The application of medical terminologies to free-text in routine databases using the example of strategies to reduce infant mortality

Hintergrund Die Säuglingssterblichkeitsrate (IMR), ein wichtiger Indikator für die Qualität eines Gesundheitssystems, liegt in Deutschland seit 10 Jahren bei rund 3.5‰. Generische Qualitätsindikatoren (QIs), wie sie seit 2010 in Deutschland verwendet werden, tragen wesentlich zu einem so guten Wert bei, scheinen aber nicht in der Lage zu sein, den IMR weiter zu reduzieren. Die neonatale Sterblichkeitsrate (NMR) trägt zu 65-70% der IMR bei. Der vorgestellte Ansatz schlägt daher eine Einzelfallanalyse neonataler Todesfälle auf der Grundlage von Krankenakten vor. Die meisten elektronischen Krankenakten enthalten noch immer große Mengen an Freitextdaten. Die semantische Auswertung solcher Daten erfordert, dass die Daten mit ausreichenden Klassifizierungen kodiert oder in eine wissensbasierte Datenbank umgewandelt werden. Methodik Die Nordic-Baltic-Classification (NBC) wurde zur Erkennung vermeidbarer neonataler Todesfälle verwendet. Diese Klassifikation wurde auf eine Stichprobe von 1.968 neonatalen Todesfällen angewandt, die über 90% aller neonatalen Todesfälle in Ost-Berlin von 1973 bis 1989 darstellen. Alle Fälle wurden damals von einer speziellen Kommission verschiedener Experten auf der Grundlage der vollständigen perinatalen und klinischen Daten auf ihre Vermeidbarkeit hin analysiert. Der entwickelte Ansatz ermöglicht es, Datenbanken, die über SQL (Structured Query Language) zugänglich sind, direkt über semantische Abfragen zu durchsuchen, ohne dass weitere Transformationen erforderlich sind. Dazu wurden 1.) eine Erweiterung von SQL „Ontology-SQL“ (O-SQL) entwickelt, die es ermöglicht, semantische Ausdrücke zu verwenden, 2.) ein Framework entwickelt, das einen Standardterminologieserver verwendet, um Freitext enthaltende Datenbanktabellen zu annotieren und 3.) ein Parser entwickelt, der O-SQL Ausdrücke in SQL konvertiert, so dass semantische Abfragen direkt an den Datenbankserver weitergeleitet werden können. Ergebnisse Die NBC wurde verwendet, um die Gruppe der Fälle auszuwählen, die ein hohes Vermeidungspotenzial hatten. Die ausgewählte Gruppe stellte 6,0% aller Fälle dar und 60,4% der Fälle innerhalb dieser Gruppe wurden tatsächlich als vermeidbar oder bedingt vermeidbar beurteilt. Die automatische Erkennung von Fehlbildungen ergab einen F1-Wert von 0,94. Darüber hinaus wurde die Verallgemeinerbarkeit des Ansatzes mit verschiedenen semantischen Abfragen nachgewiesen und dessen Güte mit F1-Werten von 0,91 bis 0,98 gemessen. Zusammenfassung Die Ergebnisse zeigen, dass die vorgestellte Methode automatisch anwendbar ist und ein leistungsfähiges und hochsensitives und -spezifisches Werkzeug zur Auswahl potenziell vermeidbarer neonataler Todesfälle und damit zur Unterstützung einer effizienten Einzelfallanalyse darstellt. Die nahtlose Verknüpfung von Ontologien und Standardtechnologien aus dem Datenbankbereich stellt einen wichtigen Bestandteil der unstrukturierten Datenanalyse dar. Die entwickelte Technologie lässt sich problemlos auf aktuelle Daten anwenden und unterstützt das immer wichtiger werdende Feld der translationalen Forschung.Background The infant mortality rate (IMR), a key indicator of the quality of a healthcare system, has remained at approximately 3.5‰ for the past 10 years in Germany. Generic quality indicators (QIs), as used in Germany since 2010, greatly help to ensure such a good value but do not seem to be able to further reduce the IMR. The neonatal mortality rate (NMR) contributes to 65-70% of the IMR. The presented approach therefore proposes single-case analysis of neonatal deaths on base of medical records. Most electronic medical records still contain large amounts of free-text data. Semantic evaluation of such data requires the data to be encoded with sufficient classifications or transformed into a knowledge-based database. Methods The Nordic-Baltic classification (NBC) was used to detect avoidable neonatal deaths. This classification has been applied to a sample of 1,968 neonatal death records, which represent over 90% of all neonatal deaths in East Berlin from 1973 to 1989. All cases were analyzed as to their preventability based on the complete perinatal and clinical data by a special commission of different experts. The developed approach allows databases accessible via SQL (Structured Query Language) to be searched directly through semantic queries without the need for further transformations. Therefore, I) an extension to SQL named Ontology-SQL (O-SQL) that allows to use semantic expressions, II) a framework that uses a standard terminology server to annotate free-text containing database tables and III) a parser that rewrites O-SQL to SQL, so that such queries can be passed to the database server, have been developed. Results The NBC was used to select the group of cases that had a high potential of avoidance. The selected group represented 6.0% of all cases, and 60.4% of the cases within that group were judged avoidable or conditionally avoidable. The automatic detection of malformations showed an F1 score of 0.94. Furthermore, the generability has been proved with different semantic queries and was measured with between 0.91 and 0.98. Conclusion The results show, that the presented method can be applied automatically and is a powerful and highly specific tool for selecting potentially avoidable neonatal deaths and thus for supporting efficient single case analysis. The seamless connection of ontologies and standard technologies from the database field represents an important constituent of unstructured data analysis. The developed technology can be readily applied to current data and supports the increasingly important field of translational research

Routing on the Visibility Graph

We consider the problem of routing on a network in the presence of line segment constraints (i.e., obstacles that edges in our network are not allowed to cross). Let $P$ be a set of $n$ points in the plane and let $S$ be a set of non-crossing line segments whose endpoints are in $P$. We present two deterministic 1-local $O(1)$-memory routing algorithms that are guaranteed to find a path of at most linear size between any pair of vertices of the \emph{visibility graph} of $P$ with respect to a set of constraints $S$ (i.e., the algorithms never look beyond the direct neighbours of the current location and store only a constant amount of additional information). Contrary to {\em all} existing deterministic local routing algorithms, our routing algorithms do not route on a plane subgraph of the visibility graph. Additionally, we provide lower bounds on the routing ratio of any deterministic local routing algorithm on the visibility graph.Comment: An extended abstract of this paper appeared in the proceedings of the 28th International Symposium on Algorithms and Computation (ISAAC 2017). Final version appeared in the Journal of Computational Geometr

Data Driven Model Discovery - Petroleum application

The SINDy algorithm is a data driven algorithm that discovers dynamical system in data that evolves over time. The method can be utilized for every dataset that evolves over time. In this study we have looked the Lorenz system, covid-19 data and production data from two different oil fields on the Norwegian shelf. The aim of the study was to investigate if SINDy can be used on the well data to extract sparse and suitable well models. The complexity of the models are decided by the user when using prior knowledge to choose the candidate function. If you have limited knowledge about the system a handful of different models are tested and parameters are optimized to fit the data. Noisy and spiky data are an issue for the SINDy method due to its use of the differentiated data. Therefor filtering is needed on production data to minimize the large spikes and smooth out the data. The SINDy algorithm gives good results to the production data using polynomials to describe the data. The results are good for data from Draugen and Statfjord Øst. And the results from the covid-19 data are promising

Business ownership and economic growth: an emperial investigation

Studie naar de relatie tussen het aantal zelfstandige ondernemers en economische groei. Onderzocht wordt het bestaan van een lange termijn relatie tussen het aantal ondernemers en de fase van economische ontwikkeling. Daarnaast wordt ingegaan op het feit dat zelfstandige ondernemerschap aantrekkelijker wordt doordat een daling van de economische groei leidt tot hoge werkloosheid. Lage werkloosheid stimuleert mensen, die het moeilijk hebben om een baan te vinden of wiens cariëre bedreigt wordt in bestaande ondernemingen, om zelfstandige ondernemer te worden.� Dit leidt tot een omgekeerde�effect van de invloed van economische groei� op het aantal zelfstandige ondernemers per beroepsbevolking.

From nascent to actual entrepreneurship: the effect of entry barriers

This exploratory study focuses on the convention from nascent to actual entrepreneurship and the role of entry barriers in this process. Evidence is found for a strong conversion from nascent to actual entrepreneurship. Also positive effects are found on entrepreneurial activity rates of labour flexibility and tertiary enrollment and a negative effect of social expenditure.

Social security arrangements and early-stage entrepreneurial activity; an empirical analysis

This exploratory study focuses on the relation between social security arrangements and the rate of early-stage entrepreneurial activity at the country level. Using a sample of countries participating in the Global Entrepreneurship Monitor, we explore how various measures of entrepreneurial activity are related to various measures of social security arrangements. On the one hand we look at aggregate indicators such as the social security contributions or premiums paid by employers and employees. On the other hand we look at micro level based indicators such as 'replacement rates', measuring the benefits an individual is entitled to in case of unemployment or illness/disability. Our analysis using aggregate indicators shows that the height of employer premiums negatively influences entrepreneurial activity at the macro level, but that the height of employee premiums has no impact. The results of our analysis using micro level based indicators suggest that the replacement rate of employees has a significantly negative influence on the level of early-stage entrepreneurship at the macro level.

Self-employment across 15 European countries: the role of dissatisfaction

This paper deals with differences in the rate of self-employment (business ownership) in 15 European countries for the period 1978-2000, focusing on the influence of dissatisfaction and using the framework of occupational choice. Using two different measures of dissatisfaction, in addition to the level of economic development, the unemployment rate and income differentials, we find that dissatisfaction at the level of societies is the most significant factor for explaining differences in self-employment levels. Dissatisfaction with life and with the way democracy works are both found to be positively related to self-employment. It is concluded that these are proxies for job dissatisfaction and at the same time represent other negative 'displacements' known to promote self-employment.

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period