arXiv:2512. 03296v2 Announce Type: replace-cross Abstract: Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams.
arXiv:2606. 04499v1 Announce Type: cross Abstract: Cancer care requires a longitudinal approach in which treatments are planned and delivered over time according to the needs of each individual patient.
arXiv:2408. 02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era.
Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relevant to cancer genomics, yet their translation in...
arXiv:2608.30912v1 Announce Type: new Abstract: Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge rele...
The paper evaluates the cost‑effectiveness of consensus‑based learning (CBL) versus federated learning (FL) across seven medical datasets, three tasks, and eight modalities involving 3 to 23 clients. CBL achieves accuracy comparable to FL while dramatically cutting training time (15‑fold) and communication cost (60‑fold). The study suggests that CBL offers a more sustainable and democratized approach to deploying collaborative AI in real‑world healthcare settings.
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which conditions. However, such...
The study re‑implements 12 AI algorithms for electronic health records within a unified framework and evaluates them on MIMIC‑IV and NWICU datasets. It compares expert‑authored clinically meaningful tasks with randomly generated tasks, finding that pairwise algorithm comparisons transfer well across task families and datasets, yet clinically meaningful tasks show stronger task‑method interactions. The results also reveal that newer algorithms do not consistently outperform older ones, with gradient‑boosted trees remaining highly competitive when combined with modern EHR representations.
arXiv:2606. 14608v1 Announce Type: cross Abstract: Survival prediction plays a central role for healthcare providers and clinical researchers.
arXiv:2605. 01189v2 Announce Type: replace Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability.
arXiv:2606. 28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts.
arXiv:2608. 14657v1 Announce Type: new Abstract: Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single data modalities and their inability to leverage structured clinical knowledge.