Using the PharmCAT tool for Pharmacogenetic clinical decision support.

Here, we will provide our insights into the usage of PharmCAT as part of a pharmacogenetic clinical decision support pipeline, which addresses the challenges in mapping clinical dosing guidelines to variants to be extracted from genetic datasets. After a general outline of pharmacogenetics, we describe some features of PharmCAT and how we integrated it into a pharmacogenetic clinical decision support system within a clinical information system. We conclude with promising developments regarding future PharmCAT releases.

An ontology-based tool for modeling and documenting events in neurosurgery.

BACKGROUND: Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool. METHODS: We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology's creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool's functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool's productive utilization. RESULTS: The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool's evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise. CONCLUSIONS: Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it's crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.

Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles.

BACKGROUND: Even for an experienced neurophysiologist, it is challenging to look at a single graph of an unlabeled motor evoked potential (MEP) and identify the corresponding muscle. We demonstrate that supervised machine learning (ML) can successfully perform this task. METHODS: Intraoperative MEP data from supratentorial surgery on 36 patients was included for the classification task with 4 muscles: Extensor digitorum (EXT), abductor pollicis brevis (APB), tibialis anterior (TA) and abductor hallucis (AH). Three different supervised ML classifiers (random forest (RF), k-nearest neighbors (kNN) and logistic regression (LogReg)) were trained and tested on either raw or compressed data. Patient data was classified considering either all 4 muscles simultaneously, 2 muscles within the same extremity (EXT versus APB), or 2 muscles from different extremities (EXT versus TA). RESULTS: In all cases, RF classifiers performed best and kNN second best. The highest performances were achieved on raw data (4 muscles 83%, EXT versus APB 89%, EXT versus TA 97% accuracy). CONCLUSIONS: Standard ML methods show surprisingly high performance on a classification task with intraoperative MEP signals. This study illustrates the power and challenges of standard ML algorithms when handling intraoperative signals and may lead to intraoperative safety improvements.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

[Quo Vadis Data Linkage in Germany? An Initial Inventory]. / Quo vadis Datenlinkage in Deutschland? Eine erste Bestandsaufnahme.

In recent years, linking different data sources, also called data linkage or record linkage, to address scientific questions, is being increasingly used in Germany. However, there are very few published reports and new projects develop the necessary tools independently of each other. Therefore, a team of researchers joined together to exchange their experiences on data linkage and to give suggestions on how linkage could be done for scientists, reviewers as well as members of data privacy boards and ethics committees. It is the aim of this article to assist future projects that want to link German data on an individual level. In addition to the legal framework conditions (data privacy), also examples of types of data linkage, their fields of application und potential pitfalls as well as the methods of preventing them will be described in an application-oriented fashion.

A boosting approach for adapting the sparsity of risk prediction signatures based on different molecular levels.

Risk prediction models can link high-dimensional molecular measurements, such as DNA methylation, to clinical endpoints. For biological interpretation, often a sparse fit is desirable. Different molecular aggregation levels, such as considering DNA methylation at the CpG, gene, or chromosome level, might demand different degrees of sparsity. Hence, model building and estimation techniques should be able to adapt their sparsity according to the setting. Additionally, underestimation of coefficients, which is a typical problem of sparse techniques, should also be addressed. We propose a comprehensive approach, based on a boosting technique that allows a flexible adaptation of model sparsity and addresses these problems in an integrative way. The main motivation is to have an automatic sparsity adaptation. In a simulation study, we show that this approach reduces underestimation in sparse settings and selects more adequate model sizes than the corresponding non-adaptive boosting technique in non-sparse settings. Using different aggregation levels of DNA methylation data from a study in kidney carcinoma patients, we illustrate how automatically selected values of the sparsity tuning parameter can reflect the underlying structure of the data. In addition to that, prediction performance and variable selection stability is compared to the non-adaptive boosting approach.

How to justify avoidance of communications related to death anxiety in the health care system.

It might seem obvious that dealing with death anxiety in the health care system is desirable. Hence, there are either voices that demand more research on how this openness can be fostered or those who consider this topic unworthy of further investigations because of its triviality. The idea behind both deficient perspectives is that the health care system as a communication system can assume the position of a second-order observer who can account for his deficits. However, in terms of Luhmannian systems theory, external perturbations cannot force a functional system to reflect and change the structure of his communications in a certain way. The health care system as a communication system cannot do more than integrating the topic of death anxiety in terms of its functional perpetuation. For example, in hospitals, neither health care staff nor external counselors are able to address existential issues without being affected by functional and structural requirements of the hospital. We present an outline for the justification of the avoidance of death-anxiety related talk in the health care system by reference to systems theory and existential philosophy.

Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.

BACKGROUND: Molecular data, e.g. arising from microarray technology, is often used for predicting survival probabilities of patients. For multivariate risk prediction models on such high-dimensional data, there are established techniques that combine parameter estimation and variable selection. One big challenge is to incorporate interactions into such prediction models. In this feasibility study, we present building blocks for evaluating and incorporating interactions terms in high-dimensional time-to-event settings, especially for settings in which it is computationally too expensive to check all possible interactions. RESULTS: We use a boosting technique for estimation of effects and the following building blocks for pre-selecting interactions: (1) resampling, (2) random forests and (3) orthogonalization as a data pre-processing step. In a simulation study, the strategy that uses all building blocks is able to detect true main effects and interactions with high sensitivity in different kinds of scenarios. The main challenge are interactions composed of variables that do not represent main effects, but our findings are also promising in this regard. Results on real world data illustrate that effect sizes of interactions frequently may not be large enough to improve prediction performance, even though the interactions are potentially of biological relevance. CONCLUSION: Screening interactions through random forests is feasible and useful, when one is interested in finding relevant two-way interactions. The other building blocks also contribute considerably to an enhanced pre-selection of interactions. We determined the limits of interaction detection in terms of necessary effect sizes. Our study emphasizes the importance of making full use of existing methods in addition to establishing new ones.

Effect of nighttime aircraft noise exposure on endothelial function and stress hormone release in healthy adults.

AIMS: Aircraft noise disturbs sleep, and long-term exposure has been shown to be associated with increases in the prevalence of hypertension and an overall increased risk for myocardial infarction. The exact mechanisms responsible for these cardiovascular effects remain unclear. METHODS AND RESULTS: We performed a blinded field study in 75 healthy volunteers (mean age 26 years), who were exposed at home, in random order, to one control pattern (no noise) and two different noise scenarios [30 or 60 aircraft noise events per night with an average maximum sound pressure level (SPL) of 60 dB(A)] for one night each. We performed polygraphy during each study night. Noise caused a worsening in sleep quality (P < 0.0001). Noise60, corresponding to equivalent continuous SPLs of 46.3 dB (Leq) and representing environmental noise levels associated with increased cardiovascular events, caused a blunting in FMD (P = 0.016). As well, although a direct comparison among the FMD values in the noise groups (control: 10.4 ± 3.8%; Noise30: 9.7 ± 4.1%; Noise60: 9.5 ± 4.3%, P = 0.052) did not reach significance, a monotone dose-dependent effect of noise level on FMD was shown (P = 0.020). Finally, there was a priming effect of noise, i.e. the blunting in FMD was particularly evident when subjects were exposed first to 30 and then to 60 noise events (P = 0.006). Noise-induced endothelial dysfunction (ED) was reversed by the administration of Vitamin C (P = 0.0171). Morning adrenaline concentration increased from 28.3 ± 10.9 to 33.2 ± 16.6 and 34.1 ± 19.3 ng/L (P = 0.0099). Pulse transit time, reflecting arterial stiffness, was also shorter after exposure to noise (P = 0.003). CONCLUSION: In healthy adults, acute nighttime aircraft noise exposure dose-dependently impairs endothelial function and stimulates adrenaline release. Noise-induced ED may be in part due to increased production in reactive oxygen species and may thus be one mechanism contributing to the observed association of chronic noise exposure with cardiovascular disease.

Large Language Models for Synthetic Tabular Health Data: A Benchmark Study.

Synthetic tabular health data plays a crucial role in healthcare research, addressing privacy regulations and the scarcity of publicly available datasets. This is essential for diagnostic and treatment advancements. Among the most promising models are transformer-based Large Language Models (LLMs) and Generative Adversarial Networks (GANs). In this paper, we compare LLM models of the Pythia LLM Scaling Suite with varying model sizes ranging from 14M to 1B, against a reference GAN model (CTGAN). The generated synthetic data are used to train random forest estimators for classification tasks to make predictions on the real-world data. Our findings indicate that as the number of parameters increases, LLM models outperform the reference GAN model. Even the smallest 14M parameter models perform comparably to GANs. Moreover, we observe a positive correlation between the size of the training dataset and model performance. We discuss implications, challenges, and considerations for the real-world usage of LLM models for synthetic tabular data generation.

Modelling Events in Biomedical Decision Support Systems Using Ontologies.

Biomedical decision support systems play a crucial role in modern healthcare by assisting clinicians in making informed decisions. Events, such as physiological changes or drug reactions, are integral components of these systems, influencing patient outcomes and treatment strategies. However, effectively modeling events within these systems presents significant challenges due to the complexity and dynamic nature of medical data. Especially the differentiation between events and processes as well as the nature of events is often unclear. This paper explores approaches to modeling events in biomedical decision support systems, considering factors such as ontology-based representation. By addressing these challenges, we strive to provide the means for enhancing the functionality and interpretability of biomedical decision support systems concerning events.

What Kind of Transformer Models to Use for the ICD-10 Codes Classification Task.

Coding according to the International Classification of Diseases (ICD)-10 and its clinical modifications (CM) is inherently complex and expensive. Natural Language Processing (NLP) assists by simplifying the analysis of unstructured data from electronic health records, thereby facilitating diagnosis coding. This study investigates the suitability of transformer models for ICD-10 classification, considering both encoder and encoder-decoder architectures. The analysis is performed on clinical discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset, which contains an extensive collection of electronic health records. Pre-trained models such as BioBERT, ClinicalBERT, ClinicalLongformer, and ClinicalBigBird are adapted for the coding task, incorporating specific preprocessing techniques to enhance performance. The findings indicate that increasing context length improves accuracy, and that the difference in accuracy between encoder and encoder-decoder models is negligible.

Is Medical Informatics a Scientific Discipline or Just Applied Computer Science?

The aim of this paper is to investigate whether and how medical informatics can claim to have a sound scientific basis. Why is such clarification fruitful? First, it provides a common ground for the core principles, theories and methods used to gain knowledge and to guide the practice. Without such a ground, medical informatics might be subsumed to medical engineering at one institution and to life sciences at another institution or might be just regarded as an application domain within computer science. We will provide a succinct outline of the philosophy of science, after which we provide an application of the related notions in order to decide the scientific status of medical informatics. We justify viewing medical informatics as an interdisciplinary field with a paradigm that can be formulated as "user-centered process-orientation in the healthcare setting". Even if MI is not merely applied computer science, it still remains uncertain whether it will attain the status of a mature science, especially without comprehensive theories.

Implementing Informative-Based Active Learning in Biomedical Record Linkage for the Splink Package in Python.

In biomedical record linkage, efficient determination of a threshold to decide at which level of similarity two records should be classified as belonging to the same patient is frequently still an open issue. Here, we describe how to implement an efficient active learning strategy that puts into practice a measure of usefulness of training sets for such a task. Our results show that active learning should always be considered when training data is to be produced via manual labeling. In addition to that, active learning gives a quick indication how complex a problem is by looking into the label frequencies: If the most difficult entities are always stemming from the same class, then the classifier will probably have less problems in distinguishing the classes. In big data applications, these two properties are essential, as the problems of under- and overfitting are exacerbated in such contexts.

What Kind of Ontologies Do We Need in the Biomedical Domain?

We tackle the question as to what sort of ontologies we primarily need in the biomedical domain. For this purpose, we will first provide a simple categorization of ontologies and describe an important use case related to modeling and documenting events. Then, the impact of using upper-level ontologies as a basis to address our use case will be shown in order to derive an answer to our research question. Although formal ontologies can serve as a starting point to understand conceptualization in a domain and facilitate interesting inferences, it is even more important to account for the dynamic and changing nature of knowledge. Being unconstrained by pre-defined categories and relationships can facilitate timely enrichment of a conceptual scheme and provide links and dependency structures in an informal manner. Semantic enrichment can be achieved by other mechanisms such as tagging or the creation of synsets as, for example, provided in WordNet.

Nurse Scheduling with State-of-the Art Open-Source Tools.

Nurse scheduling is still an unsolved issue, as it is NP-hard and highly context-dependent. Despite this fact, the practice needs guidance on how to tackle this problem without using costly commercial tools. Concretely, we have the following use case: a Swiss hospital is planning a new station designed for nurse training. The capacity planning is finished, and the hospital wants to assess whether shift planning with known constraints leads to valid solutions. Here, a mathematical model is combined with a genetic algorithm. We trust the solution of the mathematical model more, but if it does not provide a valid solution, we try out an alternative. Our solutions indicate that actual capacity planning together with the hard constraints cannot lead to valid staff schedules. The central conclusion is that more degrees of freedom are necessary and that open-source tools OMPR and DEAP are valuable alternatives to commercial products such as Wrike or Shiftboard, in which the degree of freedom of customization is reduced in favor of easiness of use.

Medical Informatics in a Tension Between Black-Box AI and Trust.

For medical informaticians, it became more and more crucial to assess the benefits and disadvantages of AI-based solutions as promising alternatives for many traditional tools. Besides quantitative criteria such as accuracy and processing time, healthcare providers are often interested in qualitative explanations of the solutions. Explainable AI provides methods and tools, which are interpretable enough that it affords different stakeholders a qualitative understanding of its solutions. Its main purpose is to provide insights into the black-box mechanism of machine learning programs. Our goal here is to advance the problem of qualitatively assessing AI from the perspective of medical informaticians by providing insights into the central notions, namely: explainability, interpretability, understanding, trust, and confidence.

Explaining Contextualized Word Embeddings in Biomedical Research - A Qualitative Investigation.

Contextualized word embeddings proved to be highly successful quantitative representations of words that allow to efficiently solve various tasks such as clinical entity normalization in unstructured texts. In this paper, we investigate how the Saussurean sign theory can be used as a qualitative explainable AI method for word embeddings. Our assumption is that the main goal of XAI is to produce confidence and/or trust, which can be gained through quantitative as well as quantitative approaches. One important result is related to the fact that the differential structure of language as explained by Saussure corresponds to the possibility of adding and subtracting word embeddings. On the other hand, these mathematical structures provide insights into the inner workings of natural language.

The Sortal Concept in the Context of Biomedical Record Linkage.

Biomedical Record Linkage is especially designed for linking data of patients in different data repositories. An important question in this context is whether singling-out is sufficient for identifying a patient, and if not, what is in general required for identification. To provide hints for an answer, we will extend previous works on the concept of identity and extend the sortal concept, stemming from analytical philosophy and upper-level ontologies. A sortal is a concept that is associated with an identity criterion. For example, the concept "set" has the identity criterion "having the same members". Based on a description of a record linkage setting, we operationalize the sortal concept by providing a distinction between the digital representation of a person (d-sortal) and the person in flesh (b-sortal).

Embedding Risk-Based Anonymization into Data Access Control for Providing Individual-Level Health Data in a Secure Way.

Especially in biomedical research, individual-level data must be protected due to the sensitivity of the data that is associated with patients. The broad goal of scientific data re-use is to allow many researchers to derive new hypotheses and insights from the data while preserving privacy. Data usage control (DUC) as an attribute-based access mechanism promises to overcome the limitations of traditional access control models achieving that goal. Park and Sandhu provided the usage control (UCON) model as an instance of DUC, which defines policies that evaluate certain attributes. Here, we present an UCON-based architecture, which is augmented with risk-based anonymization as provided by the R package sdcMicro and an extensible Access Control Markup Language (XACML) environment with a core policy decision point as implemented by authzforce.