health informatics papers

Experimental multi-center validation of a radiomics-based photonic quantum precision medicine architecture for lesion-level prediction of anti-PD-1 response in non-small cell lung cancer

This multi-center study validates the external performance of a radiomics-based photonic quantum machine learning architecture, demonstrating that a model trained on a statistically reduced feature space can match or outperform classical baselines in predicting anti-PD-1 treatment response for non-small cell lung cancer lesions.

Olgiati, S., Santona, F., Meloni, D. + 5 more2026-03-11📄 health informatics

The Risk Factors, Detection and Classification of Esophageal Cancer Using Ensemble Machine Learning Models

This study presents a robust ensemble machine learning framework utilizing a multi-seed strategy and Random Forest-based feature ranking to achieve near-perfect accuracy (98.3%) and zero false negatives in detecting esophageal cancer in Ethiopia, demonstrating that reduced feature sets focusing on dietary and environmental risk factors can effectively support early diagnosis in resource-limited settings.

Gaso, M. S., Mekuria, R. R., Cankurt, S. + 3 more2026-03-11📄 health informatics

Cross-Attention Enables Context-Aware Multimodal Skin Lesion Diagnosis

This study demonstrates that a cross-attention-based multimodal deep learning framework, which effectively integrates dermoscopic images with patient metadata, outperforms both image-only and conventional fusion models in diagnosing skin lesions with superior accuracy and calibration.

Mridha, K., Islam, H.2026-03-11📄 health informatics

Co-designing a virtual reality based mindfulness application to address diabetes distress using Artificial Intelligence-informed Experience-Based Co-Design (AI-EBCD): a feasibility study

This feasibility study utilized Artificial Intelligence-informed Experience-Based Co-Design (AI-EBCD) to gather insights from adults with type 2 diabetes and mindfulness practitioners, resulting in a tailored prototype for a virtual reality mindfulness application aimed at alleviating diabetes distress.

Ghosal, S., Zhang, M., Stanmore, E. + 7 more2026-03-11📄 health informatics

Regression vs. Medical LLMs: A Comprehensive Study for CVD and Mortality Risk Prediction

This study evaluates the performance of traditional regression models against medical large language models (MedLLMs) for predicting cardiovascular disease and mortality risks using the LURIC dataset, finding that while optimized MedLLMs and boosting techniques achieve competitive AUROCs up to 85%, MedLLMs require calibration adjustments to correct systematic over-prediction.

KOM SANDE, S. D., Skorski, M., Theobald, M. + 2 more2026-03-11📄 health informatics

Evaluating linkage approaches for address-level socioenvironmental exposure assessment

This study demonstrates that while address tag fuzzy matching achieves perfect accuracy for linking National Address Database records to parcel data, geocoding-based methods perform significantly worse—particularly in densely populated and deprived neighborhoods—highlighting the critical need for precise, standardized linkage approaches to prevent exposure misclassification in health research.

Hartlage, C. S., Manning, E. R., Brokamp, C.2026-03-10📄 health informatics

PrivateBoost: Privacy-Preserving Federated Gradient Boosting for Cross-Device Medical Data

PrivateBoost is a privacy-preserving federated XGBoost system designed for cross-device medical scenarios where clients hold minimal data, utilizing m-of-n Shamir secret sharing and commitment-based anonymous aggregation to achieve high model accuracy and robustness against client dropout without requiring client-to-client communication or revealing individual identities.

Specht, B., Garbaya, S., Ermis, O. + 4 more2026-03-10📄 health informatics

Variability in Automated Sepsis Case Detection: A Systematic Analysis of Implementation Methods in Clinical Data Repositories

This systematic review reveals that significant methodological heterogeneity in automated sepsis case detection across MIMIC-III and eICU-CRD databases leads to widely varying detection rates, underscoring the urgent need for standardized reporting and open-source code to ensure reproducibility in sepsis research.

Meyer-Eschenbach, F., Schmiedler, R., Stoephasius, J. v. + 13 more2026-03-10📄 health informatics

RAPEX HARM from AIS 2015 Coded Injuries

This paper presents a novel post-processing method that assigns RAPEX HARM values to AIS 2015-coded injuries by aggregating the three most severe injuries per person to holistically combine lethality (AIS-CD) and long-term consequences (pFCI), thereby providing a more comprehensive severity assessment than the current AIS-CD-only approach.

Krampe, J., Junge, M.2026-03-10📄 health informatics

AI-Powered Pipeline for Annotating Echocardiography Notes and Prognostic Variable Analysis in Critical Care

This study presents a secure, locally deployable AI pipeline that automatically extracts prognostic echocardiography variables from unstructured ICU notes, demonstrating that integrating these data points with clinical features significantly improves mortality prediction accuracy compared to the standard APACHE IV score.

Xu, S., Ma, T., Duan, C. + 8 more2026-03-10📄 health informatics

Adversarial Robustness of Capsule Networks for Medical Image Classification

This study demonstrates that Capsule Networks exhibit superior intrinsic adversarial robustness and stable feature representations compared to CNNs and Vision Transformers across multiple medical imaging datasets, highlighting their potential as reliable alternatives for clinical diagnostic applications.

Srinivasan, A., Sritharan, D. V., Chadha, S. + 4 more2026-03-10📄 health informatics

Measurement strategy alters inferred age-dependent accumulation and mortality risk of mosaic Y loss

This study demonstrates that the choice between phase-based and intensity-based measurement strategies significantly alters the inferred age-dependent accumulation, mortality risk thresholds, and population prevalence of mosaic Y chromosome loss, with phase-based approaches revealing steeper accumulation and identifying excess mortality risk at lower burdens compared to conventional intensity-based metrics.

Ware, A., Weyrich, M., Fatima, S. + 12 more2026-03-10📄 health informatics

Machine Unlearning for GDPR Right-to-Erasure in Antimicrobial Resistance Prediction Models

This study demonstrates that Sharded, Isolated, Sliced and Aggregated (SISA) training offers a computationally efficient and accurate alternative to full retraining for machine unlearning in antimicrobial resistance prediction, enabling scalable compliance with GDPR right-to-erasure requirements while maintaining clinical performance thresholds.

Saniya, S., Khan, A. A.2026-03-10📄 health informatics

More Signal vs. More Noise - Comparing Full Text and Abstract as Inputs for Large Language Model-based Classification of Oncology Trial Eligibility Criteria

This study demonstrates that providing full-text articles rather than just abstracts significantly improves the accuracy of GPT-5 in classifying oncology trial eligibility criteria, as the additional signal from the complete text outweighs the potential negative impact of increased noise.

Weyrich, J., Dennstaedt, F., Foerster, R. + 4 more2026-03-10📄 health informatics

Accelerating Exploratory Clinical Research: An LLM-Powered Framework for Cross-Study Data Harmonization and Natural Language Querying

This paper presents an LLM-powered framework that automates the harmonization of disparate CDISC SDTM clinical trial datasets and enables natural language querying, thereby overcoming interoperability barriers to accelerate secondary analysis and evidence generation in clinical research.

Garg, A., Sett, A., Baumann, B. + 4 more2026-03-09📄 health informatics

AI-Driven Feature Selection Using Only Survey Variable Descriptions: Large Language Models Identify Adolescent Vaping Predictors

This study demonstrates that instruction-tuned Large Language Models can effectively identify key predictors of adolescent vaping initiation using only survey variable descriptions, enabling the training of high-performing classifiers without access to raw data.

Zhang, K., Zhao, Z., Hu, Y. + 1 more2026-03-09📄 health informatics

Time-to-event modeling with multimodal clinical and genetic features improves risk stratification of liver complications in chronic hepatitis C

This study demonstrates that an interpretable, multimodal time-to-event framework integrating clinical, genetic, and socioeconomic data significantly improves the risk stratification of cirrhosis, hepatocellular carcinoma, and mortality in chronic hepatitis C patients compared to traditional fibrosis-based assessment.

Islam, H., Arian, A., Franses, J. W. + 1 more2026-03-09📄 health informatics

Predictors of COVID-19 hospital outcomes: a machine learning analysis of the National COVID Cohort Collaborative

This study utilized machine learning on a large, harmonized cohort of hospitalized COVID-19 patients to demonstrate that while common structured electronic health record features offer moderate utility for predicting mortality, they are insufficient for forecasting length of stay, and that applying SMOTE to address class imbalance creates a critical trade-off between recall and discrimination that necessitates reporting threshold-dependent metrics for clinical utility.

Vazquez, J., Taylor, L., Chen, Y.-Y. K. + 5 more2026-03-09📄 health informatics

Extracting patient reported cannabis use and reasons for use from electronic health records: a benchmarking study of large language models

This study demonstrates that combining fine-tuned clinical language models with large language model classifiers enables the high-precision, scalable extraction of patient-reported cannabis use status and reasons for use from unstructured electronic health records of patients with autoimmune rheumatic diseases.

Wang, Y., Bozkurt, S., Le, N. + 6 more2026-03-09📄 health informatics

A Novel Blended Hybrid Care Model for Maternal Mental Health: Cohort Study of Pregnant and Postpartum Patients

This pilot cohort study demonstrates that a novel blended hybrid care model combining synchronous virtual CBT with the asynchronous mindLAMP app significantly reduced anxiety and depression symptoms in pregnant and postpartum women, suggesting its potential to bridge critical treatment gaps in maternal mental health.

Calvert, E. I., Chen, K., Moon, K. + 4 more2026-03-09📄 health informatics

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