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Background and Purpose: Differentiation between glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) remains a challenge in neuroradiology with up to 40% of the cases to be incorrectly classified using only conventional MRI. The inclusion of perfusion MRI parameters provides characteristic features that could support the distinction of these pathological entities. On these grounds, we aim to use a perfusion gradient in the peritumoral edema., Methods: Twenty-four patients with GBM or an SBM underwent conventional and perfusion MR imaging sequences before tumors' surgical resection. After postprocessing of the images, quantification of dynamic susceptibility contrast (DSC) perfusion parameters was made. Three concentric areas around the tumor were defined in each case. The monocompartimental and pharmacokinetics parameters of perfusion MRI were analyzed in both series., Results: DSC perfusion MRI models can provide useful information for the differentiation between GBM and SBM. It can be observed that most of the perfusion MR parameters (relative cerebral blood volume, relative cerebral blood flow, relative Ktrans, and relative volume fraction of the interstitial space) clearly show higher gradient for GBM than SBM. GBM also demonstrates higher heterogeneity in the peritumoral edema and most of the perfusion parameters demonstrate higher gradients in the area closest to the enhancing tumor., Conclusion: Our results show that there is a difference in the perfusion parameters of the edema between GBM and SBM demonstrating a vascularization gradient. This could help not only for the diagnosis, but also for planning surgical or radiotherapy treatments delineating the real extension of the tumor., (© 2021 American Society of Neuroimaging.)

Background: There is increasing recognition that health care providers need to focus attention, and be judged against, the impact they have on the health outcomes experienced by patients. The measurement of health outcomes as a routine part of clinical documentation is probably the only scalable way of collecting outcomes evidence, since secondary data collection is expensive and error-prone. However, there is uncertainty about whether routinely collected clinical data within electronic health record (EHR) systems includes the data most relevant to measuring and comparing outcomes and if those items are collected to a good enough data quality to be relied upon for outcomes assessment, since several studies have pointed out significant issues regarding EHR data availability and quality., Objective: In this paper, we first describe a practical approach to data quality assessment of health outcomes, based on a literature review of existing frameworks for quality assessment of health data and multistakeholder consultation. Adopting this approach, we performed a pilot study on a subset of 21 International Consortium for Health Outcomes Measurement (ICHOM) outcomes data items from patients with congestive heart failure., Methods: All available registries compatible with the diagnosis of heart failure within an EHR data repository of a general hospital (142,345 visits and 12,503 patients) were extracted and mapped to the ICHOM format. We focused our pilot assessment on 5 commonly used data quality dimensions: completeness, correctness, consistency, uniqueness, and temporal stability., Results: We found high scores (>95%) for the consistency, completeness, and uniqueness dimensions. Temporal stability analyses showed some changes over time in the reported use of medication to treat heart failure, as well as in the recording of past medical conditions. Finally, the investigation of data correctness suggested several issues concerning the characterization of missing data values. Many of these issues appear to be introduced while mapping the IMASIS-2 relational database contents to the ICHOM format, as the latter requires a level of detail that is not explicitly available in the coded data of an EHR., Conclusions: Overall, results of this pilot study revealed good data quality for the subset of heart failure outcomes collected at the Hospital del Mar. Nevertheless, some important data errors were identified that were caused by fundamentally different data collection practices in routine clinical care versus research, for which the ICHOM standard set was originally developed. To truly examine to what extent hospitals today are able to routinely collect the evidence of their success in achieving good health outcomes, future research would benefit from performing more extensive data quality assessments, including all data items from the ICHOM standards set and across multiple hospitals., (©Hannelore Aerts, Dipak Kalra, Carlos Sáez, Juan Manuel Ramírez-Anguita, Miguel-Angel Mayer, Juan M Garcia-Gomez, Marta Durà-Hernández, Geert Thienpont, Pascal Coorevits. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 04.08.2021.)

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence., Competing Interests: Competing interestsL.F. is a shareholder of Salumedia, a digital health company that provides mHealth solutions for patient empowerment. The remaining authors declare no competing interests., (© The Author(s) 2020.)

The evidence that quality of life is a positive variable for the survival of cancer patients has prompted the interest of the health and pharmaceutical industry in considering that variable as a final clinical outcome. Sustained improvements in cancer care in recent years have resulted in increased numbers of people living with and beyond cancer, with increased attention being placed on improving quality of life for those individuals. Connected Health provides the foundations for the transformation of cancer care into a patient-centric model, focused on providing fully connected, personalized support and therapy for the unique needs of each patient. Connected Health creates an opportunity to overcome barriers to health care support among patients diagnosed with chronic conditions. This paper provides an overview of important areas for the foundations of the creation of a new Connected Health paradigm in cancer care. Here we discuss the capabilities of mobile and wearable technologies; we also discuss pervasive and persuasive strategies and device systems to provide multidisciplinary and inclusive approaches for cancer patients for mental well-being, physical activity promotion, and rehabilitation. Several examples already show that there is enthusiasm in strengthening the possibilities offered by Connected Health in persuasive and pervasive technology in cancer care. Developments harnessing the Internet of Things, personalization, patient-centered design, and artificial intelligence help to monitor and assess the health status of cancer patients. Furthermore, this paper analyses the data infrastructure ecosystem for Connected Health and its semantic interoperability with the Connected Health economy ecosystem and its associated barriers. Interoperability is essential when developing Connected Health solutions that integrate with health systems and electronic health records. Given the exponential business growth of the Connected Health economy, there is an urgent need to develop mHealth (mobile health) exponentially, making it both an attractive and challenging market. In conclusion, there is a need for user-centered and multidisciplinary standards of practice to the design, development, evaluation, and implementation of Connected Health interventions in cancer care to ensure their acceptability, practicality, feasibility, effectiveness, affordability, safety, and equity., (©Gabriel Ruiz Ruiz Signorelli, Fedor Lehocki, Matilde Mora Fernández, Gillian O'Neill, Dominic O'Connor, Louise Brennan, Francisco Monteiro-Guerra, Alejandro Rivero-Rodriguez, Santiago Hors-Fraile, Juan Munoz-Penas, Mercè Bonjorn Dalmau, Jorge Mota, Ricardo B Oliveira, Bela Mrinakova, Silvia Putekova, Naiara Muro, Francisco Zambrana, Juan M Garcia-Gomez. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.10.2019.)

Nursing homes usually host large accounts of persons with different levels of dementia. Detecting dementia process in early stages may allow the application of mechanisms to reduce or stop the cognitive impairment. Our ultimate objective is to demonstrate that the use of persuasive techniques may serve to motivate these subjects and induct re-learning mechanisms to stop mental impairment. Nevertheless, this requires the study of the behaviour of each patient individually in order to detect conduct disorders in their living ambient. This study presents a behavior pattern detection architecture based on the Ambient Assisted Living paradigm and Workflow Mining technology to enable re-learning mechanisms in dementia processes via providing tools to automate the conduct disorder detection. This architecture fosters the use of Workflows as representation languages to allow health professionals to represent persuasive motivation protocols in the AAL environment to react individually to dementia symptoms detected.

This study examines the effect of feature extraction methods prior to automated pattern recognition based on magnetic resonance spectroscopy (MRS) for brain tumor diagnosis. Since individual inspection of spectra is time-consuming and requires specific spectroscopic expertise, the introduction of clinical decision support systems (DSSs) is expected to strongly promote the clinical use of MRS. This study focuses on the feature extraction step in the preprocessing protocol of MRS when using a DSS. On two independent data sets, encompassing single-voxel and multi-voxel data, it is observed that the use of the full spectra together with a kernel-based technique, handling high dimensional data, or using an automated pattern recognition method based on independent component analysis or Relief-F achieves accurate performances. In addition, these approaches have low cost and are easy to automate. When sophisticated quantification methods are used in a DSS, user interaction should be minimized. The computationally intensive quantification techniques do not tend to increase the performance in these circumstances. The results suggest to simplify the feature reduction step in the preprocessing protocol when using a DSS purely for classification purposes. This can greatly speed up the execution of classifiers and DSSs and may accelerate their introduction into clinical practice., ((c) 2008 Wiley-Liss, Inc.)

In this paper, a new method for modelling tRNA secondary structures is presented. This method is based on the combination of stochastic context-free grammars (SCFG) and Hidden Markov Models (HMM). HMM are used to capture the local relations in the loops of the molecule (nonstructured regions) and SCFG are used to capture the long term relations between nucleotides of the arms (structured regions). Given annotated public databases, the HMM and SCFG models are learned by means of automatic inductive learning methods. Two SCFG learning methods have been explored. Both of them take advantage of the structural information associated with the training sequences: one of them is based on a stochastic version of the Sakakibara algorithm and the other one is based on a Corpus based algorithm. A final model is then obtained by merging of the HMM of the nonstructured regions and the SCFG of the structured regions. Finally, the performed experiments on the tRNA sequence corpus and the non-tRNA sequence corpus give significant results. Comparative experiments with another published method are also presented.

This paper introduces a novel distributed decision support system to help radiologists in the diagnosis of soft tissue tumors (STT). Decision support systems are based on pattern recognition engines that discriminate between benign/malignant character and histological groups with a satisfactory estimated efficiency. This system is based on a distributed architecture with three specialized nodes: Radiologist Visual Interface, Information System and Decision Support Web-services. The visual interface is the radiologists and clinicians' point of access to local and remote STT registers, statistical analysis tools and distributed pattern recognition engines. A location-independent and multi-platform system has been developed in order to connect hospitals and institutions to research useful tools in clinical and laboratory environments. The nodes maintenance and upgrade are automatically controlled by the architecture. This tool will be useful regarding the objective methodology to assist radiologist decision in a new case and will help the education of the new radiologists with no expertise in STT.

tRNA molecule has a well-known second structure in which it folds by pairing of far-off nucleotides. This paper shows a syntactic pattern recognition methodology for model tRNA second structure using stochastic context-free grammars. In order to learn models, structural regions (paired nucleotides) have been learned from categorized samples with full labelled tree with a Corpus based estimation algorithm. Nonstructural regions have been modelled by hidden Markov models and transformed to stochastic regular grammars to fusion together the structural regions. Test with positive samples and negative samples in comparison with Sakakibara achieved 1.81% in sequences error rate, 98.43% in precision and 100% in recall and 100% of SER in negative test. Corpus based algorithm is computational time efficient and required less training samples for converge to the correct model of the tRNA second structure.