Iqvia Inc.

William McClellan

Yong Cai

Emily Zhao

Yilian Yuan

Yunlong Wang

Fan Zhang

Yanping Liu

Ruoxin Li

John H Eichert, Jr

Kezi Yu

Steve Eichert

D Bruce West

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

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

Growing community of inventors

Collegeville, PA, United States of America

William McClellan