Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice.

Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased availability for patients who urgently need it. Common risk assessment tool, like the ASA score, lack objectivity and do account only for some influencing parameters. The aim of our study was (1) to develop a reliable machine learning model predicting ICU admission risk after elective surgery, and (2) to implement it in a clinical workflow. We used electronic medical records from more than 61,000 patients for modelling. A random forest model outperformed other methods with an area under the curve of 0.91 in the retrospective test set. In the prospective implementation, the model achieved a sensitivity of 73.3% and a specificity of 80.8%. Further research is essential to determine physicians' attitudes to machine learning models and assess the long term improvement of ICU management.

Enhanced information retrieval from narrative German-language clinical text documents using automated document classification.

The amount of narrative clinical text documents stored in Electronic Patient Records (EPR) of Hospital Information Systems is increasing. Physicians spend a lot of time finding relevant patient-related information for medical decision making in these clinical text documents. Thus, efficient and topical retrieval of relevant patient-related information is an important task in an EPR system. This paper describes the prototype of a medical information retrieval system (MIRS) for clinical text documents. The open-source information retrieval framework Apache Lucene has been used to implement the prototype of the MIRS. Additionally, a multi-label classification system based on the open-source data mining framework WEKA generates metadata from the clinical text document set. The metadata is used for influencing the rank order of documents retrieved by physicians. Combining information retrieval and automated document classification offers an enhanced approach to let physicians and in the near future patients define their information needs for information stored in an EPR. The system has been designed as a J2EE Web-application. First findings are based on a sample of 18,000 unstructured, clinical text documents written in German.

Photodynamic activation of ion transport through lipid membranes and its correlation with an increased dielectric constant of the membrane.

Illumination of biological membranes with visible light in the presence of membrane-active sensitizers (e.g. rose bengal) is known to inactivate transport proteins such as ion channels and ion pumps. In some cases, however, illumination gives rise to an activation of transport. This is shown here for ion channels formed by alamethicin in lipid membranes, and for porin channels, which were isolated from the outer membrane of E. coli (OmpC) and from the outer membrane of mitochondria (VDAC) and were reconstituted in lipid membranes. An activation (in the form of an increased conductance) was also observed in the presence of the cation carriers valinomycin and nonactin. The activation phenomena were only present, if the membranes were made from lipids containing unsaturated double bonds. Activation was reduced in the presence of the antioxidant vitamin E. We suggest that the activation of the different transport systems has a common physical basis, namely an increase of the dielectric constant, epsilon(m), of the membrane interior by the presence of polar oxidation products of photodynamically induced lipid peroxidation. Experimental evidence for an enhanced dielectric constant was obtained from the finding of a light-induced increase of the membrane capacitance in the presence of rose bengal.