Making sense of violence risk predictions using clinical notes

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to their full potential. Previous studies have attempted to assess violence risk in psychiatric patients using such notes, with acceptable performance. However, they do not explain why classification works and how it can be improved. We explore two methods to better understand the quality of a classifier in the context of clinical note analysis: random forests using topic models, and choice of evaluation metric. These methods allow us to understand both our data and our methodology more profoundly, setting up the groundwork for improved models that build upon this understanding. This is particularly important when it comes to the generalizability of evaluated classifiers to new data, a trustworthiness problem that is of great interest due to the increased availability of new data in electronic format

Representations of Menger (2,n)(2,n)-semigroups by multiplace functions

Investigation of partial multiplace functions by algebraic methods plays an important role in modern mathematics were we consider various operations on sets of functions, which are naturally defined. The basic operation for $n$-place functions is an $(n+1)$-ary superposition $[ ]$, but there are some other naturally defined operations, which are also worth of consideration. In this paper we consider binary Mann's compositions \op{1},...,\op{n} for partial $n$-place functions, which have many important applications for the study of binary and $n$-ary operations. We present methods of representations of such algebras by $n$-place functions and find an abstract characterization of the set of $n$-place functions closed with respect to the set-theoretic inclusion

Automated business goal extraction from e-mail repositories to bootstrap business understanding

The Cross-Industry Standard Process for Data Mining (CRISP-DM), despite being the most popular data mining process for more than two decades, is known to leave those organizations lacking operational data mining experience puzzled and unable to start their data mining projects. This is especially apparent in the first phase of Business Understanding, at the conclusion of which, the data mining goals of the project at hand should be specified, which arguably requires at least a conceptual understanding of the knowledge discovery process. We propose to bridge this knowledge gap from a Data Science perspective by applying Natural Language Processing techniques (NLP) to the organizations' e-mail exchange repositories to extract explicitly stated business goals from the conversations, thus bootstrapping the Business Understanding phase of CRISP-DM. Our NLP-Automated Method for Business Understanding (NAMBU) generates a list of business goals which can subsequently be used for further specification of data mining goals. The validation of the results on the basis of comparison to the results of manual business goal extraction from the Enron corpus demonstrates the usefulness of our NAMBU method when applied to large datasets.Algorithms and the Foundations of Software technolog

Redox signals at the ER-mitochondria interface control melanoma progression.

Reactive oxygen species (ROS) are emerging as important regulators of cancer growth and metastatic spread. However, how cells integrate redox signals to affect cancer progression is not fully understood. Mitochondria are cellular redox hubs, which are highly regulated by interactions with neighboring organelles. Here, we investigated how ROS at the endoplasmic reticulum (ER)-mitochondria interface are generated and translated to affect melanoma outcome. We show that TMX1 and TMX3 oxidoreductases, which promote ER-mitochondria communication, are upregulated in melanoma cells and patient samples. TMX knockdown altered mitochondrial organization, enhanced bioenergetics, and elevated mitochondrial- and NOX4-derived ROS. The TMX-knockdown-induced oxidative stress suppressed melanoma proliferation, migration, and xenograft tumor growth by inhibiting NFAT1. Furthermore, we identified NFAT1-positive and NFAT1-negative melanoma subgroups, wherein NFAT1 expression correlates with melanoma stage and metastatic potential. Integrative bioinformatics revealed that genes coding for mitochondrial- and redox-related proteins are under NFAT1 control and indicated that TMX1, TMX3, and NFAT1 are associated with poor disease outcome. Our study unravels a novel redox-controlled ER-mitochondria-NFAT1 signaling loop that regulates melanoma pathobiology and provides biomarkers indicative of aggressive disease

The Fermat-Torricelli problem in normed planes and spaces

We investigate the Fermat-Torricelli problem in d-dimensional real normed spaces or Minkowski spaces, mainly for d=2. Our approach is to study the Fermat-Torricelli locus in a geometric way. We present many new results, as well as give an exposition of known results that are scattered in various sources, with proofs for some of them. Together, these results can be considered to be a minitheory of the Fermat-Torricelli problem in Minkowski spaces and especially in Minkowski planes. This demonstrates that substantial results about locational problems valid for all norms can be found using a geometric approach

Detailing radio frequency heating induced by coronary stents: a 7.0 tesla magnetic resonance study

The sensitivity gain of ultrahigh field Magnetic Resonance (UHF-MR) holds the promise to enhance spatial and temporal resolution. Such improvements could be beneficial for cardiovascular MR. However, intracoronary stents used for treatment of coronary artery disease are currently considered to be contra-indications for UHF-MR. The antenna effect induced by a stent together with RF wavelength shortening could increase local radiofrequency (RF) power deposition at 7.0 T and bears the potential to induce local heating, which might cause tissue damage. Realizing these constraints, this work examines RF heating effects of stents using electro-magnetic field (EMF) simulations and phantoms with properties that mimic myocardium. For this purpose, RF power deposition that exceeds the clinical limits was induced by a dedicated birdcage coil. Fiber optic probes and MR thermometry were applied for temperature monitoring using agarose phantoms containing copper tubes or coronary stents. The results demonstrate an agreement between RF heating induced temperature changes derived from EMF simulations versus MR thermometry. The birdcage coil tailored for RF heating was capable of irradiating power exceeding the specific-absorption rate (SAR) limits defined by the IEC guidelines by a factor of three. This setup afforded RF induced temperature changes up to +27 K in a reference phantom. The maximum extra temperature increase, induced by a copper tube or a coronary stent was less than 3 K. The coronary stents examined showed an RF heating behavior similar to a copper tube. Our results suggest that, if IEC guidelines for local/global SAR are followed, the extra RF heating induced in myocardial tissue by stents may not be significant versus the baseline heating induced by the energy deposited by a tailored cardiac transmit RF coil at 7.0 T, and may be smaller if not insignificant than the extra RF heating observed under the circumstances used in this study

A multi-vendor, multi-center study on reproducibility and comparability of fast strain-encoded cardiovascular magnetic resonance imaging

Myocardial strain is a convenient parameter to quantify left ventricular (LV) function. Fast strain-encoding (fSENC) enables the acquisition of cardiovascular magnetic resonance images for strain-measurement within a few heartbeats during free-breathing. It is necessary to analyze inter-vendor agreement of techniques to determine strain, such as fSENC, in order to compare existing studies and plan multi-center studies. Therefore, the aim of this study was to investigate inter-vendor agreement and test-retest reproducibility of fSENC for three major MRI-vendors. fSENC-images were acquired three times in the same group of 15 healthy volunteers using 3 Tesla scanners from three different vendors: at the German Heart Institute Berlin, the Charité University Medicine Berlin-Campus Buch and the Theresien-Hospital Mannheim. Volunteers were scanned using the same imaging protocol composed of two fSENC-acquisitions, a 15-min break and another two fSENC-acquisitions. LV global longitudinal and circumferential strain (GLS, GCS) were analyzed by a trained observer (Myostrain 5.0, Myocardial Solutions) and for nine volunteers repeatedly by another observer. Inter-vendor agreement was determined using Bland-Altman analysis. Test-retest reproducibility and intra- and inter-observer reproducibility were analyzed using intraclass correlation coefficient (ICC) and coefficients of variation (CoV). Inter-vendor agreement between all three sites was good for GLS and GCS, with biases of 0.01-1.88%. Test-retest reproducibility of scans before and after the break was high, shown by ICC- and CoV values of 0.63-0.97 and 3-9% for GLS and 0.69-0.82 and 4-7% for GCS, respectively. Intra- and inter-observer reproducibility were excellent for both parameters (ICC of 0.77-0.99, CoV of 2-5%). This trial demonstrates good inter-vendor agreement and test-retest reproducibility of GLS and GCS measurements, acquired at three different scanners from three different vendors using fSENC. The results indicate that it is necessary to account for a possible bias (< 2%) when comparing strain measurements of different scanners. Technical differences between scanners, which impact inter-vendor agreement, should be further analyzed and minimized.DRKS Registration Number: 00013253. Universal Trial Number (UTN): U1111-1207-5874