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\"This practical guide to biosimulation provides the hands-on experience needed to devise, design and analyze simulations of biophysical processes for applications in biological and biomedical sciences. Through real-world case studies and worked examples, students will develop and apply basic operations through to advanced concepts, covering a wide range of biophysical topics including chemical kinetics and thermodynamics, transport phenomena, and cellular electrophysiology. Each chapter is built around case studies in a given application area, with simulations of real biological systems developed to analyze and interpret data. Open-ended project-based exercises are provided at the end of each chapter, and with all data and computer codes available online (www.cambridge.org/biosim) students can quickly and easily run, manipulate, explore and expand on the examples inside. This hands-on guide is ideal for use on senior undergraduate/graduate courses and also as a self-study guide for anyone who needs to develop computational models of biological systems\"-- Provided by publisher.

This cutting edge book provides all the important aspects dealing with the basic science involved in materials in biomedical technology, especially structure and properties, techniques and technological innovations in material processing and characterizations, as well as the applications.

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Biomedical image analysis challenges have increased in the last ten years, but common practices have not been established yet. Here the authors analyze 150 recent challenges and demonstrate that outcome varies based on the metrics used and that limited information reporting hampers reproducibility.

Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI’s value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.

Depression and anxiety co-occur with chronic pain, and all three are thought to be caused by dysregulation of shared brain systems related to emotional processing associated with body sensations. Understanding the connection between emotional states, pain, and bodily sensations may help understand chronic pain conditions. We developed a mobile platform for measuring pain, emotions, and associated bodily feelings in chronic pain patients in their daily life conditions. Sixty-five chronic back pain patients reported the intensity of their pain, 11 emotional states, and the corresponding body locations. These variables were used to predict pain 2 weeks later. Applying machine learning, we developed two predictive models of future pain, emphasizing interpretability. One model excluded pain-related features as predictors of future pain, and the other included pain-related predictors. The best predictors of future pain were interactive effects of (a) body maps of fatigue with negative affect and (b) positive affect with past pain. Our findings emphasize the contribution of emotions, especially emotional experience felt in the body, to understanding chronic pain above and beyond the mere tracking of pain levels. The results may contribute to the generation of a novel artificial intelligence framework to help in the development of better diagnostic and therapeutic approaches to chronic pain.

BackgroundSubstantial amounts of public funds are invested in health research worldwide. Publicly funded randomised controlled trials (RCTs) often recruit participants at a slower than anticipated rate. Many trials fail to reach their planned sample size within the envisaged trial timescale and trial funding envelope.ObjectivesTo review the consent, recruitment and retention rates for single and multicentre randomised control trials funded and published by the UK's National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme.Data sources and study selectionHTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed.Data extractionInformation was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers.Main outcome measuresTarget sample size and whether it was achieved; recruitment rates (number of participants recruited per centre per month) and retention rates (randomised participants retained and assessed with valid primary outcome data).ResultsThis review identified 151 individually RCTs from 787 NIHR HTA reports. The final recruitment target sample size was achieved in 56% (85/151) of the RCTs and more than 80% of the final target sample size was achieved for 79% of the RCTs (119/151). The median recruitment rate (participants per centre per month) was found to be 0.92 (IQR 0.43–2.79) and the median retention rate (proportion of participants with valid primary outcome data at follow-up) was estimated at 89% (IQR 79–97%).ConclusionsThere is considerable variation in the consent, recruitment and retention rates in publicly funded RCTs. Investigators should bear this in mind at the planning stage of their study and not be overly optimistic about their recruitment projections.

Background Evaluation of health technology programmes should be theoretically informed, interdisciplinary, and generate in-depth explanations. The NASSS (non-adoption, abandonment, scale-up, spread, sustainability) framework was developed to study unfolding technology programmes in real time—and in particular to identify and manage their emergent uncertainties and interdependencies. In this paper, we offer a worked example of how NASSS can also inform ex post (i.e. retrospective) evaluation. Methods We studied the TORPEDO (Treatment of Cardiovascular Risk in Primary Care using Electronic Decision Support) research programme, a multi-faceted computerised quality improvement intervention for cardiovascular disease prevention in Australian general practice. The technology ( HealthTracker ) had shown promise in a cluster randomised controlled trial (RCT), but its uptake and sustainability in a real-world implementation phase was patchy. To explain this variation, we used NASSS to undertake secondary analysis of the multi-modal TORPEDO dataset (results and process evaluation of the RCT, survey responses, in-depth professional interviews, videotaped consultations) as well as a sample of new, in-depth narrative interviews with TORPEDO researchers. Results Ex post analysis revealed multiple areas of complexity whose influence and interdependencies helped explain the wide variation in uptake and sustained use of the HealthTracker technology: the nature of cardiovascular risk in different populations, the material properties and functionality of the technology, how value (financial and non-financial) was distributed across stakeholders in the system, clinicians’ experiences and concerns, organisational preconditions and challenges, extra-organisational influences (e.g. policy incentives), and how interactions between all these influences unfolded over time. Conclusion The NASSS framework can be applied retrospectively to generate a rich, contextualised narrative of technology-supported change efforts and the numerous interacting influences that help explain its successes, failures, and unexpected events. A NASSS-informed ex post analysis can supplement earlier, contemporaneous evaluations to uncover factors that were not apparent or predictable at the time but dynamic and emergent.

GRADE and other evidence to decision (EtD) frameworks are widely used by guideline development groups (GDG) and other decision-makers. When GDGs judge the magnitude of desirable and undesirable health outcomes on EtDs, they typically categorize them as trivial, small, moderate, or large. However, generic judgment or decision thresholds (DTs) that could guide the user about such estimates of effect size or serve as references for interpretation of findings are not yet available. The objective of this study was to empirically derive DTs for EtD judgments about the magnitude of dichotomously assessed health benefits and harms. We conducted a methodological randomized controlled trial to derive empirical DTs across conditions and health outcomes. We invited stakeholders, including clinicians, epidemiologists, decision scientists, health research methodologists, experts in health technology assessment (HTA), members of GDGs, patient representatives, and the public to participate in the trial. We employed randomly assigned case scenarios to elicit ranges of absolute risk differences judged as small and moderate effects from study participants. We then used the collected data to derive empirical DTs. We also investigated the validity of our DTs by measuring the agreement between judgments that were made by GDGs in the past and the judgments that our DTs approach would suggest if applied to the same guideline data. A total of 445 stakeholders accessed the survey of which 409 were randomised and 288 rated at least one case scenario. Based on these participants, the study findings support our a priori hypothesis of a difference in the DTs for trivial, small, moderate, and large effects and are suggestive of a relation between raters' judgments and the joint measure of absolute effects and outcome values. The results permit the use and calculation of DTs for a variety of scenarios and we present three ways of how to use the results practically. In this trial we confirmed that empirically derived DTs discriminate between judgments on the EtDs. These DTs can be used for judgments about desirable and undesirable health effects in systematic reviews or to initiate and inform a discussion with a GDG. This ensures consistency in judgments across different guideline questions and promotes transparency in judgments. Decision thresholds (DTs) help with determining if effects of interventions should be considered absent, small, moderate or large. In this study we derived an overarching approach for these thresholds across conditions and outcomes. The results of this study, a randomized experiment, will help guideline developers and other decision-makers to make these judgments objectively. They will be particularly relevant for the use in Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence to decision (EtD) frameworks.

Despite investments in infrastructure and evidence for high acceptability, few mHealth interventions have been implemented in sub-Saharan Africa. We sought to (1) identify predictors of uptake of an mHealth application for a low-literacy population of people living with HIV (PLWH) in rural Uganda and (2) evaluate the efficacy of various short message service (SMS) text message formats to optimize the balance between confidentiality and accessibility. The trial evaluated the efficacy of a SMS text messaging app to notify PLWH of their laboratory results and request return to care for those with abnormal test results. Participants with a normal laboratory result received a single SMS text message indicating results were normal. Participants with an abnormal test result were randomized to 1 of 3 message formats designed to evaluate trade-offs between clarity and privacy: (1) an SMS text message that stated results were abnormal and requested return to clinic (\"direct\"), (2) the same message protected by a 4-digit PIN code (\"PIN\"), and (3) the message \"ABCDEFG\" explained at enrollment to indicate abnormal results (\"coded\"). Outcomes of interest were (1) self-reported receipt of the SMS text message, (2) accurate identification of the message, and (3) return to care within 7 days (for abnormal results) or on the date of the scheduled appointment (for normal results). We fit regression models for each outcome with the following explanatory variables: sociodemographic characteristics, CD4 count result, ability to read a complete sentence, ability to access a test message on enrollment, and format of SMS text message. Seventy-two percent (234/385) of participants successfully receiving a message, 87.6% (219/250) correctly identified the message format, and 60.8% (234/385) returned to clinic at the requested time. Among participants with abnormal tests results (138/385, 35.8%), the strongest predictors of reported message receipt were the ability to read a complete sentence and a demonstrated ability to access a test message on enrollment. Participants with an abnormal result who could read a complete sentence were also more likely to accurately identify the message format (AOR 4.54, 95% CI 1.42-14.47, P=.01) and return to clinic appropriately (AOR 3.81, 95% CI 1.61-9.03, P=.002). Those who were sent a PIN-protected message were less likely to identify the message (AOR 0.11, 95% CI 0.03-0.44, P=.002) or return within 7 days (AOR 0.26, 95% CI 0.10-0.66, P=.005). Gender, age, and socioeconomic characteristics did not predict any outcomes and there were no differences in outcomes between those receiving direct or coded messages. Confirmed literacy at the time of enrollment was a robust predictor of SMS text message receipt, identification, and appropriate response for PLWH in rural Uganda. PIN-protected messages reduced odds of clinic return, but coded messages were as effective as direct messages and might augment privacy. Clinicaltrials.gov NCT 01579214; https://clinicaltrials.gov/ct2/show/NCT01579214 (Archived by WebCite at http://www.webcitation.org/6Ww8R4sKq).