Should I Bug You? Identifying Domain Experts in Software Projects Using Code Complexity Metrics

In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90% of all cases

Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats

In this article we describe an efficient approximation of the stochastic Galerkin matrix which stems from a stationary diffusion equation. The uncertain permeability coefficient is assumed to be a log-normal random field with given covariance and mean functions. The approximation is done in the canonical tensor format and then compared numerically with the tensor train and hierarchical tensor formats. It will be shown that under additional assumptions the approximation error depends only on smoothness of the covariance function and does not depend either on the number of random variables nor the degree of the multivariate Hermite polynomials

Digitalisierte Gesundheitsversorgung im Jahr 2030 – ein mögliches Szenario

Due to the rapidly advancing digitalization, healthcare will also change significantly in the next few years. For example, in Germany, new legal framework conditions have already set the course for the electronic patient record (ePA), the e‑prescription, and the integration of digital health applications (DiGA). The new fast-track procedure of the Federal Institute for Drugs and Medical Devices (BfArM) for evaluating the reimbursability of DiGA is an important step that will be followed by others in the coming years.This article uses a future scenario for the year 2030 to describe the legal, technical, and practical changes that could occur by then. In 2030, healthcare could be organized in individual and integrated treatment pathways that offer comprehensive support to the insured. Interoperable digital components could, for example, make structured data available for research purposes. Doubts concerning data protection could become a thing of the past if data protection law is reformed and harmonized and new consent procedures for patients are developed. New professional fields could become established and market access for innovative digital medical products could be further improved.Another important aspect that can help to exploit the potential of digital healthcare is the creation of a European data space based on a technical infrastructure that upholds high ethical and social standards. Active measures on the part of legislators can create the necessary conditions for innovations to be incorporated into the system for the benefit of patients and for the German healthcare system to be able to cope with the ongoing changes in medical technology

PyDPLib: Python Differential Privacy Library for Private Medical Data Analytics

Pharmaceutical and medical technology companies accessing real-world medical data are not interested in personally identifiable data but rather in cohort data such as statistical aggregates, patterns, and trends. These companies cooperate with medical institutions that collect medical data and want to share it but they need to protect the privacy of individuals on the shared data. We present PyDPLib, a Python Differential Privacy library for private medical data analytics. We illustrate an application of differential privacy using PyDPLib in our platform for visualizing private statistics on a database of prostate cancer patients. Our experimental results show that PyDPLib allows creating statistical data plots without compromising patients’ privacy while preserving underlying data distributions. Even though PyDPLib has been developed to be used in our platform for reporting the radiological examinations and procedures, it is general enough to be used to provide differential privacy on data in any data analytics and visualization platform, service or application

Feasibility of local field potential-guided programming for deep brain stimulation in Parkinson's disease: A comparison with clinical and neuro-imaging guided approaches in a randomized, controlled pilot trial

Background: Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced Parkinson's disease (PD). Clinical outcomes after DBS can be limited by poor programming, which remains a clinically driven, lengthy and iterative process. Electrophysiological recordings in PD patients undergoing STN-DBS have shown an association between STN spectral power in the beta frequency band (beta power) and the severity of clinical symptoms. New commercially-available DBS devices now enable the recording of STN beta oscillations in chronically-implanted PD patients, thereby allowing investigation into the use of beta power as a biomarker for DBS programming. Objective: To determine the potential advantages of beta-guided DBS programming over clinically and image-guided programming in terms of clinical efficacy and programming time. Methods: We conducted a randomized, blinded, three-arm, crossover clinical trial in eight Parkinson's patients with STN-DBS who were evaluated three months after DBS surgery. We compared clinical efficacy and time required for each DBS programming paradigm, as well as DBS parameters and total energy delivered between the three strategies (beta-, clinically- and image-guided). Results: All three programming methods showed similar clinical efficacy, but the time needed for programming was significantly shorter for beta- and image-guided programming compared to clinically-guided programming (p < 0.001). Conclusion: Beta-guided programming may be a useful and more efficient approach to DBS programming in Parkinson's patients with STN-DBS. It takes significantly less time to program than traditional clinically-based programming, while providing similar symptom control. In addition, it is readily available within the clinical DBS programmer, making it a valuable tool for improving current clinical practice

Reduced Programming Time and Strong Symptom Control Even in Chronic Course Through Imaging-Based DBS Programming

Objectives: Deep brain stimulation (DBS) programming is based on clinical response testing. Our clinical pilot trial assessed the feasibility of image-guided programing using software depicting the lead location in a patient-specific anatomical model. Methods: Parkinson's disease patients with subthalamic nucleus-DBS were randomly assigned to standard clinical-based programming (CBP) or anatomical-based (imaging-guided) programming (ABP) in an 8-week crossover trial. Programming characteristics and clinical outcomes were evaluated. Results: In 10 patients, both programs led to similar motor symptom control (MDS-UPDRS III) after 4 weeks (medicationOFF/stimulationON; CPB: 18.27 ± 9.23; ABP: 18.37 ± 6.66). Stimulation settings were not significantly different, apart from higher frequency in the baseline program than CBP (p = 0.01) or ABP (p = 0.003). Time spent in a program was not significantly different (CBP: 86.1 ± 29.82%, ABP: 88.6 ± 29.0%). Programing time was significantly shorter (p = 0.039) with ABP (19.78 ± 5.86 min) than CBP (45.22 ± 18.32). Conclusion: Image-guided DBS programming in PD patients drastically reduces programming time without compromising symptom control and patient satisfaction in this small feasibility trial