Dideriksenstarr2959

The current movement in Medical Informatics towards comprehensive Electronic Health Records (EHRs) has enabled a wide range of secondary use cases for this data. However, due to a number of well-justified concerns and barriers, especially with regards to information privacy, access to real medical records by researchers is often not possible, and indeed not always required. An appealing alternative to the use of real patient data is the employment of a generator for realistic, yet synthetic, EHRs. However, we have identified a number of shortcomings in prior works, especially with regards to the adaptability of the projects to the requirements of the German healthcare system. Based on three case studies, we define a non-exhaustive list of requirements for an ideal generator project that can be used in a wide range of localities and settings, to address and enable future work in this regard.The automation of medical documentation is a highly desirable process, especially as it could avert significant temporal and monetary expenses in healthcare. With the help of complex modelling and high computational capability, Automatic Speech Recognition (ASR) and deep learning have made several promising attempts to this end. However, a factor that significantly determines the efficiency of these systems is the volume of speech that is processed in each medical examination. compound library chemical In the course of this study, we found that over half of the speech, recorded during follow-up examinations of patients treated with Intra-Vitreal Injections, was not relevant for medical documentation. In this paper, we evaluate the application of Convolutional and Long Short-Term Memory (LSTM) neural networks for the development of a speech classification module aimed at identifying speech relevant for medical report generation. In this regard, various topology parameters are tested and the effect of the model performance on different speaker attributes is analyzed. The results indicate that Convolutional Neural Networks (CNNs) are more successful than LSTM networks, and achieve a validation accuracy of 92.41%. Furthermore, on evaluation of the robustness of the model to gender, accent and unknown speakers, the neural network generalized satisfactorily.Clinical trials are carried out to prove the safety and effectiveness of new interventions and therapies. As diseases and their causes continue to become more specific, so do inclusion and exclusion criteria for trials. Patient recruitment has always been a challenge, but with medical progress, it becomes increasingly difficult to achieve the necessary number of cases. link2 In Germany, the Medical Informatics Initiative is planning to use the central application and registration office to conduct feasibility analyses at an early stage and thus to identify suitable project partners. This approach aims to technically adapt/integrate the envisioned infrastructure in such a way that it can be used for trial case number estimation for the planning of multicenter clinical trials. We have developed a fully automated solution called APERITIF that can identify the number of eligible patients based on free-text eligibility criteria, taking into account the MII core data set and based on the FHIR standard. The evaluation showed a precision of 62.64 % for inclusion criteria and a precision of 66.45 % for exclusion criteria.Access to hospitals has been dramatically restricted during the COVID 19 pandemic. Indeed, due to the high risk of contamination by patients and by visitors, only essential visits and medical appointments have been authorized. Restricting hospital access to authorized visitors was an important logistic challenge. link3 To deal with this challenge, our institution developed the ExpectingU app to facilitate patient authorization for medical appointments and for visitors to enter the hospital. This article analyzes different trends regarding medical appointments, visitors' invitations, support staff hired and COVID hospitalizations to demonstrate how the ExpectingU system has helped the hospital to maintain accessibility to the hospital. Results shows that our system has allowed us to maintain the hospital open for medical appointments and visits without creating bottlenecks.Chatbots potentially address deficits in availability of the traditional health workforce and could help to stem concerning rates of youth mental health issues including high suicide rates. While chatbots have shown some positive results in helping people cope with mental health issues, there are yet deep concerns regarding such chatbots in terms of their ability to identify emergency situations and act accordingly. Risk of suicide/self-harm is one such concern which we have addressed in this project. A chatbot decides its response based on the text input from the user and must correctly recognize the significance of a given input. We have designed a self-harm classifier which could use the user's response to the chatbot and predict whether the response indicates intent for self-harm. With the difficulty to access confidential counselling data, we looked for alternate data sources and found Twitter and Reddit to provide data similar to what we would expect to get from a chatbot user. We trained a sentiment analysis classifier on Twitter data and a self-harm classifier on the Reddit data. We combined the results of the two models to improve the model performance. We got the best results from a LSTM-RNN classifier using BERT encoding. The best model accuracy achieved was 92.13%. We tested the model on new data from Reddit and got an impressive result with an accuracy of 97%. Such a model is promising for future embedding in mental health chatbots to improve their safety through accurate detection of self-harm talk by users.Hospital-acquired infections, particularly in ICU, are becoming more frequent in recent years, with the most serious of them being Gram-negative bacterial infections. Among them, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa are considered the most resistant bacteria encountered in ICU and other wards. Given the fact that about 24 hours are usually required to perform common antibiotic resistance tests after the bacteria identification, the use of machine learning techniques could be an additional decision support tool in selecting empirical antibiotic treatment based on the sample type, bacteria, and patient's basic characteristics. In this article, five machine learning (ML) models were evaluated to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We suggest implementing ML techniques to forecast antibiotic resistance using data from the clinical microbiology laboratory, available in the Laboratory Information System (LIS).Data integration is an increasing need in medical informatics projects like the EU Precise4Q project, in which multidisciplinary semantically and syntactically heterogeneous data across several institutions needs to be integrated. Besides, data sharing agreements often allow a virtual data integration only, because data cannot leave the source repository. We propose a data harmonization infrastructure in which data is virtually integrated by sharing a semantically rich common data representation that allows their homogeneous querying. This common data model integrates content from well-known biomedical ontologies like SNOMED CT by using the BTL2 upper level ontology, and is imported into a graph database. We successfully integrated three datasets and made some test queries showing the feasibility of the approach.The Fast Healthcare Interoperability Resources (FHIR) contain multiple data-exchange standards that aim at optimizing healthcare information exchange. One of them, the FHIR AdverseEvent, may support post-market safety surveillance. We examined its readiness using the Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS). Our methodology focused on mapping the public FAERS data fields to the FHIR AdverseEvent Resource elements and developing a software tool to automate this process. We mapped directly nine and indirectly two of the twenty-six FAERS elements, while six elements were assigned default values. This exploration further revealed opportunities for adding extra elements to the FHIR standard, based on critical FAERS pieces of information reviewed at the FDA. The existing version of the FHIR AdverseEvent Resource may standardize some of the FAERS information but has to be modified and extended to maximize its value in post-market safety surveillance.This work aims to describe how EHRs have been used to meet the needs of healthcare providers and researchers in a 1,300-beds tertiary Hospital during COVID-19 pandemic. For this purpose, essential clinical concepts were identified and standardized with LOINC and SNOMED CT. After that, these concepts were implemented in EHR systems and based on them, data tools, such as clinical alerts, dynamic patient lists and a clinical follow-up dashboard, were developed for healthcare support. In addition, these data were incorporated into standardized repositories and COVID-19 databases to improve clinical research on this new disease. In conclusion, standardized EHRs allowed implementation of useful multi- purpose data resources in a major Hospital in the course of the pandemic.The integration of surgical knowledge into virtual planning systems plays a key role in computer-assisted surgery. The knowledge is often implicitly contained in the implemented algorithms. However, a strict separation would be desirable for reasons of maintainability, reusability and readability. Along with the Department of Oral and Maxillofacial Surgery at Heidelberg University Hospital, we are working on the development of a virtual planning system for mandibular reconstruction. In this work we describe a process for the structured acquisition and representation of surgical knowledge for mandibular reconstruction. Based on the acquired knowledge, an RDF(S) ontology was created. The ontology is connected to the virtual planning system via a SPARQL interface. The described process of knowledge acquisition can be transferred to other surgical use cases. Furthermore, the developed ontology is characterised by a reusable and easily expandable data model.Metadata management is an essential condition to follow the FAIR principles. Therefore, metadata management was one asset of an accompanying project within a funding scheme for registries in health services research. The metadata of the funded projects were acquired, combined in a database compatible with the metamodel of ISO/IEC 11179 "Information technology - Metadata registries" third edition (ISO/IEC 11179-3), and analyzed in order to support the development and the operation of the registries. In the second phase of the funding scheme, six registries delivered a complete update of their metadata. The mean number of data elements increased from 245.7 to 473.5 and the mean number of values from 569.5 to 1,306.0. The conceptual core of the database had to be extended by one third to cover the new elements. The reason for this increase remained unclear. Constraints from the grant might be causal, a deviation from an evidence-based development process as well. It is questionable, whether the revealed quality of the metadata is sufficient to fulfill the FAIR principles.