Annotated Data Research Articles

PIS-Net: Efficient weakly supervised instance segmentation network based on annotated points for rice field weed identification

Weed damage in rice fields is one of the main causes of reduced rice yields and quality. Accurate and efficient weed identification is the prerequisite for realizing intelligent and precise weeding in paddies. Recently, Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to the convolutional neural network (CNN)-based models. However, the lack of fully labeled weed datasets hinders the potential application of deep learning models in weed identification. To address the above issues, this study customizes a novel point-supervised instance segmentation network (PIS-Net) for weakly supervised instance segmentation of weeds in rice fields. More correctly, we first propose a novel instance segmentation point labeling scheme that utilizes randomly generated annotation points within each instance, aiming to decrease both labeling time and difficulty. Additionally, to make optimal use of point labels, this study puts forth a mask generation strategy based on the adaptive selection of pyramid levels. In this sense, the network model can flexibly choose the pyramid level expected to generate the most suitable instance mask based on the network's reliability. Finally, we establish the pseudo label refinement network (PLR-Net) to refine rough instance masks. The proposed PIS-Net utilizes 13 randomly generated annotation points for each instance, yet achieving an AP of 38.5 and an AP50 of 68.3, which is superior to the baseline mask-R-CNN with an AP of 8.2 and AP50 of 6.9, achieving 90 % fully supervised performance. This method effectively utilizes point labels, annotated with high efficiency, as a robust source of weak supervision to address challenges in weed data annotation and the low accuracy of existing weakly supervised models. Experiments show that the point annotation scheme of the PIS-Net is faster than full-object mask annotation, and the AP is also higher than the current semi-supervised weed segmentation model, enjoying high potentials in practical paddy fields.

Highly contiguous genome assembly and gene annotation of the short-finned eel (Anguilla bicolor pacifica)

In East Asia, anguillid eels are commercially important. However, unlike other species, they have not been successfully cultivated throughout their lifecycle. Facing population decline due to overharvesting and environmental pressures, the industry is turning to alternatives, such as Anguilla bicolor pacifica (short-finned eel). However, genomic data for short-finned eels are unavailable. Here, we present in-depth whole-genome sequencing results for short-finned eel obtained using two sequencing platforms (PacBio Revio, and Illumina). In this study, we achieved a highly contiguous genome assembly of the short-finned eel, comprising 19 pseudochromosomes encompassing 99.76% of the 1.087 Gb genome sequence with an N50 of 16.88 and 61.07 Mb from contig and scaffold, respectively. Transcripts from four different tissues led to the annotation of 23,095 protein-coding genes in the eel genome, 98.66% of which were functionally annotated. This high-quality genome assembly, along with the annotation data, provides a foundation for future functional genomic studies of short-finned eels.

Transcriptome Analysis of Multiple Plant Parts in the Woody Oil Tree Camellia drupifera Loureiro

Camellia drupifera is mainly used in forestry for its high-value industrial products; however, limited information is available on its transcriptome. This study aimed to construct a full-length transcriptome sequence based on the PacBio sequencing platform for various plant parts of C. drupifera, including flower buds, leaves, leaf buds, branches, the pericarp, and seed kernels. The transcriptomes were annotated with 23,207 genes, with 58 subgroups in the GO classification. The KEGG database revealed 10,407 genes involved in the metabolic pathway analysis, with 68,192 coding sequences, 3352 TF families, 48,541 SSRs, 1421 IncRNAs, and 2625 variable shears predicted. The transcriptomes of different parts were analyzed and compared. The majority of differentially expressed genes (DEGs) were found between the pericarp and seed kernels, followed by leaves and the pericarp with 5662 DEGs, and flower buds and leaf buds with 1616 DEGs. GO and KEGG enrichment analyses showed that KEGG differential genes were significant in microbial metabolism, carbon metabolism, and other functions. The data annotation and analysis of the full-length transcriptome and the comparative analysis between different plant parts provided a theoretical basis for studying gene function, metabolic pathway regulation, and gene expression analysis in KEGG.

Identifying Legal, BIM Data and Visualization Requirements to Form Legal Spaces and Developing a Web-Based 3D Cadastre Prototype: A Case Study of Condominium Building

Over the past decade, numerous countries and researchers have been investigating the potential of 3D cadastre based on the Building Information Model (BIM). In Türkiye, the General Directorate of Land Registry and Cadastre (GDLRC) has been engaged in the “Production of 3D City Models and Creation of 3D Cadastral Bases Project” since 2018. One of the objectives is to develop 3D (physical) digital building models (and databases) through the digitization of floor plans of architectural drawings. In order to ensure the long-term viability of this project, a new regulation was issued in 2021. This regulation mandates the submission of 3D digital building models to the land registry in order to register condominiums. The future goals of the GDLRC include utilizing the 3D digital building models produced to create legal spaces in condominium buildings for the 3D cadastre. However, no research has yet been carried out for this goal. The objective of this research is to identify the legal and BIM data requirements for deriving legal spaces in condominium buildings, in light of the legislative analysis, and to develop a web-based 3D cadastre visualization prototype (showing both legal spaces and physical objects) based on the requirements obtained from the scientific literature. The result of this research demonstrates that well-structured and annotated BIM data can be used to develop a 3D cadastre prototype that meets the legal requirements in the case of Türkiye. Moreover, it is evident that, although the GDLRC has initiated the construction of a robust foundation for a 3D cadastre based on BIM, further enhancements and resolutions must be implemented from a legal and technical standpoint. It is postulated that the identified requirements and the proposed methodology in this research may assist decision-makers in Türkiye and globally in formulating their strategic plans for a 3D cadastre.

Enhanced Crime Prediction with Computer Vision-Yolov4 Approach

This research paper presents the development of an artificial intelligence safety application on an HP Pavilion gaming machine, utilizing criminal footage from reputable databases like the UCF-Crime open-source dataset. The system underwent meticulous data annotation to identify five distinct classes crucial for anomaly detection: Person, Short Gun, Handgun, Knife, and Rifle. Supervised machine learning techniques were applied, focusing on monitoring human trajectories and employing deep-SORT and Euclidean distance computations to track individuals, simulating real-world crime scenarios. The AI safety model showcased outstanding performance with an average precision rate of approximately 86.43%, exceeding 90% after 2000 iterations, demonstrating versatility across all categories with notable average precision accuracies for rifles (98.90%), handguns (96.93%), and knives (97.66%). Enhancements to the Python script improved the system's ability to detect weapons sub-objects in human subjects and classify potential perpetrators as high risk, a novel aspect of this study.

One-Shot Learning from Prototype Stock Keeping Unit Images

This paper highlights the importance of one-shot learning from prototype Stock Keeping Unit (SKU) images for efficient product recognition in retail and inventory management. Traditional methods require large supervised datasets to train deep neural networks, which can be costly and impractical. One-shot learning techniques mitigate this issue by enabling classification from a single prototype image per product class, thus reducing data annotation efforts. We introduce the Variational Prototyping Encoder (VPE), a novel deep neural network for one-shot classification. Utilizing a support set of prototype SKU images, VPE learns to classify query images by capturing image similarity and prototypical concepts. Unlike metric learning-based approaches, VPE pre-learns image translation from real-world object images to prototype images as a meta-task, facilitating efficient one-shot classification with minimal supervision. Our research demonstrates that VPE effectively reduces the need for extensive datasets by utilizing a single image per class while accurately classifying query images into their respective categories, thus providing a practical solution for product classification tasks.

SPORT: A Subgraph Perspective on Graph Classification with Label Noise

Graph neural networks (GNNs) have achieved great success recently on graph classification tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels could exist in the real world because of the complicated processes of manual graph data annotations, which may significantly degrade the performance of GNNs. Therefore, we investigate the problem of graph classification with label noise, which is demanding because of the complex graph representation learning issue and serious memorization of noisy samples. In this work, we present a novel approach called S ubgra p h Set Netw or k with Sample Selection and Consis t ency Learning (SPORT) for this problem. To release the overfitting of GNNs, SPORT proposes to characterize each graph as a set of subgraphs generated by certain predefined stratagems, which can be viewed as samples from its underlying semantic distribution in graph space. Then we develop an equivariant network to encode the subgraph set with the consideration of the symmetry group. To further release the influences of noisy examples, we leverage the predictions of subgraphs to measure the likelihood of a sample being clean or noisy, followed by effective label updating. In addition, we propose a joint loss to advance the model generalizability by introducing consistency regularization. Comprehensive experiments on a wide range of graph classification datasets demonstrate the effectiveness of our SPORT. Specifically, SPORT outperforms the most competing baseline by up to 6.4%.

Towards Cross-lingual Social Event Detection with Hybrid Knowledge Distillation

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the common lesser-spoken languages relatively unexplored. Thus, in this work, we present a GNN-based framework that integrates cross-lingual word embeddings into the process of graph knowledge distillation for detecting events in low-resource language data streams. To achieve this, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Specifically, to extract sufficient useful knowledge, we propose a hybrid distillation method that consists of both feature-wise and relation-wise information. To transfer both kinds of knowledge in an effective way, we add a cross-lingual module in the feature-wise distillation to eliminate the language gap and selectively choose beneficial relations in the relation-wise distillation to avoid distraction caused by teachers’ misjudgments. Our proposed CLKD framework also adopts different configurations to suit both offline and online situations. Experiments on real-world datasets show that the framework is highly effective at detection in languages where training samples are scarce.

Enhancing semi‐supervised contrastive learning through saliency map for diabetic retinopathy grading

AbstractDiabetic retinopathy (DR) is a severe ophthalmic condition that can lead to blindness if not diagnosed and provided timely treatment. Hence, the development of efficient automated DR grading systems is crucial for early screening and treatment. Although progress has been made in DR detection using deep learning techniques, these methods still face challenges in handling the complexity of DR lesion characteristics and the nuances in grading criteria. Moreover, the performance of these algorithms is hampered by the scarcity of large‐scale, high‐quality annotated data. An innovative semi‐supervised fundus image DR grading framework is proposed, employing a saliency estimation map to bolster the model's perception of fundus structures, thereby improving the differentiation between lesions and healthy regions. By integrating semi‐supervised and contrastive learning, the model's ability to recognise inter‐class and intra‐class variations in DR grading is enhanced, allowing for precise discrimination of various lesion features. Experiments conducted on publicly available DR grading datasets, such as EyePACS and Messidor, have validated the effectiveness of our proposed method. Specifically, our approach outperforms the state of the art on the kappa metric by 0.8% on the full EyePACS dataset and by 3.2% on a 10% subset of EyePACS, demonstrating its superiority over previous methodologies. The authors’ code is publicly available at https://github.com/500ZhangJC/SCL‐SEM‐framework‐for‐DR‐Grading.

Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson's Disease Detection and Speech Features Extraction.

Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.

Domain knowledge-driven encoder–decoder for nasopharyngeal carcinoma segmentation

Accurate image segmentation is vital for the diagnosis and treatment of nasopharyngeal carcinoma (NPC). Although deep neural networks have shown promising performance in NPC segmentation, they depend on large-scale pixel-level annotation datasets for purely data-driven model training, which poses practical limitations. To address this challenge, this paper explores the domain knowledge, namely the expert knowledge from radiologists, and proposes a domain knowledge-driven encoder–decoder architecture. Specifically, the domain knowledge on image spatial information is formulated as Gaussian mixture distribution and then transformed into an optimal transport-based expert-prior regularization of the encoder, which enhances the model’s ability in capturing discriminative features. To project the encoded features onto pixel space and obtain the segmentation maps, a cross-scale feature refinement module is built in the decoder with theoretical justification, which integrates the domain knowledge that radiologists segment NPC in a gradual refinement process. Experimental results verify the effectiveness of the proposed method for NPC segmentation. Remarkably, despite using only 10% of pixel-level annotation data, the domain knowledge-driven model outperforms recent deep neural networks that use the entire training data.

Human sleeping pose estimation from IR images for in-bed patient monitoring using image processing and deep learning techniques

Human Pose Estimation (HPE) is a crucial step towards understanding people in images and videos. HPE provides geometric and motion information of the human body, which has been applied to a wide range of applications (e.g., human-computer interaction, motion analysis, augmented reality, virtual reality, healthcare, etc.). An extremely useful task of this kind is the 2D pose estimation of bedridden patients from infrared (IR) images. Here, the IR imaging modality is preferred due to privacy concerns and the need for monitoring both uncovered and covered patients at different levels of illumination. The major drawback of this research problem is the unavailability of covered examples, which are very costly to collect and time-consuming to label. In this work, a deep learning-based framework was developed for human sleeping pose estimation on covered images using only the uncovered training images. In the training scheme, two different image augmentation techniques, a statistical approach as well as a GAN-based approach, were explored for domain adaptation, where the statistical approach performed better. The accuracy of the model trained on the statistically augmented dataset was improved by 124 % as compared with the model trained on non-augmented images. To handle the scarcity of training infrared images, a transfer learning strategy was used by pre-training the model on an RGB pose estimation dataset, resulting in a further increment in accuracy of 4 %. Semi-supervised learning techniques, with a novel pose discriminator model in the loop, were adopted to utilize the unannotated training data, resulting in a further 3 % increase in accuracy. Thus, significant improvement has been shown in the case of 2D pose estimation from infrared images, with a comparatively small amount of annotated data and a large amount of unannotated data by using the proposed training pipeline powered by heavy augmentation.

A Dataset for Detection and Segmentation of Underwater Marine Debris in Shallow Waters

Robust object detection is crucial for automating underwater marine debris collection. While supervised deep learning achieves state-of-the-art performance in discriminative tasks, replicating this success on underwater data is challenging. The generalization of these methods suffers due to a lack of available annotated data considering different sources of variation in the unstructured underwater environment and imaging conditions. In this paper, we present the Seaclear Marine Debris Dataset, the first publicly available shallow-water marine debris dataset annotated for instance segmentation/object detection. The dataset contains 8610 images collected using ROVs at multiple locations and with different cameras, annotated for 40 object categories, encompassing not only litter but also observed animals, plants, and robot parts. As part of the technical validation, we provide baseline results for object detection using Faster RCNN and YOLOv6 models. Furthermore, we demonstrate the non-triviality of generalizing the trained model performance to unseen sites and cameras due to domain shift. This underscores the value of the presented dataset in further developing robust models for underwater debris detection.

Cross-modal Transfer Learning Based on an Improved CycleGAN Model for Accurate Kidney Segmentation in Ultrasound Images

ObjectiveDeep-learning algorithms have been widely applied in the field of automatic kidney ultrasound (US) image segmentation. However, obtaining a large number of accurate kidney labels clinically is very difficult and time-consuming. To solve this problem, we have proposed an efficient cross-modal transfer learning method to improve the performance of the segmentation network on a limited labeled kidney US dataset. MethodsWe aim to implement an improved image-to-image translation network called Seg-CycleGAN to generate accurate annotated kidney US data from labeled abdomen computed tomography images. The Seg-CycleGAN framework primarily consists of two structures: (i) a standard CycleGAN network to visually simulate kidney US from a publicly available labeled abdomen computed tomography dataset; (ii) and a segmentation network to ensure accurate kidney anatomical structures in US images. Based on the large number of simulated kidney US images and small number of real annotated kidney US images, we then aimed to employ a fine-tuning strategy to obtain better segmentation results. ResultsTo validate the effectiveness of the proposed method, we tested this method on both normal and abnormal kidney US images. The experimental results showed that the proposed method achieved a segmentation accuracy of 0.8548 in dice similarity coefficient on all testing datasets and 0.7622 on the abnormal testing dataset. ConclusionsCompared with existing data augmentation and transfer learning methods, the proposed method improved the accuracy and generalization of the kidney US image segmentation network on a limited number of training datasets. It therefore has the potential to significantly reduce annotation costs in clinical settings.

A domain adaptation framework for cross-modality SAR 3D reconstruction point clouds segmentation utilizing LiDAR data

Synthetic aperture radar (SAR) 3D point clouds reconstruction can eliminate the problems of layover in 2D SAR image projections, the recognition of the reconstructed point cloud can significantly enhance target identification and information extraction. Current SAR 3D point cloud segmentation methods, based on traditional machine learning clustering approaches, suffer from poor automation and accuracy. However, the absence of publicly labeled SAR datasets impedes the progress of deep learning-based SAR 3D point cloud segmentation methods. To tackle the aforementioned challenges, we introduce, for the first time, an alternative training approach for SAR 3D point cloud segmentation using LiDAR annotated data, which offers a more abundant sample pool, thus alleviating the lack of training sets. Nevertheless, a segmentation model trained on LiDAR point clouds exhibits a significant decline in performance when directly applied to SAR 3D reconstructed point clouds due to the cross-domain discrepancy. This research presents a pioneering domain adaptation 3D semantic segmentation framework to implement cross-modal learning for SAR point clouds. In our scheme, a simple yet effective technique is developed to achieve segmentation of SAR double-bounce scattering regions called SARDBS-Mix, which employs a mixing strategy, to capture the distinctive reflection characteristics of SAR data. Furthermore, we implement approaches including center alignment and normalization, local augmentation, and weighted cross entropy to mitigate LiDAR and SAR domain gap and class imbalances. The experimental results validate the feasibility and effectiveness of the proposed method for SAR 3D reconstructed point cloud segmentation.

Genome-Wide Association Studies Reveal That the Abietane Diterpene Isopimaric Acid Promotes Rice Growth through Inhibition of Defense Pathways.

Plants are an important source for the discovery of novel natural growth regulators. We used activity screening to demonstrate that treatment of Nipponbare seeds with 25 μg/mL isopimaric acid significantly increased the resulting shoot length, root length, and shoot weight of rice seedlings by 11.37 ± 5.05%, 12.96 ± 7.63%, and 27.98 ± 10.88% and that it has a higher activity than Gibberellin A3 (GA3) at the same concentration. A total of 213 inbred lines of different rice lineages were screened, and we found that isopimaric acid had different growth promotional activities on rice seedlings of different varieties. After induction with 25 μg/mL isopimaric acid, 15.02% of the rice varieties tested showed increased growth, while 15.96% of the varieties showed decreased growth; the growth of the remaining 69.02% did not show any significant change from the control. In the rice varieties showing an increase in growth, the shoot length and shoot weight significantly increased, accounting for 21.88% and 31.25%. The root length and weight significantly increased, accounting for 6.25% and 3.13%. Using genome-wide association studies (GWASs), linkage disequilibrium block, and gene haplotype significance analysis, we identified single nucleotide polymorphism (SNP) signals that were significantly associated with the length and weight of shoots on chromosomes 2 and 8, respectively. After that, we obtained 17 candidate genes related to the length of shoots and 4 candidate genes related to the weight of shoots. Finally, from the gene annotation data and gene tissue-specific expression; two genes related to this isopimaric acid regulation phenotype were identified as OsASC1 (LOC_Os02g37080) on chromosome 2 and OsBUD13 (LOC_Os08g08080) on chromosome 8. Subcellular localization analysis indicated that OsASC1 was expressed in the plasma membrane and the nuclear membrane, while OsBUD13 was expressed in the nucleus. Further RT-qPCR analysis showed that the relative expression levels of the resistance gene OsASC1 and the antibody protein gene OsBUD13 decreased significantly following treatment with 25 μg/mL isopimaric acid. These results suggest that isopimaric acid may inhibit defense pathways in order to promote the growth of rice seedlings.

Provenance Information for Biomedical Data and Workflows: Scoping Review.

The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research. This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. This scoping review followed the methodological framework developed by Arksey and O'Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term provenance is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles. As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists. RR2-10.2196/31750.

Low-resource multi-granularity academic function recognition based on multiple prompt knowledge

PurposeFine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.Design/methodology/approachSpecifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.FindingsExtensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.Originality/valueIn addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

Integrated Perspectives on Clinical Decision Support: A Comparative Analysis of Knowledge Management Approaches.

We analyze five approaches to knowledge management in clinical decision support (CDS) systems: pattern recognition based on annotated imaging data, mining of stored structured medical data, text mining of published texts, computable knowledge design, and general or specific text corpora for large language models. Each method's strengths and limitations in automating clinical knowledge management while striving for a zero-error policy are evaluated, offering insights into their roles in enhancing healthcare through intelligent decision support. The study aims to inform decisions in the development of effective, transparent CDS systems in clinical and patient care settings.

Integration of Oncological Data into openEHR: A Path Towards Improved Cancer Care and Research.

The integration of tumor-related diagnosis and therapy data is a key factor for cancer-related collaborative projects and research projects on-site. The Medical Data Integration Center (MeDIC) of the University Hospital Schleswig-Holstein, resulting from the Medical Informatics Initiative and Network University Medicine in Germany, has agreed on an openEHR-based data management based on a centralized repository with harmonized annotated data. Consequently, the oncological data should be integrated into the MeDIC to interconnect the information and thus gain added value. A uniform national data set for tumor-related reports is already defined for the cancer registries. Therefore, this work aims to transform the national oncological basis data set for tumor documentation (oBDS) so that it can be stored and utilized properly in the openEHR repository of the MeDIC. In a previous work openEHR templates representing the oncological basis data set were modeled. These templates were used to implement a processing pipeline including a metadata repository, which defines the mappings between the elements, a FHIR terminology service for annotation and validation, resulting in a tool to automatically build openEHR compositions from oBDS data. The prototype proved the feasibility of the referred mapping, integration into the MeDIC is straightforward and the architecture introduced is adaptable to future needs by design.