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Accurate computer models of a treatment and its deployment, as well as patient characteristics, are necessary precursors for the development of in silico clinical trials. [5] [6] [8] [9] In such a scenario, ‘virtual’ patients would be given a ‘virtual’ treatment, enabling observation through a computer simulation of how the candidate biomedical product performs and whether it produces ...
Furthermore, virtual patients can be endlessly replayed and easily modified to allow the user to explore different clinical scenarios and patient outcomes. [8] [10] Compared to simulated patients, virtual patients make observation and assessment more robust and easier to control, and they can be used as a standardized assessment method. [11]
The simulations enable precise & comprehensive predictions of response to therapy while providing researchers and clinicians keys insights into mechanisms of resistance. By virtualizing cancer, clinicians and patients are empowered with a better understanding of the disease and can assess all available options computationally to truly ...
The In Silico Oncology Group (ISOG) [9] at the Institute of Communication and Computer Systems, National Technical Institute of Athens (ICCS-NTUA) aims at developing clinically driven and oriented multiscale simulation models of malignant tumors (cancer) to be utilized as patient individualized decision support and treatment planning systems ...
cTAKES ("clinical Text Analysis Knowledge Extraction Software") is a natural language processing system for extracting information from electronic medical record clinical free-text, an Apache top level project (TLP) since 2013, developed by the Mayo Clinic and others. It is available under the Apache license. [55]
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The app was released as a trial for 5,000 twins, [8] using patients involved in other Zoe research projects. [2] It was later expanded for use by non-twins. [2] The app entered the UK Apple App Store and Google Play Store on 24 March, and the US App and Play stores on 29 March. [5]
Biosimulation is becoming increasingly important for drug development. [1] Since on average only 11% of all drug candidates are approved, [2] it is anticipated that biosimulation may be the tool to predict whether a candidate drug will fail in the development process e.g. in clinical trials due to adverse side effects, bad pharmacokinetics or even toxicity.
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