BMC Medical Informatics and Decision Making

Background: Adverse event (AE) coding is essential for safety monitoring in oncology clinical trials, particularly in acute myeloid leukemia (AML), where intensive therapies are associated with frequent and heterogeneous toxicities requiring standardized MedDRA (Medical Dictionary for Regulatory Activities) coding. However, manual Low-Level Term (LLT) assignment remains labor-intensive, subjective, and difficult to scale. Although large language models (LLMs) have emerged as promising decision-support tools for automated coding, unguided zero-shot generation remains insufficient for reliable fine-grained MedDRA coding. Objective: To develop and evaluate a retrieval-augmented reasoning pipeline for clinically aligned LLT-level MedDRA coding of free-text adverse events from prospective AML clinical trials. Methods: We implemented a retrieval-augmented reasoning pipeline inspired by the retrieval-augmented generation (RAG) paradigm using LLaMA-3.3-70B-Instruct as the primary backbone and benchmarked the framework across multiple open instruction-tuned LLMs. Dense semantic retrieval first generated a constrained top-100 LLT candidate set for each AE, followed by structured LLM reasoning to select a single best-matching LLT and deterministic mapping to Preferred Term (PT) and System Organ Class (SOC) levels. The pipeline was evaluated retrospectively on AE datasets from three prospective AML clinical trials (MOSAIC, DELTA, and DaunoDouble) with automated LLT/PT/SOC metrics and expert-assessed Clinical Correctness Rate (CCR). Results: Clinical expert review showed high clinical acceptability of the RAG pipeline across datasets (91-97%). Under automated evaluation, the pipeline achieved LLT exact accuracy of 50-58%, PT accuracy of 78-85%, and SOC accuracy of 90-93%. Zero-shot generation and random candidate selection performed substantially worse. Semantic retrieval more often included the coder-assigned LLT among the candidate terms available to the model than retrieval based on lexical similarity. Multi-model benchmarking showed that backbone choice mainly affected LLT exact agreement, whereas PT and SOC performance remained comparatively stable. Conclusions: Retrieval-augmented reasoning supports clinically aligned MedDRA coding of free-text adverse events under realistic candidate constraints in AML clinical trials. Evaluation across three AML clinical trials showed that strict LLT-level string agreement underestimated clinical ap-propriateness, highlighting the importance of combining hierarchical evaluation metrics with clini-cal expert validation for AI-assisted MedDRA coding in hematology trials.

Oncology knowledge spans structured and unstructured datasets of drugs, regimens, conditions, and identifiers, as well as clinician-authored narratives describing treatment sequencing across disease settings. Answering clinical questions may require accessing both databases and free-text narratives, but existing intelligent retrieval systems favor either relational facts or narrative context. We developed HemOncAgent, an artificial intelligence agent that selects among retrieval tools for the HemOnc knowledge ecosystem: HemOncKB, a curated knowledge graph for pharmacologic relationships, and HemOnc.org, a narrative resource for care pathways. Across structured and narrative benchmarks, HemOncAgent maintained high-fidelity retrieval across question types for which single-source systems showed domain-specific limitations, supporting hybrid tool-based retrieval for more reliable oncology knowledge access.

Objective: Retrospective evaluation of clinical pathway adherence using electronic medical record (eMR) data is limited by heterogeneous data structures and statistical measures that do not account for patient-specific context, care sequence, or timing. We designed a modular event-based architecture to dynamically evaluate the New South Wales Adult Sepsis Pathway over time. Method: Data from eMRs were collected from adult emergency department encounters across four hospitals between July 2022 and June 2024 and processed through three sequential layers: event-stream transformation, pathway-relevance filtering, and state-based pathway evaluation. Heterogeneous records were standardized and organized into a chronological timeline for each encounter. The framework then updated the pathway state as new clinical information became available and assessed whether recommended care was delivered within the relevant timeframe and sequence. Result: The architecture processed 451,124 encounters. This included 57,631,084 clinical events extracted and transformed into encounter-level event streams. After filtering for pathway relevance, 43,951,146 events remained (76.3%). A total of 129,907 (34.6%) encounters entered the sepsis pathway, 50,118 (38.6%) entered YELLOW state, indicating progression to suspected sepsis, and 3,349 (2.6%) encounters progressed to RED state from GREEN, indicating the initial shock. Of 50,118 encounters entering YELLOW state, 2,426 (4.8%) progressed to shock, 8,700 (17.4%) encounters were recognized with sepsis through a blood culture order, and 4,287 (8.6%) encounters were recognized with sepsis through an antibiotic order. This demonstrates the ability of the architecture to prioritize higher-acuity states during evaluation rather than going through an intermediate state. Conclusion: The architecture developed shows the feasibility of transforming fragmented routine eMR data into a reusable longitudinal representation that supports context-dependent pathway evaluation. Separating data preparation from pathway rules improves transparency, maintainability, and adaptability and provides a foundation for future interoperable, near-real-time pathway monitoring

Background: As required by the 21st Century Cures Act, all test results, including final radiology reports, are released to patients as soon as the results are finalized. However, radiology reports are primarily intended for communication between medical professionals and can often be difficult for patients to interpret, particularly in cancer care settings. We developed a tool that leverages a large language model (LLM; ChatGPT-5.2, OpenAI, San Francisco, CA) to draft a patient-friendly plain language version of imaging results (Patient-Friendly Scan Result Generation [PFSG] tool) that oncologists can incorporate into their communication with patients via patient portals. This article describes our mixed-methods study protocol. Objective: Our primary aims are to: 1) assess whether patients who receive patient-friendly versions of scan results report better understanding of their results than patients who do not; 2) assess whether introduction of the PFSG tool affects provider workload; and 3) explain how and under what circumstances the PFSG tool enhances communication and workflows, and affects patient and provider outcomes. Methods: We will use an embedded mixed-methods, prospective cohort design within a quality improvement (QI) initiative. The PFSG tool will be implemented using a modified three-wave staircase trial design, with providers in randomized cohorts of oncology disease groups progressively gaining access to the tool over the course of the study. Approximately 60 providers from 11 oncology disease focus groups will be randomized. Longitudinal provider outcomes will be measured through surveys at one month pre-implementation, one month post-implementation, and three months post-implementation. Patient outcomes will be measured cross-sectionally through surveys at one month pre-implementation (n=350) and at post-implementation result point-of-contact (n=350). Additionally, we will conduct interviews with patients and oncologists to inform the iterative design of the tool and its implementation. Results: This study was funded in September 2025 and data collection began in February 2026. As of September 2026, the PFSG tool has been rolled out to all oncology providers, with Cohort 3 post-implementation data collection beginning in August 2026. A total of 738 patient baseline surveys have been collected and patient post-implementation data collection is in progress. Conclusions: This is the protocol for a study of the implementation of a patient-friendly results tool in oncology provider workflows to facilitate improved communication of radiology results to oncology patients. The results of this study will lend evidence to the utility and acceptability of the study tool, and tools like it, to close an important oncology care gap. If the tool is shown to improve key oncology patient and provider outcomes, it may lead to development of a wider release for more general patient populations.

Background Hepatocellular carcinoma (HCC) treatment selection demands nuanced integration of heterogeneous patient data, yet prevailing predictive models rely on restricted data modalities and oversimplified therapeutic frameworks, compromising clinical translation. Objective We developed and validated a multimodal artificial intelligence framework to guide optimal treatment strategy selection across the full spectrum of HCC interventions. Methods This retrospective study comprised 1,043 HCC patients (development cohort, January 2017-December 2023) and 55 external validation patients (2023) from Wuxi Peoples Hospital. We engineered Embedding-Augmented Extra Trees (ET-Emb), a novel model fusing structured clinical variables with contextual text embeddings derived from medical histories and radiology reports. ET-Emb quantifies probabilities for five primary treatments: open/laparoscopic resection, transarterial chemoembolization, radiofrequency ablation (RFA), and chemotherapy. Model performance was rigorously assessed via 10-fold cross-validation and external validation using ROC-AUC and PR-AUC metrics. Results ET-Emb demonstrated robust performance in the development cohort (ROC-AUC: 0.84 {+/-} 0.04; PR-AUC: 0.55 {+/-} 0.06), significantly outperforming established benchmarks. This generalizability was preserved in external validation (ROC-AUC: 0.77 {+/-} 0.02; PR-AUC: 0.47 {+/-} 0.03). SHAP analysis identified textual clinical narratives and socioeconomic determinants as critical predictive drivers. Conclusions By unifying structured and unstructured data modalities, ET-Emb delivers accurate, multi-treatment strategy prediction for HCC. Its clinical validity and the demonstrated significance of textual features establish multimodal AI as an essential paradigm for simulating complex oncological decision-making, positioning ET-Emb as a transformative tool for precision HCC management.

Longitudinal electronic health records (EHRs) contain rich information about the evolution of a patient's clinical state, but temporal measurements and events are often represented using summary statistics or complex learned representations that can be difficult to interpret clinically. We propose an interpretable trajectory-based feature representation that transforms heterogeneous longitudinal EHR data into compact, human-readable predictive features. Numerical temporal variables are represented by trajectory states such as INCREASING, DECREASING, and STABLE, while categorical clinical events are represented by interpretable states describing repeated occurrence, stability, or change. These trajectory features are combined with static patient and encounter characteristics and evaluated using L1-regularized logistic regression, which provides sparse feature selection and direct identification of positive and negative predictive features. The approach is evaluated using MIMIC-IV in two clinically distinct prediction tasks: in- hospital mortality following ICU admission and hospital readmission within 30 days after discharge. For ICU mortality, multiple observation windows ranging from 1 to 48 hours are investigated to examine how the availability of longitudinal information affects prediction and the relative contribution of trajectory features. The highest AUROC of 0.8751 is obtained with a 32-hour observation window. For 30-day readmission, trajectories from the final 72 hours of the index hospitalization are evaluated using both three-state and five-state categorical representations. The two representations produce essentially identical discriminative performance (AUROC 0.6875 and 0.6876, respectively), indicating that increasing trajectory granularity provides little predictive benefit in this setting. The results demonstrate that heterogeneous longitudinal EHR observations can be transformed into compact and directly interpretable temporal features while retaining useful predictive information. The proposed framework therefore provides a simple and model-compatible approach for identifying clinically interpretable patterns of patient evolution and can be extended with more detailed trajectory definitions or applied with other predictive models.

Background: Machine learning models leveraging electronic health records (EHRs) can support earlier detection of sepsis in intensive care units (ICUs). However, their clinical utility depends on reproducibility across institutions and patient populations. Building on a published pipeline from the Children's Hospital of Philadelphia (CHOP), this study examines how a neonatal sepsis prediction framework performs and can be adapted to a range of intensive care environments, paediatric, cardiac, and neonatal, at Great Ormond Street Hospital (GOSH). Methods: We extracted de-identified ICU EHR data from GOSH and applied feature derivation, unit harmonisation, and temporal sampling to align with the CHOP dataset used by Masino et al. (2019). Seven classifiers were first evaluated using CHOP-trained weights to characterise cross-domain behaviour and then retrained on local data to assess recoverability and site-specific adaptation. Model discrimination was summarised by AUC and F1, and learning curves were used to explore sample efficiency and bias-variance dynamics. Results: Models achieved strong discrimination on the CHOP neonatal cohort but demonstrated reduced performance when transferred to the mixed GOSH ICU population, reflecting anticipated domain and population shift. Retraining on GOSH data restored discrimination (AUC range 0.69-0.86), with Gradient Boosting (AUC 0.86 vs AUC 0.87 at CHOP) and KNN (AUC 0.80 vs AUC 0.79 at CHOP) models performing comparably to their CHOP benchmarks. DeLong's test confirmed statistically significant gains across all classifiers (p < 0.001). Conclusion: ICU cohort and baseline demographic differences between CHOP and GOSH introduced domain shift that limited direct model transfer. Elements of the original preprocessing pipeline could not be reproduced, further constraining transportability. Yet, retraining on local data restored high discrimination, showing that the modelling framework remains robust when re-estimated in new settings. These results highlight local adaptation as a practical route to recover performance and support safe, generalisable deployment of clinical prediction models in mixed clinical environments.

Machine learning workflows frequently incorporate data preprocessing to enhance predictive performance. However, the need for Super Learner ensembles made up only of preprocessing-invariant tree-based algorithms remains unexplored. Using three benchmark clinical classification datasets, this study examined how preprocessing affected the Super Learner's prediction performance, learner weight distribution, and oracle behavior. The Heart Disease (207 observations), Indian Liver Patient Dataset (583 observations), and Pima Indians Diabetes (768 observations) datasets were used to create a Super Learner ensemble model that included Classification and Regression Trees (CART), Random Forest, Ranger, and Extreme Gradient Boosting (XGBoost). Models were evaluated under raw and preprocessed data conditions using repeated cross-validation. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), and Brier score. Learner weight allocation and Oracle Gap were compared using paired Wilcoxon signed-rank tests with Benjamini-Hochberg adjustment. Preprocessing produced negligible changes in predictive performance for the Heart Disease and Pima datasets. For the ILPD dataset, preprocessing significantly improved AUC (0.746 to 0.752; adjusted p = 0.0017) and reduced the Brier score (0.177 to 0.175; adjusted p < 0.001). Learner weights remained largely stable, although Random Forest replaced Ranger as the dominant learner for the Heart Disease dataset. Oracle Gaps remained extremely small (<0.002) across all datasets and did not differ significantly between preprocessing conditions. Preprocessing provides limited benefit for Super Learner ensembles composed of preprocessing-invariant learners and does not materially alter their oracle behavior. Preprocessing decisions should therefore be guided by dataset characteristics rather than adopted as a universal modelling practice.

Accurate prediction of adverse events in the intensive care unit (ICU) following cardiothoracic surgery is crucial for timely interventions, improving patient outcomes and optimizing value in healthcare. By leveraging advanced machine learning techniques, this study demonstrates the transformative potential of predictive analytics to enhance postoperative care using a dataset from the Society of Thoracic Surgeons' (STS) database and time-series intraoperative data. We developed a multi-modal late fusion approach integrating static patient variables with intraoperative time-series data, utilizing an ensemble of models including residual neural networks for static features, CNN and Bidirectional GRU for time-series processing, XGBoost, logistic regression, SVM and Gradient Boosting. Through five-fold cross-validation, our ensemble model with balanced threshold selection achieved an AUC of 0.87, sensitivity of 0.76, specificity of 0.83, PPV of 0.40, and NPV of 0.96. Preoperative heart failure, IABP insertion, cardiopulmonary bypass (CPB) duration and white blood cell (WBC) count emerged as key predictors. Training the same pipeline on each modality separately showed that the static registry variables alone reached the same AUC of 0.87, and that the intraoperative signals alone reached 0.83, so on this cohort the two modalities act as substitutes rather than complements and fusion changed the sensitivity and precision trade-off rather than the amount of separable signal. A per-signal ablation found that no individual monitoring channel was load bearing, with mean arterial pressure and central venous pressure the least substitutable. This balanced approach optimizes the trade-off between sensitivity and specificity, providing a screening tool with high negative predictive value for monitoring high-risk patients, and the modality comparison indicates that comparable performance is attainable at sites holding registry data alone.

Acute kidney injury (AKI) is a frequent complication within intensive care units, with its sudden onset often missed. This is especially important because a timely window for intervention is required as delayed detection leads to progressively worse outcomes. Existing machine learning and deep learning models have contributed to closing this gap, but their complexity, requiring hundreds to thousands of features, and lack of generalisation pose a limitation that prevents them from being integrated into clinical workflows across different electronic health-record ecosystems. This study presents a 37-feature XGBoost model trained on the MIMIC-IV dataset with 5.4% positive cases, with hyperparameters optimised via Optuna and probabilities calibrated using isotonic regression, designed for transportability across clinical settings. Validation was conducted internally using a temporal patient-level split simulating prospective deployment, training on 2008-2016 data and testing on 2017-2022 data"External validation was performed on the eICU Collaborative Research Database, a multi-centre dataset spanning 208 US hospitals, using the trained model without retraining. SHAP TreeExplainer was used to provide feature-level explainability for individual predictions. Internal testing yielded an AUROC score of 0.794 for predicting AKI onset within a 12-24 hour window. External validation produced a 0.750 AUROC without retraining. Equitable discrimination was observed across gender, age, chronic kidney disease presence, race, and AKI stages on both datasets, with a 95% internal CI of 0.789-0.799 confirming the model's estimate stability. These results suggest that clinically useful prediction systems are achievable with substantially fewer features than current models require.

Objective: To evaluate whether multi-agent LLM architectures with explicit safety verification maintain guideline compliance when their clinical knowledge bases undergo temporal or institutional distribution shift. Materials and Methods: We designed a controlled evaluation framework using 50,000 synthetic type 2 diabetes patients with CKD and hypertension comorbidities (500 per experimental condition). Four architecture modes (single-agent, naive RAG, linear multi-agent, stateful graph with safety floor) were tested under four shift regimes: baseline, temporal drift (updated eGFR thresholds), institutional vocabulary transformation (11 term-pair substitutions producing 0.36 cosine similarity degradation), and metadata erasure. The clinical task was medication reconciliation with contraindication detection. Two embedding models (all-MiniLM-L6-v2, PubMedBERT) and two LLM backends (Llama3-8B, Mistral-7B) were compared. Results: Under institutional vocabulary shift, the linear pipeline's Guideline Compliance Score dropped from 1.00 to 0.36 because retrieval degradation rendered critical contraindication guidelines unretrievable. The stateful graph architecture maintained GCS = 1.00 across all shift conditions through its regime-aware safety floor, which operates independently of retrieval quality. This pattern held across both LLM backends and both embedding models. The safety mechanism added 32.2s latency per patient under shift versus 12.5s for single-agent mode. Discussion: Architectural choice (specifically whether audit findings are routed back to the summary agent) determines compliance under shift more than retrieval quality or model scale. The safety floor's value is compliance maintenance, not semantic fidelity improvement. Conclusion: Stateful multi-agent graphs with programmatic safety floors bound error propagation under clinical knowledge shift. The framework is reproducible on consumer hardware with no external API dependencies.

Background: The growing burden of lifestyle-related chronic diseases has increased the need for clinically interpretable decision-support tools capable of integrating artificial intelligence with evidence-based preventive nutrition. Although machine learning has shown considerable potential for health risk prediction, most existing approaches remain limited to isolated predictive models or conventional nutritional software, with little integration of multidimensional clinical assessment and personalized recommendations. Objective: To develop and internally validate NutrIA, a hybrid web-based Clinical Decision Support System (CDSS) that combines machine learning, validated clinical assessment, structured clinical reasoning and personalized nutritional recommendations for preventive medicine. Methods: NutrIA was developed using harmonized data from the National Health and Nutrition Examination Survey (NHANES, 1988 to 2018). A supervised machine learning model was trained to estimate 5-, 10- and 20-year all-cause mortality risk and subsequently integrated with an adaptive clinical questionnaire, validated screening instruments, nutritional indicators, dietary clustering, clinical phenotyping and a transparent rule-based recommendation engine within a unified web-based platform. Results: The predictive model achieved ROC-AUC values of 0.894, 0.914 and 0.923 for 5-, 10- and 20-year mortality prediction, respectively. The implemented CDSS incorporates an adaptive questionnaire (151 items), 39 validated clinical assessment instruments, 17 clinical phenotypes and 31 dietary clustering modules to generate individualized nutritional and lifestyle recommendations together with an automated clinical report. The integrated framework translates probabilistic risk estimates into clinically interpretable decision support for personalized preventive nutrition. Conclusions: NutrIA demonstrates the technical feasibility of integrating machine learning with knowledge-based clinical reasoning within a single web-based CDSS for preventive nutrition. Although external validation and prospective clinical evaluation are required before routine implementation, the proposed architecture represents a promising step toward clinically interpretable artificial intelligence for personalized nutritional care.

Background: Patients with intracerebral hemorrhage (ICH) in intensive care units (ICUs) frequently receive antibiotics early in critical illness. Such exposure reflects suspected infection, physiological deterioration, monitoring intensity, and clinical management and may vary across institutions and electronic health record databases. We evaluated target-domain adaptation for modeling sustained antibiotic exposure in critically ill patients with ICH. Methods: We analyzed derived ICH cohorts from MIMIC-IV and the eICU Collaborative Research Database. MIMIC-IV included 2,024 ICU stays and served as the source cohort. The eICU cohort comprised 1,277 stays and was divided, at the ICU-stay level, into a development subset (766 stays) and a held-out evaluation subset (511 stays). The eICU outcome was sustained antibiotic exposure on at least two ICU days. Candidate models incorporated demographic, comorbidity, physiological, laboratory, neurologic, respiratory, temporal, and measurement-process features. Timestamped observations for static and temporal tabular families were censored at the recorded first-antibiotic time when available, whereas the compact sequence component and explicit measurement-density features used the full first 24 hours. For base-model families using eICU development data, meta-learner inputs were generated by five-fold stratified cross-fitting. A 42-input L2-penalized logistic meta-learner combined 16 model probabilities with model-summary, clinical, availability, and measurement-density features. Results: The held-out eICU subset contained 48 positive outcomes (9.39%). The adapted ensemble achieved an area under the receiver operating characteristic curve (AUROC) of 0.841 (95% CI 0.775-0.899) and an average precision (AP) of 0.540 (95% CI 0.399-0.676), with a Brier score of 0.063 and expected calibration error of 0.029. The MIMIC-IV source-only stack achieved an AUROC of 0.782; the paired AUROC difference was 0.059 (95% CI 0.003-0.112). In a separate patient-grouped five-fold out-of-fold sensitivity analysis of 503 linked eICU stays, AUROC was 0.807 (95% CI 0.691-0.910). Conclusions: Target-domain adaptation improved held-out discrimination relative to a source-only stack for sustained antibiotic exposure. Because some model components and measurement-process features used information from the broader first 24 hours, the model should be interpreted as a first-24-hour treatment-association model rather than a strictly prospective pre-treatment infection predictor.

Background: Japan faces critical challenges in medical data interoperability, conceptualized as the "Ten Walls" obstructing the Japanese Health Data Space (JHDS) [1]. The Japanese Intensive Care Patient Database (JIPAD) - Japan's largest national ICU registry with 151 participating facilities - represents a high-quality critical care dataset that remains isolated from international data ecosystems. Objective: To develop a formal mapping of all 122 JIPAD variables to HL7 FHIR R4, characterize the nature and magnitude of semantic gaps, and assess the feasibility of JIPAD integration into the JHDS. Methods: All 122 JIPAD variables (Data Dictionary v3.7.2; Linkage Items List 20231020) were evaluated using ISO 21564 [8]-based semantic equivalence scoring across three tiers: High (direct FHIR R4 Core mapping), Partial (mapping via JP-Core Implementation Guide extensions [3]), and Low/No Equivalence (structural institutional gap). Semantically identical multi-instance fields (e.g., secondary disease codes x5) were consolidated into single mapping entries, yielding 114 mapping entries. Pseudonymization architecture was characterized from primary documentation. Results: Of 114 mapping entries representing the 122 JIPAD variables, 97 (85.1%) achieved High Equivalence via LOINC/SNOMED CT, and 12 (10.5%) achieved Partial Equivalence via JP-Core extensions, value-set translation, or FHIR R4 Core extension mechanisms - yielding a combined technical feasibility of 95.6% (109/114). Only 5 entries (4.4%) were classified as Low/No Equivalence, all attributable to Japan's proprietary disease classification system (288 adult codes; 165 pediatric codes) embedded in the DPC reimbursement framework, plus one Japan-specific procedure (PMX endotoxin adsorption) absent from international terminology systems. Variable-level mapping details are provided in Supplementary Table S1. Critically, JIPAD employs pseudonymization with record-linkage capability, enabling 99% DPC data matching - demonstrating that technical and design-level barriers to FHIR integration have already been resolved. Conclusion: JIPAD is technically and architecturally ready for FHIR integration at a 95.6% level. The remaining 4.4% barrier is exclusively institutional - rooted in MHLW policy frameworks governing the DPC disease classification system [6] - rather than technical. FHIR integration would further unlock pharmacoepidemiological and social epidemiological research currently inaccessible due to data isolation. As the sole national ICU registry providing high-acuity anchor data unavailable in general health records, JIPAD integration is essential for a clinically meaningful JHDS by 2027.

Diagnostic errors, including misdiagnoses and delayed clinical diagnoses, could affect outcomes of a significant patient population, particularly individuals presenting with rare diseases or non-specific symptoms. From rule-based diagnostic decision supporting systems (DDSS) to large language model (LLM) based tools for clinical reasoning have been developed to address these limitations. However, existing DDSS are often proprietary and difficult to integrate, and recent LLM-based tools remain hindered by operational challenges such as cost, resources constraint, and privacy concerns. Moreover, existing systems interpret electronic medical records (EMR) and generate diagnoses separately, limiting continuous evidence-based analysis and imposing repeated clinician involvement. In this paper, we present DDx-Finder, an open-source framework that leverages Model Context Protocol (MCP) servers for direct EMR and literature access, enabling prompt-driven clinical state extraction and reliable case-report re- trieval via generating searching query by LLM, while addressing limitations related to resource demands and privacy concerns. A clinical case study demonstrates the systems feasibility and its potential to provide accessible, transparent, and systematic differential diagnostic support for complex cases.

De-identifying temporal protected health information (PHI) is challenging because temporal expressions appear in diverse formats and must be transformed while preserving clinically meaningful timelines. This study proposes and evaluates an LLM-based framework for temporal PHI de-identification in real-world clinical notes. Using 1,148 notes from 30 sarcoma patients (28,431 annotated temporal entities), we evaluated multiple modern LLMs for temporal entity extraction and surrogate generation. Proprietary models achieved strong extraction performance, with GPT-4o achieving the best overall results. Most models preserved temporal formatting (>99%), but surrogate generation remained challenging. GPT-5.4 achieved the highest shift correctness (90.2% on a shared-entity subset) and the lowest order violations. Error analysis revealed systematic shift deviations ({+/-}1, {+/-}30/31, {+/-}365 days), highlighting persistent limitations in LLM temporal reasoning. These findings suggest that reliable temporal de-identification will require hybrid or multi-agent approaches beyond standalone LLMs.

Background: Much of the symptom burden in colorectal cancer (CRC) patients is documented in unstructured discharge-note narrative, and manual extraction is not scalable. Whether large language models (LLMs) outperform rule-based and named entity recognition (NER) methods has not been rigorously benchmarked. Objective: To benchmark rule-based, NER, and zero-shot LLM methods for extracting 46 cancer-related symptoms from CRC discharge notes against an adjudicated ground truth. Methods: We analyzed 2,704 discharge notes from CRC patients in MIMIC-IV. A 46-symptom target list was built from the Memorial Symptom Assessment Scale and the EORTC QLQ-CR29. Four approaches -- dictionary-based rule matching, pretrained clinical NER, and zero-shot Claude Haiku and Gemini 3.5 Flash -- plus two hybrid variants (LLM output with post-hoc rule-based negation filtering) were evaluated against a 200-note gold standard adjudicated by two raters (pooled kappa=0.71, macro kappa=0.49), using Macro/Micro F1, precision, and recall. Results: Gemini 3.5 Flash performed best (Macro F1=0.70, Micro F1=0.86, Macro Precision=0.74), followed by Claude Haiku (Macro F1=0.63, Macro Recall=0.71); both substantially outperformed rule-based (Macro F1=0.44) and NER (Macro F1=0.38) methods. Post-hoc negation filtering paradoxically degraded LLM performance (Gemini+Hybrid Macro F1=0.58; Claude+Hybrid Macro F1=0.54) by overriding correct predictions through rigid, fixed-window matching. Conclusions: Zero-shot LLMs substantially outperform rule-based and NER approaches for CRC symptom extraction; post-hoc negation correction should not be applied to LLM outputs without syntactic scope validation. Implications for Practice: Zero-shot LLM extraction offers a scalable, accurate alternative to manual chart review and traditional NLP pipelines for oncology symptom surveillance, without institution-specific rule development or model training.

Background: Warfarin therapy requires repetitive dose adjustments based on INR (International Normalised Ratio) monitoring. We evaluated the long-term real-world performance of Forsante Warfarin Advisor (WA), a CE-marked class IIb guideline-based decision support and care automation medical device used in anticoagulation management. Methods: Retrospective real-world data from routine clinical use between 2016 and 2026 were analysed. Treatment quality was assessed using Time in Therapeutic Range (TTR). Recommendation performance was evaluated by comparing achievement of target INR after clinician acceptance or modification of Warfarin Advisor recommendations. Results: Among 1348 patients in March 2026 median TTR was 83%, compared with 70% in March 2016. Dosages congruent with Warfarin Advisor recommendations were strongly associated with achieving target INR at follow-up in INR target ranges of 2.0-3.0 and 2.5-3.5. Treatment quality remained consistently high across years of deployment. No serious device-attributable safety incidents, regulatory incident reports, or CAPA cases were identified during 12 calendar years and 82,709 patient years of routine use. Conclusions: The findings provide real-world long-term evidence that a guideline-based warfarin dosing and care automation system can support sustained high-quality anticoagulation control in routine clinical practice. The findings support the feasibility of deploying workflow-integrated execution of selected guideline-driven clinical processes, while the causal effects on clinical outcomes require prospective confirmation. Keywords: Clinical decision support systems, Guideline execution, Real-world evidence, Warfarin, Anticoagulation

Background: Adjuvant therapy decision in early-stage node-negative breast cancer remains one of the most challenging problems in oncology. Although most patients in this population achieve favorable outcomes with endocrine therapy alone, a subset remains at risk of relapse and may benefit from chemotherapy. Accurate identification of high-risk and low-risk patients is therefore essential to prevent both undertreatment and overtreatment. Methods: In this study, machine learning models were assessed using MSK-IMPACT (n=645) and METABRIC (n=503) cohorts comprising node-negative stage I-II breast cancer patients. Three experimental settings were evaluated: training on MSK-IMPACT and testing on METABRIC, the reverse direction, and five-fold cross-validation on the combined cohort. Sensitivity analysis and data harmonization were applied to identify and mitigate cohort-specific bias. Survival analysis, SHapley Additive exPlanations (SHAP) interpretability, and ablation analyses were performed to validate clinical relevance and characterize feature contributions. Results: The combined cohort model achieved an area under the receiver operating characteristic curve (AUC) of 0.788{+/-}0.028 across five-fold cross-validation, while bidirectional cross-cohort validation yielded AUC of 0.741 when testing on MSK-IMPACT and AUC of 0.714 when testing on METABRIC. Survival validation on METABRIC out-of-fold predictions from the combined cohort model showed hazard ratios of 3.01 (95\% CI: 2.28-3.98) for disease-free survival and 2.35 (95\% CI: 1.79-3.09) for overall survival, consistent across Luminal A, Luminal B, and Triple-Negative subtypes. Conclusion: Validated across two geographically independent cohorts, the proposed model enables cost-effective, gene expression-free identification of patients at high-risk of distant relapse and may facilitate adjuvant chemotherapy decision-making in early-stage node-negative breast cancer.

Background: Public facing large language models (LLMs) are increasingly used for health guidance, including triage recommendations. We evaluated whether augmenting LLM prompts with standardized clinical concepts from the Unified Medical Language System (UMLS) could improve the safety and robustness of clinical triage recommendations. Methods: We used a publicly available dataset comprising 60 clinician-authored clinical vignettes, each represented in 16 demographic and narrative variations, yielding 960 vignette-factor combinations. Clinical entities were extracted using a two-stage pipeline combining ClinicalBERT-based named entity recognition with rule-based identification of laboratory abnormalities. Extracted entities were mapped to UMLS Concept Unique Identifiers (CUIs). Negated concepts were excluded. A confidence-weighted CUI voting classifier was trained using empirical associations between CUIs and clinician-assigned triage categories. We compared five approaches: CUI-only classification, MedGemma 27B, MedGemma 27B augmented with CUIs, GPT-4o-mini, and GPT-4o-mini augmented with CUIs. Outcomes included overall accuracy, under-triage, over-triage, emergency-case accuracy, and sensitivity to anchoring statements. Results: CUI augmentation decreased under-triage but increased over-triage in both models tested (GPT-4o-mini and MedGemma 27B). It improved high-acuity recognition while reducing recognition of low-acuity cases. CUI augmentation had mixed effects on overall triage accuracy; accuracy increased for MedGemma 27B but decreased for GPT-4o-mini. Emergency-case accuracy improved from 73.0% to 80.7% for GPT-4o-mini and from 60.5% to 68.5% for MedGemma 27B. CUI augmentation also reduced susceptibility to anchoring statements. These findings suggest that the principal value of CUI augmentation may be shifting model behavior toward safety-oriented behavior rather than uniformly improving overall accuracy. Conclusion: Ontology-grounded prompt augmentation shifted LLM triage recommendations toward greater sensitivity to high-acuity presentations and reduced overall under-triage. These safety gains were accompanied by increased over-triage and mixed effects on overall accuracy. A hybrid architecture combining LLM-based language understanding with interpretable UMLS-derived clinical concepts may improve the safety and robustness of AI-assisted triage. Further evaluation using real-world patient communications and clinical outcomes is warranted.