ÜBER DIE BEI DER REINEN BIEGUNG DER GERADEN UND DER EBENEN GEKRÜMMTEN STÄBE SICH ZEIGENDEN ÄHNLICHKEITEN

Objective The aim of this repeated cross-sectional study was to compare patients from a psychiatric intensive care unit (PICU) over 30years regarding their diagnostic and therapeutic characteristics. Method Three samples including 100 consecutive inpatients each from the Viennese PICU were submitted to a chart review: sample no. 1 from the years 1985/86, no. 2 from 1995/96 and no. 3 from 2007/08. Results Changes in referral modes were associated with a decrease of patients with substance induced disorders and an increase of patients with affective disorders over time. The rate of admissions after accidents and suicides was stable. The use of cranial MRI increased, while intravenous psychopharmacotherapy and parenteral nutrition decreased. Involuntary admission occurred in 43% and in 37% of patients physical restraints were necessary. We saw a shift from tricyclic antidepressants to SSRIs and SNRIs from sample 1 to 3. Likewise, we observed the emergence of atypical antipsychotics and a reduction of use of typical neuroleptics mainly from sample 2 to 3. The percentage of patients receiving benzodiazepines increased over time, while the mean dosage of benzodiazepines decreased. 7% of patients received electroconvulsive therapy. Conclusions The changes over time in our samples reflect the medical progress made during the last decades. Future studies should focus on evaluation of efficacy of psychiatric intensive care using standardized measurements.(VLID)490129

Generalisability of deep learning-based early warning in the intensive care unit: a retrospective empirical evaluation

Deep learning (DL) can aid doctors in detecting worsening patient states early, affording them time to react and prevent bad outcomes. While DL-based early warning models usually work well in the hospitals they were trained for, they tend to be less reliable when applied at new hospitals. This makes it difficult to deploy them at scale. Using carefully harmonised intensive care data from four data sources across Europe and the US (totalling 334,812 stays), we systematically assessed the reliability of DL models for three common adverse events: death, acute kidney injury (AKI), and sepsis. We tested whether using more than one data source and/or explicitly optimising for generalisability during training improves model performance at new hospitals. We found that models achieved high AUROC for mortality (0.838-0.869), AKI (0.823-0.866), and sepsis (0.749-0.824) at the training hospital. As expected, performance dropped at new hospitals, sometimes by as much as -0.200. Using more than one data source for training mitigated the performance drop, with multi-source models performing roughly on par with the best single-source model. This suggests that as data from more hospitals become available for training, model robustness is likely to increase, lower-bounding robustness with the performance of the most applicable data source in the training data. Dedicated methods promoting generalisability did not noticeably improve performance in our experiments

Know an Emotion by the Company It Keeps: Word Embeddings from Reddit/Coronavirus

Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, “You shall know a word by the company it keeps,” highlighting the importance of context in NLP. Meanwhile, “Context is everything in Emotion Research.” Therefore, we aimed to train a model (W2V) for generating word associations (also known as embeddings) using a popular Coronavirus Reddit forum, validate them using public evidence and apply them to the discovery of context for specific emotions previously reported as related to psychological resilience. We used Pushshiftr, quanteda, broom, wordVectors, and superheat R packages. We collected all 374,421 posts submitted by 104,351 users to Reddit/Coronavirus forum between January 2020 and July 2021. W2V identified 64 terms representing the context for seven positive emotions (gratitude, compassion, love, relief, hope, calm, and admiration) and 52 terms for seven negative emotions (anger, loneliness, boredom, fear, anxiety, confusion, sadness) all from valid experienced situations. We clustered them visually, highlighting contextual similarity. Although trained on a “small” dataset, W2V can be used for context discovery to expand on concepts such as psychological resilience

Origins of the midlatitude Pacific decadal variability

Analysis of multiple climate simulations shows much of the midlatitude Pacific decadal variability to be composed of two simultaneously occurring elements: One is a stochastically driven, passive ocean response to the atmosphere while the other is oscillatory and represents a coupled mode of the ocean‐atmosphere system. ENSO processes are not required to explain the origins of the decadal variability. The stochastic variability is driven by random variations in wind stress and heat flux associated with internal atmospheric variability but amplified by a factor of 2 by interactions with the ocean. We also found a coupled mode of the ocean‐atmosphere system, characterized by a significant power spectral peak near 1 cycle/20 years in the region of the midlatitude North Pacific and Kuroshio Extension. Ocean dynamics appear to play a critical role in this coupled air/sea mode

Evidence from Focal Brain Lesions

Neuroimaging and neuropsychological experiments suggest that modality- preferential cortices, including motor- and somatosensory areas, contribute to the semantic processing of action related concrete words. Still, a possible role of sensorimotor areas in processing abstract meaning remains under debate. Recent fMRI studies indicate an involvement of the left sensorimotor cortex in the processing of abstract-emotional words (e.g., “love”) which resembles activation patterns seen for action words. But are the activated areas indeed necessary for processing action-related and abstract words? The current study now investigates word processing in two patients suffering from focal brain lesion in the left frontocentral motor system. A speeded Lexical Decision Task on meticulously matched word groups showed that the recognition of nouns from different semantic categories – related to food, animals, tools, and abstract-emotional concepts – was differentially affected. Whereas patient HS with a lesion in dorsolateral central sensorimotor systems next to the hand area showed a category-specific deficit in recognizing tool words, patient CA suffering from lesion centered in the left supplementary motor area was primarily impaired in abstract-emotional word processing. These results point to a causal role of the motor cortex in the semantic processing of both action-related object concepts and abstract-emotional concepts and therefore suggest that the motor areas previously found active in action-related and abstract word processing can serve a meaning-specific necessary role in word recognition. The category-specific nature of the observed dissociations is difficult to reconcile with the idea that sensorimotor systems are somehow peripheral or ‘epiphenomenal’ to meaning and concept processing. Rather, our results are consistent with the claim that cognition is grounded in action and perception and based on distributed action perception circuits reaching into modality-preferential cortex

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods

Multimodal Fusion Strategies for Outcome Prediction in Stroke

Data driven methods are increasingly being adopted in the medical domain for clinical predictive modeling. Prediction of stroke outcome using machine learning could provide a decision support system for physicians to assist them in patient-oriented diagnosis and treatment. While patient-specific clinical parameters play an important role in outcome prediction, a multimodal fusion approach that integrates neuroimaging with clinical data has the potential to improve accuracy. This paper addresses two research questions: (a) does multimodal fusion aid in the prediction of stroke outcome, and (b) what fusion strategy is more suitable for the task at hand. The baselines for our experimental work are two unimodal neural architectures: a 3D Convolutional Neural Network for processing neuroimaging data, and a Multilayer Perceptron for processing clinical data. Using these unimodal architectures as building blocks we propose two feature-level multimodal fusion strategies: 1) extracted features , where the unimodal architectures are trained separately and then fused, and 2) end-to-end, where the unimodal architectures are trained together. We show that integration of neuroimaging information with clinical metadata can potentially improve stroke outcome prediction. Additionally, experimental results indicate that the end-to-end fusion approach proves to be more robust

The impact of COVID-19 on home, social, and productivity integration of people with chronic traumatic brain injury or stroke living in the community

Compare community integration of people with stroke or traumatic brain injury (TBI) living in the community before and during the coronavirus severe acute respiratory syndrome coronavirus 2 disease (COVID-19) when stratifying by injury: participants with stroke (G1) and with TBI (G2); by functional independence in activities of daily living: independent (G3) and dependent (G4); by age: participants younger than 54 (G5) and older than 54 (G6); and by gender: female (G7) and male (G8) participants. Prospective observational cohort study In-person follow-up visits (before COVID-19 outbreak) to a rehabilitation hospital in Spain and on-line during COVID-19. Community dwelling adults (≥18 years) with chronic stroke or TBI. Community integration questionnaire (CIQ) the total-CIQ as well as the subscale domains (ie, home-CIQ, social-CIQ, productivity CIQ) were compared before and during COVID-19 using the Wilcoxon ranked test or paired t test when appropriate reporting Cohen effect sizes (d). The functional independence measure was used to assess functional independence in activities of daily living. Two hundred four participants, 51.4% with stroke and 48.6% with TBI assessed on-line between June 2020 and April 2021 were compared to their own in-person assessments performed before COVID-19. When analyzing total-CIQ, G1 (d = −0.231), G2 (d = −0.240), G3 (d = −0.285), G5 (d = −0.276), G6 (d = −0.199), G7 (d = −0.245), and G8 (d = −0.210) significantly decreased their scores during COVID-19, meanwhile G4 was the only group with no significant differences before and during COVID-19. In productivity-CIQ, G1 (d = −0.197), G4 (d = −0.215), G6 (d = −0.300), and G8 (d = −0.210) significantly increased their scores, meanwhile no significant differences were observed in G2, G3, G5, and G7. In social-CIQ, all groups significantly decreased their scores: G1 (d = −0.348), G2 (d = −0.372), G3 (d = −0.437), G4 (d = −0.253), G5 (d = −0.394), G6 (d = −0.319), G7 (d = −0.355), and G8 (d = −0.365). In home-CIQ only G6 (d = −0.229) significantly decreased, no significant differences were observed in any of the other groups. The largest effect sizes were observed in total-CIQ for G3, in productivity-CIQ for G6, in social-CIQ for G3 and in home-CIQ for G6 (medium effect sizes). Stratifying participants by injury, functionality, age or gender allowed identifying specific CIQ subtotals where remote support may be provided addressing them

A precision medicine framework for personalized simulation of hemodynamics in cerebrovascular disease

Background: Cerebrovascular disease, in particular stroke, is a major public health challenge. An important biomarker is cerebral hemodynamics. To measure and quantify cerebral hemodynamics, however, only invasive, potentially harmful or time-to-treatment prolonging methods are available. Results: We present a simulation-based approach which allows calculation of cerebral hemodynamics based on the patient-individual vessel configuration derived from structural vessel imaging. For this, we implemented a framework allowing segmentation and annotation of brain vessels from structural imaging followed by 0-dimensional lumped simulation modeling of cerebral hemodynamics. For annotation, a 3D-graphical user interface was implemented. For 0D-simulation, we used a modified nodal analysis, which was adapted for easy implementation by code. The simulation enables identification of areas vulnerable to stroke and simulation of changes due to different systemic blood pressures. Moreover, sensitivity analysis was implemented allowing the live simulation of changes to simulate procedures and disease progression. Beyond presentation of the framework, we demonstrated in an exploratory analysis in 67 patients that the simulation has a high specificity and low-to-moderate sensitivity to detect perfusion changes in classic perfusion imaging. Conclusions: The presented precision medicine approach using novel biomarkers has the potential to make the application of harmful and complex perfusion methods obsolete

On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking

Average Hausdorff distance is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, it is used to compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average Hausdorff distance making it less suitable for applications in segmentation performance assessment. To mitigate this error, we present a modified calculation of this performance measure that we have coined "balanced average Hausdorff distance". To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using both performance measures. We calculated the Kendall rank correlation coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by balanced average Hausdorff distance had a significantly higher median correlation (1.00) than those by average Hausdorff distance (0.89). In 200 total rankings, the former misranked 52 whilst the latter misranked 179 segmentations. Balanced average Hausdorff distance is more suitable for rankings and quality assessment of segmentations than average Hausdorff distance