Synthetic biomedical data generation in support of In Silico Clinical Trials

Living in the era of Big Data, one may advocate that the additional synthetic generation of data is redundant. However, to be able to truly say whether it is valid or not, one needs to focus more on the meaning and quality of data than on the quantity. In some domains, such as biomedical and translational sciences, data privacy still holds a higher importance than data sharing. This by default limits access to valuable research data. Intensive discussion, agreements, and conventions among different medical research players, as well as effective techniques and regulations for data anonymization, already made a big step toward simplification of data sharing. However, the situation with the availability of data about rare diseases or outcomes of novel treatments still requires costly and risky clinical trials and, thus, would greatly benefit from smart data generation. Clinical trials and tests on animals initiate a cyclic procedure that may involve multiple redesigns and retesting, which typically takes two or three years for medical devices and up to eight years for novel medicines, and costs between 10 and 20 million euros. The US Food and Drug Administration (FDA) acknowledges that for many novel devices, practical limitations require alternative approaches, such as computer modeling and engineering tests, to conduct large, randomized studies. In this article, we give an overview of global initiatives advocating for computer simulations in support of the 3R principles (Replacement, Reduction, and Refinement) in humane experimentation. We also present several research works that have developed methodologies of smart and comprehensive generation of synthetic biomedical data, such as virtual cohorts of patients, in support of In Silico Clinical Trials (ISCT) and discuss their common ground

Modeling of Reconfigurable Medical Ultrasonic Applications in BIP

Medical ultrasonic imaging applications require high quality of images produced in real-time often with limited resources available. Deadlock-freedom and confluency must be guaranteed to ensure the correctness of the applications, while feasibility and optimality properties are required to provide the best Quality of Service (QoS) within available resources. In this paper we introduce BIP (Behavior-Interaction-Priority) framework components as main building blocks to model such applications in a correct-by-construction manner. Based on those components we model a reconfigurable multi-mode processing pipeline for ultrasonic imaging that supports QoS management by topology reconfiguration. Finally, as a proof of concept, we present a simple quality controller as a well-triggered component, which when combined with the processing pipeline can manipulate the quality of image processing

TAT-based Formal Representation of Medical Guidelines : Imatinib Case-study

Computer-based interpretation of medical guide- lines (GLs) has drawn lots of attention in the past three decades. It is essential to use a formalism for GLs representation that would enable the validation of GLs structural properties, be able to map medical actions into the time scale and support the automatic formal verification of GLs without additional translation paths. In this paper we preset a novel approach based on Timed Automata extended with Tasks (TAT) for the medical protocol formal representation using the TIMES toolbox. We discuss the verification issues with the help of the Imatinib case study

Cascaded PID controller for anaesthesia delivery

The technologies for continuous measurement of the anaesthetic agents circulating in body fluids are not mature yet, though some preliminary prototypes exist already. We present a control algorithm that based on the real measurement of propofol plasma concentration may adjust the delivery rate. This opens a possibility for a safer anesthesia when the technologies for online measurement of drug concentration will be mature enough to be combined with our model

Parameterized SVM for Personalized Drug Concentration Prediction

This paper proposes a parameterized Support Vector Machine (ParaSVM) approach for modeling the Drug Concentration to Time (DCT) curves. It combines the merits of Support Vector Machine (SVM) algorithm that considers various patient features and an analytical model that approxi- mates the predicted DCT points and enables curve calibrations using occasional real Therapeutic Drug Monitoring (TDM) measurements. The RANSAC algorithm is applied to construct the parameter library for the relevant basis functions. We show an example of using ParaSVM to build DCT curves and then calibrate them by TDM measurements on imatinib case study

Modelling Resource Dependencies

The major research in the resource management literature focuses primarily on two directions: 1) specification languages for formulating resource requests and 2) constraint problems modelling allocation and scheduling. Both directions assume the knowledge of the underlying platform architecture and the dependencies it induces on the usage of the various resources. In this report we bridge this gap, by introducing Constraint-Flow Nets (cfNets). A cfNet is defined by a set of resources and dependencies between them, each dependency having an associated constraint. The model is inspired by Petri Nets, with resources corresponding to places and dependencies—to transitions. Given an architecture of dependent resources, an initial resource request is propagated through the dependencies. The generated constraints are then conjuncted into the global allocation constraint. We study the notion of conflicts in cfNets and prove that for conflict-free cfNets the global allocation constraint can be constructed unambiguously. Furthermore, we provide an efficient algorithm for conflict detection

Drug concentration prediction and delivery

In medical practice, the decision-making process regarding drug dose is critical to patients’ health and recovery. For drugs with narrow therapeutic ranges, the medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of patients’ parameters. Computer-aided tools for drug dose administration makes the prescription procedure faster, more accurate, more objective, and less expensive. We describe an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose/frequency computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions

Safe Implementation of Embedded Software for a Portable Device Supporting Drug Administration

Poor adherence to medical regimen causes approximately 33% to 69% of medication-related hospitalizations and accounts for $100 billion in annual health care costs. In this paper we address the problem of unintentional non adherence, when patient fails to take a medication due to forgetfulness or carelessness. We present the safe approach to software implementation of a portable reminder device with enabled personalization of medical regimen. The presented prototype is designed for imatinib administration, a drug used to treat Chronic Myeloid Leukemia (CML). However, thanks to the component-based structure of the software, the method can be applied to other cases by replacing implementation of certain components

Assessment of Image Quality vs. Computation Cost for Different Parameterizations of Ultrasound Imaging Pipelines

Ultrasound imaging is a technique widely used in medicine to visualize organs and other body structures, capturing their position, size, morphology and any pathological lesions. Its use is unfortunately limited to specialized centers with trained personnel, and it would be beneficial to expand its applicability to environments like on-the-sheld emergency response and family physician cabinets. This requires the development of new ultrasound platforms that must be faster, lower-power, easier to use, safe and reliable. One of the major challenges to be met is to dynamically manage a myriad of different imaging options and configuration parameters, which impact image quality and computation cost at the same time. Focusing on this challenge, in this paper we first give an overview of ultrasound imaging techniques and of their possible configuration and parametrization options. We then discuss the impact of these options on computation cost and image quality, showing outcomes from a prototype Matlab ultrasound imaging pipeline