Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Computer-interpretable guidelines driven clinical decision support systems : an approach to the treatment personalisation routes of patients with multi-diseases

Clinical Decision Support Systems help the delivery of care by supplementing generic clinical guidelines with decision support. This is achieved by encompassing patient specific recommendations that support the implementation of the computer-interpretable guidelines (CIGs). CIG implementation involves understanding the risks and outcomes of a treatment, which may show diversifications between patients with multiple diseases and those without. The objective of this study is to present a state-of-the-art approach for CIG based treatment personalisation routes and stages for patients with multiple diseases

APPRAISE-RS: Automated, updated, participatory, and personalized treatment recommender systems based on GRADE methodology

Attention deficit hyperactivity disorder; Evidence-based medicine; Meta-analysisTrastorn per dèficit d'atenció amb hiperactivitat; Medicina basada en l'evidència; MetaanàlisiTrastorno por déficit de atención con hiperactividad; Medicina basada en la evidencia; MetanálisisPurpose: Clinical practice guidelines (CPGs) have become fundamental tools for evidence-based medicine (EBM). However, CPG suffer from several limitations, including obsolescence, lack of applicability to many patients, and limited patient participation. This paper presents APPRAISE-RS, which is a methodology that we developed to overcome these limitations by automating, extending, and iterating the methodology that is most commonly used for building CPGs: the GRADE methodology.Method: APPRAISE-RS relies on updated information from clinical studies and adapts and automates the GRADE methodology to generate treatment recommendations. APPRAISE-RS provides personalized recommendations because they are based on the patient's individual characteristics. Moreover, both patients and clinicians express their personal preferences for treatment outcomes which are considered when making the recommendation (participatory). Rule-based system approaches are used to manage heuristic knowledge.Results: APPRAISE-RS has been implemented for attention deficit hyperactivity disorder (ADHD) and tested experimentally on 28 simulated patients. The resulting recommender system (APPRAISE-RS/TDApp) shows a higher degree of treatment personalization and patient participation than CPGs, while recommending the most frequent interventions in the largest body of evidence in the literature (EBM). Moreover, a comparison of the results with four blinded psychiatrist prescriptions supports the validation of the proposal.Conclusions: APPRAISE-RS is a valid methodology to build recommender systems that manage updated, personalized and participatory recommendations, which, in the case of ADHD includes at least one intervention that is identical or very similar to other drugs prescribed by psychiatrists.This work was supported by European Regional Development Fund (ERDF), the Spanish Ministry of the Economy, Industry and Competitiveness (MINECO) and the Carlos III Research Institute [PI19/00375], Fundació Pascual i Prats & Campus Salut, UdG [AIN2018E], Generalitat de Catalunya [2017 SGR 1551]

JNER at 15 years: analysis of the state of neuroengineering and rehabilitation.

On JNER's 15th anniversary, this editorial analyzes the state of the field of neuroengineering and rehabilitation. I first discuss some ways that the nature of neurorehabilitation research has evolved in the past 15 years based on my perspective as editor-in-chief of JNER and a researcher in the field. I highlight increasing reliance on advanced technologies, improved rigor and openness of research, and three, related, new paradigms - wearable devices, the Cybathlon competition, and human augmentation studies - indicators that neurorehabilitation is squarely in the age of wearability. Then, I briefly speculate on how the field might make progress going forward, highlighting the need for new models of training and learning driven by big data, better personalization and targeting, and an increase in the quantity and quality of usability and uptake studies to improve translation

Human Factors As A Parameter For Improving Interface Usability And User Satisfaction

The endeavour to optimize HCI should integrate a wide array of user characteristics that have an effect throughout users’ interactions with a system. Human factors such as cognitive traits and current state, from a psychological point of view, are undoubtedly significant in the shaping of the perceived and objective quality of interactions with a system. The research that is presented in this paper focuses on identifying human factors that relate to users’ performance in Web applications that involve information processing, and a framework of personalization rules that are expected to increase users’ performance is depicted. The empirical results that are presented are derived from environments both learning and commercial; in the case of e-learning personalization was beneficial, while the interaction with a commercial site needs to be further investigated due to the implicit character of information processing in the Web

RFID-Integrated Retail Supply Chain Services: Lessons Learnt From The Smart Project

This paper proposes a service-oriented architecture that utilizes the automatic, unique identification capabilities of RFID technology, data stream management systems and web services, to support RFID-integrated supply chain services. In the lifespan of SMART project (IST-2005, FP6) two services have been deployed supporting dynamic-pricing of fresh products and management of promotion events. The two services have been field-tested in three retail stores in Greece, Ireland, and Cyprus. The valuable lessons learnt, concerning RFID readability challenges, consumer privacy, customers and store staff health concerns, investment cost, and so on, are reported to provide guidance to future developers of RFID-integrated supply chain services as well as to set an agenda for academic research

Biopsychosocial rehabilitation in the working population with chronic low back pain:a concept analysis

OBJECTIVE: To identify the essential attributes of biopsychosocial rehabilitation for chronic low back pain in the working population.DESIGN: A concept analysis was conducted according to the 8-step method of Walker and Avant. This framework provides a clear concept and theoretical and operational definitions.METHODS: Five databases were searched, followed by a systematic screening. Subsequently, attributes, illustrative cases, antecedents, consequences and empirical referents were formulated.RESULTS: Of the 3793 studies identified, 42 unique references were included. Eleven attributes were identified: therapeutic exercise, psychological support, education, personalization, self-management, participation, follow-up, practice standard, goal-setting, social support, and dietary advice. Subsequently, illustrative cases were described. Antecedents, such as motivation, preparedness and a multidisciplinary team, were found, together with consequences such as decreased pain, less sick-leave and increased function and work status. Finally, examples of empirical referents were given.CONCLUSION: This study identified the attributes that are necessary to develop biopsychosocial rehabilitation intervention programmes for chronic low back pain. The defined concept of biopsychosocial rehabilitation for chronic low back pain may serve as a solid base to further develop and apply interventions. Future research should focus on the objectification of biopsychosocial rehabilitation and conceptualization regarding how personalization is done.</p