Methods of Information in Medicine最新文献

Background: Standardized clinical terminology is essential for semantic interoperability. Typically, a hospital's terminology expert manually maps local terminology with international standards such as SNOMED CT. The manual mapping process is demanding, labor-intensive, and time-consuming, and its effectiveness relies on the expertise of the professional handling it.

Objective: We developed a method to map clinical terms to SNOMED CT concept descriptions using an information retrieval (IR) approach with rich synonyms. We also provide a free mapping support service to help terminology experts alleviate the challenges of manual mapping without the need for additional manipulation.

Methods: We created indexes using edge n-grams and synonyms. We adopted Elasticsearch for indexing and query processing, incorporating data from the SPECIALIST Lexicon to enrich the synonym database. Eight different indexes were initially created, but only four were retained based on performance. We tested indexes individually and in combination, using a dataset of 1,753 one-to-one mapped instances from the National Library of Medicine ICD-9-CM Procedure codes to the SNOMED CT Map. We compared our approach with MetaMap for evaluation.

Results: We found that using rich synonyms and edge n-gram indexing significantly improved the accuracy of mapping clinical terms to SNOMED CT. The indexes incorporating synonyms and edge n-grams performed better than those using either technique alone. Combining these methods captured more relevant terms and synonyms, resulting in more precise mappings. Our method outperformed the baseline provided by MetaMap, demonstrating enhanced capability in handling complex medical terminology and improving the overall mapping quality.

Conclusions: Our study introduced an IR method with rich synonyms for mapping clinical terms to SNOMED CT, analyzing 42 unmapped terms, and identifying key issues. The approach shows promise in improving terminology mapping, and future work will explore advanced methods to enhance accuracy further, aiming to reduce manual mapping efforts and improve result evaluation.

Multicentre evaluations are key to provide generalisable results on medication safety interventions. Yet, for computerised physician order entry (CPOE) assessment, evaluations are mostly singe-centred and poorly comparable.We performed a multicentre simulation-based lab study at three independent study sites implementing varying CPOE systems with the aim to compare the effects of CPOE implementation on workflow changes, time requirement, and quality of medication documentation.At each study site, medication documentation processes with and without CPOE usage were analysed. Based on patient case scenarios, a simulation-based lab study was performed where the time required to document medication according to the analysed processes was measured. Additionally, the quality of medication documentation with and without CPOE usage was evaluated. Results were compared between the three study sites.At two hospitals, CPOE implementation led to a streamlining of the medication documentation process with less documentation systems and professional groups involved and an elimination of double documentation. However, at one hospital multiple documentation of medication orders persisted even after CPOE implementation. In total, the time required to document medication according to the case scenarios was faster without than with a CPOE system (median time without CPOE = 13:56 minutes [range = 07:29-32:17], median time with CPOE = 16:51 minutes [09:18-33:41], p = 0.047, +20.8%). One hospital was taking considerably longer for medication documentation than the two others, both with and without CPOE usage. Medication documentation quality rose significantly and to a median of 100.0% at each study site with CPOE usage.This study showed that a simulation-based lab study methodology is suitable for comparing CPOE effects across study sites. Furthermore, it provides evidence on the changes in medication documentation workflow, time requirement, and quality that occur with a CPOE implementation.

Cancer registries collect extensive data on cancer patients, including diagnoses, treatments, and disease progression. These data offer valuable insights into cancer care, but it is challenging to analyze due to its complexity. Machine learning techniques, particularly clustering, enable the exploration of treatment data to uncover previously unknown patterns and relationships.This work aimed to develop a method for clustering breast cancer patients in cancer registries based on their treatment courses, to demonstrate the usefulness of clustering for gaining insights, improving data quality, and identifying clinically relevant patterns.We developed a similarity measure adapted from the Levenshtein distance to compare treatment courses, incorporating cancer diagnosis, surgeries, radiotherapies, and systemic therapies. The method was evaluated on 17,822 breast cancer cases diagnosed in 2019 from the cancer registry of North Rhine-Westphalia. Evaluation involved two stages: first, domain experts reviewed the clustering results to assess clinical relevance and interpretability. Second, an intercluster survival analysis was performed to identify clinically relevant differences between treatment patterns.Expert evaluations confirmed that clustering produced clinically plausible groups while also uncovering unexpected treatment patterns and potential data inconsistencies. The survival analysis showed differences in survival between clusters in both prognostically favorable and unfavorable subgroups. These results demonstrate that treatment-course clustering can identify patient groups with differing survival outcomes. However, registry data incompleteness and unmeasured confounders may influence these findings.Clustering treatment courses in cancer registries can reveal data quality issues, distinguish groups with different prognostic profiles, and support exploratory analyses of treatment patterns. While these findings are not intended to guide clinical decision making or evaluate treatment effectiveness, they can help generate hypotheses, identify unexpected care pathways, and support quality monitoring within cancer registries. Future work should focus on improving treatment data completeness, incorporating additional clinical variables, and refining clustering methods for broader applicability.

Existing drug recommendation systems lack integration with up-to-date clinical guidelines (the latest diabetes association standards of care and clinical guidelines that align with local government health care regulations) and lack high-precision drug interaction processing, explainability, and dynamic dosage adjustment. As a result, the recommendations generated by these systems are often inaccurate and do not align with local standards, greatly limiting their practicality.To develop a personalized drug recommendation and dosage optimization system named Diabetes Drug Recommendation System (DDRs), integrating Fast Healthcare Interoperability Resources-standardized electronic health record (EHR) data and up-to-date clinical guidelines for accurate and practical recommendations.We analyzed patients' EHR and International Classification of Diseases-tenth edition codes and integrated them with a drug interaction database to reduce adverse reactions. ADA guidelines and Taiwan's National Health Insurance (NHI) chronic disease guidelines served as data sources. Bio-GPT and Retrieval-Augmented Generation (RAG) were used to build the clinical guideline database and ensure recommendations align with the latest standards, with references provided for interpretability. Finally, optimal dosage was dynamically calculated by integrating patient disease progression trends from the EHR.DDRs achieved superior drug recommendation accuracy (Precision-Recall Area Under the Curve = 0.7951, Jaccard = 0.5632, F1-score = 0.7158), with a low drug-drug interaction rate (4.73%) and dosage error (±6.21%). Faithfulness of recommendations reached 0.850. Field validation with three physicians showed that the system reduced literature review time by 30 to 40% and delivered clinically actionable recommendations.DDRs is the first system to integrate EHR data, LLMs, RAG, ADA guidelines, and Taiwan NHI policies for diabetes treatment. The system demonstrates high accuracy, safety, and interpretability, offering practical decision support in routine clinical settings.

Background:  Textual datasets (corpora) are crucial for the application of natural language processing (NLP) models. However, corpus creation in the medical field is challenging, primarily because of privacy issues with raw clinical data such as health records. Thus, the existing clinical corpora are generally small and scarce. Medical NLP (MedNLP) methodologies perform well with limited data availability.

Objectives:  We present the outcomes of the Real-MedNLP workshop, which was conducted using limited and parallel medical corpora. Real-MedNLP exhibits three distinct characteristics: (1) limited annotated documents: the training data comprise only a small set (∼100) of case reports (CRs) and radiology reports (RRs) that have been annotated. (2) Bilingually parallel: the constructed corpora are parallel in Japanese and English. (3) Practical tasks: the workshop addresses fundamental tasks, such as named entity recognition (NER) and applied practical tasks.

Methods:  We propose three tasks: NER of ∼100 available documents (Task 1), NER based only on annotation guidelines for humans (Task 2), and clinical applications (Task 3) consisting of adverse drug effect (ADE) detection for CRs and identical case identification (CI) for RRs.

Results:  Nine teams participated in this study. The best systems achieved 0.65 and 0.89 F1-scores for CRs and RRs in Task 1, whereas the top scores in Task 2 decreased by 50 to 70%. In Task 3, ADE reports were detected by up to 0.64 F1-score, and CI scored up to 0.96 binary accuracy.

Conclusion:  Most systems adopt medical-domain-specific pretrained language models using data augmentation methods. Despite the challenge of limited corpus size in Tasks 1 and 2, recent approaches are promising because the partial match scores reached ∼0.8-0.9 F1-scores. Task 3 applications revealed that the different availabilities of external language resources affected the performance per language.

Objective:  This is the first Malaysian machine learning model to detect and disambiguate abbreviations in clinical notes. The model has been designed to be incorporated into MyHarmony, a natural language processing system, that extracts clinical information for health care management. The model utilizes word embedding to ensure feasibility of use, not in real-time but for secondary analysis, within the constraints of low-resource settings.

Methods:  A Malaysian clinical embedding, based on Word2Vec model, was developed using 29,895 electronic discharge summaries. The embedding was compared against conventional rule-based and FastText embedding on two tasks: abbreviation detection and abbreviation disambiguation. Machine learning classifiers were applied to assess performance.

Results:  The Malaysian clinical word embedding contained 7 million word tokens, 24,352 unique vocabularies, and 100 dimensions. For abbreviation detection, the Decision Tree classifier augmented with the Malaysian clinical embedding showed the best performance (F-score of 0.9519). For abbreviation disambiguation, the classifier with the Malaysian clinical embedding had the best performance for most of the abbreviations (F-score of 0.9903).

Conclusion:  Despite having a smaller vocabulary and dimension, our local clinical word embedding performed better than the larger nonclinical FastText embedding. Word embedding with simple machine learning algorithms can decipher abbreviations well. It also requires lower computational resources and is suitable for implementation in low-resource settings such as Malaysia. The integration of this model into MyHarmony will improve recognition of clinical terms, thus improving the information generated for monitoring Malaysian health care services and policymaking.

Syndrome is a unique and crucial concept in traditional Chinese medicine (TCM). However, much of the syndrome knowledge lacks systematic organization and correlation, and current information technologies are unsuitable for TCM ancient texts.We aimed to develop a knowledge graph that presents this knowledge in a more orderly, structured, and semantically oriented manner, providing a foundation for computer-aided diagnosis and treatment.We developed a construction framework of TCM syndrome knowledge from ancient books, using a pretrained model and rules (TCMSF). We conducted fine-tuning training on Enhanced Representation through Knowledge Integration (ERNIE), Bidirectional Encoder Representation from Transformers pretrained language models, and chatGLM3-6b large language models for named entity recognition (NER) tasks. Furthermore, we employed the progressive entity relationship extraction method based on the dual pattern feature combination to extract and standardize entities and relationships between entities in these books.We selected Yin deficiency syndrome as a case study and constructed a model layer suitable for the expression of knowledge in these books. Compared with multiple NER methods, the combination of ERNIE and Conditional Random Fields performs the best. By utilizing this combination, we completed the entity extraction of Yin deficiency syndrome, achieving an average F1 value of 0.77. The relationship extraction method we proposed reduces the number of incorrectly connected relationships compared with fully connected pattern layers. We successfully constructed a knowledge graph of ancient books on Yin deficiency syndrome, including over 120,000 entities and over 1.18 million relationships.We developed TCMSF in line with the knowledge characteristics of ancient TCM books and improved the accuracy of knowledge graph construction.

Symptom phenotypes are crucial for diagnosing and treating various disease conditions. However, the diversity of symptom terminologies poses a significant challenge to analyzing and sharing of symptom-related medical data, particularly in the field of traditional Chinese medicine (TCM). This study aims to construct an Integrated Symptom Phenotype Ontology (ISPO) to support data mining of Chinese electronic medical records (EMRs) and real-world studies in the TCM field.We manually annotated and extracted symptom terms from 21 classical TCM textbooks and 78,696 inpatient EMRs, and integrated them with five publicly available symptom-related biomedical vocabularies. Through a human-machine collaborative approach for terminology editing and ontology development, including term screening, semantic mapping, and concept classification, we constructed a high-quality symptom ontology that integrates both TCM and Western medical terminology.ISPO provides 3,147 concepts, 23,475 terms, and 23,363 hierarchical relationships. Compared with international symptom-related ontologies such as the Symptom Ontology, ISPO offers significant improvements in the number of terms and synonymous relationships. Furthermore, evaluation across three independent curated clinical datasets demonstrated that ISPO achieved over 90% coverage of symptom terms, highlighting its strong clinical usability and completeness.ISPO represents the first clinical ontology globally dedicated to the systematic representation of symptoms. It integrates symptom terminologies from historical and contemporary sources, encompassing both TCM and Western medicine, thereby enhancing semantic interoperability across heterogeneous medical data sources and clinical decision support systems in TCM.