Koninklijke Philips Corporation N.v.

Cold Spring Harbor Laboratory

Sitharthan Kamalakaran

Nevenka Dimitrova

Angel Janevski

Nilanjana Banerjee

Vinay Varadan

Henry Lin

Robert Lucito

Pramod Mayigowda

Sonia Chothani

Aleksandra Popovic

Balasundar Iyyavu Raju

Douglas Allen Stanton

Yasser Alsafadi

James Bruce Hicks

Sayan Maity

Mine Danisman Tasar

Lyndsay Harris

Anca Ioana Daniela Bacur

Jasper Van Leeuwen

In a clinical decision support method, outputs of computer-implemented analytical modules are computed for a patient. Information is displayed for the patient pertaining to a clinical question comprising outputs computed for the patient of analytical modules associated with the clinical question. The analytical modules may include modules configured to perform in silico genetic/genomic tests using genetic/genome sequencing (whole genome, whole exome, whole transcriptome, targeted gene panels, etc) or microarray data. A clinical question-module matrix (CQ-M matrix) may be generated for the patient associating clinical questions with analytical modules, and the method may further include populating the clinical questions with outputs computed for the patient of the analytical modules associated with the clinical questions by the CQ-M matrix. Such populating advantageously re-uses outputs computed for the patient when an analytical module is associated with two or more different clinical questions by the CQ-M matrix. This system empowers the clinician to focus on the clinical aspects of patient management while allowing the data complexities of patient genomic data interpretation to be handled by the clinical decision support system.

System and method for real time clinical questions presentation and management

Methods and systems for monitoring and determining antimicrobial resistance and antimicrobial treatment using genomic subtype information. Various embodiments utilize molecular epidemiology and next-generation sequencing technologies (NGS) to monitor multi-drug resistant pathogens and provide early insight into emergent microbial threats.

Methods of displaying the antimicrobial sensitivity of biological isolates

When generating visual representations of gene activity pathways for clinical decision support, a validated pathway database that stores a plurality of validated pathways is accessed, wherein each pathway describes at least one interaction between a plurality of genes. A processor () is configured to execute computer-executable instructions stored in a memory (), the instructions comprising visually representing gene activity level () for at least one gene across a plurality of populations, retrieving a pathway () from the validated pathway database, wherein the pathway includes the at least one gene, and visually representing gene activity levels for all genes in the pathway. The instructions further comprise a formal visual grammar representation visually representing information flow () through interactions between genes in pathway, linking the visual representation of the information flow to actionable information () for a clinician, and outputting the visual representation of the information flow and the linked actionable information to the to the clinician.

Pathway visualization for clinical decision support

Methods and apparatus to identify and quantify the microorganisms present in a sample. Sequence reads are classified using existing methods, but the classification results are corrected to account for the number of reads expected to be falsely classified as determined through simulation. With statistics on the expected number of reads misclassified, a linear least squares method (non-negative or otherwise) or other related technique can be used to adjust the number of reads that are classified to various taxonomic units (e.g., species) and to determine more accurate values for the quantities of those taxonomic units actually present in the sample, eliminating microorganisms in taxonomic units falsely determined to be present in the sample.

Method and apparatus for estimating the quantity of microorganisms within a taxonomic unit in a sample

The present invention provides for a method to identify a hospital acquired infection. The method includes computing the number of changes over time between an infection sample and at least a subset of the plurality of infection samples and determining if the number of changes over time is within an interval of an expected number of changes. If so, marking the infection sample as a hospital acquired infection.

Tracking infections in hospital environments

Infection management and control

A catalog () of molecular marker tests specifies molecular marker tests annotated with clinical applicability annotations. An electronic patient medical record () stores genetic sequencing data () of a patient. A clinical decision support (CDS) system () is configured to track the clinical context of the patient wherein the clinical context includes at least a disease diagnosis and a current patient care stage. A catalog search module () is configured to search the catalog of molecular marker tests to identify a molecular marker test having clinical applicability to the patient in the clinical context tracked by the CDS system. The search is automatically triggered by occurrence of a trigger event defined by a set of triggering rules. A testing module () is configured to perform a molecular marker test identified by the identification module in silico using the genetic sequencing data of the patient stored in the electronic patient medical record.

Retroactive extraction of clinically relevant information from patient sequencing data for clinical decision support

In a predictive outcome assessment test for predicting whether a patient undergoing a breast cancer treatment regimen will achieve pathological complete response (pCR), differential gene expression level information are generated for an input set of genes belonging to the TGF-&#x3b2; signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample () of a breast tumor of a patient acquired before initiating () a breast cancer therapy regimen to the patient and response gene expression level information from a response sample () of the breast tumor acquired after initiating the breast cancer therapy regimen by administering a first dose of bevacizumab to the patient. A pCR prediction for the patient is computed based on the differential gene expression level information for the input set of genes belonging to the TGF-&#x3b2; signaling pathway. Related predictive outcome assessment test development methods are also disclosed.

Predictive outcome assessment for chemotherapy with neoadjuvant bevacizumab

A force feedback gripping device employs a mechanical gripper (), an electromagnetic actuator () and a force feedback controller (). The mechanical gripper () is operable to be actuated to one of a plurality of gripping poses for gripping an object. The electromagnetic actuator () includes a magnetorheological elastomer ('MRE'), wherein the MRE is operable to be transitioned between a plurality of shapes dependent upon a variable strength of a magnetic field applied to the MRE, and wherein each shape of the MRE actuates the mechanical gripper () to one of the gripping poses. The force feedback controller () is operable to control the variable strength of the magnetic field applied to the MRE based on an estimation of a gripping force of the mechanical gripper () and on a sensing of a load force of the object responsive to the gripping force of the mechanical gripper ().

Force feedback gripping device with magnetorheological based actuator

A medical device and methods for locating the device include a structure having a length dimension and a surface (). A volume () is associated with the surface and extends along a portion of the length dimension. Nanomaterials () are incorporated in the volume and configured to be responsive to an excitation signal such that the excitation signal generates a response from the nanomaterials to enable location of the structure within a subject.

Real-time medical device visualization using nanomaterials

The present invention relates to a method for identifying multi-modal associations between biomedical markers which allows for the determination of network nodes and/or high ranking network members or combinations thereof, indicative of having a diagnostic, prognostic or predictive value for a medical condition, in particular ovarian cancer. The present invention further relates to a biomedical marker or group of biomedical markers associated with a high likelihood of responsiveness of a subject to a cancer therapy, preferably a platinum based cancer therapy, wherein said biomedical marker or group of biomedical markers comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 8, 19, 20 or all markers selected from PKMYT1, SKIL, RAB8A, HIRIP3, CTNNB1, NGFR, ZCCHC11, LSP1, CD200, PAX8, CYBRD1, HOXC11, TCEAL1, FZD10, FZD1, BBS4, IRS2, TLX3, TSPAN2, TXN, and CFLAR. Furthermore, an assay for detecting, diagnosing, graduating, monitoring or prognosticating a medical condition, or for detecting, 1 diagnosing, monitoring or prognosticating the responsiveness of a subject to a therapy against said medical condition, in particular ovarian cancer, is provided, as well as a corresponding method for classifying a subject comprising and a medical decision support system.

Identification of multi-modal associations between biomedical markers

The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.

Medical analysis system

An imaging visualization workstation () includes a graphical display device () and an electronic data processor, and is configured to perform a method including: spatially registering a biopsy sample extracted from a medical subject with a medical image () of the medical subject; combining the medical image with a graphical representation of information () generated from the biopsy sample to generate a combined image in which the graphical representation is spatially delineated based on the spatial registration of the biopsy sample; and displaying the combined image on the graphical display device of the imaging visualization workstation. A method comprises extracting a biopsy sample spatial sample from a medical subject, processing the biopsy sample to generate biopsy information, acquiring a medical image of the subject, spatially registering the biopsy sample with the medical image, and displaying the medical image modified to include an annotation generated from the biopsy information.

Clinical workstation integrating medical imaging and biopsy data and methods using same

Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.

Integrated phenotyping employing image texture features

The invention relates to a method for the analysis of ovarian cancer disorders, comprising determining the genomic methylation status of one or more CpG dinucleotides in a sequence selected from the group of sequences according to SEQ ID NO. 1 to 10 and/or SEQ ID NO. 50 to SEQ ID NO. 60. Optionally, additionally following steps are performed, the one or more results from the methylation status test is input into a classifier that is obtained from a Diagnostic Multi Variate Model, calculating a likelihood as to whether the sample is from a normal tissue or an ovarian cancer tissue and/or, calculating an associated p-value for the confidence in the prediction.

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Pelham, NY, United States of America

Sitharthan Kamalakaran