Automated Mortality Prediction in Critically-ill Patients with Thrombosis using Machine Learning

Venous thromboembolism (VTE) is the third most common cardiovascular condition. Some high risk patients diagnosed with VTE need immediate treatment and monitoring in intensive care units (ICU) as the mortality rate is high. Most of the published predictive models for ICU mortality give information on in-hospital mortality using data recorded in the first day of ICU admission. The purpose of the current study is to predict in-hospital and after-discharge mortality in patients with VTE admitted to ICU using a machine learning (ML) framework. We studied 2,468 patients from the Medical Information Mart for Intensive Care (MIMIC-III) database, admitted to ICU with a diagnosis of VTE. We formed ML classification tasks for early and late mortality prediction. In total, 1,471 features were extracted for each patient, grouped in seven categories each representing a different type of medical assessment. We used an automated ML platform, JADBIO, as well as a class balancing combined with a Random Forest classifier, in order to evaluate the importance of class imbalance. Both methods showed significant ability in prediction of early mortality (AUC=0.92). Nevertheless, the task of predicting late mortality was less efficient (AUC=0.82). To the best of our knowledge, this is the first study in which ML is used to predict short-term and long-term mortality for ICU patients with VTE based on a multitude of clinical features collected over time

Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction

Background: Acute myocardial damage is common in severe COVID-19. Post-mortem studies have implicated microvascular thrombosis, with cardiovascular magnetic resonance (CMR) demonstrating a high prevalence of myocardial infarction and myocarditis-like scar. The microcirculatory sequelae are incompletely characterized. Perfusion CMR can quantify the stress myocardial blood flow (MBF) and identify its association with infarction and myocarditis. Objectives: To determine the impact of the severe hospitalized COVID-19 on global and regional myocardial perfusion in recovered patients. Methods: A case-control study of previously hospitalized, troponin-positive COVID-19 patients was undertaken. The results were compared with a propensity-matched, pre-COVID chest pain cohort (referred for clinical CMR; angiography subsequently demonstrating unobstructed coronary arteries) and 27 healthy volunteers (HV). The analysis used visual assessment for the regional perfusion defects and AI-based segmentation to derive the global and regional stress and rest MBF. Results: Ninety recovered post-COVID patients {median age 64 [interquartile range (IQR) 54-71] years, 83% male, 44% requiring the intensive care unit (ICU)} underwent adenosine-stress perfusion CMR at a median of 61 (IQR 29-146) days post-discharge. The mean left ventricular ejection fraction (LVEF) was 67 ± 10%; 10 (11%) with impaired LVEF. Fifty patients (56%) had late gadolinium enhancement (LGE); 15 (17%) had infarct-pattern, 31 (34%) had non-ischemic, and 4 (4.4%) had mixed pattern LGE. Thirty-two patients (36%) had adenosine-induced regional perfusion defects, 26 out of 32 with at least one segment without prior infarction. The global stress MBF in post-COVID patients was similar to the age-, sex- and co-morbidities of the matched controls (2.53 ± 0.77 vs. 2.52 ± 0.79 ml/g/min, p = 0.10), though lower than HV (3.00 ± 0.76 ml/g/min, p< 0.01). Conclusions: After severe hospitalized COVID-19 infection, patients who attended clinical ischemia testing had little evidence of significant microvascular disease at 2 months post-discharge. The high prevalence of regional inducible ischemia and/or infarction (nearly 40%) may suggest that occult coronary disease is an important putative mechanism for troponin elevation in this cohort. This should be considered hypothesis-generating for future studies which combine ischemia and anatomical assessment

OntoCAT -- simple ontology search and integration in Java, R and REST/JavaScript

<p>Abstract</p> <p>Background</p> <p>Ontologies have become an essential asset in the bioinformatics toolbox and a number of ontology access resources are now available, for example, the EBI Ontology Lookup Service (OLS) and the NCBO BioPortal. However, these resources differ substantially in mode, ease of access, and ontology content. This makes it relatively difficult to access each ontology source separately, map their contents to research data, and much of this effort is being replicated across different research groups.</p> <p>Results</p> <p>OntoCAT provides a seamless programming interface to query heterogeneous ontology resources including OLS and BioPortal, as well as user-specified local OWL and OBO files. Each resource is wrapped behind easy to learn Java, Bioconductor/R and REST web service commands enabling reuse and integration of ontology software efforts despite variation in technologies. It is also available as a stand-alone MOLGENIS database and a Google App Engine application.</p> <p>Conclusions</p> <p>OntoCAT provides a robust, configurable solution for accessing ontology terms specified locally and from remote services, is available as a stand-alone tool and has been tested thoroughly in the ArrayExpress, MOLGENIS, EFO and Gen2Phen phenotype use cases.</p> <p>Availability</p> <p><url>http://www.ontocat.org</url></p

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function.Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e.,human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups:normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval.Finally,a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Ophthalmological assessment of OCT and electrophysiological changes in migraine patients

Background: A cross-sectional study to investigate the morphological and functional changes of the visual pathway taking place in patients with migraine. Methods: Fifteen patients (14 female, 1 male) diagnosed with migraine with aura and 23 patients (21 female, 2 male) diagnosed with migraine without aura were compared with 20 healthy volunteers (18 female, 2 male). All the participants underwent optical coherence tomography scan, electroretinogram (ERG), visual evoked potentials, and multifocal electroretinogram (mf-ERG) recording. Results: Assessing ERG recordings, no significant differences in mean N1-P1 amplitudes were measured among the groups. The mean visual evoked potentials N80-P100 amplitudes were not significantly different among the three groups (one way analysis of variance: P 0.075, F 2.718). No significant difference was found in P100 latency times among groups. The mean retinal response density of mf-ERG in ring 1 was higher in healthy individuals compared with migraineurs, with statistical significance (Kruskal-Wallis analysis of variance and Dunn multiple comparisons test; P &lt; 0.001, mean rank difference -24.857 and P &lt; 0.001, mean rank difference -20.9, for migraine with aura-control and migraine without aura-control comparisons, respectively). In migraine with aura subjects, retinal nerve fiber layer thickness in superior and inferior quadrants was significantly decreased compared with healthy individuals, whereas in migraine without aura group, only the superior quadrant was significantly thinner compared with the control group. Conclusions: Retinal response density in mfERG of all migraineurs was significantly lessened compared with healthy individuals. There was no significant difference in visual evoked potentials N80-P100 amplitudes or P100 latencies among the groups. Moreover, retinal nerve fiber layer thinning observed in patients with migraine compared with control subjects, appeared statistically significant in some quadrants. The authors may be able to defend the retinal blood flow decrease theory in migraine. The results also indicate that several levels of the visual pathway seem to be affected in migraineurs. © 2016 by the American Clinical Neurophysiology Society

Influence of Polyamines and Related Macromolecules on Silicic Acid Polycondensation: Relevance to “Soluble Silicon Pools”?

The influence of a number of N-containing macromolecules on the polycondensation of silicic acid to form amorphous silica is studied by the combined use of ²⁹Si NMR spectroscopy and the silicomolybdate test. Polymeric additives include poly(allylamine hydrochloride) (PAH), the poly(aminoamide) dendrimer of generation 1 (PAMAM-1), poly(ethyleneimine) (PEI), and poly(vinylpyrrolidone) (PVP). These studies were performed under biologically relevant conditions (pH 5.4 and 7.0) using aqueous solutions of isotope-labeled sodium [²⁹Si]metasilicate as the precursor compound. It was found at pH 5.4 that all additives accelerate silicic acid polycondensation, except for PVP, which exerts a minor silicic acid stabilizing effect. At pH 7.0, polycondensation is much faster in the presence of PAMAM-1, PEI, and PAH. However, PVP significantly stabilizes mono- and disilicic acid. Silica precipitates were also studied by ²⁹Si NMR spectroscopy. The effect observed for PVP is striking and indicates that the silicic acid polycondensation is slowed, although the oligomers are immobilized by the PVP polymer. In contrast, the charged PAH attracts the oligomeric species and enhances the silicic acid polycondensation