Heat conduction in live tissue during radiofrequency electrosurgery.

In this paper, we propose a method to model radiofrequency electrosurgery to capture the phenomena at higher temperatures and present the methods for parameter estimation. Experimental data taken from our surgical trials performed on in vivo porcine liver show that a non-Fourier Maxwell-Cattaneo-type model can be suitable for this application when used in combination with an Arrhenius-type model that approximates the energy dissipation in physical and chemical reactions. The resulting model structure has the advantage of higher accuracy than existing ones, while reducing the computation time required.

Minimally Invasive Live Tissue High-Fidelity Thermophysical Modeling Using Real-Time Thermography.

We present a novel thermodynamic parameter estimation framework for energy-based surgery on live tissue, with direct applications to tissue characterization during electrosurgery. This framework addresses the problem of estimating tissue-specific thermodynamics in real-time, which would enable accurate prediction of thermal damage impact to the tissue and damage-conscious planning of electrosurgical procedures. Our approach provides basic thermodynamic information such as thermal diffusivity, and also allows for obtaining the thermal relaxation time and a model of the heat source, yielding in real-time a controlled hyperbolic thermodynamics model. The latter accounts for the finite thermal propagation time necessary for modeling of the electrosurgical action, in which the probe motion speed often surpasses the speed of thermal propagation in the tissue operated on. Our approach relies solely on thermographer feedback and a knowledge of the power level and position of the electrosurgical pencil, imposing only very minor adjustments to normal electrosurgery to obtain a high-fidelity model of the tissue-probe interaction. Our method is minimally invasive and can be performed in situ. We apply our method first to simulated data based on porcine muscle tissue to verify its accuracy and then to in vivo liver tissue, and compare the results with those from the literature. This comparison shows that parameterizing the Maxwell-Cattaneo model through the framework proposed yields a noticeably higher fidelity real-time adaptable representation of the thermodynamic tissue response to the electrosurgical impact than currently available. A discussion on the differences between the live and the dead tissue thermodynamics is also provided.

Minimally Invasive Live Tissue High-fidelity Thermophysical Modeling using Real-time Thermography.

We present a novel thermodynamic parameter estimation framework for energy-based surgery on live tissue, with direct applications to tissue characterization during electrosurgery. This framework addresses the problem of estimating tissue-specific thermodynamics in real-time, which would enable accurate prediction of thermal damage impact to the tissue and damage-conscious planning of electrosurgical procedures. Our approach provides basic thermodynamic information such as thermal diffusivity, and also allows for obtaining the thermal relaxation time and a model of the heat source, yielding in real-time a controlled hyperbolic thermodynamics model. The latter accounts for the finite thermal propagation time necessary for modeling of the electrosurgical action, in which the probe motion speed often surpasses the speed of thermal propagation in the tissue operated on. Our approach relies solely on thermographer feedback and a knowledge of the power level and position of the electrosurgical pencil, imposing only very minor adjustments to normal electrosurgery to obtain a high-fidelity model of the tissue-probe interaction. Our method is minimally invasive and can be performed in situ. We apply our method first to simulated data based on porcine muscle tissue to verify its accuracy and then to in vivo liver tissue, and compare the results with those from the literature. This comparison shows that parameterizing the Maxwell-Cattaneo model through the framework proposed yields a noticeably higher fidelity real-time adaptable representation of the thermodynamic tissue response to the electrosurgical impact than currently available. A discussion on the differences between the live and the dead tissue thermodynamics is also provided.

Viability Under Degraded Control Authority.

In this letter, we solve the problem of quantifying and mitigating control authority degradation in real time. Here, our target systems are controlled nonlinear affine-in-control evolution equations with finite control input and finite- or infinite-dimensional state. We consider two cases of control input degradation: finitely many affine maps acting on unknown disjoint subsets of the inputs and general Lipschitz continuous maps. These degradation modes are encountered in practice due to actuator wear and tear, hard locks on actuator ranges due to over-excitation, as well as more general changes in the control allocation dynamics. We derive sufficient conditions for identifiability of control authority degradation, and propose a novel real-time algorithm for identifying or approximating control degradation modes. We demonstrate our method on a nonlinear distributed parameter system, namely a one-dimensional heat equation with a velocity-controlled moveable heat source, motivated by autonomous energy-based surgery.

Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain Temperature Estimation of 3D Electrosurgical Processes using Surface Thermography Sensing.

We present a novel 3D adaptive observer framework for use in the determination of subsurface organic tissue temperatures in electrosurgery. The observer structure leverages pointwise 2D surface temperature readings obtained from a real-time infrared thermographer for both parameter estimation and temperature field observation. We introduce a novel approach to decoupled parameter adaptation and estimation, wherein the parameter estimation can run in real-time, while the observer loop runs on a slower time scale. To achieve this, we introduce a novel parameter estimation method known as attention-based noise-robust averaging, in which surface thermography time series are used to directly estimate the tissue's diffusivity. Our observer contains a real-time parameter adaptation component based on this diffusivity adaptation law, as well as a Luenberger-type corrector based on the sensed surface temperature. In this work, we also present a novel model structure adapted to the setting of robotic surgery, wherein we model the electrosurgical heat distribution as a compactly supported magnitude- and velocity-controlled heat source involving a new nonlinear input mapping. We demonstrate satisfactory performance of the adaptive observer in simulation, using real-life experimental ex vivo porcine tissue data.

Heat conduction in porcine muscle and blood: experiments and time-fractional telegraph equation model.

This paper presents experimental evidence for the damped-hyperbolic nature of transient heat conduction in porcine muscle tissue and blood. An examination of integer order and Maxwell-Cattaneo heat conduction models indicates that the latter, in effect resulting in a time-fractional telegraph (TFT) equation, provides the best fit to transient heat phenomena in such materials. The numerical method is verified on Dirichlet and Neumann initial boundary value problems using existing analytical results. Overall, the TFT equation captures the wave-like nature of heat conduction and temperature profiles obtained in experiments, while reducing the need for further tunable parameters.

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions.

In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

Supporting Emergency Medical Care Teams with an Integrated Status Display Providing Real-Time Access to Medical Best Practices, Workflow Tracking, and Patient Data.

The work of a hospital's medical staff is safety critical and often occurs under severe time constraints. To provide timely and effective cognitive support to medical teams working in such contexts, guidelines in the form of best practice workflows for healthcare have been developed by medical organizations. However, the high cognitive load imposed in such stressful and rapidly changing environments poses significant challenges to the medical staff or team in adhering to these workflows. In collaboration with physicians and nurses from Carle Foundation Hospital, we first studied and modeled medical team's individual responsibilities and interactions in cardiac arrest resuscitation and decomposed their overall task into a set of distinct cognitive tasks that must be specifically supported to achieve successful human-centered system design. We then developed a medical Best Practice Guidance (BPG) system for reducing medical teams' cognitive load, thus fostering real-time adherence to best practices. We evaluated the resulting system with physicians and nurses using a professional patient simulator used for medical training and certification. The evaluation results point to a reduction of cognitive load and enhanced adherence to medical best practices.

Systems Medicine-Complexity Within, Simplicity Without.

This paper presents a brief history of Systems Theory, progresses to Systems Biology, and its relation to the more traditional investigative method of reductionism. The emergence of Systems Medicine represents the application of Systems Biology to disease and clinical issues. The challenges faced by this transition from Systems Biology to Systems Medicine are explained; the requirements of physicians at the bedside, caring for patients, as well as the place of human-human interaction and the needs of the patients are addressed. An organ-focused transition to Systems Medicine, rather than a genomic-, molecular-, or cell-based effort is emphasized. Organ focus represents a middle-out approach to ease this transition and to maximize the benefits of scientific discovery and clinical application. This method manages the perceptions of time and space, the massive amounts of human- and patient-related data, and the ensuing complexity of information.

Preventable Medical Errors Driven Modeling of Medical Best Practice Guidance Systems.

In a medical environment such as Intensive Care Unit, there are many possible reasons to cause errors, and one important reason is the effect of human intellectual tasks. When designing an interactive healthcare system such as medical Cyber-Physical-Human Systems (CPHSystems), it is important to consider whether the system design can mitigate the errors caused by these tasks or not. In this paper, we first introduce five categories of generic intellectual tasks of humans, where tasks among each category may lead to potential medical errors. Then, we present an integrated modeling framework to model a medical CPHSystem and use UPPAAL as the foundation to integrate and verify the whole medical CPHSystem design models. With a verified and comprehensive model capturing the human intellectual tasks effects, we can design a more accurate and acceptable system. We use a cardiac arrest resuscitation guidance and navigation system (CAR-GNSystem) for such medical CPHSystem modeling. Experimental results show that the CPHSystem models help determine system design flaws and can mitigate the potential medical errors caused by the human intellectual tasks.

A Pathophysiological Model-Driven Communication for Dynamic Distributed Medical Best Practice Guidance Systems.

There is a great divide between rural and urban areas, particularly in medical emergency care. Although medical best practice guidelines exist and are in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. In this paper, we propose the concept of distributed executable medical best practice guidance systems to assist adherence to best practice from the time that a patient first presents at a rural hospital, through diagnosis and ambulance transfer to arrival and treatment at a regional tertiary hospital center. We codify complex medical knowledge in the form of simplified distributed executable disease automata, from the thin automata at rural hospitals to the rich automata in the regional center hospitals. However, a main challenge is how to efficiently and safely synchronize distributed best practice models as the communication among medical facilities, devices, and professionals generates a large number of messages. This complex problem of patient diagnosis and transport from rural to center facility is also fraught with many uncertainties and changes resulting in a high degree of dynamism. A critically ill patient's medical conditions can change abruptly in addition to changes in the wireless bandwidth during the ambulance transfer. Such dynamics have yet to be addressed in existing literature on telemedicine. To address this situation, we propose a pathophysiological model-driven message exchange communication architecture that ensures the real-time and dynamic requirements of synchronization among distributed emergency best practice models are met in a reliable and safe manner. Taking the signs, symptoms, and progress of stroke patients transported across a geographically distributed healthcare network as the motivating use case, we implement our communication system and apply it to our developed best practice automata using laboratory simulations. Our proof-of-concept experiments shows there is potential for the use of our system in a wide variety of domains.

Sepsis Patient Detection and Monitor Based on Auto-BN.

Sepsis is a life-threatening condition caused by an inappropriate immune response to infection, and is a leading cause of elderly death globally. Early recognition of patients and timely antibiotic therapy based on guidelines improve survival rate. Unfortunately, for those patients, it is often detected late because it is too expensive and impractical to perform frequent monitoring for all the elderly. In this paper, we present a risk driven sepsis screening and monitoring framework to shorten the time of onset detection without frequent monitoring of all the elderly. Within this framework, the sepsis ultimate risk of onset probability and mortality is calculated based on a novel temporal probabilistic model named Auto-BN, which consists of time dependent state, state dependent property, and state dependent inference structures. Then, different stages of a patient are encoded into different states, monitoring frequency is encoded into the state dependent property, and screening content is encoded into different state dependent inference structures. In this way, the screening and monitoring frequency and content can be automatically adjusted when encoding the sepsis ultimate risk into the guard of state transition. This allows for flexible manipulation of the tradeoff between screening accuracy and frequency. We evaluate its effectiveness through empirical study, and incorporate it into existing medical guidance system to improve medical healthcare.

The Design of Safe Networked Supervisory Medical Systems Using Organ-Centric Hierarchical Control Architecture.

There are growing demands to leverage network connectivity and interoperability of medical devices in order to improve patient safety and the effectiveness of medical services. However, if not properly designed, the integration of medical devices through networking could significantly increase the complexity of the system and make the system more vulnerable to potential errors, jeopardizing patient safety. The system must be designed and verified to guarantee the safety of patients and the effectiveness of medical services in the face of potential problems such as network failures. In this paper, we propose organ-centric hierarchical control architecture as a viable solution that reduces the complexity in system design and verification. In our approach, medical devices are grouped into clusters according to organ-specific human physiology. Each cluster captures common patterns arising out of medical device interactions and becomes a survivable semiautonomous unit during network failures. Further, safety verification and runtime enforcement can be modularized along organ-centric hierarchical control structure. We show the feasibility of the proposed approach under Simulink's model-based development framework. A simplified scenario for airway laser surgery is used as a case study.

Immune response to mutant neo-antigens: Cancer's lessons for aging.

Extending observations on the immunogenicity of neo-antigens that arise in the course of oncogenesis and tumor progression, we suggest that somatic mutations affecting normal tissues also lead to generation of new epitopes. We hypothesize that, at least under inflammatory conditions, immune responses against such neo-antigens may lead to the elimination or functional impairment of normal cells, thus contributing to aging.

Designing and evaluating a clustering system for organizing and integrating patient drug outcomes in personal health messages.

Patient outcomes to drugs vary, but physicians currently have little data about individual responses. We designed a comprehensive system to organize and integrate patient outcomes utilizing semantic analysis, which groups large collections of personal comments into a series of topics. A prototype implementation was built to extract situational evidences by filtering and digesting user comments provided by patients. Our methods do not require extensive training or dictionaries, while categorizing comments based on expert opinions from standard source, or patient-specified categories. This system has been tested with sample health messages from our unique dataset from Yahoo! Groups, containing 12M personal messages from 27K public groups in Health and Wellness. We have performed an extensive evaluation of the clustering results with medical students. Evaluated results show high quality of labeled clustering, promising an effective automatic system for discovering patient outcomes from large volumes of health information.

Predicting adverse drug events from personal health messages.

Adverse drug events (ADEs) remain a large problem in the United States, being the fourth leading cause of death, despite post market drug surveillance. Much post consumer drug surveillance relies on self-reported "spontaneous" patient data. Previous work has performed datamining over the FDA's Adverse Event Reporting System (AERS) and other spontaneous reporting systems to identify drug interactions and drugs correlated with high rates of serious adverse events. However, safety problems have resulted from the lack of post marketing surveillance information about drugs, with underreporting rates of up to 98% within such systems. We explore the use of online health forums as a source of data to identify drugs for further FDA scrutiny. In this work we aggregate individuals' opinions and review of drugs similar to crowd intelligence3. We use natural language processing to group drugs discussed in similar ways and are able to successfully identify drugs withdrawn from the market based on messages discussing them before their removal.

Insufflation vs intubation during esophagogastroduodenoscopy in children.

OBJECTIVES: We compared adverse airway events during esophagogastroduodenoscopy (EGD) in children managed with insufflation vs intubation. BACKGROUND: Optimum airway management during EGD in children remains undecided. METHODS/MATERIALS: Following IRB approval and written informed parental consent, children between 1 and 12 years of age presenting for EGD were randomized to airway management with insufflation (Group I), intubation/awake extubation (Group A), or intubation/deep extubation (Group D). All subjects received a standardized anesthetic with sevoflurane in oxygen. Using uniform definitions, airway adverse events during and after EGD recovery were recorded. Categorical data were analysed with Chi-square contingency tables or Fisher's exact test as appropriate. RESULTS: Analyzable data were available for 415 subjects (Group I: 209; Group A: 101; Group D: 105). Desaturation, laryngospasm, any airway adverse event, and multiple airway adverse events during EGD were significantly more common in subjects in Group I compared to those in Groups A and D. Complaints of sore throat, hoarseness, stridor, and/or dysphagia were more common in subjects in Groups A and D. Analysis of confounders suggested that younger age, obesity, and midazolam premedication were independent predictors of airway adverse events during EGD. CONCLUSIONS: Insufflation during EGD was associated with a higher incidence of airway adverse events, including desaturation and laryngospasm; intubation during EGD was associated with more frequent complaints related to sore throat. As our results show that insufflation during EGD offers no advantage in terms of operational efficiency and is associated with more airway adverse events, we recommend endotracheal intubation during EGD, especially in patients who are younger, obese, or have received midazolam premedication.

Measuring population health using personal health messages.

Personal health messages - inter patient communications within online communities; represent a new path towards providing continuous information about patient derived health status. We apply natural language processing techniques to personal health messages from online message boards to demonstrate the ability to track trends in people's positive or negative opinion (sentiment) regarding particular drugs over time. The significant changes in sentiment correspond to FDA announcements and other publicity. We envision such analysis as a scalable tool for pharmacovigilance hypothesis generation for possible adverse drug reactions.