Healthcare Powered by QLAYM
QLAYM TURNS PREDICTIVE MEDICINE INTO REALITY
In clinical studies and personalized medicine, the main task is to cluster data from individuals to form patient groups and to detect whether this grouping reflects relevant information. QLAYM exactly detects that.
Q-USDTM recognizes therapy options, vaccination results, higher risk for adverse events and many other effects hidden in the data. This gives background for faster and more cost efficient drug development, better decisions for the right therapy, safety and reliability for clinical studies.
We understand that often data generated from clinical studies and biological matter can be inaccurate, in-complete, erroneous and even unreliable. Q-USDTM is designed to recognize such difficulties and overcome them.
Q-USDTM integrates all kinds of patient data regardless from their source – like micro array data for gene expression, SNP and CNV data, medical images, preclinical and clinical study data – and extracts systematics with a predictive value in respect of the up-front defined outcome (e.g., efficacy, side effects).
As the predictability of any outcome mostly correlates not to only one or two but a network of variables, we are dealing with complex multi-variable and interdependent systems. We success by translating these networks’ “talks” into usable insights with significant value for our clients.
QLAYM IN HEALTHCARE – THE DETAILS
What kinds of topics? Predictive medicine, prediction of effects/side effects and disease processes, adaptive clinical tests, identification of molecular networks, prediction of resistances, epidemiology, genetic and cultural disposition, patient report outcomes, time to market and market acceptance.
What kinds of data? Genomics, general laboratory data, sensor, image and 3D data, clinical data, animal data, demographic and socioeconomic data, videos, instrument data, fund and insurance data.
What kinds of solutions? Identification of complex networks in all kinds of -omics data, optimized patient stratification, optimized individual diagnosis and treatment strategies, success and cost optimization of clinical tests, identification of valid targets for therapy development, individual cost-optimized treatment plans, market positioning for new and existing therapies, epidemiology, prediction of disease progression, prediction and monitoring of compliance, reimbursement models
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