98 results about "Clinical scenario" patented technology

The invention provides a scientific and technological literature intelligent retrieval method and related equipment based on generative artificial intelligence, and the method comprises the steps: carrying out the multi-layer semantic annotation of medical scientific and technological literatures, constructing a symptom-disease dynamic association map, and building a medical scientific and technological literature knowledge base; performing medical context analysis and multi-modal feature extraction on user query, and generating a unified retrieval vector in combination with Boolean operation nested analysis and semantic alignment processing; performing evidence grading retrieval and clinical scene matching based on the unified retrieval vector to obtain a preliminary candidate literature set, and optimizing the preliminary candidate literature set into a target candidate literature set through fine-grained semantic recalculation; and calculating a retrieval prior probability according to the evaluation dimension, performing knowledge weighted fusion on the candidate literature, and generating a medical science and technology literature recommendation report. According to the method, the medical term association relationship is deeply understood through the association map, the result is ensured to be matched with the patient characteristics through evidence grading retrieval and clinical scene matching and screening, and the accuracy of document retrieval is improved.

The invention relates to the technical field of medical big data intelligent analysis, and discloses a medical big data intelligent analysis method and system based on deep learning, and the method comprises the steps: obtaining patient image data, case text data and wearable equipment monitoring data, carrying out the preprocessing, and carrying out the standardized coding of each modal data; and calculating the dynamic weight of each modal data, and synthesizing a multi-level fusion weight. And in combination with the fusion weight and the clinical state parameters of the patient, constructing a modal value-clinical urgency degree two-factor resource decision model to obtain a priority score. According to the priority scores, the patients are divided into different emergency treatment levels, and different treatment strategies are provided. The model provided by the invention can maintain efficient prediction capability in various clinical scenes, improves the accuracy of clinical decisions, and enables the resource allocation of hospitals to be more scientific and reasonable. In an emergency treatment environment, the real-time priority evaluation of the patient can significantly improve the efficiency of emergency treatment, reduce the waiting time, and improve the survival rate and the treatment effect of the patient.

The invention relates to the technical field of tumor treatment, in particular to an intelligent decision-making system for tumor precise targeted medication based on individual mutation information, which comprises a data processing layer, a variation annotation and function prediction layer, a knowledge base integration layer and a scheme decision-making engine and report visualization module. According to the intelligent decision-making system for tumor precise targeted medication based on individual mutation information, a rule engine and a prediction model are combined, dynamic priority ranking is output, multi-model fusion decision making is achieved, and clinical scene deep adaptation is achieved by predicting primary and secondary drug resistance, calculating liver and kidney function adjusting dosage and generating a combined medication time sequence scheme; through an individualized drug delivery scheme, combination drug use optimization is achieved, a visual clinical report is generated, clinical executable operation is further strengthened, and through algorithm quantification, a dynamic knowledge graph, AI auxiliary decision making and a clinical operation closed loop, the next-generation technical research direction of a tumor precise drug use system can be represented.

The invention belongs to the technical field of biomedical materials, and relates to ultrasonic response adhesion integrated hydrogel as well as a preparation method and application thereof, the hydrogel comprises a hydrogel matrix, an interface anchoring agent and a functional load component; the preparation method comprises the following steps: 1, preparing a hydrogel precursor solution, and preparing a hydrogel matrix into a solution; 2, carrying out mold molding or injection crosslinking on the solution prepared in the step 1 to form hydrogel; 3, preparing an interface anchoring agent solution, and dissolving or dispersing an interface anchoring agent in a solvent to prepare the interface anchoring agent solution; the ultrasonic responsive adhesive hydrogel platform provided by the invention can provide an efficient, controllable and universal solution for tissue adhesion, targeted therapy, wearable attachment and the like in a complex microenvironment, and is suitable for a plurality of clinical scenes such as chronic wound management, surgical postoperative closure, infection control, adhesive bioelectronic fixation, soft tissue engineering and the like.

The invention provides a ward rotation evaluation and model construction method based on multi-modal information, and belongs to the technical field of deep learning. The method comprises the following steps: firstly, constructing a multi-modal clinical data set, integrating five types of heterogeneous data including operation records, vital sign time sequence data, medical images, medication records and nursing logs, labeling postoperative state labels of patients, and simulating a clinical real scene to generate a training set and a test set containing random missing modals; secondly, constructing a ward rotation evaluation model, wherein the model is used for improving and designing a traditional teacher-student network architecture into a double-path heterogeneous distillation model; meanwhile, designing a multi-task loss joint optimization strategy for aligning the characteristics of the teacher-student network and constraining the evaluation result of the teacher network, and finally deploying a trained model to an edge computing device, receiving a multi-modal data stream in real time and outputting a transfer suggestion and a risk score; the invention provides a brand new intelligent solution under a ward rotation scene, and improves the evaluation robustness of a multi-modal missing scene.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively improved.

The invention provides a fusion platform intelligent teaching method and system based on virtual simulation, and the method comprises the steps: obtaining an operation behavior of a student at each interaction point in a virtual simulation scene, and forming an IFM data set; constructing an evaluation function, and comparing the actual performance data with the ideal performance value; forming a key evidence source data set based on actual performance data in the IFM data set; and constructing an optimization model containing data conflict items and consistency items of each key evidence source, generating teaching guidance data, and quantitatively evaluating the learning effect of students. According to the invention, students can perform operation exercises in a simulated real clinical scene, such as key nursing skills of intravenous injection, external chest compression and the like, and through multi-dimensional data collection including operation accuracy, answer conditions, interactive records and the like, more comprehensive learning evaluation is provided, so that scoring deviation caused by single data is avoided, and the learning efficiency is improved. And a rich information basis is provided for ability evaluation of students.

The invention discloses an emergency treatment high-risk patient real-time grading method based on an improved multi-mode Transform algorithm. The method comprises the following steps: S1, generating a time synchronization multi-mode event sequence; s2, obtaining a corresponding single-mode feature representation tensor; s3, obtaining a risk weighted cross-modal attention matrix; s4, in the improved multi-modal Transform fusion network, generating a fusion feature representation tensor by using a time sequence sliding window cache and incremental updating mechanism, and outputting a risk grading label and a corresponding risk grading confidence coefficient based on the fusion feature representation tensor; s5, inputting the risk grading label and the risk grading confidence into the interpretive sub-network, and generating clinical causal chain prompt information; and S6, synchronizing the risk grading label, the risk grading confidence and the clinical causal chain prompt information. According to the method, the accuracy of high-risk patient identification and the adaptability of the model to a clinical complex scene are remarkably improved, and a test result shows that the real-time identification accuracy of a high-risk case is improved compared with that of a conventional multi-modal model.

The present invention relates to the field of intelligent medical technology, and in particular to an intelligent outpatient triage method and system based on a large model. An intelligent outpatient triage system based on a large model comprises: a data acquisition module and an outpatient triage module. The present invention determines the triage priority corresponding to the patient by sending the patient's structured data into the outpatient triage model for processing, and performs triage operations on the patient based on the patient's corresponding triage priority, without the need for medical staff to make judgments based on experience; four different judgment criteria are incorporated into the outpatient triage model, and the feature information expression effect and feature information fusion effect under the perspectives of the four different judgment criteria are improved through a multi-standard feature extraction layer, a feature transmission layer, and a difference complementary enhancement layer, so that the outpatient triage model can be adapted to more clinical scenarios and has higher adaptability to complex patient conditions.

Systems and methods are described for creating and deploying immersive, interactive, and dynamic medical simulation training experiences within augmented reality (AR) and virtual reality (VR) environments. Embodiments include a creation framework that decomposes complex clinical scenarios into modular components, allowing users to construct simulations using an intuitive, block-based interface without requiring programming expertise. These components capture the medical, physiological, and psychological elements of the scenario, enabling the creation of detailed, multidimensional simulations. An integrated AI engine further enhances the system by leveraging a large language model (LLM) to automate and assist in the scenario creation process. The AI engine interprets high-level natural language inputs, predicting and generating detailed simulation instructions, thereby accelerating the development and deployment of custom training scenarios. Once created, the scenarios are processed by a scenario engine that controls various elements of the simulation, including virtual characters, environments, tools, physiological responses, and assessable actions within AR / VR settings.

The invention relates to the technical field of health data processing, in particular to a clinical patient self-health management recommendation method and system. Obtaining multi-dimensional health data of the target patient, including a clinical measurement index set and a self-report symptom set, and performing standardized preprocessing on the multi-dimensional health data to generate standardized health data; constructing a personal health state map based on the standardized health data; performing health risk traceability analysis according to the personal health state map, and identifying a key risk node set and a risk propagation path; generating an initial health management recommendation strategy set in combination with a predefined medical knowledge base and patient personalized constraints; and dynamically optimizing the initial health management recommendation strategy set based on the patient feedback data and the health state change information, and outputting an optimized personalized health management recommendation scheme. According to the invention, personalized, precise and dynamic patient self-health management recommendation in a clinical scene can be realized.

The invention relates to the technical field of intelligent traditional Chinese medicine diagnosis and treatment, and discloses a thermosensitive moxibustion tracking algorithm and system based on long-acting moxibustion therapy sensing and posture three-dimensional sensing, and the algorithm comprises a long-acting flexible thermosensitive sensing subsystem, a three-dimensional posture sensing module, a multi-modal data fusion processing unit, a dynamic thermal field modeling algorithm, and a self-adaptive moxibustion applying control subsystem. Through full-chain innovation of long-acting sensing, three-dimensional sensing, coupling modeling and intelligent control, the performance stability problem (drift error is less than or equal to + / -0.3 DEG C) of the flexible sensor under long-term work (more than or equal to 72 hours) is solved; establishing a posture-thermal field-time four-dimensional coupling model, and realizing submillimeter thermosensitive point tracking under a dynamic posture; a millisecond-level response (less than or equal to 50ms) closed-loop control system is constructed, and the adaptability to complex clinical scenes is improved; therefore, the key technical problem in the dynamic moxibustion therapy scene is systematically solved.

The invention relates to the technical field of medical instruments, and discloses a surgical instrument preparation early warning system and method based on big data, and the system comprises an association module, an urgent module and an early warning module. The method corresponds to the system. According to the application, the instrument set association library is constructed based on the historical operation content through the association module, and accurate matching of the operation content and the doctor habits is realized in combination with the knowledge relationship and the preference relationship; the urgent module uses a traceability system to automatically compare the inventory and finely identify the urgent instrument package, so that manual counting errors are avoided; the early warning module determines the circulation starting time of the instrument set, monitors the time difference value in real time, and quickly responds to the operation demand of the day; therefore, automatic and refined surgical instrument preparation early warning based on big data is realized, matching accuracy and circulation efficiency are improved, resource management is optimized, personal errors and resource waste are reduced, adaptability to complex clinical scenes is enhanced, and surgical safety and high efficiency are guaranteed.

The invention provides a hospital equipment department consumable management method based on an AIGC technology. The hospital equipment department consumable management method comprises the following steps of S1, performing multi-modal data acquisition and fusion modeling; s2, a step of constructing a generative demand prediction model; s3, generating a dynamic inventory optimization strategy; s4, performing semantic generation of an intelligent replenishment strategy; s5, a supplier portrait and intelligence matching step is carried out; s6, automation of compliance review is carried out; s7, carrying out three-dimensional digital twin modeling; s8, performing an anomaly detection and self-healing mechanism; s9, performing consumable full life cycle tracing; and S10, performing a cognitive evolution system. According to the method, mathematical modeling and clinical scenes are deeply fused, compared with a traditional SPD mode, leap-type upgrading is achieved in three dimensions of inventory cost, management efficiency and medical safety, and technical support is provided for hospital refined operation under DRG / DIP payment reform.

The invention relates to the technical field of medical temperature control, and discloses an ice blanket machine dynamic temperature control method based on body temperature feedback. The method comprises the following steps: acquiring body surface temperature sensing data and core temperature simulation data of a target object through body temperature multi-source sensing, and constructing an original body temperature sensing data set; generating a physiological state feature vector according to the data, and performing physiological state evaluation; based on the feature vector, generating a multi-modal body temperature state tensor through multi-source sensing fusion modeling; then, an optimized temperature control strategy is generated according to the multi-modal body temperature state tensor by adopting a reinforcement learning method for dynamic binding of a clinical constraint rule component; performing integrity verification and clinical safety rule conflict detection on the optimization strategy to generate a safety control instruction sequence; and finally, the ice blanket machine is driven to execute temperature regulation and control operation according to the sequence. According to the method, the accuracy and safety of temperature regulation and control of the ice blanket machine are improved, and complex clinical scene requirements are met.

The invention discloses a patient rehabilitation effect evaluation system based on nursing information processing. The system comprises an evaluation data acquisition module, a data preprocessing module, an effect evaluation model construction module, a model hyper-parameter optimization module and a rehabilitation effect evaluation module. The invention relates to the technical field of intelligent patient rehabilitation effect evaluation, in particular to a patient rehabilitation effect evaluation system based on nursing information processing, and the system comprises the steps: obtaining an effect evaluation original data set through data collection; a data preprocessing method of data cleaning, feature extraction, time sequence alignment, data standardization and data set segmentation is adopted; a double-flow deep network model is adopted as an effect evaluation model, and it is ensured that an evaluation result conforms to clinical logic while feature collaboration of heterogeneous medical data is achieved; an improved leapfrog algorithm is adopted to optimize model hyper-parameters, dynamic balance exploration and development are achieved, meanwhile, the stability of the algorithm is enhanced through population division, and stable performance of an evaluation system in different clinical scenes is ensured.

The invention relates to the technical field of medical image processing, in particular to a dynamic ultrasonic noise reduction model training method and a dynamic ultrasonic noise reduction method and device. The method comprises the following steps: acquiring an ultrasonic video covering multiple clinical scenes such as antral pneumatosis, hydrops, solid contents, long antral antrum and cardiac anatomical occlusion; marking the precise boundary of the organ frame by frame by a clinical expert, segmenting and complementing a missing section, optimizing the contour of a fuzzy region, and inhibiting or erasing artifacts; a deep learning model is trained based on the labeled data, so that the model can call an exclusive noise reduction algorithm according to organ categories (the antral sinuses adopt morphological reconstruction filtering guided by ellipse / long axis prior, and the heart adopts time-space joint deblurring driven by a cardiac cycle and combining motion compensation and anisotropic filtering); and weighted noise reduction is carried out on the suspected lesion or the user indication area. In the reasoning stage, a high-fidelity and low-noise video stream can be output in real time by inputting any dynamic ultrasonic video, and organ boundary metadata is attached.

The invention provides an application of a biomarker in platelet contamination degree evaluation. The biomarker comprises IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TREML1, GP5, PRDX3, ITGB1, MDH2 and GNB1. Based on the marker combination, platelet contamination samples can be accurately recognized and eliminated, and large-scale application of plasma proteomics to clinical scenes such as cancer early screening and personalized medical treatment is facilitated.

The invention discloses a clinical medicine practice skill evaluation system, and relates to the technical field of clinical medicine education, and the system comprises the following components: a comprehensive data processing module which collects and preprocesses real patient electronic medical record data and examinee multi-modal data, fuses all modal data features, cleans the electronic medical record data, and stores the cleaned electronic medical record data; providing data for subsequent modules; the personalized intelligent virtual patient is generated based on the electronic medical record data of the real patient, rich and diversified clinical simulation scenes are provided for examinees, the complexity and diversity of the real patient are achieved, the illness state can be adjusted in real time according to decisions of the examinees, the uncertainty and complexity of the clinical scenes are simulated, and the clinical simulation experience of the examinees is improved. The system has the advantages that the system is simple in structure and convenient to use, clinical thinking of examinees can be cultivated and improved in practice, the examinees can learn to make accurate judgment and decisions when the illness state changes, and meanwhile, the examinees can exercise the strain capacity of the examinees through interaction with virtual patients so as to better adapt to various challenges in actual clinical work.

The invention relates to the technical field of medical informatization and intelligent nursing, in particular to a general surgery department patient postoperative complication risk prediction and nursing decision-making system, which comprises a baseline manifold construction module for initializing an individualized steady state manifold in a preset multi-dimensional state space based on historical operation parameters and basic state data; the perturbation response topology evaluation module is used for calculating a recovery attenuation coefficient after the real-time phase trajectory deviates from the individualized steady state manifold so as to generate a steady state dissipation index and a growth rate; the offset course quantity decoding module is used for adjusting a dynamic threshold value based on a time delay parameter and an embedded dimension parameter on the basis of the basic state data; the risk prediction module is used for weighting, merging and normalizing the growth rate of the steady-state dissipation index and the modulus length of the course deviation vector to generate a risk prediction evaluation result; the decision closed-loop module is used as a secondary perturbation variable and sends the secondary perturbation variable to the perturbation response topology evaluation module for continuous monitoring; according to the invention, the baseline anchoring precision in different clinical scenes is improved.

The application discloses a dynamic knowledge graph construction method, an electronic device and a computer readable storage medium. The method comprises the following steps: determining a four-diagnosis element set for a patient according to a medical record text of the patient, wherein the four-diagnosis element set comprises a plurality of four-diagnosis elements, and the four-diagnosis element is a characteristic related to a disease possessed by the patient described from the perspective of four-diagnosis of traditional Chinese medicine; performing traditional Chinese medicine long logic chain deduction based on the four-diagnosis element set, performing through different thinking threads, and obtaining at least one target syndrome element, scores of each target syndrome element, at least one target syndrome matching the target syndrome element, and scores of each target syndrome; and generating a dynamic knowledge graph according to the four-diagnosis element set, the scores of each target syndrome element and the scores of each target syndrome. Through the scheme, a knowledge graph based on dynamic changes of clinical conditions can be obtained, so that fast, efficient and accurate clinical thinking training for traditional Chinese medicine is realized.

Arthroscopes and laparoscopes are available in several lens cuts to cover different clinical situations of interest, with the surgeon having to exchange the optics in order to change the direction of view of the camera. The presently disclosed embodiments disclose real-time image processing systems and methods that enable the user to arbitrarily change the direction of view of a surgical camera with the advantage of avoiding the disruption in workflow caused by the physical exchange of the optics. In addition, the presently disclosed embodiments disclose performing zoom along an arbitrary viewing direction (directional zoom) that enables to increase the scale of a region of interest without decreasing the overall field-of-view or losing image contents.

This application relates to a method and system for training neurosurgical specialist nurses based on a virtual teaching and research room. The method includes: acquiring patient data and clinical scenario parameters corresponding to preset neurosurgical training tasks from a medical database; constructing a virtual training environment using virtual simulation technology based on the patient data and clinical scenario parameters; collecting user interaction behavior data in real time through a sensor network deployed in the environment; analyzing the behavior data in real time and driving the virtual environment to generate real-time feedback effects; identifying key operation points using a pre-trained LSTM analysis model based on the behavior data and feedback effects; and generating a training feedback report based on the key operation points. This method can improve the realism of neurosurgical specialist nurses' operations in the virtual environment, the assessability of the training process, and the real-time nature of feedback, effectively assisting in professional skills training and assessment.

This invention discloses a seamless switching medical simulation training system that integrates virtual and real-world childbirth and postpartum hemorrhage scenarios. Its features include: a maternal model, a childbirth simulation mechanism, and a postpartum simulation mechanism, one of which can be selectively mounted on the maternal model; the childbirth model mechanism includes an infant model, a birthing belly component, a birthing vagina component, and a drive device. The drive device is located within the maternal model, and the infant model is mounted on the drive device, allowing the drive device to move the infant model linearly and rotate towards the birthing vagina component; the birthing belly component is equipped with undulating components; this system achieves seamless switching between childbirth and postpartum hemorrhage scenarios through a modular and detachable structure, realistically simulating clinical scenarios such as the childbirth process and postpartum hemorrhage care. It is comprehensive in function, realistic in operation, and can effectively improve the standardized operation and emergency response capabilities of medical personnel.

This invention provides a sepsis consultation method and system based on multi-turn dialogue, belonging to the field of intelligent consultation equipment technology. This invention achieves effective integration of standardized medical knowledge and personalized clinical experience through the construction of a two-dimensional knowledge graph and cross-graph interactive retrieval, improving the accuracy and reliability of sepsis diagnosis and treatment recommendations. Through structured sepsis diagnosis and treatment chain constraints and a confidence-driven dynamic strategy, it alleviates the illusion problem of large language models in complex clinical reasoning, ensuring that the generated content conforms to clinical diagnosis and treatment logic norms. Through semantic anchor guidance, hierarchical activation, and knowledge feedback mechanisms, it achieves multi-granular semantic association from macro-clinical scenarios to micro-medical concepts, balancing retrieval accuracy and knowledge coverage. The confidence evaluation system comprehensively considers knowledge sufficiency, redundancy, and consistency, adaptively balancing knowledge utilization and information exploration, improving the reasoning efficiency and diagnosis and treatment quality of multi-turn dialogue.

The invention discloses a method for evaluating the curative effect of depressive disorder treatment based on heart rate variability circadian rhythm, and belongs to the technical field of mental disease treatment evaluation, and the method comprises the following steps: carrying out 24-hour continuous electrocardiogram monitoring on a patient to obtain HRV data of the corresponding patient, and preprocessing the HRV data; dividing the HRV data according to a preset unit time length to obtain HRV unit data of a corresponding unit time period, integrating the HRV unit data according to a corresponding patient to obtain patient analysis data of the patient, and determining circadian rhythm parameter data according to the patient analysis data; establishing a treatment monitoring model, performing data acquisition on a target patient to obtain target analysis data, and analyzing the target analysis data through the treatment monitoring model to obtain a treatment effect of the target patient; the method is not limited to treatment evaluation of depressive disorder, and can be applied to evaluation of other clinical scenes and disease treatment effects requiring continuous monitoring of heart rate variability circadian rhythm parameters.

The invention relates to the cross technical field of natural language processing and medical semantic understanding, and discloses a dialect medical term recognition capability improvement method based on a natural language error correction model, and the method comprises the steps: collecting dialect medical term voice data and text data, and covering a plurality of dialect types and clinical scenes; based on the dialect medical term voice data and the text data, carrying out structured corpus construction processing to generate structured dialect medical term corpus data; voice data and text data covering multiple dialect types and clinical scenes are collected, the universality and representativeness of data sources are guaranteed, meanwhile, structured corpus construction processing is conducted, pronunciation annotation, expression annotation, semantic annotation and scene annotation data are generated by adopting a four-dimensional annotation rule, corpus information is complete and standard, and the data annotation efficiency is improved. And the accuracy and reliability of subsequent model training are ensured.

The present disclosure discloses a method, electronic device, and storage medium for simulating coil placement in an aneurysm. The method includes: obtaining a coil model, a three-dimensional rigid body model, and an aneurysm model, wherein the coil model is a non-rigid body model; compressing the coil model using the three-dimensional rigid body model; placing the compressed coil model into the aneurysm model so that the coil model deforms under its own internal force; and obtaining a final simulation result in response to the deformation parameters of the coil model meeting a preset condition. Through the solution of the embodiment of the present disclosure, it is possible to impose constraints on the non-rigid coil model through the three-dimensional rigid body model, and use the internal force of the coil model itself to achieve expansion deformation in the aneurysm model, thereby directly simulating the coil compression and placement process quickly through physical methods, which can improve the simulation speed and have higher clinical value for emergency clinical scenarios.

Improved perioperative medicine involving semantic AI The invention provides, amongst other aspects, a device comprising: an input interface; a control module connected to the input interface; an output interface connected to said control module; wherein the control module is configured, upon initiation of a respective next measurement cycle, 5 to: receive, via the input interface, respective next measurement data measured with respect to a patient; process the respective next measurement data, based at least on a respective previous graph associated with a respective previous measurement cycle, for obtaining a respective next graph associated with the respective next measurement cycle; output, via the output interface, the respective next graph; wherein the previous graph and the next graph each relate to knowledge 10 graphs and each comprise: a plurality of entities relevant to a clinical scenario to which the respective measurement cycles relate, and a plurality of relations between the entities; and wherein the previous and the next graph differ in at least one entity or relation.

Systems and methods for controlling fluid delivery via a manually administrable medication container to a patient through a fluid delivery pathway are provided. The systems and methods described herein incorporate rules-based clinical decision support logic to drive a flow control valve within a flow pathway based on a determination of whether or not an IV fluid connected to an input port on the pathway is appropriate for patient administration by considering such factors as patient-specific clinical circumstances, current medical orders, and accepted delivery protocols. Related apparatus, systems, methods and articles are also described.