Koninklijke Philips Corporation N.v.

Yong Mao

Alexander Ryan Mankovich

Nevenka Dimitrova

Raymond J Krasinski

Jin Qu

Robbert Christiaan Van Ommering

Fubiao Xia

Kostyantyn Volyanskyy

Samuel Frank Pilato

Woei-Jye Yee

Qingxin Wu

Weijian Qin

A patient-trial matching system () includes a structuralizer () configured to convert input non-structured patient health data and input non-structured clinical trial eligibility criteria into structured patient health data and structured clinical trial eligibility criteria by organizing a content of the non-structured data as known data elements. The patient-trial matching system further includes a semantic matcher () configured to match the structured patient health data and the structured clinical trial eligibility criteria based on user input matching criteria and outputs matched results. The patient-trial matching system further includes a ranking engine () configured to rank the matched results using ranking criteria (), which include ranking patients matched to a clinical trial of interest in response to matching to find a group of trial patients and ranking clinical trials matched to a particular patient in response to matching to find a clinical trial.

Efficient clinical trial matching

A method () for standardizing gene nomenclature, comprising: (i) receiving () a source; (ii) tokenizing () the source; (iii) comparing () a first token to a prefix tree structure with a root node, edges, and leaf nodes; (iv) determining () which edge extending from the root node to associated first leaf nodes the first token matches; (v) updating () an identification pointer with the location of the first leaf node; (vi) determining () which of one or more edges that a second token matches; (vii) updating () the identification pointer with the location of the second leaf node; (viii) repeating () the determining () and updating () steps with subsequent tokens until a subsequent token fails to match an edge extending from a leaf node or there is no edge extending from the leaf node; and (ix) providing () an identification of a canonical gene name.

Method and system for normalization of gene names in medical text

A method for assessing risk associated with a request from an inquirer for allele frequency from a database of genetic data, comprising: (i) generating an allele database comprising allele frequency information and request frequency information for each of a plurality of alleles; (ii) generating an inquirer database comprising allele request information about a plurality of inquirers; (iii) receiving a request for genetic data from an inquirer, comprising a request for allele frequency for one or more alleles and an identifier of the inquirer; (iv) updating the request frequency information based on the received request; (v) updating the allele request information for the requesting inquirer; (vi) calculating an allele risk score; (vii) calculating an inquirer risk score; and (viii) assessing, based on the allele risk score and the inquirer risk score, a risk associated with the received request.

Proactive resistance to re-identification of genomic data

The present disclosure pertains to a system, a method of using such a system, and a non-transitory computer-readable medium containing instructions to such a system for generating annotated gene fusion data from processing both a patient's DNA and RNA sequence information thereby filtering out weak candidate gene fusions. Thus the annotated gene fusion data contains clinically relevant information and accurate gene fusion detections (low false-positives) for use in clinical and/or R&D settings. The system, method and computer-readable medium allows a user to generate gene fusion data by detecting breakpoints from a patient's DNA-SEQ and RNA-SEQ, creating candidate breakpoint data by combining matching breakpoints from the DNA-SEQ and RNA-SEQ breakpoint data, determining confidence levels of the candidate breakpoint, identifying corresponding gene fusions, and annotating clinically relevant information about the gene fusions.

System and method for detecting gene fusion

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Hawthorne, NY, United States of America

Yong Mao