Studies in health technology and informatics最新文献

Data-driven technologies have improved the efficiency, reliability and effectiveness of healthcare services, but come with an increasing demand for data, which is challenging due to privacy-related constraints on sharing data in healthcare contexts. Synthetic data has recently gained popularity as potential solution, but in the flurry of current research it can be hard to oversee its potential. This paper proposes a novel taxonomy of synthetic data in healthcare to navigate the landscape in terms of three main varieties. Data Proportion comprises different ratios of synthetic data in a dataset and associated pros and cons. Data Modality refers to the different data formats amenable to synthesis and format-specific challenges. Data Transformation concerns improving specific aspects of a dataset like its utility or privacy with synthetic data. Our taxonomy aims to help researchers in the healthcare domain interested in synthetic data to grasp what types of datasets, data modalities, and transformations are possible with synthetic data, and where the challenges and overlaps between the varieties lie.

This study leverages the DCTPep database, a comprehensive repository of cancer therapy peptides, to explore the application of machine learning in accelerating cancer research. We applied Principal Component Analysis (PCA) and K-means clustering to categorize cancer therapy peptides based on their physicochemical properties. Our analysis identified three distinct clusters, each characterized by unique features such as sequence length, isoelectric point (pI), net charge, and mass. These findings provide valuable insights into the key properties that influence peptide efficacy, offering a foundation for the design of new therapeutic peptides. Future work will focus on experimental validation and the integration of additional data sources to refine the clustering and enhance the predictive power of the model, ultimately contributing to the development of more effective peptide-based cancer treatments.

We propose a deep learning method to model and generate synthetic aortic shapes based on representing shapes as the zero-level set of a neural signed distance field, conditioned by a family of trainable embedding vectors with encode the geometric features of each shape. The network is trained on a dataset of aortic root meshes reconstructed from CT images by making the neural field vanish on sampled surface points and enforcing its spatial gradient to have unit norm. Empirical results show that our model can represent aortic shapes with high fidelity. Moreover, by sampling from the learned embedding vectors, we can generate novel shapes that resemble real patient anatomies, which can be used for in-silico trials.

The paper presents perceptions and feedback from speech therapists and parents embracing the idea of using digital tools in improving the language of the children with speech disorders. The authors investigated the perception of speech therapists and parents and their readiness to use digital tools in speech therapy starting from several digital applications from the domain. The feedback was positive, 88.3% of the parents agree to use digital apps at home, between face-to-face speech therapy sessions coordinated by speech therapists and 75% of parents agree to use them several days a week.

Medication review is a collaborative process between pharmacists and general practitioners (GPs) aimed at optimizing patient care by identifying and eliminating harmful medications. This paper proposes a collaborative platform to enhance pharmacist-GP interactions, assess drug-drug interactions, evaluate adverse effects, and manage dosages. The platform uses the issue mapping function of IBIS to structure dialogues and systematically evaluates proposed actions using the QuAD framework to support decision-making. An ontology based on medical knowledge ensures consistency, while visual enhancements such as varying edge width, color coding, and highlighting preferred actions enable swift, informed decisions. These tools improve collaboration and patient care outcomes.

The German National Medication Plan (GNMP) can be a valuable and interoperable data source for clinical studies, due to its digital specification and mandatory provisioning for chronically ill patients. Digital transfer of a patients current GNMP from the Patient Data Management System (PDMS) into electronic case report forms would avoid error prone manual data capturing. It is also essential for studies in practice-based research networks (PBRN), where data capturing must have as little impact as possible on everyday practice. The following issues are currently preventing seamless digital integration: There is no standardized interoperable export of the GNMP from PDMS. In the current form, pharmaceutical catalogs are needed to decode the contained pharmaceutical registration numbers. As accessibility to the pharmaceutical catalogs is restricted, there is no generic access to the actual information needed for study data evaluation. In order to conduct studies, feasible workarounds for these issues had to be implemented in the standard operating procedures, tools and participating GP practices. To overcome the GNMP's current lack of digital interoperability, the proposed solution combines semi-automated data export from PDMS at the GP practice and manual database search at the study center with a semi-automated processing pipeline to balance workload between GP practices, study management and evaluation.

Precision Forestry is an emerging approach that uses digital technologies for data-driven decision-making in environmental management. Traditional methods for assessing tree risk are often subjective and focus on individual trees using mechanical approaches. The #SecureTree model offers an innovative alternative by deploying sensors to measure biophysical parameters like temperature, humidity, and acceleration. Data from these sensors is processed to create a risk assessment map based on the progression of trees' behaviors. This model is non-invasive and objective, addressing risk more effectively than current methods. Field tests validated the model's accuracy and highlighted its potential to identify long-term risk trends, enabling better planning for disruptive events and the development of digital strategies for emergency management.

Infectious intestinal disease (IID) is a syndrome consisting of diarrhoea and vomiting symptoms linked to a causative pathogen. The Third Study of IID (IID3) will report its incidence in the community within the UK and assess how it has changed since the second IID study (IID2) in 2012. We implemented an automated, online patient recruitment process within a national sentinel surveillance network and compared its performance versus IID2 in terms of: Patient recruitment rates and demographic characteristics of recruited participants. We utilised a text messaging system (TMS) running off a computerised medical record systems (CMR) application programme interface (API). Demographic analysis showed that the majority of those recruited to IID3/IID2 studies were >65 years and female. However, the recruitment of participants of non-white ethnicity was statistically significantly different between IID3/IID2. Further work is required to improve recruitment in the younger patient demographic and in ethnic minority populations.

We present a new methodological approach based on integrating Arden-Syntax-based clinical decision support (CDS) with an upstream ontology service. Incoming linguistic patient data, such as single reports about detected germs or viruses, shall be identified by the applied ontology at a low level. Then, higher-level concepts are activated by ontology-based bottom-up reasoning. Access to these high-level concepts is then provided by Arden-Syntax-based CDS. The results suggest promising directions for future enhancements in knowledge-based artificial intelligence.

The Arden Syntax is a language designed for the encoding of medical knowledge into clinical decision support systems. Its evolution is overseen by Health Level 7. A significant enhancement in its new version 3.0 is the incorporation of FHIR for data retrieval, which addresses the long-standing curly braces problem. We introduce a newly developed compiler for Arden Syntax 3.0, which employs modern tools such as ANTLR for lexical and parsing analysis and GraalVM with the Truffle Language Implementation Framework for semantic processing, optimization, and language execution. Comprehensive testing against a legacy compiler revealed substantial improvements in execution speed, memory efficiency, and code quality. These advancements, coupled with superior maintainability and extensibility, position the Truffle compiler as a robust replacement, supporting future development and enhancing the user experience with Arden-Syntax-based clinical decision support.