering of the individuals. Multiple parameters (inter-graph weights, consensus regularization function) may be jointly tuned based on a joint optimization function. The collaborative communities may be displayed such that relative positions of the individuals represent measures of influence exerted by the respective individuals within the respective collaborative communities.

Methods and systems to identify collaborative communities from multiplex healthcare providers

A deep learning model implements continuous, lifelong machine learning (LML) based on a Bayesian neural network using an inventive framework including wide, deep, and prior components that employ diverse algorithms to leverage available real-world healthcare data differently to improve prediction performance. The outputs from each component of the framework are fed into a wide and shallow neural network and the posterior structure of the final model output may be utilized as a prior structure when the deep learning model is refreshed with new data in a deep learning process. Lifelong learning is implemented by dynamically integrating present learning from the wide and deep learning components with past learning from traditional tree models in the prior component into future predictions. Thus, the present Bayesian deep neural network-based LML model increases accuracy in identifying patient profiles by continuously learning, as new data become available, without forgetting prior knowledge.

Lifelong machine learning (LML) model for patient subpopulation identification using real-world healthcare data

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.

Professional network-based identification of influential thought leaders and measurement of their influence via deep learning

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.

System for predicting patient health conditions

Systems and techniques are disclosed for using machine-learning to identify potential opportunity patients that are more likely to adjust his/her preference for a healthcare provider or service. In some implementations, integrated patient data is obtained. A patient sequence feature vector, a provider sequence feature vector, and a set of entity-specific feature vectors are generated. A set of opportunity patients is identified. A notification is transmitted to the set of opportunity patients about a second treatment plan.

Machine learning techniques for identifying opportunity patients

Some implementations provide a computer-implemented method that includes retrieving, from a customer relationship (CRM) database, data documenting exposures of healthcare professionals to information of healthcare products from more than one channels and at various time points; processing the retrieved data to model the exposure of each healthcare professional such that an effectiveness of each of the more than one channels for the particular healthcare professional is determined; retrieving, from a prescription database, data recording each healthcare professional prescribing healthcare products at various time points; longitudinally associating the processed data from the customer relationship database and the retrieved data from the prescription database such that a multi-channel CRM and prescription database is generated; and, determining a next healthcare professional to whom information of a particular healthcare product should be directed as well as a channel for the next healthcare professional to receive the information of the particular healthcare product.

System and method to improve dynamic multi-channel information synthesis

A computer-assisted method to provide timely multi-channel notification of treatments to healthcare providers and patients, the method including receiving de-identified longitudinal medical records, treatment prescription records of healthcare providers, and notification data. Relationships between the healthcare providers, the anonymized patients, and the notifications are identified using the de-identified longitudinal medical records, the treatment prescription records of the healthcare providers, and the notification data. An impact of notifications being received by both the healthcare provider for the anonymized patient and the anonymized patient on whether the anonymized patient received the treatment is determined. A plan to timely provide notifications of treatments to the healthcare provider and the anonymized patients is determined based at least on the impact of the notifications being received by both the healthcare provider for the anonymized patient and the anonymized patient on whether the anonymized patient received the treatment.

System and method for timely notification of treatments to healthcare providers and patient

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North Wales, PA, United States of America

Yilian Yuan