Local detection of microvessels in IDH-wildtype glioblastoma using relative cerebral blood volume: an imaging marker useful for astrocytoma grade 4 classification.

BACKGROUND: The microvessels area (MVA), derived from microvascular proliferation, is a biomarker useful for high-grade glioma classification. Nevertheless, its measurement is costly, labor-intense, and invasive. Finding radiologic correlations with MVA could provide a complementary non-invasive approach without an extra cost and labor intensity and from the first stage. This study aims to correlate imaging markers, such as relative cerebral blood volume (rCBV), and local MVA in IDH-wildtype glioblastoma, and to propose this imaging marker as useful for astrocytoma grade 4 classification. METHODS: Data from 73 tissue blocks belonging to 17 IDH-wildtype glioblastomas and 7 blocks from 2 IDH-mutant astrocytomas were compiled from the Ivy GAP database. MRI processing and rCBV quantification were carried out using ONCOhabitats methodology. Histologic and MRI co-registration was done manually with experts' supervision, achieving an accuracy of 88.8% of overlay. Spearman's correlation was used to analyze the association between rCBV and microvessel area. Mann-Whitney test was used to study differences of rCBV between blocks with presence or absence of microvessels in IDH-wildtype glioblastoma, as well as to find differences with IDH-mutant astrocytoma samples. RESULTS: Significant positive correlations were found between rCBV and microvessel area in the IDH-wildtype blocks (p < 0.001), as well as significant differences in rCBV were found between blocks with microvascular proliferation and blocks without it (p < 0.0001). In addition, significant differences in rCBV were found between IDH-wildtype glioblastoma and IDH-mutant astrocytoma samples, being 2-2.5 times higher rCBV values in IDH-wildtype glioblastoma samples. CONCLUSIONS: The proposed rCBV marker, calculated from diagnostic MRIs, can detect in IDH-wildtype glioblastoma those regions with microvessels from those without it, and it is significantly correlated with local microvessels area. In addition, the proposed rCBV marker can differentiate the IDH mutation status, providing a complementary non-invasive method for high-grade glioma classification.

Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma.

INTRODUCTION: IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature. PURPOSE: To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long-term survivors (LTSs) and short-term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow-up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients. MATERIALS AND METHODS: Ninety-nine IDH1/2 wt GB patients were divided into LTSs (OS ≥ 400 days) and STSs (OS < 400 days). Mann-Whitney and Fisher, uni- and multiparametric Cox, Aalen's additive regression and Kaplan-Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform). RESULTS: For LTSs, we found a significant association between a moderate value of rCBVmean and higher OS (uni- and multiparametric Cox and Aalen's regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBVmean and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs. CONCLUSION: We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBVmean at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.

A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning.

BACKGROUND: Postpartum depression (PPD) is a disorder that often goes undiagnosed. The development of a screening program requires considerable and careful effort, where evidence-based decisions have to be taken in order to obtain an effective test with a high level of sensitivity and an acceptable specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective. The purpose of this article is twofold: first, to develop classification models for detecting the risk of PPD during the first week after childbirth, thus enabling early intervention; and second, to develop a mobile health (m-health) application (app) for the Android(®) (Google, Mountain View, CA) platform based on the model with best performance for both mothers who have just given birth and clinicians who want to monitor their patient's test. MATERIALS AND METHODS: A set of predictive models for estimating the risk of PPD was trained using machine learning techniques and data about postpartum women collected from seven Spanish hospitals. An internal evaluation was carried out using a hold-out strategy. An easy flowchart and architecture for designing the graphical user interface of the m-health app was followed. RESULTS: Naive Bayes showed the best balance between sensitivity and specificity as a predictive model for PPD during the first week after delivery. It was integrated into the clinical decision support system for Android mobile apps. CONCLUSIONS: This approach can enable the early prediction and detection of PPD because it fulfills the conditions of an effective screening test with a high level of sensitivity and specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective.

Mobile clinical decision support systems and applications: a literature and commercial review.

The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Ninety-two relevant papers and 192 commercial apps were found. Forty-four papers were focused only on mobile clinical decision support systems. One hundred seventy-one apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.

Unlocking Bevacizumab's Potential: rCBVmax as a Predictive Biomarker for Enhanced Survival in Glioblastoma IDH-Wildtype Patients.

BACKGROUND: Aberrant vascular architecture and angiogenesis are hallmarks of glioblastoma IDH-wildtype, suggesting that these tumors are suitable for antiangiogenic therapy. Bevacizumab was FDA-approved in 2009 following promising results in two clinical trials. However, its use for recurrent glioblastomas remains a subject of debate, as it does not universally improve patient survival. PURPOSES: In this study, we aimed to analyze the influence of tumor vascularity on the benefit provided by BVZ and propose preoperative rCBVmax at the high angiogenic tumor habitat as a predictive biomarker to select patients who can benefit the most. METHODS: Clinical and MRI data from 106 patients with glioblastoma IDH-wildtype have been analyzed. Thirty-nine of them received BVZ, and the remaining sixty-seven did not receive a second-line treatment. The ONCOhabitats method was used to automatically calculate rCBV. RESULTS: We found a median survival from progression of 305 days longer for patients with moderate vascular tumors who received BVZ than those who did not receive any second-line treatment. This contrasts with patients with high-vascular tumors who only presented a median survival of 173 days longer when receiving BVZ. Furthermore, better responses to BVZ were found for the moderate-vascular group with a higher proportion of patients alive at 6, 12, 18, and 24 months after progression. CONCLUSIONS: We propose rCBVmax as a potential biomarker to select patients who can benefit more from BVZ after tumor progression. In addition, we propose a threshold of 7.5 to stratify patients into moderate- and high-vascular groups to select the optimal second-line treatment.

Genetic polymorphisms located in genes related to immune and inflammatory processes are associated with end-stage renal disease: a preliminary study.

BACKGROUND: Chronic kidney disease progression has been linked to pro-inflammatory cytokines and markers of inflammation. These markers are also elevated in end-stage renal disease (ESRD), which constitutes a serious public health problem. OBJECTIVE: To investigate whether single nucleotide polymorphisms (SNPs) located in genes related to immune and inflammatory processes, could be associated with ESRD development. DESIGN AND METHODS: A retrospective case-control study was carried out on 276 patients with ESRD and 288 control subjects. Forty-eight SNPs were genotyped via SNPlex platform. Logistic regression was used to assess the relationship between each sigle polymorphism and the development of ESRD. RESULTS: Four polymorphisms showed association with ESRD: rs1801275 in the interleukin 4 receptor (IL4R) gene (OR: 0.66 (95%CI = 0.46-0.95); p = 0.025; overdominant model), rs4586 in chemokine (C-C motif) ligand 2 (CCL2) gene (OR: 0.70 (95%CI = 0.54-0.90); p = 0.005; additive model), rs301640 located in an intergenic binding site for signal transducer and activator of transcription 4 (STAT4) (OR: 1.82 (95%CI = 1.17-2.83); p = 0.006; additive model) and rs7830 in the nitric oxide synthase 3 (NOS3) gene (OR: 1.31 (95%CI = 1.01-1.71); p = 0.043; additive model). After adjusting for multiple testing, results lost significance. CONCLUSION: Our preliminary data suggest that four genetic polymorphisms located in genes related to inflammation and immune processes could help to predict the risk of developing ESRD.

Organizing data quality assessment of shifting biomedical data.

Low biomedical Data Quality (DQ) leads into poor decisions which may affect the care process or the result of evidence-based studies. Most of the current approaches for DQ leave unattended the shifting behaviour of data underlying concepts and its relation to DQ. There is also no agreement on a common set of DQ dimensions and how they interact and relate to these shifts. In this paper we propose an organization of biomedical DQ assessment based on these concepts, identifying characteristics and requirements which will facilitate future research. As a result, we define the Data Quality Vector compiling a unified set of DQ dimensions (completeness, consistency, duplicity, correctness, timeliness, spatial stability, contextualization, predictive value and reliability), as the foundations to the further development of DQ assessment algorithms and platforms.

Glioblastoma versus solitary brain metastasis: MRI differentiation using the edema perfusion gradient.

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.

Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis.

In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce.

Robust estimation of infant feeding indicators by data quality assessment of longitudinal electronic health records from birth up to 18 months of life.

BACKGROUND AND OBJECTIVE: The Baby-Friendly Hospital Initiative (BFHI) is an international strategy aimed at improving breastfeeding practices in health care services. Regular monitoring of indicators is key for BFHI implementation and maintenance. Currently, routine data collected from electronic health records (EHR) is an excellent source for infant feeding monitoring, however data quality (DQ) assessment should be undertaken. The aim of this research is to enable robust estimations of infant feeding indicators through DQ assessment of routine EHR data. MATERIALS AND METHODS: We use the longitudinal series of healthcare contacts belonging to 6427 children born from 2009 to 2018 in the Health Area V of Murcia (Spain). Longitudinal data came from EHR at hospital discharge and community infant health reviews up to 18 months. The data of each healthcare contact contained a 24-h recall of infant feeding. We perform a DQ process in three phases: (1) an assessment of each-single-contact and the definition of their infant feeding status; (2) a longitudinal DQ assessment of completeness and consistency of the series of contacts to obtain meta-information that guides the duration calculus, for each case, of the different types of breastfeeding: exclusive breastfeeding (EBF), full breastfeeding (FBF) and any breastfeeding (ABF); and finally (3) a robust estimation of indicators and description of DQ of each indicator. RESULTS: We found deficiencies of DQ in 30.42% of single contacts for EBF, 19.02% for FBF and 22.50% for ABF that were used to establish the infant feeding status. However, after longitudinal DQ assessment, we obtained valid and reliable data rates for most indicators such as "median duration of breastfeeding" nearly 90%, both for FBF and ABF, not so for EBF. CONCLUSIONS: Despite the DQ deficiencies found in raw data, the DQ assurance approach by indicators proposed in this work, allowed us to obtain a robust estimation of indicators with a significant percentage of subjects with valid information for ABF and FBF monitoring. The estimations were consistent with results previously published. The methodology provided with this study allows a continuous and reliable population monitoring of infant feeding indicators of BFHI from routine EHR data.

High-throughput functional annotation and data mining with the Blast2GO suite.

Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.

Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015).

OBJECTIVES: To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015. DESIGN: Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording. SETTING: 400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES). MAIN OUTCOMES: The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45). RESULTS: Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines. CONCLUSIONS: Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.

Definition of loss functions for learning from imbalanced data to minimize evaluation metrics.

Most learning algorithms for classification use objective functions based on regularized and/or continuous versions of the 0-1 loss function. Moreover, the performance of the classification models is usually measured by means of the empirical error or misclassification rate. Nevertheless, neither those loss functions nor the empirical error is adequate for learning from imbalanced data. In these problems, the empirical error is uninformative about the performance of the classifier and the loss functions usually produce models that are shifted to the majority class. This study defines the loss function L BER whose associated empirical risk is equal to the BER. Our results show that classifiers based on our L BER loss function are optimal in terms of the BER evaluation metric. Furthermore, the boundaries of the classifiers were invariant to the imbalance ratio of the training dataset. The L BER-based models outperformed the 0-1-based models and other algorithms for imbalanced data in terms of BER, regardless of the prevalence of the positive class. Finally, we demonstrate the equivalence of the loss function to the method of inverted prior probabilities, and we define the family of loss functions L WER that is associated with any WER evaluation metric by the generalization of L BER.

Incremental logistic regression for customizing automatic diagnostic models.

In the last decades, and following the new trends in medicine, statistical learning techniques have been used for developing automatic diagnostic models for aiding the clinical experts throughout the use of Clinical Decision Support Systems. The development of these models requires a large, representative amount of data, which is commonly obtained from one hospital or a group of hospitals after an expensive and time-consuming gathering, preprocess, and validation of cases. After the model development, it has to overcome an external validation that is often carried out in a different hospital or health center. The experience is that the models show underperformed expectations. Furthermore, patient data needs ethical approval and patient consent to send and store data. For these reasons, we introduce an incremental learning algorithm base on the Bayesian inference approach that may allow us to build an initial model with a smaller number of cases and update it incrementally when new data are collected or even perform a new calibration of a model from a different center by using a reduced number of cases. The performance of our algorithm is demonstrated by employing different benchmark datasets and a real brain tumor dataset; and we compare its performance to a previous incremental algorithm and a non-incremental Bayesian model, showing that the algorithm is independent of the data model, iterative, and has a good convergence.

Actigraphy pattern analysis for outpatient monitoring.

The actigraphy is a cost-effective method for assessing specific sleep disorders such as diagnosing insomnia, circadian rhythm disorders, or excessive sleepiness. Due to recent advances in wireless connectivity and motion activity sensors, the new actigraphy devices allow the non-intrusive and non-stigmatizing monitoring of outpatients for weeks or even months facilitating treatment outcome measure in daily life activities. This possibility has propitiated new studies suggesting the utility of actigraphy to monitor outpatients with mood disorders such as major depression, or patients with dementia. However, the full exploitation of data acquired during the monitoring period requires the use of automatic systems and techniques that allow the reduction of inherent complexity of the data, the extraction of most informative features, and the interpretability and decision-making. In this study we purpose a set of techniques for actigraphy patterns analysis for outpatient monitoring. These techniques include actigraphy signal pre-processing, quantification, nonlinear registration, feature extraction, detection of anomalies, and pattern visualization. In addition, techniques for daily actigraphy signals modelling and simulation are included to facilitate the development and test of new analysis techniques in controlled scenarios.

Knowledge-Based Personal Health System to empower outpatients of diabetes mellitus by means of P4 Medicine.

Diabetes Mellitus (DM) affects hundreds of millions of people worldwide and it imposes a large economic burden on healthcare systems. We present a web patient empowering system (PHSP4) that ensures continuous monitoring and assessment of the health state of patients with DM (type I and II). PHSP4 is a Knowledge-Based Personal Health System (PHS) which follows the trend of P4 Medicine (Personalized, Predictive, Preventive, and Participative). It provides messages to outpatients and clinicians about the achievement of objectives, follow-up, and treatments adjusted to the patient condition. Additionally, it calculates a four-component risk vector of the associated pathologies with DM: Nephropathy, Diabetic retinopathy, Diabetic foot, and Cardiovascular event. The core of the system is a Rule-Based System which Knowledge Base is composed by a set of rules implementing the recommendations of the American Diabetes Association (ADA) (American Diabetes Association: http://www.diabetes.org/ ) clinical guideline. The PHSP4 is designed to be standardized and to facilitate its interoperability by means of terminologies (SNOMED-CT [The International Health Terminology Standards Development Organization: http://www.ihtsdo.org/snomed-ct/ ] and UCUM [The Unified Code for Units of Measure: http://unitsofmeasure.org/ ]), standardized clinical documents (HL7 CDA R2 [Health Level Seven International: http://www.hl7.org/index.cfm ]) for managing Electronic Health Record (EHR). We have evaluated the functionality of the system and its users' acceptance of the system using simulated and real data, and a questionnaire based in the Technology Acceptance Model methodology (TAM). Finally results show the reliability of the system and the high acceptance of clinicians.

Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data.

Research biobanks are often composed by data from multiple sources. In some cases, these different subsets of data may present dissimilarities among their probability density functions (PDF) due to spatial shifts. This, may lead to wrong hypothesis when treating the data as a whole. Also, the overall quality of the data is diminished. With the purpose of developing a generic and comparable metric to assess the stability of multi-source datasets, we have studied the applicability and behaviour of several PDF distances over shifts on different conditions (such as uni- and multivariate, different types of variable, and multi-modality) which may appear in real biomedical data. From the studied distances, we found information-theoretic based and Earth Mover's Distance to be the most practical distances for most conditions. We discuss the properties and usefulness of each distance according to the possible requirements of a general stability metric.

An HL7-CDA wrapper for facilitating semantic interoperability to rule-based Clinical Decision Support Systems.

The success of Clinical Decision Support Systems (CDSS) greatly depends on its capability of being integrated in Health Information Systems (HIS). Several proposals have been published up to date to permit CDSS gathering patient data from HIS. Some base the CDSS data input on the HL7 reference model, however, they are tailored to specific CDSS or clinical guidelines technologies, or do not focus on standardizing the CDSS resultant knowledge. We propose a solution for facilitating semantic interoperability to rule-based CDSS focusing on standardized input and output documents conforming an HL7-CDA wrapper. We define the HL7-CDA restrictions in a HL7-CDA implementation guide. Patient data and rule inference results are mapped respectively to and from the CDSS by means of a binding method based on an XML binding file. As an independent clinical document, the results of a CDSS can present clinical and legal validity. The proposed solution is being applied in a CDSS for providing patient-specific recommendations for the care management of outpatients with diabetes mellitus.

Behaviour patterns detection for persuasive design in Nursing Homes to help dementia patients.

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.

Automated brain tumor biopsy prediction using single-labeling cDNA microarrays-based gene expression profiling.

AIMS: Gene signatures obtained from microarray experiments may be of use to improve the prediction of brain tumor diagnosis. Nevertheless, automated and objective prediction with accuracy comparable to or better than the gold standard should be convincingly demonstrated for possible clinician uptake of the new methodology. Herewith, we demonstrate that primary brain tumor types can be discriminated using microarray data in an automated and objective way. METHODS: Postsurgical biopsies from 35 patients [17 glioblastoma multiforme (Gbm) and 18 meningothelial meningioma (Mm)] were stored in liquid nitrogen, total RNA was extracted, and cDNA was labeled with Cy3 fluorochrome and hybridized onto a cDNA-based microarray containing 11,500 cDNA clones representing 9300 loci. Scanned data were preprocessed, normalized, and used for predictor development. The predictive functions were fitted to a subset of samples and their performance evaluated with an independent subset. Expression results were validated by means of real time-polymerase chain reaction. RESULTS: Some gene expression-based predictors achieved 100% accuracy both in training resampling validation and independent testing. One of them, composed of GFAP, PTPRZ1, GPM6B and PRELP, produced a 100% prediction accuracy for both training and independent test datasets. Furthermore, the gene signatures obtained, increased cell detoxification, motility and intracellular transport in Gbm, and increased cell adhesion and cytochrome-family genes in Mm, agree well with the expected biologic and pathologic characteristics of the studied tumors. CONCLUSIONS: The ability of gene signatures to automate prediction of brain tumors through a fully objective approach has been demonstrated. A comparison of gene expression profiles between Gbm and Mm may provide additional clues about patterns associated with each tumor type.