Using Topological Data Analysis for diagnosis pulmonary embolism

Pulmonary Embolism (PE) is a common and potentially lethal condition. Most patients die within the first few hours from the event. Despite diagnostic advances, delays and underdiagnosis in PE are common.To increase the diagnostic performance in PE, current diagnostic work-up of patients with suspected acute pulmonary embolism usually starts with the assessment of clinical pretest probability using plasma d-Dimer measurement and clinical prediction rules. The most validated and widely used clinical decision rules are the Wells and Geneva Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE based on topological data analysis and artificial neural network. Filter or wrapper methods for features reduction cannot be applied to our dataset: the application of these algorithms can only be performed on datasets without missing data. Instead, we applied Topological data analysis (TDA) to overcome the hurdle of processing datasets with null values missing data. A topological network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE patient topology identified two ares in the pathological group and hence two distinct clusters of PE patient populations. Additionally, the topological netowrk detected several sub-groups among healthy patients that likely are affected with non-PE diseases. TDA was further utilized to identify key features which are best associated as diagnostic factors for PE and used this information to define the input space for a back-propagation artificial neural network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is greater than the AUCs of the scores (Wells and revised Geneva) used among physicians. The results demonstrate topological data analysis and the BP-ANN, when used in combination, can produce better predictive models than Wells or revised Geneva scores system for the analyzed cohortComment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with arXiv:cs/0308031 by other authors without attributio

The symptom and genetic diversity of cassava brown streak viruses infecting cassava in East Africa

The genetic and symptom diversity of six virus isolates causing cassava brown streak disease (CBSD) in the endemic (Kenya, Mozambique, and Tanzania) and the recently affected epidemic areas (Uganda) of eastern Africa was studied. Five cassava varieties; Albert, Colombian, Ebwanateraka, TMS60444 (all susceptible) and Kiroba (tolerant) were graft inoculated with each isolate. Based on a number of parameters including the severity of leaf and root symptoms, and the extent of virus transmission by grafting, the viruses were classified as either severe or relatively mild. These results were further confirmed by the mechanical inoculation of 13 herbaceous hosts in which the virulent isolates caused plant death in Nicotiana clevelandii and N. benthamiana whereas the milder isolates did not. Phylogenetic analysis of complete coat protein gene sequences of these isolates together with sequences obtained from 14 other field-collected samples from Kenya and Zanzibar, and reference sequences grouped them into two distinct clusters, representing the two species of cassava brown streak viruses. Put together, these results did not suggest the association of a hypervirulent form of the virus with the current CBSD epidemic in Uganda. Identification of the severe and milder isolates, however, has further implications for disease management and quarantine requirements

Cognitive conflicts in major depression : Between desired change and personal coherence

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposesThe notion of intrapsychic conflict has been present in psychopathology for more than a century within different theoretical orientations. However, internal conflicts have not received enough empirical attention, nor has their importance in depression been fully elaborated. This study is based on the notion of cognitive conflict, understood as implicative dilemma (ID), and on a new way of identifying these conflicts by means of the Repertory Grid Technique. Our aim was to explore the relevance of cognitive conflicts among depressive patientsPeer reviewedFinal Published versio

Hypothesis exploration with visualization of variance.

BackgroundThe Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes-to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics-wide-scale, systematic study of phenotypes-to neuropsychiatry research.ResultsThis paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles-patterns of values across phenotypes-that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.ConclusionsThe ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports 'natural selection' on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics

Setting a research agenda for progressive multiple sclerosis: The International Collaborative on Progressive MS

Despite significant progress in the development of therapies for relapsing MS, progressive MS remains comparatively disappointing. Our objective, in this paper, is to review the current challenges in developing therapies for progressive MS and identify key priority areas for research. A collaborative was convened by volunteer and staff leaders from several MS societies with the mission to expedite the development of effective disease-modifying and symptom management therapies for progressive forms of multiple sclerosis. Through a series of scientific and strategic planning meetings, the collaborative identified and developed new perspectives on five key priority areas for research: experimental models, identification and validation of targets and repurposing opportunities, proof-of-concept clinical trial strategies, clinical outcome measures, and symptom management and rehabilitation. Our conclusions, tackling the impediments in developing therapies for progressive MS will require an integrated, multi-disciplinary approach to enable effective translation of research into therapies for progressive MS. Engagement of the MS research community through an international effort is needed to address and fund these research priorities with the ultimate goal of expediting the development of disease-modifying and symptom-relief treatments for progressive MS

Diagnostic, demographic, memory quality, and cognitive variables associated with acute stress disorder in children and adolescents

To date, no studies have investigated factors associated with acute stress disorder (ASD) in children and adolescents. Relationships between ASD and a number of demographic, trauma, cognitive, and trauma memory variables were therefore investigated in a sample (N=93) of children and adolescents involved in assaults and motor vehicle accidents. Several cognitive variables and the quality of trauma memories, but not demographic or trauma variables, were correlated with ASD and also mediated the relationship between peritraumatic threat and ASD. Finally, nosological analyses comparing ASD with indexes of posttraumatic stress disorder in the month posttrauma revealed little support for the dissociation mandate that uniquely characterizes ASD. The results are discussed with respect to assessment and treatment for the acute traumatic stress responses of children and young people

What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media

Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. Depression detection is key for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability. Although a few depression detection studies attempt to explain the decision based on the importance score or attention weights, these explanations misalign with the clinical depression diagnosis criterion that is based on depressive symptoms. To fill this gap, we follow the computational design science paradigm to develop a novel Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and interprets depressive symptoms as well as how long they last. Extensive empirical analyses using a large-scale dataset show that MSTPNet outperforms state-of-the-art depression detection methods with an F1-score of 0.851. This result also reveals new symptoms that are unnoted in the survey approach, such as sharing admiration for a different life. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media. In practice, our proposed method can be implemented in social media platforms to provide personalized online resources for detected depressed patients.Comment: 56 pages, 10 figures, 21 table