Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Nearly a quarter of visits to the Emergency Department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of big data sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease community. Sickle cell disease is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to predict pain dynamics given patients' reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data driven recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table

Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples

Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain. Our results may help physicians and patients better understand and manage patients\u27 pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond

The factors that impact the adoption and Usage of Telemedicine in Chronic Diseases: Systematic Review

Chronic diseases are one of the most common diseases that pertain to a large number of people. Most people suffer from one or more chronic diseases, such as diabetes, heart failure, rheumatoid arthritis, cancer, and others. According to a recent statistic, 9% of Jordanian have at least one type of chronic disease; it is a high percentage for a country of limited resources. Chronic diseases are costly to manage; the cost is not only associated with the treatment alone but also to monitoring patients, healthcare professionals labor, and continuous lab testing. During the past few years, the world witnessed a significant increase in the number of mobile Health users. This increase also translated into an increase in using Telehealth services. This paper aims to conduct a systematic literature review of the different adoption factors of Telemedicine for chronic diseases. We provide an analysis and a synthesis of recently published research in the past ten years. We follow the methodological literature review proposed by Ramey and Rao to examine and extract related scholarly work. By providing a thematic analysis of relevant literature, we classify the current research into the main themes of the Telehealth in the chronic field. We also develop a taxonomy of positive and negative factors that influence Telehealth. We also highlight the main limitations and gaps in the literature to guide future research

Novel approaches for effective design of controlled drug release systems, employing hybrid semi-parametric mathematical systems

The controlled release of a drug from a carrier into a medium over a defined period of time is referred to as Controlled Drug Release (CDR). A major challenge for a sustainable and reproducible CDR is the unintentional initial burst, which occurs in the first hours/days of immersion and during which a large amount of drug is released. Also it can have deleterious effects on the host. Burst release happens with both small drug molecules and large proteins and for both drug-loaded PLGA micro- and nanoparticles. Particle design can, in principal, be used to control the amount of burst but no systematic methods are to date available and the design process is governed by trial and error. One reason might be that the available models for burst release do not explicitly account for the particle design parameters. This thesis proposes novel methodologies that allow for rational design of drug-loaded PLGA micro- and nanoparticles. It is divided in three main parts. Firstly, a quantitative analysis of the physicochemical factors that impact on the amount of burst release and the burst release rate using partial least squares and decision tree methods is performed. The factors with the greatest impact are selected for the subsequent modelling activities. Next, a bootstrap aggregated hybrid model (HM) is developed, which can successfully predict the cumulative drug release of an independent set of CDR experiments. Lastly, a new rational design method is presented for the optimization of the formulation characteristics of protein-loaded PLGA nanoparticles. The method is successfully applied to design the carrier of a mock-protein, α- chymotrypsin, yielding a close to desired release profile. The method can also help to judge upon the similarity of the mock protein with a target protein in terms of their similarities in burst release behavior. This thesis proposes the first rational PLGA particle design method requiring only the specification of the drug and the desired burst release profile. The application of the method can be expected to significantly reduce the time for PLGA particle development. With the increasing availability of CDR data the predictive power of the method can be further improved towards a systematic and reliable tool. The engine of the method is the hybrid model which links the release profile to the design parameters and is the first of its kind in drug release modeling

Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications

Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or process, but it is based on fundamental laws of physics or engineering that lead to systems of equations able to represent all the variables that characterize the process. Conversely, Machine Learning methods require a large amount of data to find solutions, remaining detached from the problem that generated them and trying to infer the behavior of the object, material or process to be examined from observed samples. Mathematics allows us to formulate complex models with effectiveness and creativity, describing nature and physics. Together with the potential of Artificial Intelligence and data collection techniques, a new way of dealing with practical problems is possible. The insertion of the equations deriving from the physical world in the data-driven models can in fact greatly enrich the information content of the sampled data, allowing to simulate very complex phenomena, with drastically reduced calculation times. Combined approaches will constitute a breakthrough in cutting-edge applications, providing precise and reliable tools for the prediction of phenomena in biological macro/microsystems, for biotechnological applications and for medical diagnostics, particularly in the field of precision medicine

Evidence Based Medicine

Evidence-based medicine (EBM) was introduced to the best benefit of the patient. It has transformed the pathophysiological approach to the outcome approach of today's treatments. Disease-oriented to patient-oriented medicine. And, for some, daily medical practice from patient oriented to case oriented medicine. Evidence has changed the paternalistic way of medical practice. And gave room to patients, who show a tendency towards partnership. Although EBM has introduced a different way of thinking in the day to day medical practice, there is plenty of space for implementation and improvement. This book is meant to provoke the thinker towards the unlimited borders of caring for the patient

Facilitating implementation of research evidence (FIRE): A randomised controlled trial and process evaluation of two models of facilitation informed by the promoting action on research implementation in health services (PARIHS) framework

Background: The PARIHS framework proposes that successful implementation of research evidence results from the complex interplay between the evidence to be implemented, the context of implementation and the facilitation processes employed. Facilitation is defined as a role (the facilitator) and a process (facilitation strategies/methods). Empirical evidence comparing different facilitation approaches is limited; this paper reports a trial of two different types of facilitation represented in the PARIHS framework. Methods: A pragmatic cluster randomised controlled trial with embedded process evaluation was undertaken in 24 long-term nursing care settings in four European countries. In each country, sites were randomly allocated to standard dissemination of urinary incontinence guideline recommendations and one of two types of external-internal facilitation, labelled Type A and B. Type A facilitation was a less resource intensive approach, underpinned by improvement methodology; Type B was a more intensive, emancipatory model of facilitation, informed by critical social science. The primary outcome was percentage documented compliance with guideline recommendations. Process evaluation was framed by realist methodology and involved quantitative and qualitative data collection from multiple sources. Findings: Quantitative data were obtained from reviews of 2313 records. Qualitative data included over 332 hours of observations of care; 39 hours observation of facilitation activity; 471 staff interviews; 174 resident interviews; 120 next of kin/carer interviews; and 125 stakeholder interviews. There were no significant differences in the primary outcome between study arms and all study arms improved over time. Process data revealed three core mechanisms that influenced the trajectory of the facilitation intervention: alignment of the facilitation approach to the needs and expectations of the internal facilitator and colleagues; engagement of internal facilitators and staff in attitude and action; and learning over time. Data from external facilitators demonstrated that the facilitation interventions did not work as planned, issues were cumulative and maintenance of fidelity was problematic. Implications for D&I Research: Evaluating an intervention - in this case facilitation - that is fluid and dynamic within the methodology of a randomised controlled trial is complex and challenging. For future studies, we suggest a theoretical approach to fidelity, with a focus on mechanisms, as opposed to dose and intensity of the intervention

Faculty Publications and Creative Works 2003

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. It serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

Hierarchies of evidence in evidence-based medicine

Hierarchies of evidence are an important and influential tool for appraising evidence in medicine. In recent years, hierarchies have been formally adopted by organizations including the Cochrane Collaboration [1], NICE [2,3], the WHO [4], the US Preventive Services Task Force [5], and the Australian NHMRC [6,7]. The development of such hierarchies has been regarded as a central part of Evidence-Based Medicine (e.g. [8-10]), a movement within healthcare which prioritises the use of epidemiological evidence such as that provided by Randomised Controlled Trials (RCTs). Philosophical work on the methodology of medicine has so far mostly focused on claims about the superiority of RCTs, and hence has largely neglected the questions of what hierarchies are, what assumptions they require, and how they affect clinical practice. This thesis shows that there is great variation in the hierarchies defended and in the interpretations they are, and can be, given. The interpretative assumptions made in using hierarchies are crucial to the content and defensibility of the underlying philosophical commitments concerning evidence and medical practice. Once this variation is been identified, it becomes clear that the little philosophical work that has been done so far affects only some hierarchies, under some interpretations. Modest interpretations offered by La Caze [11], conditional hierarchies like GRADE [12-14], and heuristic approaches such as that defended by Howick et al. [15,16] all survive previous philosophical criticism. This thesis extends previous criticisms by arguing that modest interpretations are so weak as to be unhelpful for clinical practice; that GRADE and similar conditional models omit clinically relevant information, such as information about variation in treatments’ effects and the causes of different responses to therapy; and that heuristic approaches lack the necessary empirical support. The conclusion is that hierarchies in general embed untenable philosophical assumptions: principally that information about average treatment effects backed by high-quality evidence can justify strong recommendations, and that the impact of evidence from individual studies can and should be appraised in isolation. Hierarchies are a poor basis for the application of evidence in clinical practice. The Evidence-Based Medicine movement should move beyond them and explore alternative tools for appraising the overall evidence for therapeutic claims

2021 - The Second Annual Fall Symposium of Student Scholars

The full program book from the Fall 2020 Symposium of Student Scholars, held on November 18, 2021. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1024/thumbnail.jp