Learning from high-dimensional and class-imbalanced datasets using random forests

Class imbalance and high dimensionality are two major issues in several real-life applications, e.g., in the fields of bioinformatics, text mining and image classification. However, while both issues have been extensively studied in the machine learning community, they have mostly been treated separately, and little research has been thus far conducted on which approaches might be best suited to deal with datasets that are class-imbalanced and high-dimensional at the same time (i.e., with a large number of features). This work attempts to give a contribution to this challenging research area by studying the effectiveness of hybrid learning strategies that involve the integration of feature selection techniques, to reduce the data dimensionality, with proper methods that cope with the adverse effects of class imbalance (in particular, data balancing and cost-sensitive methods are considered). Extensive experiments have been carried out across datasets from different domains, leveraging a well-known classifier, the Random Forest, which has proven to be effective in high-dimensional spaces and has also been successfully applied to imbalanced tasks. Our results give evidence of the benefits of such a hybrid approach, when compared to using only feature selection or imbalance learning methods alone

Using Artificial Intelligence for COVID-19 Detection in Blood Exams: A Comparative Analysis

COVID-19 is an infectious disease that was declared a pandemic by the World Health Organization (WHO) in early March 2020. Since its early development, it has challenged health systems around the world. Although more than 12 billion vaccines have been administered, at the time of writing, it has more than 623 million confirmed cases and more than 6 million deaths reported to the WHO. These numbers continue to grow, soliciting further research efforts to reduce the impacts of such a pandemic. In particular, artificial intelligence techniques have shown great potential in supporting the early diagnosis, detection, and monitoring of COVID-19 infections from disparate data sources. In this work, we aim to make a contribution to this field by analyzing a high-dimensional dataset containing blood sample data from over forty thousand individuals recognized as infected or not with COVID-19. Encompassing a wide range of methods, including traditional machine learning algorithms, dimensionality reduction techniques, and deep learning strategies, our analysis investigates the performance of different classification models, showing that accurate detection of blood infections can be obtained. In particular, an F-score of 84% was achieved by the artificial neural network model we designed for this task, with a rate of 87% correct predictions on the positive class. Furthermore, our study shows that the dimensionality of the original data, i.e. the number of features involved, can be significantly reduced to gain efficiency without compromising the final prediction performance. These results pave the way for further research in this field, confirming that artificial intelligence techniques may play an important role in supporting medical decision-making

Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data

Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications. The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies proposed for processing raw data, extracting suitable features, and inducing predictive models capable of recognizing multiple types of daily activities. Since many HAR systems are implemented in resource-constrained mobile devices, the efficiency of the induced models is a crucial aspect to consider. This paper highlights the importance of exploiting dimensionality reduction techniques that can simplify the model and increase efficiency by identifying and retaining only the most informative and predictive features for activity recognition. More in detail, a large experimental study is presented that encompasses different feature selection algorithms as well as multiple HAR benchmarks containing mobile sensor data. Such a comparative evaluation relies on a methodological framework that is meant to assess not only the extent to which each selection method is effective in identifying the most predictive features but also the overall stability of the selection process, i.e., its robustness to changes in the input data. Although often neglected, in fact, the stability of the selected feature sets is important for a wider exploitability of the induced models. Our experimental results give an interesting insight into which selection algorithms may be most suited in the HAR domain, complementing and significantly extending the studies currently available in this field

The Portrayal of Complementary and Alternative Medicine in Mass Print Magazines Since 1980

Objectives: The objectives of this study were to examine and describe the portrayal of complementary and alternative medicine (CAM) in mass print media magazines. Design: The sample included all 37 articles found in magazines with circulation rates of greater than 1 million published in the United States and Canada from 1980 to 2005. The analysis was quantitative and qualitative and included investigation of both manifest and latent magazine story messages. Results: Manifest analysis noted that CAM was largely represented as a treatment for a patient with a medically diagnosed illness or specific symptoms. Discussions used biomedical terms such as patient rather than consumer and disease rather than wellness. Latent analysis revealed three themes: (1) CAMs were described as good but not good enough; (2) individualism and consumerism were venerated; and (3) questions of costs were raised in the context of confusion and ambivalence

Paediatric radiology seen from Africa. Part I: providing diagnostic imaging to a young population

Article approval pendingPaediatric radiology requires dedicated equipment, specific precautions related to ionising radiation, and specialist knowledge. Developing countries face difficulties in providing adequate imaging services for children. In many African countries, children represent an increasing proportion of the population, and additional challenges follow from extreme living conditions, poverty, lack of parental care, and exposure to tuberculosis, HIV, pneumonia, diarrhoea and violent trauma. Imaging plays a critical role in the treatment of these children, but is expensive and difficult to provide. The World Health Organisation initiatives, of which the World Health Imaging System for Radiography (WHIS-RAD) unit is one result, needs to expand into other areas such as the provision of maintenance servicing. New initiatives by groups such as Rotary and the World Health Imaging Alliance to install WHIS-RAD units in developing countries and provide digital solutions, need support. Paediatric radiologists are needed to offer their services for reporting, consultation and quality assurance for free by way of teleradiology. Societies for paediatric radiology are needed to focus on providing a volunteer teleradiology reporting group, information on child safety for basic imaging, guidelines for investigations specific to the disease spectrum, and solutions for optimising imaging in children

Odorant binding proteins : a biotechnological tool for odour control

The application of an odorant binding protein for odour control and fragrance delayed release from a textile surface was first explored in this work. Pig OBP-1 gene was cloned and expressed in Escherichia coli , and the purified protein was biochemically characterized. The IC50 values(concentrations of competitor that caused a decay of fluorescence to half-maximal intensity) were determined for four distinct fragrances, namely, citronellol, benzyl benzoate,citronellyl valerate and ethyl valerate. The results showed a strong binding of citronellyl valerate,citronellol and benzyl benzoate to the recombinant protein, while ethyl valerate displayed weaker binding. Cationized cotton substrates were coated with porcine odorant binding protein and tested for their capacity to retain citronellol and to mask the smell of cigarette smoke. The immobilized protein delayed the release of citronellol when compared to the untreated cotton. According to a blind evaluation of 30 assessors, the smell of cigarette smoke, trapped onto the fabrics’ surface, was successfully attenuated by porcine odorant binding protein (more than 60 % identified the weakest smell intensity after protein exposure compared to β-cyclodextrin-treated and untreated cotton fabrics). This work demonstrated that porcine odorant binding protein can be an efficient solution to prevent and/orremove unpleasant odours trapped on the large surface of textiles. Its intrinsic properties make odorant binding proteins excellent candidates for controlled release systems which constitute a new application for this class of proteins.This work was co-funded by the European Social Fund through the management authority POPH and FCT. The authors Carla Silva and Teresa Matama would like to acknowledge their post-doctoral fellowships: SFRH/BPD/46515/2008 and SFRH/BPD/47555/2008, respectively

Novel contrast-enhanced ultrasound imaging in prostate cancer

The purposes of this paper were to present the current status of contrast-enhanced transrectal ultrasound imaging and to discuss the latest achievements and techniques now under preclinical testing. Although grayscale transrectal ultrasound is the standard method for prostate imaging, it lacks accuracy in the detection and localization of prostate cancer. With the introduction of contrast-enhanced ultrasound (CEUS), perfusion imaging of the microvascularization became available. By this, cancer-induced neovascularisation can be visualized with the potential to improve ultrasound imaging for prostate cancer detection and localization significantly. For example, several studies have shown that CEUS-guided biopsies have the same or higher PCa detection rate compared with systematic biopsies with less biopsies needed. This paper describes the current status of CEUS and discusses novel quantification techniques that can improve the accuracy even further. Furthermore, quantification might decrease the user-dependency, opening the door to use in the routine clinical environment. A new generation of targeted microbubbles is now under pre-clinical testing and showed avidly binding to VEGFR-2, a receptor up-regulated in prostate cancer due to angiogenesis. The first publications regarding a targeted microbubble ready for human use will be discussed. Ultrasound-assisted drug delivery gives rise to a whole new set of therapeutic options, also for prostate cancer. A major breakthrough in the future can be expected from the clinical use of targeted microbubbles for drug delivery for prostate cancer diagnosis as well as treatmen

Diagnostic accuracy of the primary care screener for affective disorder (PC-SAD) in primary care

Background: Depression goes often unrecognised and untreated in non-psychiatric medical settings. Screening has recently gained acceptance as a first step towards improving depression recognition and management. The Primary Care Screener for Affective Disorders (PC-SAD) is a self-administered questionnaire to screen for Major Depressive Disorder (MDD) and Dysthymic Disorder (Dys) which has a sophisticated scoring algorithm that confers several advantages. This study tested its performance against a ‘gold standard’ diagnostic interview in primary care. Methods: A total of 416 adults attending 13 urban general internal medicine primary care practices completed the PC-SAD. Of 409 who returned a valid PC-SAD, all those scoring positive (N=151) and a random sample (N=106) of those scoring negative were selected for a 3-month telephone follow-up assessment including the administration of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID-I) by a psychiatrist who was masked to PC-SAD results. Results: Most selected patients (N=212) took part in the follow-up assessment. After adjustment for partial verification bias the sensitivity, specificity, positive and negative predictive value for MDD were 90%, 83%, 51%, and 98%. For Dys, the corresponding figures were 78%, 79%, 8%, and 88%. Conclusions: While some study limitations suggest caution in interpreting our results, this study corroborated the diagnostic validity of the PC-SAD, although the low PPV may limit its usefulness with regard to Dys. Given its good psychometric properties and the short average administration time, the PC-SAD might be the screening instrument of choice in settings where the technology for computer automated scoring is available

Emerging therapies in pheochromocytoma and paraganglioma: Immune checkpoint inhibitors in the starting blocks

Pheochromocytoma and paraganglioma are neuroendocrine neoplasms, originating in the adrenal medulla and in parasympathetic and sympathetic autonomic nervous system ganglia, respec-tively. They usually present as localized tumours curable with surgery. However, these tumours may exhibit heterogeneous clinical course, ranging from no/minimal progression to aggressive (progres-sive/metastatic) behavior. For this setting of patients, current therapies are unsatisfactory. Immune checkpoint inhibitors have shown outstanding results for several types of solid cancers. We therefore aimed to summarize and discuss available data on efficacy and safety of current FDA-approved immune checkpoint inhibitors in patients with pheochromocytoma and paraganglioma. After an extensive search, we found 15 useful data sources (four full-published articles, four supplements of scientific journals, seven ongoing registered clinical trials). The data we detected, even with the limit of the small number of patients treated, make a great expectation on the therapeutic use of immune checkpoint inhibitors. Besides, the newly detected predictors of response will (hopefully) be of great helps in selecting the subset of patients that might benefit the most from this class of drugs. Finally, new trials are in the starting blocks, and they are expected to shed in the next future new light on a therapy, which is considered a milestone in oncology