Baidu USA LLC

Microsoft Technology Licensing, LLC

Yi Zhen

Hongliang Fei

Wei Fan

Yu Long Cao

Shulong Tan

Chaochun Liu

Richard Chun Ching Wang

Dawen Zhou

Bo Long

Yang Yang

Ajith Muralidharan

Yiming Ma

Nan Du

Yusheng Xie

Erheng Zhong

Florian Raudies

Bocong Liu

Yung-Yu Chung

Systems and methods are disclosed for question generation to obtain more related medical information based on observed symptoms from a patient. In embodiments, possible diseases associated with the observed symptoms are generated by querying a knowledge graph. In embodiments, candidate symptoms associated with the possible diseases are also identified and are combined with the observed symptoms to obtain combined symptom sets. In embodiments, discriminative scores for the candidate symptom sets are determined and candidate symptoms with top discriminative scores are selected. In embodiments, these selected candidate symptoms may be checked for conflicts with observed symptoms and removed from further consideration if a conflict exists. In embodiments, one or more questions may be generated based on the remaining selected candidate systems to aid in collecting information about the patient. In embodiments, the process may be repeated with the updated observed symptoms.

Question generation systems and methods for automating diagnosis

Presented are systems and methods that allow healthcare providers and governments to infer demand for healthcare resources to ensure effective and timely healthcare services to patients by reducing healthcare supply shortages, emergencies, and healthcare costs. In embodiments, this is accomplished by gathering data from a number of sources to generate labeled records from which entity features and relationships between entities are extracted, correlates, and/or combined with other external healthcare data. In embodiments, this information is used to train a model that predicts healthcare resource demands given a set of input conditions or factors.

Systems and methods for estimating healthcare resource demand

The disclosed embodiments provide a system for identifying root causes of performance issues. During operation, the system obtains a call graph containing a set of call paths for a set of services. Next, the system determines, based on a load test of the set of services, severity scores for the set of services, wherein the severity scores represent levels of abnormal behavior in the set of services. The system then groups the severity scores by the set of call paths and identifies, based on the grouped severity scores, one or more services as potential root causes of performance issues in the set of services. Finally, the system outputs the identified one or more services as the potential root causes of the performance issues.

Identifying root causes of performance issues

Disclosed are systems, methods, and non-transitory computer-readable media for automated profile image generation based on scheduled video conferences. A profile image generation system generates, based on image data captured during a first video conference, a first facial feature data set for a first identified face identified from the image data. The first facial feature data set includes numeric values representing the first identified face. The profile image generation system calculates, based on the first facial feature data set and historic facial feature data sets generated from image data captured during previous video conferences, a first value indicating a likelihood that the first identified face is a first meeting participant that participated in the first video conference. The profile image generation system determines that the first value meets or exceeds a threshold value, and in response, determines that the first identified face is the first meeting participant.

Automated profile image generation based on scheduled video conferences

Systems and methods are disclosed to identify entities that have a similar meaning, and may, in embodiments, be grouped into entity groups for knowledge base construction. In embodiments, the entity relations of similarity or non-similarity for an entity pair are predicted as a binary relationship. In embodiments, the prediction may be based upon similarity score between the entities and the entity features, which features are constructed using an entity feature or representation model. In embodiments, the prediction may be an iterative process involving minimum human checking and existing knowledge update. In embodiments, one or more entity groups are formed using graph search from the predicted entity pairs. In embodiments, a group centroid entity may be selected to represent each group based on one or more factors, such as its generality or popularity.

Systems and methods for homogeneous entity grouping

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San Jose, CA, United States of America

Yi Zhen