Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clinical reasoning capabilities. Current solutions face three challenges: 1) Cloud-based LLMs pose data security risks; 2) Local model deployment demands technical expertise; 3) General LLMs lack mechanisms to integrate medical knowledge. Retrieval-augmented systems also struggle with medical document processing and clinical usability. We developed KidneyTalk-open, a desktop system integrating three technical components: 1) No-code deployment of state-of-the-art (SOTA) open-source LLMs (such as DeepSeek-r1, Qwen2.5) via local inference engine; 2) Medical document processing pipeline combining context-aware chunking and intelligent filtering; 3) Adaptive Retrieval and Augmentation Pipeline (AddRep) employing agents collaboration for improving the recall rate of medical documents. A graphical interface was designed to enable clinicians to manage medical documents and conduct AI-powered consultations without technical expertise. Experimental validation on 1,455 challenging nephrology exam questions demonstrates AddRep's effectiveness: achieving 29.1% accuracy (+8.1% over baseline) with intelligent knowledge integration, while maintaining robustness through 4.9% rejection rate to suppress hallucinations. Comparative case studies with the mainstream products (AnythingLLM, Chatbox, GPT4ALL) demonstrate KidneyTalk-open's superior performance in real clinical query. KidneyTalk-open represents the first no-code medical LLM system enabling secure documentation-enhanced medical Q&A on desktop. Its designs establishes a new framework for privacy-sensitive clinical AI applications. The system significantly lowers technical barriers while improving evidence traceability, enabling more medical staff or patients to use SOTA open-source LLMs conveniently.

Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. At present, most of the models used for predicting the progression of CKD are static models. We aim to develop a dynamic kidney failure prediction model based on deep learning (KFDeep) for CKD patients, utilizing all available data on common clinical indicators from real-world Electronic Health Records (EHRs) to provide real-time predictions. Findings: A retrospective cohort of 4,587 patients from EHRs of Yinzhou, China, is used as the development dataset (2,752 patients for training, 917 patients for validation) and internal validation dataset (917 patients), while a prospective cohort of 934 patients from the Peking University First Hospital CKD cohort (PKUFH cohort) is used as the external validation dataset. The AUROC of the KFDeep model reaches 0.946 (95\% CI: 0.922-0.970) on the internal validation dataset and 0.805 (95\% CI: 0.763-0.847) on the external validation dataset, both surpassing existing models. The KFDeep model demonstrates stable performance in simulated dynamic scenarios, with the AUROC progressively increasing over time. Both the calibration curve and decision curve analyses confirm that the model is unbiased and safe for practical use, while the SHAP analysis and hidden layer clustering results align with established medical knowledge. Interpretation: The KFDeep model built from real-world EHRs enhances the prediction accuracy of kidney failure without increasing clinical examination costs and can be easily integrated into existing hospital systems, providing physicians with a continuously updated decision-support tool due to its dynamic design.

Abstract--Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase generalization capabilities. However, MLP is not so suitable for graph-structured data like networks. MLP treats IP addresses as isolated instances and ignores the connection information, which limits geolocation accuracy. In this work, we research how to increase the generalization capability with an emerging graph deep learning method - Graph Neural Network (GNN). First, IP geolocation is re-formulated as an attributed graph node regression problem. Then, we propose a GNN-based IP geolocation framework named GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP) layers and a decoder. The preprocessor and encoder transform measurement data into the initial node embeddings. MP layers refine the initial node embeddings by modeling the connection information. The decoder maps the refined embeddings to nodes' locations and relieves the convergence problem by considering prior knowledge. The experiments in 8 real-world IPv4/IPv6 networks in North America, Europe and Asia show the proposed GNN-Geo clearly outperforms the state-of-art rule-based and learning-based baselines. P geolocation is to obtain the geographic location (geolocation) of an IP address [1, 2]. It is widely used in localized IP after the probing host [2] obtains the measurement data online advertisement, location-based content restriction, cybercrime (e.g., delay) to the target IP and landmarks [2, 5]. IP geolocation is important are IP addresses with known locations [2]. Generally, existing especially for networking devices without GPS (Global Positioning measurement-based fine-grained IP geolocation methods can be System) function, such as PCs and routers. IP geolocation categorized into two kinds: rule-based and deep learning-based can be categorized into two kinds by accuracy: coarse-grained methods (referred as learning-based methods in this paper).