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Background: About 2% of the German population are affected by psoriasis. A growing number of cost-intensive systemic treatments are available. Surveys have shown high proportions of patients with moderate to severe psoriasis are not adequately treated despite a high disease burden. Digital therapy recommendation systems (TRS) may help implement guideline-based treatment. However, little is known about the acceptance of such clinical decision support systems (CDSSs). Methods: To assess the acceptance of the TRS, a prototypical demonstrator was developed. In three scenarios, potential test patients with psoriasis but different sociodemographic and clinical characteristics, previous treatments, desire to have children, and multiple comorbidities were designed. The demonstrator and test patients were presented to a random sample of 76 dermatologists attending a national dermatology conference in a cross-sectional face-to-face survey with case vignettes. 76.8% of dermatologists rated the TRS as acceptable based on the system usability scale (SUS). Results: Acceptance of the TRS was highest for patients eligible for systemic therapy (82%). 50% of participants accepted the system for patients with additional comorbidities and 43% for patients with special subtypes of psoriasis. Dermatologists in the outpatient sector or with many patients per week were less willing to use the TRS for patients with special psoriasis-subtypes. Participants whose SUS was 10 points above average were 27% more likely to use TRS for special psoriasis-subtypes. The main barrier in using the TRS was time demand (47.4%). Participants who perceived time as an obstacle were 22.3% less willing to use TRS with systemic therapy patients. 27.6% of physicians stated that they did not understand exactly how the recommendation was generated by the TRS, with no effect on the preparedness to use the system. Conclusion: The considerably high acceptance and the preparedness to use the psoriasis CDSS suggests that a TRS appears to be implementable in routine healthcare and may improve clinical care. Main barrier is the additional time demand posed on dermatologists in a busy clinical setting. Therefore, it will be a major challenge to identify a limited set of variables that still allows a valid recommendation with precise prediction of the patient-individual benefits and harms.

Data-based clinical decision support systems (CDSSs) can provide personalized support in medical applications. Such systems are expected to play an increasingly important role in the future of healthcare. Within this work, we demonstrate an exemplary CDSS which provides individualized pharmaceutical drug recommendations to physicians and patients. The core of the proposed system is a neighborhood-based collaborative filter (CF) that yields data-based recommendations. CFs are capable of integrating data at different scale levels and a multivariate outcome measure. This publication provides a detailed literature review, a holistic comparison of various implementations of CF algorithms, and a prototypical graphical user interface (GUI). We show that similarity measures, which automatically adapt to attribute weights and data distribution perform best. The illustrated user-friendly prototype is intended to graphically facilitate explainable recommendations and provide additional evidence-based information tailored to a target patient. The proposed solution or elements of it, respectively, may serve as a template for future CDSSs that support physicians to identify the most appropriate therapy and enable a shared decision-making process between physicians and patients.

This work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learning approach can outperform the Relief-based algorithms. It is, however, inferior regarding explainability of the generated recommendations.

Intracerebral hemorrhage (ICH) is an important stroke subtype, but preclinical research is limited by a lack of translational animal models. Large animal models are useful to comparatively investigate key pathophysiological parameters in human ICH. To (i) establish an acute model of moderate ICH in adult sheep and (ii) an advanced neuroimage processing pipeline for automatic brain tissue and hemorrhagic lesion determination; 14 adult sheep were assigned for stereotactically induced ICH into cerebral white matter under physiological monitoring. Six hours after ICH neuroimaging using 1.5T MRI including structural as well as perfusion and diffusion, weighted imaging was performed before scarification and subsequent neuropathological investigation including immunohistological staining. Controlled, stereotactic application of autologous blood caused a space-occupying intracerebral hematoma of moderate severity, predominantly affecting white matter at 5 h post-injection. Neuroimage post-processing including lesion probability maps enabled automatic quantification of structural alterations including perilesional diffusion and perfusion restrictions. Neuropathological and immunohistological investigation confirmed perilesional vacuolation, axonal damage, and perivascular blood as seen after human ICH. The model and imaging platform reflects key aspects of human ICH and enables future translational research on hematoma expansion/evacuation, white matter changes, hematoma evacuation, and other aspects.

Online review sites and opinion forums contain a wealth of information regarding user preferences and experiences over multiple product domains. This information can be leveraged to obtain valuable insights using data mining approaches such as sentiment analysis. In this work we examine online user reviews within the pharmaceutical field. Online user reviews in this domain contain information related to multiple aspects such as effectiveness of drugs and side effects, which make automatic analysis very interesting but also challenging. However, analyzing sentiments concerning the various aspects of drug reviews can provide valuable insights, help with decision making and improve monitoring public health by revealing collective experience. In this preliminary work we perform multiple tasks over drug reviews with data obtained by crawling online pharmaceutical review sites. We first perform sentiment analysis to predict the sentiments concerning overall satisfaction, side effects and effectiveness of user reviews on specific drugs. To meet the challenge of lacking annotated data we further investigate the transferability of trained classification models among domains, i.e. conditions, and data sources. In this work we show that transfer learning approaches can be used to exploit similarities across domains and is a promising approach for cross-domain sentiment analysis.