International Business Machines Corporation

En Liang Xu

Eryu Xia

Shi Wan Zhao

Jing Li

Gang Hu

Jing Mei

Bibo Hao

Ke Fei Wang

Jian Min Jiang

Li Ni Zhang

Zhong Su

Shi Lei Zhang

Yong Qin

Yue Chen

Yi Qin Yu

Ke Ke Cai

Hai Feng Liu

Chang Hua Sun

A computer-implemented method for clinical decision support is provided according to an embodiment of the present disclosure. In the method, patient information can be obtained that comprises at least one clinical condition. A graph can be searched for at least one rule matching the at least one clinical condition. The graph represents a plurality of rule sets. The rule set comprises one or more rule. The rule can be indicated with a path comprising at least one first node and a second node. The first node can indicate a clinical condition, and the second node can indicate a clinical conclusion. Then, a clinical conclusion can be determined based on the at least one path in response that the at least one path matching the at least one clinical condition is searched out.

Clinical decision support

Methods and systems for using machine learning to determine electronic document similarity include extracting entities and corresponding relationships from each of two electronic documents of a corpus of electronic documents based on word embedding, computing an entity distance between the extracted entities and a relationship distance between the extracted relationships based on knowledge graph embedding, combining the entity and relationship distances to generate a similarity score between the electronic documents, and implementing the similarity score to perform a task associated with the electronic documents.

Using machine learning to determine electronic document similarity

Systems, computer-implemented methods and/or computer program products that facilitate predicting personalized risks based on intrinsic factors and extrinsic factors are provided. In one example, a computer-implemented method comprises: collecting, by a system operatively coupled to a processor, intrinsic factors and extrinsic factors associated with infectious diseases; generating, by the system, a probabilistic model based on the intrinsic factors and extrinsic factors, wherein the model incorporates node characteristics into infection probability; and refining, by the system, the model through concurrently learning respective node thresholds and hidden infection network structure of the model.

Personalized risk prediction based on intrinsic and extrinsic factors

A computer-implemented method, system, and computer program product are provided for enhanced search strategies. The method includes selecting, by a processor device, known candidate sources related to a search topic. The method also includes ranking, by the processor device, keyphrase candidates from the known candidate sources according to inter-topic weighting. The method additionally includes assembling, by the processor device, a search string of a predetermined number of top ranked keyphrase candidates. The method further includes generating, by the processor device, new candidate sources from a candidate source repository responsive to the search string. The method also includes defining, by the processor device, a candidate source pool by the known candidate sources and the new candidate sources to reduce user search times on computer interface devices.

Enhanced search construction and deployment

Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

Optimizing patient treatment recommendations using reinforcement learning combined with recurrent neural network patient state simulation

This disclosure provides a computer-implemented method. The method may include extracting one or more features based on a first utterance from a first interlocutor in a dialog and a second utterance from a second interlocutor in the dialog. The method may further include inferring one or more personality traits of the first interlocutor based on the one or more extracted features from the dialog.

Context-based linguistic analytics in dialogues

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Beijing, China

En Liang Xu