Methods of Information in Medicine最新文献

{"title":"Recent Topics in Biomedical and Health Informatics Education: Methods of Information in Medicine Student Paper Award 2025.","authors":"Sabine Koch, John H Holmes, Stefano Bonacina","doi":"10.1055/a-2904-5454","DOIUrl":"10.1055/a-2904-5454","url":null,"abstract":"","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"64 3-06","pages":"69-70"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Statistical Framework for Person-centered Analysis of Digital Service Use in Public Health and Social Care.","authors":"Ilona Rönkkö, Antti Heiskanen, Katja Antikainen, Pia Liljamo, Ulla-Mari Kinnunen","doi":"10.1055/a-2844-0874","DOIUrl":"10.1055/a-2844-0874","url":null,"abstract":"<p><strong>Background: </strong>Secondary use of health and social care registry data enables evaluation of service effectiveness. The focus is shifting from organization-based metrics to person-centered, system-level approaches that understand outcomes as the combined result of interacting actors. In this context, digital service use offers an important lens for understanding accessibility, participation, and equity within public services.</p><p><strong>Objectives: </strong>The aim of this study is to develop an interpretable analysis approach that uses a pre-existing person-centered, registry-based data model to identify individual variables associated with clients' digital service use.</p><p><strong>Methods: </strong>This registry-based study in a Finnish wellbeing services county applied a person-centered data model linking variables across health care, social services, and client experience. Digital service use was analyzed across 17 reason-for-encounter groups defined by the International Classification of Primary Care (ICPC) and one system-generated supplementary category arising from the dataset's internal structure. Subgroup analysis covered single and paired variables. Effect size was quantified using lift values from Market Basket Analysis, and generalizability was assessed aggregating uncorrected directional test outcomes across ICPC groups.</p><p><strong>Results: </strong>We developed a subgroup-based dual-loop analysis method to identify variable-specific associations across 18 ICPC categories. The method showed that digital service use was most common among younger individuals, women, and urban residents. Low service need and mental-health or family-center-related variables were associated with a higher likelihood of choosing a digital channel, whereas chronic illnesses, intensive care needs, and continuity of care were associated with a lower likelihood of digital channel selection.</p><p><strong>Conclusion: </strong>Registry data and a person-centered model reveal how individual variables influence digital service choices. The method enabled interpretable insights into single variables and their combinations. The findings support more equitable digital services and emphasize the need to tailor solutions to meet diverse client needs.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"110-128"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Quality of Electronic Discharge Summaries: A Cross-Sectional Study Using the Validated Spanish Version of the PDQI-9.","authors":"Pilar Carral, Sonia Elizabeth Benítez, Marta Patricia Del Valle, Leonora Esmeralda Duarte Sotelo, Vanesa Alejandra Ortega, María Del Rosario Lenta, María José Monopoli, Silvana Soledad Diomedi","doi":"10.1055/a-2841-4662","DOIUrl":"10.1055/a-2841-4662","url":null,"abstract":"<p><strong>Background: </strong>Hospital discharge represents a critical moment in the inpatient care process, and its documentation, known as the discharge summary, plays a fundamental role. Incomplete discharge summaries may lead to hospital readmissions, adverse events, poor understanding of treatment and disease, patient and family dissatisfaction, lack of follow-up, and discontinuity of care. In 2017, Hospital Municipal de Agudos \"Dr. Leónidas Lucero\" (Bahía Blanca, Buenos Aires Province, Argentina) implemented an electronic health record system. Although substantial experience has been gained in its use, the quality of discharge summaries has never been systematically evaluated.</p><p><strong>Objective: </strong>To assess the quality of discharge summaries produced between July 2023 and June 2024 by the Internal Medicine Inpatient Service using the Physician Documentation Quality Instrument-9 Spanish Version (PDQI-9 SV).</p><p><strong>Methods: </strong>An observational, descriptive, cross-sectional study was conducted. The PDQI-9 SV consists of nine items (up-to-date, accurate, thorough, useful, organized, comprehensible, concise, synthesized, and internally consistent), each scored on a 5-point Likert scale. Prior to data collection, an instrument adaptation process and reviewer training were carried out. The assessment was done by two trained physician reviewers.</p><p><strong>Results: </strong>A total of 135 discharge summaries were analyzed. The mean overall quality score was 34.77 (standard deviation [SD] = 4.17). The highest-scoring domains were accuracy (mean = 4.32; SD = 0.43), completeness (mean = 4.02; SD = 0.85), and internal consistency (mean = 4.01; SD = 0.65). The lowest-scoring domain was conciseness (mean = 3.28; SD = 0.90). No statistically significant differences were observed according to physician seniority (<i>p</i> = 0.757) or day of the week (<i>p</i> = 0.809). Interrater agreement was moderate (intraclass correlation coefficient = 0.63; 95% confidence interval: 0.45-0.75).</p><p><strong>Conclusion: </strong>Overall discharge summary quality was good, with strengths in accuracy, completeness, and internal consistency. The absence of differences according to physician seniority or day of completion suggests homogeneous documentation practices. Lower scores in conciseness highlight a clear opportunity for targeted educational interventions.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"80-89"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agent-Based Modeling Approach for Population Dynamics of the Biological Vector Aedes Aegypti.","authors":"Alexandra Schwaiger, Marco Schweitzer, Werner O Hackl, Bernhard Pfeifer","doi":"10.1055/a-2854-0579","DOIUrl":"10.1055/a-2854-0579","url":null,"abstract":"<p><strong>Background: </strong>The invasive mosquito <i>Aedes aegypti</i> is a major vector of arboviruses, such as Dengue, Zika, Chikungunya and Yellow Fever.</p><p><strong>Objective: </strong>As a first step toward a transmission model for vector-borne diseases, a weather-dependent population dynamic simulation for <i>Ae. aegypti</i> was developed, suitable for high weather short-term variability in the Austrian region.</p><p><strong>Methods: </strong>We developed an agent-based model, incorporating temperature- and precipitation-dependent development, mortality, movement, feeding behavior, and egg-laying. Species-specific parameters were derived from published experimental studies. Simulations were run for observed weather data from 2024 and for EURO-CORDEX climate projections (ÖKS15) for 2050 and 2080 under RCP4.5 and RCP8.5. Daily immigration of one adult female was assumed to mimic human-mediated introduction along major transport routes.</p><p><strong>Results: </strong>Under all scenarios, population development began in late May and ceased by late September. Observed 2024 conditions produced the highest population sizes (area under the curve [AUC] = 361,343 individuals), whereas all future projections resulted in substantially lower abundances, despite higher mean temperatures.</p><p><strong>Conclusion: </strong>This discrepancy was driven by stronger fluctuations in temperature and precipitation, as well as the absence of urban heat-island effects in climate projections. Under no scenario did mosquitoes survive winter, indicating that long-term establishment is highly unlikely. The model provides a foundation for future extension toward spatially explicit virus transmission simulations, and for assessing the public health implications of climate change in alpine regions.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"71-79"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13493185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Risk Factor Prediction in Chronic Kidney Disease Diagnosis Using Feature Selection and Machine Learning Algorithms.","authors":"Chowdhury Nazia Enam Prima, Martti Juhola","doi":"10.1055/a-2797-4380","DOIUrl":"10.1055/a-2797-4380","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Chronic kidney disease, CKD in short, is a kind of long-term kidney illness in which rapid deterioration of kidney function is observed over a period of time. Unlike other organs, this damage in kidney function cannot be recovered and reversed as well. Moreover, in its early stages, asymptomatic renal disease is highly prevalent, making early identification with conventional clinical approaches difficult. Thus, early and accurate detection of risk factors is a very challenging step in CKD diagnosis.This research work showed earlier and effective identification of risk factors using notable feature selection techniques for the enhancement of patient care. It also aimed at the improvement of predictive diagnosis of CKD employing different supervised and ensemble machine learning classifiers.A CKD-focused dataset consisting of 1,032 patient records and 14 features was used for this research purpose. This research emphasized on identifying the risk factors of CKD using feature importance (for tree-based model) with sequential feature selector and ReliefF algorithm as feature selection process. Based on the ranking for both feature selection techniques, the top 10 features were identified. Then utilizing those features, the classifiers such as random forest, support vector machine, Naïve Bayes, decision tree, logistic regression, gradient boosting, &lt;i&gt;K&lt;/i&gt;-nearest neighbors, and ensemble classifier voting technique were trained using stratified 5-fold and grid-based search cross-validation techniques. After that, their performances were assessed using evaluation measures, i.e., accuracy, F1 score, precision, recall, training loss, test loss, bias, and AUC, to classify the individual having presence or absence of CKD.The feature selection algorithms selected the significant data-driven top 10 features. Based on the ranking for both feature selection procedures, hemoglobin is determined to be the significant risk factor among these features. For both feature selection techniques, all the classifiers showed their best performance, having 86 to 98% of accuracy, AUC value of over 0.96 to 1.00, and bias value of 0.003 to 0.103. All the classifiers showed a very good trade-off between false positives and false negatives, with precision, recall, and F1 score ranging from 92 to 98%, 90 to 99%, and 93 to 98%, respectively, using feature importance with SFS. In both cases of the feature selection techniques, gradient boosting outperformed all other algorithms in terms of accuracy, precision, AUC, recall, F1 score, specificity, and bias.To conclude, in the suggested methodology the feature selection algorithms effectively identified the prominent features based on their importance, and the pipeline demonstrated a good performance in diagnosing individuals at risk of CKD development. Some of the classifiers showed their effectiveness in CKD prediction using the selected features by achieving higher accuracy, F1 score, precision, recall, AUC, specificity, an","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"40-53"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12991859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146195569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using a Large Language Model-Generated Prompt to Extract Features from Synthetic MRI Brain Scan Reports: A Cross-Sectional Study.","authors":"John J Hanna, Christopher S Evans, Christopher R Dennis, K Stuart Lee, Christoph U Lehmann, Richard J Medford","doi":"10.1055/a-2797-4295","DOIUrl":"10.1055/a-2797-4295","url":null,"abstract":"<p><p>Feature extraction from free text medical reports is a frequently required clinical, operational, or research procedure. Large language models (LLMs) hold a promise for automating feature extraction, which can also enable category assignment tasks.To compare the groundedness of extracted features by five LLMs from magnetic resonance imaging (MRI) brain scan reports using a clinician-engineered versus an LLM-generated prompt.Five OpenAI LLMs were evaluated for their ability to extract nine binary features from synthetic MRI brain reports. Two types of prompts, a clinician-engineered and an LLM-generated, were used. Metrics including recall, precision, accuracy, and F1 score were calculated to assess model performance.For all extracted features by all studied models from both tested prompts, the overall average recall was 0.956, the average precision was 0.9347, the average accuracy was 0.982, and the average F1 score was 0.9431. Using GPT-3.5-turbo, the LLM-generated prompt had better numerical performance than the clinician-engineered prompt. For the other four GPT-4 models examined, overall recall, precision, and accuracy were higher regardless of the prompt source.This study highlights the potential of LLMs to generate prompts and accurately extract features, with newer models like GPT-4 performing consistently well. The efficacy of feature extraction by LLMs depends on the engineered prompt and model used. Our experimentation demonstrates the potential of LLMs to engineer prompts and extract features from MRI brain scan reports.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"54-62"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Electronic Health Record Data and Up-to-Date Clinical Guidelines for High-Accuracy Clinical Diabetes Drug and Dosage Recommendation.","authors":"Jhing-Fa Wang, Ming-Jun Wei, Te-Ming Chiang, Tzu-Chun Yeh, Eric Cheng, Yuan-Teh Lee, Hong-I Chen","doi":"10.1055/a-2707-2862","DOIUrl":"10.1055/a-2707-2862","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"1-11"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multicentre Evaluation of the Impact of Computerised Physician Order Entry on Medication Documentation Workflow, Time, and Quality.","authors":"Viktoria Jungreithmayr, Alina Fischer, Jan Leininger, Raphael Kotte, Nils Keiner, Hanna M Seidling","doi":"10.1055/a-2744-7059","DOIUrl":"10.1055/a-2744-7059","url":null,"abstract":"<p><p>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], <i>p</i> = 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.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"63-67"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145846650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Terminology Mapping Service Based on Information Retrieval.","authors":"Sungwon Jung, Seung-Jong Yu, Byoung-Kee Yi","doi":"10.1055/a-2797-4219","DOIUrl":"10.1055/a-2797-4219","url":null,"abstract":"<p><p>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.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.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.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.Our study introduced an IR method with rich synonyms for mapping clinical terms to SNOMED CT, analyzing 40 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.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"31-39"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12991864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering Breast Cancer Patients Based on Their Treatment Courses Using German Cancer Registry Data.","authors":"Kolja Blohm, David Korfkamp, Florian Oesterling, Klaas Dählmann, Stefanie Schulze, Andreas Hein","doi":"10.1055/a-2753-9631","DOIUrl":"10.1055/a-2753-9631","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"12-30"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12991862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}