Leveraging Electronic Health Records to improve Patient Appointment Scheduling: A design-oriented Approach

As demand for healthcare services continues to increase, hospitals are under constant economic pressure to better manage patient appointments. It is common practice in clinical routine to schedule appointments based on average service times, resulting in overtime and waiting times for clinicians and patients. To address this problem, we propose a data-driven decision support system for scheduling patient appointments that accounts for variable service times. We take advantage of the growing amount of patient- and treatment-specific data collected in hospitals. Using a simulation study, we evaluate the decision support system on the practical example of a Gastroenterology facility. Our results demonstrate improved appointment scheduling efficiency compared to the approach currently in use

Convolutional Neural Networks for Survey Response Classification

Artificial Intelligence reveals great potential for enterprises e.g., intelligent services. However, small and medium enterprises struggle with Artificial Intelligence due to limited resources. Especially tasks such as survey response classification are yet not investigated. We address this research gap by means of a data science study. In particular, we analyze several baseline classification pipelines leveraging logistic regression, random forests, and linear support vector machines against wide headed CNN architectures with one-hot encoding or character embedding inputs. We find that the SVM model outperforms all other evaluated models in the setting at hand. In addition, we analyze the different predictions of the models and show typical prediction errors by means of a chord diagram of commonly misclassified brands

Applied image recognition: guidelines for using deep learning models in practice

In recent years, novel deep learning techniques, greater data availability, and a significant growth in computing powers have enabled AI researchers to tackle problems that had remained unassailable for many years. Furthermore, the advent of comprehensive AI frameworks offers the unique opportunity for adopting these new tools in applied fields. Information systems research can play a vital role in bridging the gap to practice. To this end, we conceptualize guidelines for applied image recognition spanning task definition, neural net configuration and training procedures. We showcase our guidelines by means of a biomedical research project for image recognition

Augmented Intelligence for Quality Control of Manual Assembly Processes using Industrial Wearable Systems

Empowered by machine learning and artificial intelligence innovations, IoT devices have become a leading driver of digital transformation. A promising approach are augmented intelligence solutions which seek to enhance human performance in complex tasks. However, there are no turn-key solutions for developing and implementing such systems. One possible avenue is to complement multi-purpose hardware with flexible AI solutions which are adapted to a given task. We illustrate the bottom-up development of a machine learning backend for an augmented intelligence system in the manufacturing sector. A wearable device equipped with highly sensitive sensors is paired with a deep convolutional neural network to monitor connector systems assembly processes in real-time. Our initial study yields promising results in an experimental environment. While this establishes the feasibility of the suggested approach, further evaluations in more complex test cases and ultimately, in a real-world assembly process have to be performed

Making the Newsvendor Smart – Order Quantity Optimization with ANNs for a Bakery Chain

Accurate demand forecasting is particularly crucial for products with short shelf life like bakery products. Over- and underestimation of customer demand affects not only profit margins of bakeries but is also responsible for 600,000 metric tons of food waste every year in Germany. To solve this problem, we develop an IT artifact based on artificial neural networks, which is automating the manual order process and capable of reducing costs as well as food waste. To test and evaluate our artifact, we cooperated with an SME bakery chain from Germany. The bakery chain runs 40 points of sale (POS) in southern Germany. After algorithm based reconstructing and cleaning of the censored sales data, we compare two different data-driven newsvendor approaches for this inventory problem. We show that both models are able to significantly improve the forecast quality (cost savings up to 30%) compared to human planners

Can magnetotail reconnection produce the auroral intensities observed in the conjugate ionosphere?

[1] In a recent case study, Borg et al. (2007) reported that an inverted V structure, caused by a field-aligned potential drop of 30 kV producing very strong X-ray aurora, was found in connection with tail reconnection. However, the in situ particle measurements indicated clearly that the particles responsible for the X-ray aurora were not accelerated by the reconnection process. In this article, we report the predicted auroral intensities of thirteen reconnection events where Cluster passed through the reconnection region. For six of the events, global auroral imaging data were available and the predicted auroral intensities could be compared with the observed intensities. Our main findings are as follows: (1) Acceleration in the reconnection region is generally not sufficient to account for the observed auroral intensities. (2) Additional acceleration between the reconnection region and the ionosphere is needed to explain the auroral intensities. Although we see signatures that point toward potential drops at the flanks of bursty bulk flows (BBFs), we also find signatures of Alfvén wave accelerated electrons at 700 km and we are not able to determine the most likely acceleration mechanism. (3) The reconnection events are observed 2–14 min after substorm onset and indicate that reconnection is an expanding process observed along the poleward boundary of the aurora.publishedVersio

Quantifying the Lobe Reconnection Rate During Dominant IMF By Periods and Different Dipole Tilt Orientations

Lobe reconnection is usually thought to play an important role in geospace dynamics only when the Interplanetary Magnetic Field (IMF) is mainly northward. This is because the most common and unambiguous signature of lobe reconnection is the strong sunward convection in the polar cap ionosphere observed during these conditions. During more typical conditions, when the IMF is mainly oriented in a dawn-dusk direction, plasma flows initiated by dayside and lobe reconnection both map to high-latitude ionospheric locations in close proximity to each other on the dayside. This makes the distinction of the source of the observed dayside polar cap convection ambiguous, as the flow magnitude and direction are similar from the two topologically different source regions. We here overcome this challenge by normalizing the ionospheric convection observed by the Super Dual Aurora Radar Network (SuperDARN) to the polar cap boundary, inferred from simultaneous observations from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). This new method enable us to separate and quantify the relative contribution of both lobe reconnection and dayside/nightside (Dungey cycle) reconnection during periods of dominating IMF By. Our main findings are twofold. First, the lobe reconnection rate can typically account for 20% of the Dungey cycle flux transport during local summer when IMF By is dominating and IMF Bz ≥ 0. Second, the dayside convection relative to the open/closed boundary is vastly different in local summer versus local winter, as defined by the dipole tilt angle.publishedVersio

Aberrant Expression of and Cell Death Induction by Engagement of the MHC-II Chaperone CD74 in Anaplastic Large Cell Lymphoma (ALCL)

SIMPLE SUMMARY: Anaplastic large cell lymphoma (ALCL) is a lymphoid malignancy considered to be derived from T cells. Currently, two types of systemic ALCL are distinguished: anaplastic lymphoma kinase (ALK)-positive and ALK-negative ALCL. Although ALK(+) and ALK(−) ALCL differ at the genomic and molecular levels, various key biological and molecular features are highly similar between both entities. We have developed the concept that both ALCL entities share a common principle of pathogenesis. In support of this concept, we here describe a common deregulation of CD74, which is usually not expressed in T cells, in ALCL. Ligation of CD74 induces cell death of ALCL cells in various conditions, and an anti-CD74-directed antibody-drug conjugate efficiently kills ALCL cell lines. Furthermore, we reveal expression of the proto-oncogene and known CD74 interaction partner MET in a fraction of ALCL cases. These data give insights into ALCL pathogenesis and might help to develop new treatment strategies for ALCL. ABSTRACT: In 50–60% of cases, systemic anaplastic large cell lymphoma (ALCL) is characterized by the t(2;5)(p23;q35) or one of its variants, considered to be causative for anaplastic lymphoma kinase (ALK)-positive (ALK(+)) ALCL. Key pathogenic events in ALK-negative (ALK(−)) ALCL are less well defined. We have previously shown that deregulation of oncogenic genes surrounding the chromosomal breakpoints on 2p and 5q is a unifying feature of both ALK(+) and ALK(−) ALCL and predisposes for occurrence of t(2;5). Here, we report that the invariant chain of the MHC-II complex CD74 or li, which is encoded on 5q32, can act as signaling molecule, and whose expression in lymphoid cells is usually restricted to B cells, is aberrantly expressed in T cell-derived ALCL. Accordingly, ALCL shows an altered DNA methylation pattern of the CD74 locus compared to benign T cells. Functionally, CD74 ligation induces cell death of ALCL cells. Furthermore, CD74 engagement enhances the cytotoxic effects of conventional chemotherapeutics in ALCL cell lines, as well as the action of the ALK-inhibitor crizotinib in ALK(+) ALCL or of CD95 death-receptor signaling in ALK(−) ALCL. Additionally, a subset of ALCL cases expresses the proto-oncogene MET, which can form signaling complexes together with CD74. Finally, we demonstrate that the CD74-targeting antibody-drug conjugate STRO-001 efficiently and specifically kills CD74-positive ALCL cell lines in vitro. Taken together, these findings enabled us to demonstrate aberrant CD74-expression in ALCL cells, which might serve as tool for the development of new treatment strategies for this lymphoma entity