1508results about "Drug references" patented technology

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 discloses an optimization analysis system for simulating an anesthesia process, relates to the technical field of anesthesia simulation optimization analysis, and is used for solving the problem of inaccurate anesthesia individualized simulation decision. By constructing an anesthesia simulation system fusing a pharmacokinetic and pharmacodynamic model and a physiological coupling model, dynamic process simulation under individual physiological states and operative characteristics is realized, state evolution trajectories of an induction period, a maintenance period and a resuscitation period can be accurately deduced, and by introducing deep learning calculation and stage continuity constraints, the accuracy of the state evolution trajectories of the induction period, the maintenance period and the resuscitation period is improved. The method comprises the following steps of: establishing a multi-path drug delivery and ventilation strategy, setting a risk-efficiency scoring function, completing state tracking and index evaluation of the whole process, generating a strategy scoring set with confidence information, further supporting an abnormal probability judgment and risk triggering mechanism by the system, and outputting key monitoring parameters and coping strategies, so as to improve the simulation accuracy and stability; therefore, closed-loop control of strategy optimization and risk early warning is realized, and the credibility of the anaesthesia simulation process is improved.

The invention relates to the technical field of artificial intelligence, and discloses a traditional Chinese medicine diabetes dry eye treatment difference evaluation system and method based on artificial intelligence, and the system comprises a four-diagnosis integrated module, a syndrome modeling module, an intelligent decision module, a curative effect feedback module, a knowledge base management module and a self-adaptive learning module. Multi-mode traditional Chinese medicine four-diagnosis data of tongue condition, pulse condition and eye diagnosis and modern physiological parameters of metabolic indexes are fused, a deep learning method is adopted for standardization processing, the problem that traditional Chinese medicine syndrome differentiation depends on subjective experience is solved, traditional Chinese medicine syndrome feature extraction of diabetes dry eye has objectivity and quantifiability, and the traditional Chinese medicine syndrome feature extraction efficiency is improved. The comprehensiveness and the accuracy of the diagnosis basis are improved; and the curative effect feedback module compares a prediction result with revival curative effect data in real time, and the knowledge base management module is linked to carry out case feature matching and strategy optimization, so that continuous optimization and long-term reliability of the treatment decision model are ensured.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention relates to the technical field of electronic prescriptions, in particular to a dynamic electronic prescription generation method and system based on artificial intelligence. The method comprises the following steps: collecting the latest physiological state parameter flow of a patient, carrying out real-time health state evaluation, and generating a personalized patient state map; historical medical records of a patient are extracted, time sequence pathological evolution tracking is carried out, and a pathological evolution trajectory is generated; performing multi-parameter time sequence difference comparison calculation and drug curative effect quantitative evaluation based on the personalized patient state map and the pathological evolution trajectory to obtain a curative effect evaluation report; performing allergic drug identification on the patient based on the historical medical record of the patient, and performing secondary drug screening to obtain a drug candidate set; and performing intelligent matching calculation on the drug candidate set according to the curative effect evaluation report, and performing combinatorial optimization analysis to generate a final effective electronic prescription. The electronic prescription is automatically updated and adjusted based on the state change of the patient, the risk of allergic prescriptions is reduced, and the safety of the prescriptions is improved.

The invention belongs to the technical field of medical risk early warning, and discloses a medical risk dynamic early warning and intervention decision-making system based on a multi-mode Internet of Things and AI, which comprises a sensing layer, a network layer, a data processing layer, an AI analysis layer and an application layer, the sensing layer comprises a wearable device, an environment sensor, a medical device, a non-contact monitoring device and an implantation device; the network layer constructs a data transmission channel and is provided with an edge computing node; the data processing layer adopts a multi-modal data fusion engine, is responsible for data alignment, data cleaning and feature extraction, and constructs a knowledge graph; the AI analysis layer comprises a dynamic risk early warning model and an intervention decision engine; the application layer is provided with a three-level early warning board, an intelligent intervention terminal and a block chain evidence storage platform. According to the invention, the early warning window is advanced, and the recognition accuracy is improved; the system promotes a medical monitoring mode to be changed from post response to active defense, the medical accident rate is further reduced, and a technical foundation is provided for constructing a new generation of smart hospitals.

The invention provides a drug-target interaction prediction method and device based on a dynamic heterogeneous double-flow graph neural network, and belongs to the field of drug research and development. The method solves the problems of low prediction accuracy and weak generalization ability caused by insufficient graph structure construction and feature expression in the prior art, and comprises the following steps: constructing a graph structure according to feature data of drugs and targets, and dynamically adjusting weights of the graph structure and edges according to data change to obtain a dynamic heterogeneous graph; the double-flow graph neural network is utilized to process feature information of the medicine and the target spot at the same time, and a complex mode of medicine-target spot interaction is effectively captured; in combination with long-range dependency modeling and random walk feature learning, on the basis of heterogeneous graph convolutional network learning, processing a remote dependency relationship between a drug and a target spot, and capturing multi-hop information in an isomorphic network by using random walk to further optimize interaction prediction; designing a decoder based on matrix completion; training and optimizing the model; the method is applied to drug-target interaction prediction.

The invention discloses an internal medicine drug curative effect evaluation method based on big data analysis of an electronic health record, and the method comprises the steps: extracting basic health data, diagnosis and treatment time sequence data and drug intervention data from the electronic health record, and carrying out the time-space alignment to generate a dynamic feature set; subgroups are obtained based on disease typing standard hierarchical clustering, and historical data and real world data are fused through transfer learning to construct a subgroup curative effect reference matrix; collecting data after medication in real time, and generating an evaluation vector containing short-term physiological response, middle-term symptom improvement and long-term prognosis risk through deep learning; dynamically matching the evaluation vector with the reference matrix, and introducing an individual weight coefficient to correct deviation; taking the deviation correction value as input, constructing a self-adaptive evaluation model through reinforcement learning, and performing iterative optimization; and generating an individualized report containing the curative effect level, the medication suggestion and the risk early warning, and quantifying the curative effect level through a fuzzy comprehensive evaluation method. According to the method, individual differences are accurately captured, full-cycle dynamic evaluation is realized, and the curative effect evaluation accuracy and the clinical decision-making efficiency are improved.

The invention discloses an automatic traditional Chinese medicine decoction method based on multi-modal data analysis, a medium and equipment, and the method comprises the steps: firstly, matching a first initial decoction strategy according to traditional Chinese medicine prescription information, and generating a second initial decoction strategy in combination with individual feature data of a patient; in the decocting process, multi-mode state data such as temperature time sequence data, visual characteristic data and volatile gas component characteristic spectrum of liquid medicine in the pot body are synchronously collected; the multi-modal data and the individual influence factors are input into a dynamic optimization decision model, and the model generates a decoction strategy adjustment instruction by calculating the deviation between a real-time component spectrum and an expected trajectory and comprehensively judging safety states such as boiling intensity and dry burning risk; by dynamically adjusting the operation parameters of the decoction equipment, the composition change of the liquid medicine approaches to the optimization target, and meanwhile, the safety of the decoction process is ensured. The problems that a traditional decoction method depends on artificial experience, the quality stability is poor, and personalized precise regulation and control are lacked are solved.

The invention relates to an intelligent purchasing method and system for medicine consumables. The method comprises the following steps: acquiring historical medicine purchasing data, patient treatment data and epidemiological data of a hospital; adopting a drug demand prediction model to predict and determine first drug purchase data according to the hospital historical drug purchase data, the patient treatment data and the epidemiological data; and determining a medicine purchasing adjustment adaptability coefficient, and adjusting the first medicine purchasing data according to the medicine purchasing adjustment adaptability coefficient to obtain second medicine purchasing data. According to the technical scheme, the adaptability coefficient is adjusted by determining the drug purchase. And the system or service logic can better fit the actual scene change. The coefficients change dynamically, errors caused by fixed values are reduced, medicine purchasing prediction is more accurate, and data accuracy is improved.

Systems and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular disease, cancer, neurological disorders, immune system disorders and genetic diseases.

The invention belongs to the technical field of antibacterial peptide activity identification and evaluation, and relates to an antibacterial peptide activity and MIC value combined prediction framework based on cross-modal deep learning, and the framework uses a protein language model ESM2 to respectively carry out token-level semantic embedding coding on an antibacterial peptide sequence and a pathogen protein sequence; performing cross-modal feature extraction and fusion through a multi-branch structure comprising a Mama module, a multi-head self-attention mechanism and DASM 1D convolution; a multi-task decoding structure is adopted to realize antibacterial peptide activity classification and MIC value regression prediction at the same time; according to the method, functional characteristics in the sequence can be effectively mined, the accuracy and generalization ability of antibacterial peptide activity and MIC value prediction are remarkably improved, and a reliable calculation tool is provided for high-throughput screening and rational design of the antibacterial peptide.

The invention discloses a drug full life cycle risk monitoring and early warning method based on multi-source data fusion, and relates to the technical field of data analysis, and the method comprises the steps: constructing a time sequence heterogeneous graph, calculating the similarity between drugs and side effect pairs in the time sequence heterogeneous graph through a comparative learning algorithm, and generating a candidate signal list; constructing a data set, and identifying hybrid factors associated with drug selection and side effect risks in the data set by using a separation characterization learning algorithm to obtain an average causal effect value; a space-time risk thermodynamic diagram is drawn, risks in the whole life cycle of the medicine are graded based on risk distribution characteristics in the space-time risk thermodynamic diagram, and a corresponding early warning strategy is triggered; and converting the early warning strategy into a risk assessment report in a standard format by using a preset report template. By accurately calculating the individual and average causal effect value, the causal relationship between the drug and the side effect of the drug is quantified, and a solid foundation is laid for deep evaluation of drug safety.

The invention discloses a lung injury evaluation system based on a vascular endothelial cell protection effect, and the system comprises a sample processing module which is configured to execute sample preprocessing and multi-dimensional biomarker collection operation; the feature calculation module is connected to the sample processing module and is configured to perform endothelial function core index quantification and feature extraction operation; the data fusion module is connected to the feature calculation module and is configured to execute lung injury degree and endothelial protection efficiency association mapping operation; and the strategy generation module is connected to the data fusion module and is configured to execute a dynamic risk assessment and assessment strategy generation stage. The method has the following advantages and effects: systematic fusion and dynamic analysis of multi-source heterogeneous data are realized, and quantitative association mapping between biomarkers and iconography features is established, so that the comprehensiveness and predictive ability of state evaluation are remarkably improved.

The invention discloses an anesthesia strategy generation system fusing time sequence data and a knowledge graph, and relates to the technical field of medical information processing. The method specifically comprises the steps that five types of vital sign data including arterial blood pressure, electrocardio and electroencephalogram are collected, correction is conducted in combination with traction marks and electrotome interference characteristics, and an analysis result is output; the inference analysis module executes double-track processing based on the analysis value, one track evaluates cortical suppression, shock and ventilation risks and generates a risk report, and the other track performs reverse arbitration when the blood oxygen data is abnormal; constructing a knowledge graph fusing the efficacy nonlinear cooperation equation, the age sensitive parameters and the drug administration sequence influence; and the strategy generation module integrates the multi-source data to generate a drug execution track, and executes fast and slow closed-loop regulation and control based on a risk report and an arbitration result. According to the invention, the adaptation capability of the system to individual differences, the abnormal signal identification and processing capability and the intelligent response level of the anesthesia strategy are improved, and the accuracy and safety of clinical anesthesia are significantly enhanced.

The invention discloses a multi-source confidence perception insomnia intelligent prescription recommendation method, a medium and equipment, and aims to solve the problem of low recommendation accuracy caused by insufficient multi-source heterogeneous data fusion, single feature representation and missing prediction confidence in the prior art. The method comprises the following steps: coding patient symptoms into a sparse binary matrix; extracting a symptom-traditional Chinese medicine prescription matrix and a symptom-symptom type matrix from the traditional Chinese medicine knowledge map; generating symptom-guided traditional Chinese medicine features and symptom type features through a double-layer graph convolutional neural network; calculating the prediction distribution probability of the traditional Chinese medicine prescriptions and syndromes; based on an energy function, quantifying uncertainty scores of the two; the multi-source probability is dynamically weighted and fused to improve the weight of a high-confidence result; and finally, outputting recommended prescriptions and syndrome type sequences. The accuracy and interpretability of insomnia prescription recommendation are remarkably improved through isomeric map modeling and multi-source confidence fusion, and the method is suitable for a traditional Chinese medicine clinical auxiliary decision making system.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

Disclosed are systems and methods for improving processes for developing cell therapies by applying machine learning to data including manufacturing process data and clinical measurements (e.g., patient response and treatment data) to determine parameters and settings for a manufacturing process for engineering cells for use in cell therapy. Parameters and settings for a manufacturing process for genetically engineered T-cells including, but not limited to, Chimeric Antigen Receptor (CAR) T cells can be determined. A method can include receiving a set of process parameters of a cell engineering process, predicting a clinical response associated with an output of the cell engineering process by applying a machine learning model on the received set of process parameters, where the machine learning model is trained on process parameter data and clinical response data, and generating a visualization for use in a graphical user interface of the predicted clinical response.

The invention provides a dynamic medication dosage optimization method fused with reinforcement learning. The method comprises the following steps: acquiring a first physiological index parameter of a target patient; obtaining a first diagnosis report of the target patient, wherein the first diagnosis report comprises basic information of the target patient and first disease information of the target patient; the first disease information comprises a first disease type and first disease description information; determining a first reinforcement learning algorithm corresponding to the first disease type; determining a first control parameter of the first reinforcement learning algorithm according to the first physiological index parameter and the basic information; and performing operation on the first disease description information through the first reinforcement learning algorithm and the first control parameter to obtain a first medication dosage parameter. Based on the application, the drug effect can be consistent with the physical condition of a patient, and poor drug effect and excessive side effects caused by too strong drug effect are avoided.

System and methods for alerting a healthcare provider to prescribed treatments having reduced or no effectiveness due to genetic composition is provided. A database containing treatments known to have reduced or no efficacy in persons having particular genetic markers is queried to determine whether any treatments prescribed by, or likely to be prescribed by, a healthcare provider to the patient are known to have reduced or no efficacy in persons having the same certain genetic markers as the patient. An alert indicating such information is displayed at a healthcare provider system.

The invention belongs to the technical field of medical informatization, and particularly relates to a medical insurance medication rationality interception system and method based on deep reinforcement learning and an FP-Growth algorithm, and the method comprises the following steps: S1, receiving electronic prescription data transmitted by a hospital HIS system in real time; s2, according to original diagnosis information in the prescription, performing standardization processing to obtain a diagnosis set; according to the original medicine information in the prescription, cleaning and integrating to obtain a medicine set; s3, based on an FP-Growth association rule algorithm, performing diagnosis-drug association analysis and drug-drug association analysis on the diagnosis set and the drug set, and identifying a low-association prescription according to a preset condition and marking the low-association prescription as a suspicious prescription; s4, starting deep reinforcement learning analysis on the suspicious prescription through DRL decision optimization, and outputting a prescription reinforcement learning feature set; and S5, outputting a prescription qualitative analysis result and a corresponding confidence score according to the prescription reinforcement learning feature set.

The invention discloses a drug perturbation diffusion prediction method fused with cell description, and relates to the technical field of drug perturbation prediction.The method comprises the steps that firstly, a cell perturbation transcriptome database is preprocessed, and a cell-drug combination containing drug characteristics, cell line gene expression and cell line description characteristics is obtained to serve as training data; and then constructing a drug disturbance prediction diffusion model based on cell description, training the drug disturbance prediction diffusion model by using the training data, and finally inputting Gaussian white noise, cell line gene expression before disturbance, drug characteristics and cell line description characteristics into the trained model to predict cell line gene expression after drug disturbance. According to the method, cell line description characteristics are introduced, so that the perception capability of the model on intercellular biological differences is enhanced, and the generalization performance of cross-drug and cross-cell lines is improved.

The invention discloses an anesthesia effect prediction method based on a graph neural network. The method comprises the following steps: obtaining a standardized synchronization time sequence data set; forming a time sliding window sequence; generating an initial topology of the dynamic physiological index-drug effect variable diagram structure; generating a node initial feature representation set for each graph node, and initializing a graph neural network model parameter set; obtaining an updated dynamic physiological index-drug effect variable diagram structure; inputting the pharmacodynamic variable graph structure into a graph neural network model corresponding to the graph neural network model parameter set to obtain a node embedding representation set and an edge embedding representation set; outputting an induction period anesthesia effect prediction result set; and generating an interpretable output result set, wherein the interpretable output result set comprises a key variable link, a dominant influence factor and a drug administration adjustment suggestion. The rapid detection capability of the abnormal state in the induction period is improved.

The invention relates to an intelligent decision-making system for nutrition metabolism collaborative management of elderly chronic disease patients, in particular to the field of nutrition metabolism collaborative management of elderly chronic disease, which is characterized in that drug molecule characteristics and nutrient metabolism paths are dynamically integrated through a multi-modal knowledge graph, and a cross-domain associated three-dimensional knowledge network is constructed; based on a reinforcement learning real-time optimization rule confidence threshold value, the early warning sensitivity is adaptively adjusted according to the degree that the metabolic index of the patient deviates from the safety interval; the streaming conflict detection engine accurately identifies the potential risk of asynchronously input medication and diet data, and triggers graded early warning through space-time alignment and sub-graph matching; the closed-loop evolution mechanism fuses patient compliance feedback and blood potassium change trend, drives the taboo rule base to continuously and autonomously evolve under the constraint of renal function layering, and realizes personalized risk prevention and control and metabolic state collaborative optimization.

The invention discloses an intelligent evaluation method and system for pharmaceutical service and teaching ability, and the method comprises the steps: building an interaction session between a scoring target and a virtual patient based on the generated virtual patient; marking key pharmaceutical behavior nodes and pharmaceutical related statement content of the scoring target in the interaction session; and based on the marked key pharmaceutical behavior node and the pharmaceutical related statement content, intelligently generating an evaluation score of the interactive performance ability of the scoring target in the pharmaceutical service scene. According to the application, the on-site strain capability of pharmacists or students can be effectively improved, the dependence on entity training resources is remarkably reduced, the fairness and reliability of assessment results are ensured, the training efficiency is greatly improved, and the differentiated training requirements of different scenes are met.

The invention discloses a bone infection and drug resistance prediction method and system fusing a knowledge graph and a graph convolutional network. The method comprises the following steps: preprocessing bone infection multi-source heterogeneous data to obtain a standardized data feature matrix; constructing a bone infection knowledge graph based on the matrix and obtaining a knowledge embedding matrix, and fusing the two to generate a medical semantic constraint fusion feature matrix; key medical variables are screened, the maximum information coefficient (MIC) of the key medical variables is calculated, and an adjacent matrix is constructed in combination with medical association strength factors; inputting the fusion feature matrix and the adjacent matrix into a GCN spatial feature extraction module and a BiGRU time sequence dependence capture module to obtain spatial and time sequence features, and fusing the spatial and time sequence features into a space-time fusion feature matrix; and inputting the result into a double-task prediction module, and outputting a bone infection and drug resistance grading probability prediction value. According to the invention, accurate and rapid prediction of bone infection prediction and drug resistance grading can be realized.

The invention discloses an anti-tumor drug tablet stability prediction method based on multi-source data fusion, and particularly relates to the field of pharmaceutical preparation stability prediction, and the method comprises the following steps: S1, data acquisition; s2, extracting multi-dimensional characteristic parameters; s3, constructing a stability prediction reference model; s4, calculating a real-time attenuation index; s5, determining a deviation threshold range; s6, analyzing a stability deviation index; s7, dynamically updating a prediction result; according to the method, accurate prediction of content attenuation, related substance growth and disintegration time limit change indexes is realized through integration of multi-source data, construction of a stability prediction reference model, simple trend analysis not limited to a single factor, key influence factor marking, critical threshold setting and theoretical attenuation curve fitting; the accuracy and timeliness of stability prediction of antitumor drug tablets are effectively improved, and a scientific basis is provided for drug validity period evaluation and quality risk management and control.

The invention discloses a drug-target interaction prediction method based on double-flow collaborative attention and sparse feature fusion, and belongs to the technical field of computational biology. The method solves the problem that the existing method cannot capture the sub-structure discrimination features and the key binding region features of the drug-target interaction pair. According to the method, a double-flow collaborative attention strategy combining a multi-scale space attention mechanism and a channel enhanced attention mechanism is adopted to cooperatively capture discriminative features of substructures, the multi-scale space attention mechanism utilizes a multi-branch convolutional layer to adaptively integrate space substructure features, and molecular representation of each substructure is enhanced; the channel-enhanced attention mechanism mitigates the inconsistency of substructure features. The sparse attention mechanism can highlight the key features while suppressing the noise, and the cross attention mechanism improves the extraction capability of the features of the key combination region through feature interaction between the modeling drug and the target. The method can be applied to drug-target interaction prediction.

Systems, devices and methods are provided for determining a medication dose for a patient or user. The dose determination can account for recent and / or historical analyte levels of the patient or user. The dose determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity, and / or behavior. Many different dose determination embodiments are set forth, pertaining to a wide array of different aspects of the system or environment in which the embodiments can be implemented.