Anwendung von Machine-Learning in medizinischer Diagnostik in der Geburtshilfe

Background Improvements in computational capacity and new algorithmic approaches to data analysis have created enormous opportunities to improve conventional diagnostics in the hospital in recent years. Especially obstetrics, a speciality with high-dimensional data and limited performances in their conventional diagnostic approaches for many adverse outcomes in pregnancy, stands to benefit greatly from the application of machine-learning. This dissertation intends to present our own work which predicts the occurrence of adverse outcomes in preeclampsia high-risk-pregnancies and to contextualise it with the current state of research for the application of machine-learning in preeclampsia as well as other obstetric/gynecologic conditions in general. Methods The presented study is based on a patient collective of 1647 women which presented to the obstetric department of the Charité Universitätsmedizin Berlin between July 2010 and March 2019. We determined predictive performance of different machine-learning algorithms (Gradient boosted trees, Random Forest) for adverse outcomes commonly associated with preeclampsia and compared them to models based on laboratory and vital parameter cutoffs (blood pressure, sFlt-1/PlGF ratio and their combination with proteinuria measurements) used in the clinic. Dataset splitting was performed in a per-patient randomised fashion using a 90-10 split and evaluation was performed using a 10x10-fold cross-validation approach. Results Our own study showed gains in predictive performance when using machine-learning models. Accuracy for gradient boosted trees was 87 ± 3 % while blood pressure cutoffs achieved only 65 ± 4 % and a cutoff of 38 applied to the sFlt-1/PlGF-ratio yielded an accuracy of 68 ± 5 %. The positive predictive value especially improved from 33 ± 9 % for the blood-pressure-cutoffs to 82 ± 10 % for the gradient-boosted trees classifier with the “full clinical model” consisting of blood pressure, sFlt-1/PlGF ratio and proteinuria achieving 44 ± 9 % PPV. Overall we found that using machine-learning methods leads to great improvements in all assessed performance metrics with potential for further enhancement using optimization on the algorithms’ output probabilities’ cutoffs. Conclusions Machine-learning greatly improves the diagnostic capabilities for preeclampsia and, as shown by many other works in this dissertation, obstetrics/gynaecology and medicine in general. This could represent a starting point for further research which leads to more sophisticated diagnostic or decision-support tools.Einleitung Verbesserungen in Rechenkapazitäten und neue algorithmische Ansätze der Datenanalyse haben große Möglichkeiten zur Verbesserung konventioneller Diagnostik in Krankenhäusern über die letzten Jahre kreiert. Besonders die Geburtshilfe, eine Fachrichtung mit hochdimensionalen Datensätzen und limitierter Performance der konventionellen diagnostischen Methoden für viele der adversen Events in der Schwangerschaft, kann stark von der Anwendung von Machine-Learning profitieren. Diese Dissertation beabsichtigt unsere eigene Arbeit, welche das Auftreten adverser Events in Präeklampsie-Hochrisikoschwangerschaften vorhersagt, vorzustellen und mit dem aktuellen stand der Forschung für Machine-Learning in der Präeklampsie sowie Gynäkologie/Geburtshilfe in Kontext zu setzen. Methoden Die vorgestellte Studie basiert auf einer Patientinnengruppe von 1647 Frauen, die sich zwischen Juli 2010 und März 2019 in der Klinik für Geburtsmedizin der Charité Universitätsmedizin Berlin vorstellten. Wir untersuchten die Leistung verschiedener Machine-Learning-Algorithmen (Gradient Boosted Trees, Random Forest) zur Vorhersage häufig mit Präeklampsie assoziierter adverser Events und verglichen diese mit Modellen basierend auf klinisch angewendeten Labor- und Vitalparameter-Grenzwerten (Blutdruck, sFlt-1/PlGF-Ratio und ihre Kombination mit Proteinurie-Messungen). Der Datensatz wurde auf einer randomisierten Pro-Patient-Basis in einem 90-10-split in Trainings- und Testsatz geteilt und mittels einer 10x 10-fachen Kreuzvalidierung evaluiert. Ergebnisse Unsere Studie zeigte Zugewinne an prädiktiver Leistung durch Nutzung von Machine-Learning-Modellen. Genauigkeit für Gradient boosted trees war 87 ± 3 %, während Blutdruckgrenzwerte lediglich 65 ± 4 % erreichen konnten und ein Grenzwert von 38 der sFlt-1/PLGF-Ratio eine Genauigkeit von 68 ± 5 %. Insbesondere der positiv prädiktive Wert verbesserte sich von 33 ± 9 % für den Blutdruckgrenzwert auf 82 ± 10 % für den Gradient-boosted Trees-Klassifizierer, während das “vollständige” klinische Modell bestehend aus Blutdruck, sFlt-1/PlGF-Ratio und Proteinurie 44 ± 9 % erreichen konnte. Insgesamt fanden wir, dass Machine-Learning Methoden zu großen Verbesserungen in allen untersuchten Performance-Metriken führt, mit Potential zu weiteren Verbesserungen durch Optimierung von Grenzwerten auf den ausgegebenen Wahrscheinlichkeiten der Modelle. Schlussfolgerung Machine-Learning führt zu immensen Verbesserungen der diagnostischen Möglichkeiten für Präeklampsie und, wie durch viele weitere Arbeiten in dieser Dissertation gezeigt, Gynäkologie/Geburtshilfe und Medizin im Allgemeinen. Dies kann einen Startpunkt für weitere Forschung repräsentieren, welche zu anspruchsvolleren Diagnostik- und Entscheidung-Support-Werkzeugen führt

A method for precise charge reconstruction with pixel detectors using binary hit information

A method is presented to precisely reconstruct charge spectra with pixel detectors using binary hit information of individual pixels. The method is independent of the charge information provided by the readout circuitry and has a resolution mainly limited by the electronic noise. It relies on the ability to change the detection threshold in small steps while counting hits from a particle source. The errors are addressed and the performance of the method is shown based on measurements with the ATLAS pixel chip FE-I4 bump bonded to a 230 {\mu}m 3D-silicon sensor. Charge spectra from radioactive sources and from electron beams are presented serving as examples. It is demonstrated that a charge resolution ({\sigma}<200 e) close to the electronic noise of the ATLAS FE-I4 pixel chip can be achieved

Perceptual Differences for Modifications of the Elevation of Early Room Reflections

Acoustic room responses usually comprise components that propagate in non-horizontal directions. Oftentimes, audio capture and reproduction systems are not capable of maintaining such elevation information reliably hence it is important to understand their perceptual significance when auralizing rooms. This work investigates the ability of the human hearing system to distinguish between early reflections with different elevation angles by performing loudspeaker- and headphone-based listening experiments using manipulated spatial room impulse responses. The results show that changing the elevation of a strong early reflection can lead to clearly perceivable differences and factors that influence the detectability are identified. Projecting all elevated reflections of a spatial room impulse response with no very prominent ceiling reflection to the horizontal plane showed no perceivable differences

Perceptual Differences for Modifications of the Elevation of Early Room Reflections

Acoustic room responses usually comprise components that propagate in non-horizontal directions. Oftentimes, audio capture and reproduction systems are not capable of maintaining such elevation information reliably hence it is important to understand their perceptual significance when auralizing rooms. This work investigates the ability of the human hearing system to distinguish between early reflections with different elevation angles by performing loudspeaker- and headphone-based listening experiments using manipulated spatial room impulse responses. The results show that changing the elevation of a strong early reflection can lead to clearly perceivable differences and factors that influence the detectability are identified. Projecting all elevated reflections of a spatial room impulse response with no very prominent ceiling reflection to the horizontal plane showed no perceivable differences

Characterization and Verification Environment for the RD53A Pixel Readout Chip in 65 nm CMOS

The RD53 collaboration is currently designing a large scale prototype pixel readout chip in 65 nm CMOS technology for the phase 2 upgrades at the HL-LHC. The RD53A chip will be available by the end of the year 2017 and will be extensively tested to confirm if the circuit and the architecture make a solid foundation for the final pixel readout chips for the experiments at the HL-LHC. A test and data acquisition system for the RD53A chip is currently under development to perform single-chip and multi-chip module measurements. In addition, the verification of the RD53A design is performed in a dedicated simulation environment. The concept and the implementation of the test and data acquisition system and the simulation environment, which are based on a modular data acquisition and system testing framework, are presented in this work

Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?

Urbanization leads to an increased demand for urban housing, which can be met by building dwellings closer to streets. Regulations often limit equivalent sound pressure levels which do not account for changes in time structure that occur when decreasing the road distance. This study investigates the effect of such temporal changes on subjective workload and cognitive performance. A group of 42 participants performed a continuous performance test as well as a NASA-TLX workload test under three different sound conditions, i.e., close traffic, far traffic, both with the same equivalent sound pressure level of LAeq ≈ 40 dB, and silence. Additionally, participants answered a questionnaire regarding their preferred acoustic environment for concentrated working. Significant effects of the sound condition on the multivariate workload results as well as on the number of commission errors in the continuous performance test were found. Post hoc tests showed no significant differences between the two noise conditions, but there were significant differences between noise and silence. This indicates that moderate traffic noise levels can influence cognitive performance and perceived workload. If there is a difference in the human response to road traffic noise with constant LAeq but different time structures, the used methods are not suitable to detect them

Measurement, Simulation and Auralization of Indoor Road Traffic Noise

Building new houses in urban areas is often hindered by the presence of road traffic noise. Thereby, it is especially difficult to achieve sufficient fa\ue7ade sound insulation at low frequencies. Current regulations are mostly based on A-weighted levels and hence not only disregard low frequencies but also omit other factors that might influence the amount of disturbance such as time structure, traffic distance, proportion of heavy vehicles, driving speed and background noise. This motivates the need for experiments evaluating the human response to indoor traffic noise in a controlled virtual environment. Thereby, one challenge is to include the incidence angle dependent transfer paths through the fa\ue7ade in the auralization. The method presented in this paper utilises wave field synthesis in order to reproduce the sound field caused by moving sound sources on the outside of a window in a, specifically for this purpose designed, living room lab. In order to evaluate the auralization, a physical model of the source position dependent transfer path from a moving outdoor sound source through a window to an indoor receiver position was developed