International Business Machines Corporation

Bibo Hao

Jian Min Jiang

Eryu Xia

En Liang Xu

Ke Fei Wang

Shi Lei Zhang

Guo Tong Xie

Yi Qin Yu

Jing Mei

Jing Li

Yong Qin

Wen Sun

Gang Hu

Changhua Sun

Shouyu Yan

Ying Xue Li

The disclosure provides a method for data instance processing. The method includes obtaining a set of data instances collected from a plurality of organizations. Each of the data instances includes at least one record formed in an organization that stores values of a plurality of attributes of the data instance. The method also includes dividing the set of data instances into groups, wherein data instances with conflicting values for the same attribute are divided into different groups. The method further includes subdividing data instances in each of the groups into clusters.

Cross-organization data instance matching

Techniques for candidate evaluation and filtering are provided. Enrollment criteria for a clinical trial are received, where the enrollment criteria include a plurality of conditions. A plurality of cost vectors is constructed for the plurality of conditions. A set of values for one or more of the plurality of conditions is determined for a candidate, where the set of values does not include a value for at least a first condition of the plurality of conditions. A utilized cost is generated for the candidate, based on the first set of values and the plurality of cost vectors. The candidate is then ranked based on the utilized cost.

Processing profiles using machine learning to evaluate candidates

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

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

A computer implemented method identifies guidelines through use of a neural network by a remote guideline server. A client computer transmits instructions to the remote guideline server to retrieve and evaluate multiple candidate guidelines. The remote guideline server utilizes a neural network to identify a string of terms found in each of the multiple candidate guidelines that match one or more strings of terms from a model guideline; to identify a semantic concept of each of the multiple candidate guidelines that matches one or more semantic concepts from the model guideline; and to identify a structural pattern of each of the multiple candidate guidelines that matches one or more structural patterns of the model guideline. The candidate guidelines that match the model guideline are then sent from the remote guideline server to the client computer.

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Beijing, China

Bibo Hao