476 results about "Specialty" patented technology

The invention discloses a disease retrieval method and device based on a knowledge graph enhanced large language model, and relates to the technical field of large language models and knowledge graph questions and answers. The method comprises the following steps: constructing a question and answer data set based on a triple form, and realizing storage and visualization of special disease knowledge maps of five themes based on Neo4j; acquiring a medical question asked by a user, and gradually expanding the input question based on thinking chain reasoning to obtain sub-questions; performing relation path retrieval and knowledge sub-graph retrieval based on the pre-training model; finely tuning the large language model to generate a fact knowledge text based on the sampled answer sub-graph; and constructing a knowledge enhancement prompt template in combination with the input question and the fact knowledge, and sending the knowledge enhancement prompt template into the large language model to obtain a final medical answer. According to the scheme, the knowledge graph is utilized to enhance the large language model, the large language model is guided to conduct reasoning and retrieval in the efficient knowledge path, and the special disease medical problem proposed by the user can be accurately and professionally answered.

The invention relates to an intelligent diagnosis and treatment method and system based on a knowledge graph and a large language model. The method comprises the steps of constructing a basic framework of a medical field knowledge graph based on collected medical field data, constructing multi-level knowledge graph expression, obtaining symptom description of a patient through multiple rounds of dialogues, diagnosing the patient and generating a diagnosis report, rechecking and adjusting the diagnosis report by a professional physician, and finally outputting the diagnosis report. The intelligent diagnosis and treatment system comprises a knowledge graph construction module, an intelligent question and answer system, an entity matching module, an intelligent diagnosis module, a doctor review and diagnosis report adjustment module, a diagnosis report output module and the like. And main diseases are screened, so that the coverage is wider. And three levels of knowledge maps are constructed, so that text matching with finer granularity can be carried out. The non-standardized expression of the patient is firstly converted into standardized medical terms through a large model, and drug allergy and the like are taken into consideration, so that the diagnosis accuracy is improved, and the diagnosis and treatment risk is reduced.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The invention provides a medical AI question and answer method based on retrieval enhancement, which comprises the following steps: acquiring a medical professional knowledge base, and constructing an entity recognition model and a medical knowledge graph; the method comprises the following steps: splitting a medical professional knowledge base, and creating a vector index and a full-text retrieval reverse index; intention classification is carried out, and initial processing is completed; performing multi-path mixed retrieval on the medical professional knowledge base to obtain a user question related text block; a filtering rule is preset, and the user question related text blocks are filtered; answering the user question according to a preset second prompt word, and answering the user question according to a preset third prompt word when the user question exceeds the range of the medical professional knowledge base or the multi-path mixed retrieval has no result. According to the method, related context information can be accurately searched, reliable reference information is provided for a generative large model to answer professional questions, the interactive experience of a medical question answering system is improved, and the accuracy and specialty of answer generation of the system are remarkably improved.

The invention relates to the technical field of large language models, in particular to a medical question and answer data generation method and system, a terminal and a medium, and the method comprises the steps: constructing a patient agent and a doctor agent, and setting a dialogue strategy between the patient agent and the doctor agent; real medical records are obtained from the medical database, and knowledge bases of the patient intelligent agent and the doctor intelligent agent are constructed based on the real medical records; and controlling the patient agent and the doctor agent to simulate doctor-patient dialogues based on the dialogue strategy and respective knowledge bases, and generating original question and answer data. Two agents are set, real doctor-patient interaction is simulated, and the authenticity and practicability of the generated original question and answer data are improved. The doctor-patient dialogue is performed based on the real medical record, it is ensured that the generated original question and answer data meets medical specifications and professional requirements, and the accuracy is high. A large amount of original question-answer data can be automatically and quickly generated, the training efficiency is improved, and the training cost is reduced.

A machine learning-based clinical workflow system processes patient encounter data to generate structured data records and predicted diagnosis classification codes. The system may obtain an input record, generate vector embeddings, and identify reference records using vector similarity operations. A machine learning (ML) component may generate a structured data record based on the input and reference records. The system may also generate an ML instruction, identify reference codes, and produce a predicted code. The predicted code may include a predicted diagnosis classification code, a predicted procedural code, or a billing code. Vector databases storing embeddings of medical codes and records may facilitate efficient retrieval of relevant information. Some implementations may include prediction of billing codes to address revenue capture. Some implementations may utilize specialty-specific processes and data to enhance accuracy for particular medical fields. The system may incorporate clinician feedback to continuously improve performance and adapt to evolving healthcare practices.

The invention relates to the technical field of diagnosis and treatment path planning, in particular to an intelligent diagnosis and treatment path dynamic planning system based on symptom ontology reasoning, which comprises a symptom specialized mapping module, a holographic panoramic navigation module, a path optimization core module and a real-time feedback module, wherein the symptom specialized mapping module generates a dynamic medical resource recommendation parameter set by fusing a symptom ontology feature vector, medical institution treatment efficiency data and a specialized association rule base, and dynamic medical resource recommendation parameters comprise a multi-dimensional matching degree tensor and a treatment efficiency quantification matrix; the holographic panoramic navigation module receives the multi-dimensional matching degree tensor and constructs a three-dimensional medical resource topological graph, wherein the three-dimensional medical resource topological graph comprises hospital nodes and department connecting lines; the path optimization core module analyzes hospital nodes in the three-dimensional medical resource topological graph, and generates a path with optimal cost performance based on a multi-objective optimization algorithm of a treatment efficiency quantification matrix; and the real-time feedback module acquires and updates the treatment efficiency quantization matrix through a back propagation algorithm to form a closed-loop optimization system.

The invention provides a medical record analysis method and device, electronic equipment and a readable storage medium, and the method comprises the steps: enabling a planning agent to divide each process step of a medical record analysis process into a plurality of minimum sub-tasks according to prompt words, and enabling the planning agent to distribute a corresponding task execution agent for each sub-task, the intelligent agent work assembly line executes the medical record analysis process, not only is the semantics of the complex medical context adapted, but also the efficiency and the accuracy of medical record analysis are improved, and based on the structure of the intelligent agent, the large language model module is a vertical domain model with professional domain task execution capability, and the large language model module is a large-language model with professional domain task execution capability. And meanwhile, the input and output of each node agent in the agent work assembly line are transparent, readable and interpretable, so that the stability of the output quality can be ensured.

The invention provides a construction method and a construction system of a telemedicine expert recommendation model, an expert recommendation method and electronic equipment. The construction method comprises the following steps: acquiring sample information; extracting entities and relationships among the entities from the preliminary diagnosis information of the historical patients and the specialist specialist information of the experts; constructing a knowledge graph according to the extracted entities and the relationship between the entities; performing feature calculation on the sample information, and inputting the calculated disease relevancy and multi-granularity relevancy between multiple groups of historical patients and experts and basic features of the experts corresponding to the historical patients into a deep neural network for training to obtain an expert recommendation model; the method has the beneficial effects that more adaptive experts are recommended for patients, the accuracy and robustness of recommendation results are effectively improved, the efficiency and quality of remote consultation are improved, and the problem of uneven medical resource allocation is solved; the method is suitable for the technical field of telemedicine expert recommendation.

The invention discloses a retrieval enhancement generation method and device based on a medical knowledge fine-tuning language model. The method comprises the steps of constructing a medical knowledge base, performing fine-tuning on the medical language model, inputting medical questions by a user, retrieving the knowledge base, screening knowledge fragments, generating answers and the like. And the medical knowledge base is constructed through semantic partitioning, so that the integrity and continuity of related knowledge are ensured. And a medical knowledge fine tuning model is used as a screener to further screen high-quality knowledge fragments most related to user questions, so that the professionality and reliability of generating answers are improved. The method is suitable for the medical field, and can provide more accurate and more professional medical consultation services for patients and doctors. The fine-adjusted medical model is innovatively used as a filter, knowledge fragments are evaluated from multiple dimensions, it is ensured that the knowledge fragments are highly related to questions, powerful support is provided for generating professional and accurate medical answers, and the precise medical consultation requirement of a user is better met.

The invention discloses a neurology rehabilitation degree evaluation method and system, and relates to the technical field of medical rehabilitation evaluation.The method comprises the following specific steps that data collection is conducted, specifically, whole genome sequencing is conducted on a neurology patient, and whole genome data of the patient is obtained, the method comprises the following steps: collecting initial disease severity degree data of a patient, and evaluating the symptom expression of the patient when the patient sees a doctor for the first time by a professional neurology doctor according to a set disease evaluation standard. A key gene screening strategy and a synaptic plasticity index establishment method are applied to construct a multi-dimensional and more accurate neurology rehabilitation degree evaluation system, so that the neural function repair level is deeply known from the root level of nerve cell connection reconstruction; and accurate prediction and real-time evaluation of the rehabilitation process are realized through the personalized rehabilitation trajectory prediction model, so that the real rehabilitation state of the patient can be reflected more comprehensively and meticulously.

The invention relates to the technical field of information security management, and discloses an information security management method and system based on digital medical treatment. The method comprises the following steps: verifying doctor professional qualification, patient authorization range, data sensitivity level and time permission validity in a multi-institution medical data access request, and generating a permission verification result; analyzing ontology semantic attributes and sensitivity characteristics of medical search keywords input by a user, and determining a corresponding hierarchical encryption strategy based on the permission verification result; performing homomorphic encryption transformation on the medical term synonym set according to the hierarchical encryption strategy, and constructing a medical ciphertext database; and receiving a medical query request, executing symptom disease association matching and ciphertext state logical reasoning operation in the medical ciphertext database, and outputting an encrypted state search result. According to the method, the privacy leakage risk caused by search behavior trajectory analysis is effectively prevented, and the security and availability of medical data are improved.

The invention discloses a case analysis method and system based on a large language model, and belongs to the technical field of case analysis, and the method comprises the following steps: S1, patient information input, S2, examination item recommendation and determination, S3, preliminary diagnosis generation, S4, case screening and diagnosis and treatment suggestion generation, S5, diagnosis and treatment process monitoring and adjustment, and S6, case report generation and viewing. By means of the powerful knowledge integration and reasoning ability of a large language model, in combination with massive historical case data and professional medical database information, multi-dimensional diagnosis thought reference is provided for doctors, the misdiagnosis and missed diagnosis risks caused by personal experience limitation are reduced, the health indexes of patients can be monitored, and the diagnosis accuracy is improved. Personalized adjustment is performed on the diagnosis and treatment plan according to the disease change and the diagnosis and treatment condition of the patient; the medical database can dynamically expand and correct contents; diagnosis and treatment plans and suggestions can be given according to diagnosis and treatment passes of previously cured patients with similar conditions.

The invention discloses a caregiver recommendation algorithm according to a matching degree, and relates to the technical field of nursing. The method comprises the following steps of patient information collection: collecting basic information of a patient, obtaining geographical location information of the patient, determining a city and a detailed address of the patient, guiding the patient to describe nursing requirements in detail, and inputting information of a caregiver through a user side information collection module; and the caregiver fills in own professional skill information which covers basic nursing skills, professional nursing specialists and skills in the special nursing field, and uploads related certificate qualification certificates. Through deep analysis and processing of the multi-dimensional demand information of the patient and the comprehensive information of the nursing staff, the intelligent matching model is used to accurately calculate the matching degree, the nursing staff highly meeting the demand of the patient can be screened for the patient, the pertinence and effectiveness of the nursing service are greatly improved, and the problems of poor nursing effect and the like caused by mismatching are avoided.

The invention provides a social worker service method and system based on long-term diseases, and the method comprises the steps: determining a patient and case data thereof, and selecting an evaluation service group; performing pathological assessment and psychological assessment on the patient through the assessment service group; determining a service object according to an evaluation result; determining whether the family member is included in a second service object needing social worker service; determining a current social worker service stage of the first service object and generating a demand for a social worker; according to the requirements, the schedules of the social workers and the skill labels of the social workers, one or more social workers meeting preset conditions are searched for providing social worker services for the patients and / or family members. According to the method, the specific requirements of the patients and the family members are accurately analyzed through the intelligent matching algorithm, high-quality and personalized social work services can be effectively provided for the long-term disease patients and the family members thereof, it is ensured that each patient and the family members thereof can obtain customized service schemes, and the specialty and pertinence of the social work services are greatly improved.

Disclosed is a system and method for standalone automated patient referral management. The system comprises an intake module that accepts and processes referrals from diverse referral communication channels as entry points for patient referrals. A processing module undertakes the analysis of patient information to pinpoint a healthcare provider based on a healthcare need, a preference, and a schedule availability. A scheduling module confirms the appointment, and engages in dynamic three-way communication between the patient, the referring entity, and the healthcare provider. A feedback module dispatches feedback to the referring entity, offering a report on an outcome of the appointment. A natural language processing (NLP) module extracts information from conversational speech. The system utilizes algorithms to match patients with the healthcare providers based on criteria including specialty, availability, and patient preferences. A feedback module communicates with the originating EHR systems and provides updates to maintain continuity of care.

The invention discloses a medical big language question and answer method based on data desensitization, and belongs to the technical field of data processing, and the method comprises the steps: obtaining training data in a medical field corpus, carrying out the preprocessing, and obtaining the processed training data through a data desensitization model; establishing a medical big language question and answer model based on the improved enhanced Transform sub-model; inputting the processed training data into a medical big language question and answer model for training to obtain a trained medical big language question and answer model; and performing medical question and answer by using the trained medical big language question and answer model. According to the method, an enhanced Transform module is adopted, the reasoning ability for long-distance context is enhanced, the generalization ability of the model for medical professional knowledge is improved through preprocessing, and more accurate and professional suggestions can be generated; through the data desensitization model, sensitive information in the medical data is identified and converted, and the accuracy and efficiency of privacy protection are improved.

Due to the characteristics of intensive knowledge, high specialty, large service amount and the like, the medical and invasive service industry faces the challenges of low efficiency and high cost of the traditional service means. Existing information retrieval and consultation services are difficult to meet high requirements of users on timeliness, accuracy and individuation. Therefore, the invention provides the method and the device for constructing the medical and invasive service agent based on the large language model, and deep understanding and accurate matching of user requirements are realized by constructing the science and technology knowledge graph and the personal knowledge graph in combination with the reasoning and generating capabilities of the large language model. Multi-modal input (texts, voices, images and the like) is supported, and the performance and the service quality of a large model are improved by utilizing a knowledge enhancement and optimization training technology. According to the invention, the efficiency, the precision and the user experience of the department and wound service are remarkably improved, and an innovative solution is provided for intelligent transformation of the department and wound service industry.

The invention discloses an agent interaction method and system for enhancing specific industry knowledge, and belongs to the technical field of artificial intelligence. According to the method, multi-modal original data of a target industry is collected and preprocessed, dominant entities and relationships are extracted based on an industry ontology to generate a triple, and implicit knowledge is fused to construct an iterative mixed knowledge graph; converting a user query into a vector, retrieving a Top-K candidate knowledge vector, and then performing interpretable scoring in combination with a query vector, a candidate vector and a triple to obtain an enhanced feature; and dynamically determining an expert sub-network set, and fusing reasoning results of the experts to generate a final answer. The system comprises a knowledge graph and a feature enhancement and reasoning module, and dynamic updating and accurate reasoning of knowledge are achieved. According to the method, the industry knowledge coverage degree, the reasoning accuracy and the interpretability are improved, the illusion problem of a large language model is relieved, and the method is suitable for intelligent interaction services in the professional fields of medical treatment, finance and the like.

The invention provides a question and answer interaction virtual case development method for nurse inquiry training, which comprises an original case history module for constructing a virtual case text development framework based on a functional health form, a nursing inquiry teaching status and clinical nursing evaluation, and establishing an original case history; the initial response text module is used for determining the development sequence and inquiry elements of each unit, and constructing an initial response text with the guidance of cultivating clinical thinking ability, nursing science professional attainment and humanistic attainment; and the final response text module is used for constructing a final response text through the subject term and the subject term combination rule based on the initial response text. According to the method, all units of the virtual case text development framework are covered, and content comprehensiveness is guaranteed; an initial response text system is designed for cultivating clinical thinking ability, nursing science professional accomplishment and humanistic accomplishment, and educational guidance is achieved; on the basis of establishing a subject term library and designing a subject term combination rule, a response text is perfected, so that the response accuracy of the virtual patient is ensured.

The invention discloses a Meta analysis method and device, equipment and a storage medium, according to the scheme, automatic semantic analysis is carried out on a research problem input by a user through a preset large language model, target literature is processed through a pre-training language model, an efficient and reliable automatic tool is provided for evidence-based medicine, and the efficiency is improved. According to the method, the dependency of Meta statistical analysis on professionals is reduced, the analysis period is shortened, personal errors are reduced, the intelligent degree, efficiency, accuracy and repeatability of Meta statistical analysis are remarkably improved, and standardized and intelligent development of evidence-based medical research is promoted; in addition, retrieval is carried out based on the target structured form, the retrieval strategy is optimized, the retrieval accuracy is improved, and therefore the performance and accuracy of Meta statistical analysis are further improved.

The invention discloses a nursing information distribution management system based on big data, and relates to the technical field of information distribution management, and the system comprises a data collection module which is used for collecting original data from a nursing data source in real time and cleaning the original data, the original data comprises patient information, nursing records, resource use conditions and nursing personnel capabilities; the data acquisition module is connected with an external system through an API (Application Program Interface); the nursing information distribution management system based on big data can comprehensively evaluate the skill level and professional ability of nursing personnel, update skill archives, and provide an accurate basis for personnel deployment; appropriate nursing personnel are allocated to appropriate tasks, so that the quality and the specialty of nursing services are improved; optimal allocation of resources and nursing personnel is realized; resource preparation is made in advance, resource waste and shortage are avoided, and the resource utilization rate is increased.

The invention relates to the technical field of medication safety retrieval, and discloses a medication safety large model method and system based on a medical knowledge graph, the medication safety large model method based on the medical knowledge graph comprises the following steps: step S101, collecting medical literature data; step S102, constructing a basic text library; step S103, constructing a medical knowledge graph; step S104, obtaining an evidence chain through a local combined online two-way evidence retrieval strategy; s105, generating a drug safety conclusion through a large language model according to the evidence chain; according to the method, the medical knowledge graph is automatically constructed through the large language model, compared with a traditional drug safety evaluation mode in which professionals participate in the whole process, the method is more efficient, and a drug safety conclusion is generated through comprehensive evidence retrieval of the medical knowledge graph and the reasoning ability of the large language model; therefore, the problems of low answer credibility and model illusion caused by lack of evidence in a medical scene of a large model are solved.

A system recommends to a healthcare professional (HCP) (230) medical literature that is of relevance to the HCP's patients. The system communicates with the HCP (230) and accesses electronic health record (EHR) documents in a database (210) associated with the HCP's patients. The system analyzes the contents of the EHR documents to query a medical-literature database (212) for publications that are deemed relevant to the EHR documents. The extracted publications are then presented to the HCP.

The invention provides an innovative integrated system which aims at monitoring the workload of medical staff and providing psychological support for patients at the same time. The system collects physiological and behavioral data of medical staff and physiological and psychological state information of a patient in real time through a multi-modal data acquisition technology. And by utilizing an advanced machine learning algorithm, the system can intelligently evaluate the workload of the medical personnel and predict the overwork risk. Meanwhile, according to individual requirements of the patient, the system generates customized psychological support schemes including emotion recognition, psychological education, social support and professional consultation. In addition, the system has the real-time feedback and intervention functions, and it is ensured that medical workers and patients get attention and help in time. The intelligent user interface design improves the user experience, and strict data security and privacy protection measures guarantee the security of user information. According to the design of the system, the efficiency and quality of medical care work are improved, the overwork risk of medical staff is reduced, comprehensive psychological care is provided for patients, the psychological welfare of the patients is enhanced, and overall rehabilitation of the patients is promoted.

The invention belongs to the technical field of remote rehabilitation guidance, and particularly relates to an intelligent remote rehabilitation guidance system. The system integrates the functions of man-machine interaction, information encryption, physiological and psychological assessment, rehabilitation grade division, physician professional assessment, rehabilitation knowledge base construction, rehabilitation matching planning, rehabilitation training monitoring and feedback, rehabilitation effect prediction and the like, and a fuzzy assessment algorithm and an emotional depression detection algorithm are integrated. The system comprehensively evaluates the physiological and psychological health conditions of a rehabilitative person, recommends appropriate rehabilitation doctors or health planning guidance suggestions for the rehabilitative person, monitors the standard degree and micro-expression changes of training actions in real time and gives timely feedback and adjustment suggestions in the rehabilitation process, and meanwhile, predicts a rehabilitation track by utilizing a machine learning algorithm, so that the rehabilitation effect is improved. Subsequent rehabilitation planning is optimized, and personalized precise rehabilitation is ensured; according to the invention, the participation degree and training scientificity of the convalescent are improved, and an effective solution is provided for remote rehabilitation guidance.

The invention relates to the technical field of medical health, and discloses a vital sign health intelligent analysis system based on a professional medical big language model, which comprises a data acquisition module, a data processing module, a big language model analysis module and a user interaction interface, the data processing module is connected with the large language model analysis module, and the large language model analysis module is connected with the user interaction interface. By using an advanced data acquisition technology (such as a non-intrusive sensor) and a deep learning model (such as BERT, GPT and LSTM), the physiological data of the user can be monitored and analyzed in real time, the health trend and potential problems can be identified, the Bayesian fusion algorithm is combined to perform weighted fusion on the physiological data and the historical data of the user, and the user experience is improved. The system can provide more personalized and accurate health suggestions; therefore, not only is the accuracy of health monitoring improved, but also the requirements of different users are better met through personalized analysis.

Described herein are systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements, in which the trained networks are then utilized in the context of medical imaging. The success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, it is tedious, laborious, and time consuming to create large annotated datasets, and demands costly, specialty-oriented skills. A novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework is presented to dramatically reduce annotation cost, starting with a pre-trained CNN to seek “worthy” samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning. The described method was evaluated using three distinct medical imaging applications, demonstrating that it can reduce annotation efforts by at least half compared with random selection.

The invention relates to a large model question answering method and system based on domain business ontology enhancement and application. By constructing the domain ontology and integrating the domain ontology into the large model question-answering system, close interaction between the large model and the domain ontology is achieved. The method comprises the steps of domain ontology construction and optimization, large model adaptation adjustment, ontology-based question and answer interaction process design, answer generation and feedback and the like, and the question and answer accuracy and specialty of the large model in a specific field can be remarkably improved. According to the method, accurate and comprehensive domain business ontology is constructed and deeply fused with a large model technology, so that accurate analysis and intelligent answering of user questions are realized. According to the method, the accuracy and specialty of intelligent question answering can be remarkably improved, the requirements of users in the professional field are met, and intelligent development of all industries is promoted. The method can be widely applied to the fields of public security criminal investigation, medical treatment, law, supply chain management and the like, and has important practical value.

The invention relates to a recording method and system of an electronic medical record, and aims to comprehensively and systematically collect health information of a patient through diversified input terminals, improve information input efficiency, ensure information integrity and promote team cooperation. The system comprises a medical professional terminal, a patient direct related terminal and other patient related terminals, supports information input in various forms of pictures, characters, videos, audios and the like, and covers various input devices such as portable devices, professional examination instruments and the like. And the information storage module ensures accurate homing of the data and supports cross-mechanism data integration and sharing. The system also has the functions of electronic medical record automatic generation, treatment tracking, symptom and sign extraction, intelligent analysis and early warning and the like, provides efficient and accurate information support for medical services, optimizes medical resource configuration, and enhances medical experience of patients. The method is suitable for modern medical management, and particularly has remarkable advantages in the aspect of improving the intelligence and integration level of an electronic medical record system.