Baidu USA LLC

Nan Du

Wei Fan

Yusheng Xie

Hongliang Fei

Chaochun Liu

Dawen Zhou

Yu Long Cao

Weicheng Zhu

Shulong Tan

Jing Zhai

Yi Zhen

Richard Chun Ching Wang

Erheng Zhong

Yaliang Li

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 relation inference methods and systems that use deep learning techniques for data mining documents to discover a relation between terms of interest in a given field covering a specific topic. For example, in the healthcare domain, various embodiments of the present disclosure provide for a relation inference system that mines large-scale medical documents in a free-text database to extract symptom and disease terms and generates relation information that aids in disease diagnosis. In embodiments, this is accomplished by training and using an RNN, such as an LSTM, a Gated Recurrent Unit (GRU), etc., that takes advantage of a term dictionary to examine co-occurrences of terms of interest within documents to discover correlations between the terms. The correlation may then be used to predict statistically most probable terms (e.g., a disease) related to a given search term (e.g., a symptom).

Systems and methods for relation inference

Systems and methods are disclosed for question generation to infer the most probable cause from the observable outcome and known Noisy-OR causal relations. In embodiments, the outcomes are sorted by indices according to an order including but not limited to the outcomes' natural frequency order, expert-labeled order, machine-learning derived order, etc. According to their assigned indices, in embodiments, observed outcomes with lower indices are assigned for exact inference while observed outcomes with higher indices are assigned for variational inference. In embodiments, results of exact inference and variational inference are combined to predict the most probable cause. The unique combination of exact inference and variational inference according to outcome indices makes the probable cause inferring process faster.

Systems and methods of determining sufficient causes from multiple outcomes

Presented are systems and methods that estimate the strength of a relationship between elements gathered from online and/or offline information sources by estimating the trustworthiness both of the gathered data and the information sources from which the data originates. In one exemplary application, the relatedness between co-occurring symptom and disease terms collected from information sources, such as health-related online databases, is iteratively evaluated based on the trustworthiness of symptom-disease pairings and the trustworthiness of the information sources themselves. In various embodiments of the present disclosure, an objective function is used to extract a knowledge base that aids in identifying a potential relationship between a set of given symptoms provided by a user of an online healthcare service and co-occurring disease terms, such that a likely disease may be inferred from the set of symptoms.

Systems and methods for data and information source reliability estimation

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Nan Du