50 results about "Diagnosis code" patented technology

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

The invention provides an AI-based medical record diagnosis and surgical operation code intelligent generation method and system, and relates to the technical field of medical information, and the method comprises the steps: obtaining multi-modal data from a hospital information system, including a text medical record, a medical image, a surgical video and a pathological report; entities and relationships are extracted through a multi-modal fusion model, and a hospital-level disease knowledge graph containing space-time dimensions is constructed; training a coding rule generation model and a verification model based on a reinforcement learning framework, and generating candidate codes and confidence scores thereof; and correcting the candidate codes according to manual feedback, and dynamically updating the knowledge graph and the rule base by using the graph neural network. Accurate disease diagnosis codes and surgical operation codes are generated in an intelligent mode, and the accuracy of the codes can be remarkably improved through automatic medical record code recommendation.

Aspects relate to monitoring timing margin of logic paths and its degradation through an integrated circuit. In one example an integrated circuit has a logic path formed in the integrated circuit. A monitor circuit is formed in the integrated circuit near the logic path and configured to monitor a condition of the logic path and to generate a diagnostic code sequence to indicate the condition of the logic path over time. A monitor controller is configured to receive diagnostic codes of the diagnostic code sequence, to determine a condition of the integrated circuit based on the diagnostic code sequence, and to initiate a remedial action in response to the condition of the integrated circuit.

The invention discloses a chronic obstructive pulmonary disease patient screening system for the respiratory medicine department, particularly relates to the field of computer-aided diagnosis, and is used for solving the technical problems that an existing screening method is low in screening efficiency, low in patient adaptability and difficult to popularize on a large scale in a basic level. According to the system, a standardized patient medical event sequence is constructed by obtaining a target population medical record containing medicine distribution and disease diagnosis codes; identifying a characteristic medication and diagnosis mode before definite diagnosis of the chronic obstructive pulmonary disease based on a sequence pattern mining technology; constructing a medical knowledge graph by using the modes, obtaining a feature embedding vector representing a chronic obstructive pulmonary disease medical trajectory through graph neural network learning, and establishing a high-risk digital portrait; inputting the medical sequence of the person to be screened into a feature extraction model based on same map training, and generating an individualized risk probability prediction value by calculating the matching degree of the medical sequence and the digital portrait; and a screening result report is automatically generated, so that efficient and noninvasive early risk screening is realized.

The invention discloses an automatic coding method and system for disease diagnosis, and belongs to the technical field of medical event identification, and the method comprises the steps: obtaining and carrying out the structured preprocessing of a disease diagnosis document, and displaying the directory and content of the document; utilizing the diagnosis coding model to extract diagnosis names, evidences and surgical operations from documents, and displaying the diagnosis names, the evidences and the surgical operations in multiple windows in a highlight mode; auditing, commenting, editing, merging and traceability positioning of diagnosis and evidence information are supported; automatically generating diagnosis coding results, sorting the diagnosis coding results, modifying main diagnosis and adjusting the sequence of associated evidences; the system supports DRG / DIP simulation in-group display and assists in coding sequence decision making; according to the method, the workload of a coder can be greatly reduced, the medical insurance settlement efficiency and the data quality are improved, and the method has good clinical application value and popularization prospects.

The invention discloses a patient diagnosis package information extraction method, which comprises the following steps: S1, acquiring a case through a diagnosis information output circuit, extracting tumor diagnosis and pathological diagnosis according to the dimension of the case, performing corresponding classification, and generating a patient diagnosis package in a matching manner according to a classification result; s2, the patient diagnosis package is filled and revised; s3, after filling and revision of the S2 are executed on each diagnosis package, a disease diagnosis code and a pathological diagnosis code are extracted, diagnosis package code conversion is carried out, and then patient diagnosis package extraction information is obtained; and S4, displaying the patient diagnosis package extraction information through the diagnosis information record display device. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

Systems and methods generate clinical documentation using large language models and artificial intelligence (AI). A template management module is provided to create customizable templates. A processing unit can receive input data from various sources and use AI to generate transcripts, summarize sessions, and produce clinical documentation such as clinical notes. The processing unit may also generate Current Procedural Terminology (CPT) and diagnosis codes, generate after-visit summaries, and generate referral letters. The AI may be trained on past clinical notes and can adapt to the clinician's style over time, with a feedback loop for continuous improvement. Additional features include cohort-based training, real-time language translation, predictive text, and analytics for documentation trends. The system supports customization of note length, style, and keywords, as well as integration with external medical databases and patient portals.

The invention provides an AUTOSAR architecture diagnosis code generation method and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a diagnosis database required for configuring the AUTOSAR diagnosis system, wherein the diagnosis database records a plurality of diagnosis units and a logic relation graph formed by the diagnosis units; generating a plurality of diagnosis registrators corresponding to the plurality of diagnosis units in a Simulink application layer model by adopting a registrator module according to the logic relation graph; performing parameter configuration on the plurality of diagnosis registrators to generate a diagnosis database configuration file; generating a diagnosis global manager in the Simulink application layer model, and then performing parameter configuration to generate a diagnosis global configuration file; and integrating the diagnosis database configuration file and the diagnosis global configuration file to obtain an AUTOSAR architecture diagnosis code file corresponding to the AUTOSAR diagnosis system. According to the invention, the AUTOSAR diagnosis function can be realized in a lightweight manner.

In some implementations, a fault diagnosis system may track information associated with a first execution of a code pipeline to deploy an updated version of software associated with the code pipeline. The fault diagnosis system may receive an indication of a failure in a second execution of the code pipeline, wherein the second execution of the code pipeline is triggered by a commit of source code to a source code repository. The fault diagnosis system may determine whether the updated version of the software associated with the code pipeline or the source code committed to the source code repository is a cause of the failure in the second execution of the code pipeline. The fault diagnosis system may send a request to plan code development to resolve the cause of the failure in the second execution of the code pipeline.

Provided are methods, systems, and computer-readable media for machine learning enabled determination of diagnostic codes. Various embodiments describe training a machine learning model, testing and / or optimizing a machine learning model, and using a machine learning model. Such embodiments may include inputting clinical notes into a trained machine learning model and obtaining a diagnostic code from the machine learning model. In various embodiments, the machine learning model is trained on descriptions associated with diagnostic codes. In various embodiments, the clinical notes are obtained from electronic health records (EHR). Certain embodiments may utilize a similarity matrix to determine differences between a clinical note and the diagnostic code description. Certain embodiments describe optimizing a trained model, which may include iteratively adjusting a parameter or hyperparameter associated with the model.

The embodiment of the application provides a code generation method, a power line loss anomaly diagnosis method, an electronic device, a storage medium and a program product. In the embodiment of the application, code generation requirement information including power data file input requirements, line loss anomaly diagnosis rule text and diagnosis result output requirements is input into a code generation agent, and initial line loss anomaly diagnosis code is automatically generated; then, in combination with a unit test case, a code error correction agent performs unit testing and automatic error correction on the line loss anomaly diagnosis code, and finally, high-reliability line loss anomaly diagnosis code is generated. Therefore, the code generation agent and the code error correction agent cooperate with each other, end-to-end automatic construction from natural language rules to executable line loss anomaly diagnosis code is realized, and safe, efficient and landable technical support is provided for power system line loss intelligent analysis.

The invention discloses a time sequence health event prediction method and system based on graph learning and a Transform framework, and relates to the technical field of medical data processing and deep learning. Comprising the following steps: constructing a patient-diagnosis code bipartite graph, converting the patient-diagnosis code bipartite graph into a patient network association graph by adopting a single-mode projection technology, and obtaining patient node representation by utilizing a Node2Vec pre-training method; constructing a diagnosis code hierarchical structure tree to obtain a diagnosis code initial embedding representation; learning the hidden representation of the diagnosis codes by adopting a multilayer graph neural network GNN; obtaining a time interval coded representation based on the learnable parameters and the interval period; and combining the diagnosis code hidden representation and the time interval code representation into a single admission record input vector to obtain a final embedding representation of the patient, and realizing time sequence health event prediction based on the final embedding representation of the patient. According to the method, static and dynamic information in the electronic health record is coded, time and space characteristics are effectively fused, and the prediction capability of the health event of the patient is remarkably improved.

The present disclosure concerns a computer-implemented method for transforming stool sample data into a diagnostic code, the method including: a) providing a stool sample from a subject; b) determining, as the stool sample data, expression levels of at least two distinct genes from a plurality of mRNA segments in the stool sample; and c) determining, by a computing system executing a machine-learning model and based on the stool sample data, a diagnostic code, wherein the machine-learning model was trained using stool diagnostic training data including a plurality of stool sample data labeled with at least one of two or more corresponding diagnostic codes.

An industrial control system includes a first data channel unit, a second data channel unit, and a retransformation unit. The first data channel unit receives first input data and a diagnosis code and transforms the first input data into first transformed data using an invertible transformation function employing the diagnosis code. The second data channel unit receives second input data and the diagnosis code and transforms the second input data into second transformed data using the invertible transformation function employing the diagnosis code. The retransformation unit receives the first transformed data and the diagnosis code from the first data channel unit and receives the second transformed data from the second data channel unit. The retransformation unit converts the first transformed data into first output data employing an inverse function of the invertible transformation function. The retransformation unit converts the second transformed data into second output data employing the inverse function.

The application discloses an ICD intelligent coding method based on a multi-level knowledge-driven large language model, and belongs to the technical field of intelligent coding. The method comprises the following steps: S1, a sub-model matching knowledge base is constructed to select an adaptive sub-model for a diagnosis text; S2, a single diagnosis coding knowledge base is constructed, the adaptive sub-model is called, and a preliminary coding result is generated for a single diagnosis of the diagnosis text; S3, a global verification knowledge base is constructed to perform global verification on all preliminary coding results; and S4, the global verification result is input into a strong rule quality control engine to perform final verification and merging. The application optimizes the knowledge processing process, only filters and vectorizes effective characters that are strongly related to ICD coding when constructing the three-layer knowledge base, eliminates redundant and invalid characters, greatly improves the recall accuracy and efficiency of retrieval enhancement, and provides accurate knowledge support for model decision-making.

Aspects relate to monitoring, by an integrated circuit, a timing margin of a logic path and its degradation. In one example, an apparatus includes an integrated circuit having a logic path formed therein; and a monitoring circuit formed in the integrated circuit. The monitoring circuit is configured to monitor a condition of the logical path and generate a sequence of diagnostic codes to indicate the condition of the logical path over time. A monitoring controller is configured to receive diagnostic codes in the sequence of diagnostic codes, store the diagnostic codes in a log along with corresponding timestamps, and determine a condition of the integrated circuit based on the sequence of diagnostic codes.

The invention relates to the technical field of disease prediction, and particularly discloses a universal disease prediction method and system based on a BERT framework, and the method comprises the following steps: S1, obtaining multiple diagnosis data of a patient, the diagnosis data comprising age during treatment, diagnosis date, diagnosis code, abnormal laboratory detection result code and operation information; s2, generating an embedded representation of the sequence according to the diagnosis data; the position in the embedded representation of the sequence is embedded with age representation during treatment; and S3, inputting the embedded representation of the sequence into a pre-training model based on a BERT framework to obtain prediction results of various disease risks. According to the method, a pre-training model based on a BERT framework is designed and is specially used for processing structured electronic health record data, discontinuous age during treatment is used as a position for embedding, complex features and relations in EHR data can be captured, and the performance of a disease prediction task is remarkably improved.

The invention relates to the technical field of electronic medical records, and particularly discloses an electronic medical record generation method and system based on a BART framework, and the method comprises the steps: obtaining a plurality of electronic medical records, and extracting the age during treatment, the diagnosis date, the treatment type, the diagnosis code, the abnormal laboratory detection result code and the operation information from the electronic medical records; the codes are input sequences; inputting the input sequence into a pre-training model based on a BART framework to generate a future electronic medical record; the pre-training model based on the BART framework comprises an encoder and a decoder. According to the method, the pre-training model based on the BART framework is used, and the age and the doctor-seeing time difference during doctor seeing are introduced, so that the model can better capture the time information, and the accuracy and the correlation of generating the medical record are improved. The next future electronic medical record can be generated according to multiple existing electronic medical records, and the medical record content is logically coherent.

A system for determining a principal diagnosis includes a computer processor configured to 5 receive structured medical claims data including at least a plurality of diagnosis codes. The computer processor is further configured to provide the medical claims data to a machine learning model and receive a plurality of partial codes and a plurality of first confidence scores. The computer processor is further configured to provide the plurality of partial codes and the plurality of first confidence scores to a computational model and receive a plurality of full codes and a plurality of second confidence scores. 0 The computational model is configured to filter and rank each full code from the plurality of diagnosis codes, such that each full code includes a corresponding partial code. The computer processor is further configured to output the plurality of full codes in an ordered recommendation based on the plurality of second confidence scores.

The application discloses a function diagnosis method, device and equipment and a storage medium. The method comprises the following steps: if a vehicle fault is detected, a fault diagnosis code is acquired, and a target record is determined in an operation log according to the fault diagnosis code; a target application program interface is determined according to the target record, and a target function matched with the target application program interface is determined; a function category of the target function is determined according to a function clustering result determined in advance based on a vehicle function; and all functions associated with the function category are diagnosed. The technical scheme solves the problems that it is difficult to analyze and diagnose a function fault of an automobile and the function fault is difficult to be completely eliminated, can improve the function fault diagnosis efficiency, increase the reliability of eliminating the function fault, and avoid the function fault from repeatedly occurring.

The application relates to the technical field of bus diagnosis, and discloses an ESPI and LPC bus diagnosis method and device. The method is as follows: the STRAP pin level of a master control chip is configured through a jump cap short circuit state, so that the master control chip enters an LPC bus working mode or an ESPI bus working mode; a target mainboard is connected; an I / O write operation initiated by the target mainboard in a POST self-checking stage is listened to; when a frame start signal is detected, a start field of a bus frame is analyzed and determined as an I / O write operation type; an address field in the bus frame of the I / O write operation type is analyzed and matched with a target listening address; when the address is matched successfully, a POST Code diagnosis code is acquired from the bus frame. The application realizes a dynamic switching mechanism of the LPC and ESPI double bus protocols through hardware jump cap configuration of the STRAP pin, solves the technical problem that a diagnosis card only supports a single bus protocol in the prior art, and enables a single diagnosis card device to be compatible with all X86 platform mainboards.

A method, non-transitory computer readable medium, and apparatus that identifies one of a plurality of diagnostic mapping tables based on a diagnostic code associated with one of a plurality of data environment formats in an electronic claim. The diagnostic code associated with one of the plurality of data environment formats is correlated to at least one of a plurality of parts and laterality associated with another one of the plurality of data environment formats based on the identified one of the plurality of diagnostic code mapping tables. One of a plurality of assessment ratings is determined based on the diagnostic code the correlated one of the plurality of parts and the laterality, and a categorization table associated with the another one of the plurality of data environment formats. Execution of one of a plurality of actions on the electronic claim in response to the determined one of the plurality of assessment ratings for the diagnostic code is initiated.

Vehicle diagnostic system compatible with a vehicle that has a vehicle communication interface (VCI) capable of generating a diagnostic code, wherein the vehicle diagnostic system comprises: a first diagnostic processor in data communication with the VCI; a first memory in data communication with the first diagnostic processor, wherein the first memory stores a first corpus of diagnostic data; a user interface in data communication with the first diagnostic processor; a second diagnostic processor in data communication with the first diagnostic processor, wherein the second diagnostic processor has a second memory which stores a second corpus of diagnostic data; and a special processor in data communication with the second diagnostic processor, wherein the special processor has a special memory which stores a special corpus containing diagnostic data, where the first diagnostic processor is designed to search within the first corpus for a vehicle diagnostic associated with the diagnostic code, The first diagnostic processor is designed to generate an initial request for vehicle diagnostics from the second diagnostic processor in response to the fact that the vehicle diagnostics are not found within the first corpus. The second diagnostic processor is designed to search for a vehicle diagnostic associated with the diagnostic code within the second corpus in response to the first request. The second diagnostic processor is designed to transmit a second request for vehicle diagnostics to the special processor in response to the vehicle diagnostics not being found within the second body, and wherein the special processor is designed to search for the vehicle diagnostics within the special body, wherein the special processor is further designed to transfer the vehicle diagnostics to the second diagnostic processor in response to the second request, and wherein the second diagnostic processor is further designed to transfer the vehicle diagnostics to the first diagnostic processor in response to the first request.

Aspects relate to monitoring, by an integrated circuit, a timing margin of a logic path and its degradation. In one example, an integrated circuit has a logic path formed in the integrated circuit. Monitoring circuitry is formed in the integrated circuit proximate to the logical path and is configured to monitor a condition of the logical path and to generate a sequence of diagnostic codes to indicate the condition of the logical path over time. A monitoring controller is configured to receive diagnostic codes in the sequence of diagnostic codes, determine a condition of the integrated circuit based on the sequence of diagnostic codes, and initiate a corrective action in response to the condition of the integrated circuit.

In a method for diagnosing control system of a process engineering plant having a plurality of actuating units for setting a process fluid and a central unit, at least one actuating-unit-specific control raw datum, such as a setpoint value, an actual value, a setpoint-actual difference value, or a control value, is detected; a particularly local diagnosis code is generated by one of the plurality of actuating units based on the at least one detected control raw datum; the diagnosis code is transmitted from the actuating unit to a central unit; a local diagnosis code, is transformed into a global diagnosis codes; a logical condition for a diagnosis deduction logic is defined such that when the logical condition is applied to at least one diagnosis code, the diagnosis deduction logic determines a predetermined deduction result; and a deduction result is determined based on the at least one diagnosis code.

The invention provides a full-process intelligent claim settlement operation method and device, and solves the problems of poor material quality, insufficient data standardization and low manual auditing efficiency in health insurance claim settlement in the prior art. The invention discloses a full-process intelligent claim settlement operation method. The method comprises the steps of obtaining perception information; the perception information is perception information of an image uploaded by a client; determining identification information according to the artificial intelligence identification perception information; the identification information comprises medical bill fields, diagnosis codes or medicine details; and determining a claim settlement type according to the combination of the identification information, auditing the perception information according to a preset rule corresponding to the claim settlement type, and outputting auditing information. According to the invention, through the innovative design of intelligent image quality detection, multi-source data fusion and five-layer decision-making architecture, full-process intelligent processing of health insurance claim settlement is realized.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and / or the like for generating a historically dynamic explanation data object for a dental image data object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform generating a historically dynamic explanation data object for a dental image data object using an encoder-decoder architecture, where the encoder machine learning framework of the encoder-decoder architecture comprises a current diagnosis identification machine learning model, a historical diagnosis identification machine learning model, a convolutional embedding machine learning model, a new diagnosis code inference machine learning model, and a feature vector combination machine learning model.

The present disclosure relates to the technical field of vehicles, and particularly relates to a fault diagnosis method, device, equipment and storage medium. The method comprises the following steps: according to a fault diagnosis sequence corresponding to a fault event set, before diagnosing a first fault event, acquiring first diagnosis state information according to a first inhibition mask of the first fault event and a first state mask corresponding to a second fault event, the second fault event being a fault event of a previous diagnosis of the first fault event, and the first state mask being a state mask acquired according to first diagnosis information of the second fault event; in a case where the first diagnosis state information indicates diagnosing the first fault event, diagnosing the first fault event, acquiring second diagnosis information of the first fault event, and updating the first state mask. The present disclosure can reduce the complexity of fault diagnosis code while reducing the accuracy of fault diagnosis, and improve the implementation convenience of fault diagnosis and the convenience of code maintenance.

A data collection procedure as a technical instruction for the treatment of Long-COVID, characterized by the application of a stage-specific, individualized, training-based approach tailored to the specific needs and symptoms of each patient. a) Independent claim The data-based, non-drug treatment of patients with the ICD-10 diagnosis code: U09.9 in the therapeutic (example: physiotherapy or rehabilitation centers), research, health, and complementary as well as medical fields.

The invention discloses an evidence perception long context refining method based on reinforcement learning. The method comprises the following steps: firstly, constructing a hierarchy-evidence matching matrix to obtain a state space, designing a discrete action space to operate an original medical record, and generating a refined context; step-by-step reasoning decision making is carried out according to a refining context and a coding decision big model, and a final diagnosis code is obtained; combining information extraction quality, format compliance and path accuracy to construct a composite reward and punishment function, and performing quantitative scoring on the refining behavior and the coding result; and finally, designing an asynchronous cooperative training mechanism based on double-layer optimization, finely adjusting the coding decision large model, and updating the refining module by adopting an actor-commentator framework. The method is suitable for a long medical record text ICD automatic coding task, and by designing a dynamic evidence perception and cooperative training mechanism, the precision of model long context processing and decision coding is remarkably improved.