Use and Evaluation of GANs for Synthetic Data Generation in Pharmacogenetics.

Pharmacogenetics (PGx) explores the influence of genetic variability on drug efficacy and tolerability. Synthetic Data Generation (SDG) has emerged as a promising alternative to the labor-intensive process of collecting real-world PGx data, which is required for high-qualitative prediction models. This study investigates the performance of two Generative Adversarial Network (GAN) models, CTGAN and CTAB-GAN+, in generating synthetic PGx data.

Mapping of Health System Performance Indicators to the WHO HSPA Framework.

Healthcare systems worldwide face escalating costs and demographic changes, necessitating effective evaluation tools to understand their underlying challenges. Switzerland's high-quality yet costly healthcare system underscores the need for robust assessment methods. Existing international rankings often lack transparency and comparability, highlighting the value of structured frameworks like the Health System Performance Assessment (HSPA) by the World Health Organization (WHO).

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.

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).

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.

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.

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.

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).

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.

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.

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.

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.

Mapping SNOMED CT Codes to Semi-Structured Texts via an NLP Pipeline.

In the project presented here, we used NLP tools for annotating German medical trainings documents with SNOMED CT codes. Following research question was addressed: Is it possible to automate the annotation of training documents with an NLP pipeline especially designed for this task but requiring translation into English? The goal of our stakeholder, an institution responsible for the continuing education of physicians, was to facilitate the switch between different medical trainings programs by coding the same requirement with the same SNOMED CT code, even if the wording is different. We first describe how we chose the concrete NLP tools, after which the concrete steps for implementing our prototype are outlined: the NLP pipeline construction, the implementation, and the validation.

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.

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.

A Consent Tool for Secondary Use of Biomedical Data.

To pursue scientific goals with patient data usually requires informed consent from the data subjects. Such a consent constitutes a contract between the research institute and the patient. Several issues must be included in the consent to be valid, for example, how the data is processed and stored as well as specifics of the research questions for which the data is going to be used.

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.

Developing an Ontology for Documenting Adverse Events While Avoiding Pitfalls.

Ontologies promise more benefits than terminologies in terms of data annotation and computer-assisted reasoning, by defining a hierarchy of terms and their relations within a domain. Here, we present central insights related to the development of an ontology for documenting events during interoperative neuromonitoring (IOM), for which we used the Basic Formal Ontology (BFO) as an upper-level ontology. This work has the following two goals: to describe the development of the IOM ontology and to guide the practice with respect to documenting of biomedical events, as available ontologies pose difficulties on certain issues.

Pro-Active Detection of Potentially Wrong Diagnoses Due to Substantial Changes of Laboratory Measurements.

For guiding decisions on medical diagnoses and diagnoses, it is crucial to receive valid laboratory test results. However, such results can be implausible for the physician, even if the measurements are within the range of known reference values. There are technical sources of implausible results that are related to the laboratory environment, which are frequently not detected through usual measures for ensuring technical validity.

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.

Digitizing Data Management for Intraoperative Neuromonitoring.

Intraoperative neurophysiological monitoring (IOM) enables a function-preserving surgical strategy for surgeries of brain or spinal cord pathologies by neurophysiological measurements. However, the IOM data management at neurosurgical institutions are often either not digitized or inefficient in terms of collecting, storing and processing of IOM data. Here, we describe the development of a web application, called IOM-Manager, as a first step towards the complete digitization of the IOM workflow.

Decentralized Digital Health Services Caught Between the Pressure for Innovation and the Burden of Regulations.

Multiple challenges await third-party digital health services when trying to enter the health market. Prominent examples of such services are clinical decision support systems provided as external software. Uncertainty about their challenges, technical as well as legal, pose serious hurdles for many innovations to be adopted early on.