公开(公告)号：US11328825B1

公开(公告)日：2022-05-10

申请号：US16847562

申请日：2020-04-13

Applicant: IQVIA Inc.

Inventor： Kezi Yu ,  Fan Zhang ,  Yunlong Wang ,  Yilian Yuan ,  Emily Zhao ,  William McClellan ,  Yong Cai

IPC: G16H50/70 ,  G16H10/60 ,  G16H40/20 ,  G16H50/20 ,  G16H20/10 ,  A61B5/00

Abstract: 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.

公开(公告)号：US20250014748A1

公开(公告)日：2025-01-09

申请号：US18347237

申请日：2023-07-05

Applicant: IQVIA Inc.

Inventor： Tong Wu ,  Yong Cai ,  Yunlong Wang ,  Fan Zhang ,  Emily Zhao ,  Yilian Yuan

IPC: G16H50/20 ,  G16H50/70

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating data structures from graphs. The computer accesses a graph having patient nodes representing patients and patient health data nodes representing health data for patients, the nodes being connected by edges. The computer generates subgraphs by identifying patient nodes and patient health data nodes associated with a particular healthcare provider. The computer generates, by a subgraph neural network, a healthcare provider data structure for a respective subgraph. The computer generates, by a first graph neural network, patient data structures for a respective patient graph network and health data structures for a respective health data graph network. Each healthcare provider data structure, patient graph network, health data structure, has a lower dimension than the corresponding subgraph, patient graph network, and health data graph network, respectively. The computer provides at least one of the data structures to a model.

公开(公告)号：US10937531B1

公开(公告)日：2021-03-02

申请号：US15948006

申请日：2018-04-09

Applicant: IQVIA Inc.

Inventor： Yunlong Wang ,  Emily Zhao ,  Yilian Yuan ,  Anthony Michael Wojeck ,  Robert Doyle ,  Yong Cai

IPC: G16H20/10 ,  G16H80/00 ,  G16H10/60

Abstract: 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.

公开(公告)号：US12079719B1

公开(公告)日：2024-09-03

申请号：US17003127

申请日：2020-08-26

Applicant: IQVIA Inc.

Inventor： Guanhao Wei ,  Yunlong Wang ,  Li Zhou ,  Lynn Lu ,  Emily Zhao ,  Lishan Feng ,  Fan Zhang ,  Frank Jing ,  Yilian Yuan

IPC: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N5/01 ,  G06N20/20 ,  G16H10/60 ,  G16H40/20 ,  G16H50/70 ,  G16H70/20 ,  G16H70/40 ,  G16H70/60

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N5/01 ,  G06N20/20 ,  G16H10/60 ,  G16H40/20 ,  G16H50/70 ,  G16H70/20 ,  G16H70/40 ,  G16H70/60

Abstract: 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.

公开(公告)号：US11621081B1

公开(公告)日：2023-04-04

申请号：US16189362

申请日：2018-11-13

Applicant: IQVIA Inc.

Inventor： Michelle O'Keefe ,  Lucas Glass ,  Kristy Morgan ,  Yunlong Wang ,  Yuliya Nigmatullina ,  Yilian Yuan ,  Yong Cai ,  Fan Zhang ,  Chaitanya Alamuri

IPC: G16H50/30 ,  G06N3/08 ,  G16H10/60 ,  G06N3/04

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.