Information processing method, system and equipment and storage medium
Through the combination of prediction model and scheduling scheme, the problem of predicting the flow time of emergency radiation examination is solved, the efficiency of emergency radiation examination is improved, and the waiting time of patients is reduced.
Patent Information
- Application Number
- CN202411067723.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-05
- Publication Date
- 2024-12-27
AI Technical Summary
The prior art is difficult to accurately predict the circulation time of emergency radiographic examinations, and lacks quantitative evaluation of the circulation efficiency of radiographic examinations, especially in public emergencies, and it is difficult to provide effective early warning and emergency solutions to decision makers.
By obtaining patient information and examination information, input it into the prediction model, predicting the examination time required for each examination stage, and determining the scheduling plan based on the average work efficiency and the number of patients to be processed in the examination stage, including adding examination machines and reporting writing, based on the average work efficiency of the examination stage and the number of patients to be processed, it is necessary to determine the scheduling plan, including adding examination machines and reporting writing Personnel and report reviewers to ensure that the inspection phase completes the required resources within the duration indicated by the target threshold.
Accurate prediction of the flow time of emergency radio examinations is achieved, reasonable and effective early warning and scheduling plans are provided, the efficiency of emergency radio examinations is improved, and the time for emergency patients to undergo radio examinations is reduced.
Smart Images
Figure CN119207818A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of computer technology, and in particular to an information processing method, system, device and storage medium. Background Art
[0002] In the medical field, emergency patients have a high degree of uncertainty in their medical risks, and doctors' diagnosis and treatment decisions for emergency patients are highly dependent on the results of their radiological examinations. However, due to the increase in the number of emergency patients and the uneven scheduling of radiological examination staff, emergency patients take a long time to undergo radiological examinations, which may delay the best time for treatment of emergency patients.
[0003] Although the existing Picture Archiving and Communication Systems (PACS) can improve the efficiency of diagnosis and treatment by optimizing the management, storage, distribution and analysis of radiological images through digital technology, PACS cannot accurately predict the turnaround time of emergency radiological examinations and lacks quantitative evaluation of the turnaround efficiency of radiological examinations. It is difficult to provide effective early warning and emergency plans for decision makers, especially in the face of public emergencies. Therefore, how to predict the turnaround time of emergency radiological examinations and determine effective early warning plans based on the prediction results is an urgent problem to be solved. Summary of the invention
[0004] The present disclosure provides an information processing method, device, equipment and storage medium to at least solve the above technical problems existing in the prior art.
[0005] According to a first aspect of the present disclosure, there is provided an information processing method, comprising: acquiring patient information and examination information; inputting the patient information and examination information into a prediction model, the prediction model outputting the examination time required for the patient in each examination stage; in response to the examination time required for the examination stage being greater than a target threshold, based on the average work efficiency of the examination stage and the number of patients to be processed, determining a scheduling scheme corresponding to the examination stage, the scheduling scheme representing other resources required to complete the examination stage within the time indicated by the target threshold.
[0006] In one possible implementation, before the patient information and examination information are input into the prediction model, the method further includes: obtaining multiple historical data, the historical data including historical patient information, historical examination information and historical examination time nodes, the historical examination time nodes being used to characterize the completion time of the patient in each examination stage; determining training data for a pre-prepared initial model based on the historical data, the training data including the historical examination time required for each examination stage, the historical number of patients to be processed, the historical average work efficiency and influencing parameters of the historical examination time; inputting the training data into the initial model for training to obtain the prediction model.
[0007] In one possible implementation, the initial model is an adaptive boosting regression algorithm, and the weak regressor of the initial model is at least one of decision tree regression, K-nearest neighbor regression and linear regression.
[0008] In one possible implementation, the inspection time required for responding to the inspection stage is greater than the target threshold, including: the inspection time required for responding to the inspection stage is greater than n times the historical average inspection time, displaying a first level alarm; the inspection time required for responding to the inspection stage is greater than m times the historical average inspection time, displaying a second level alarm, wherein m is greater than n.
[0009] In one possible implementation, the inspection stage includes a first stage from registration completion to inspection completion, a second stage from inspection completion to report writing completion, and a third stage from report writing completion to report review completion.
[0010] In one possible implementation, the scheduling plan corresponding to the inspection stage is determined based on the average work efficiency and the number of patients to be processed in the inspection stage, including: determining the number of inspection machines that need to be added in the first stage and the increased time of the inspection machines based on the average work efficiency and the number of patients to be processed in the first stage; determining the number of report writers that need to be added in the second stage and the increased time of the report writers based on the average work efficiency and the number of patients to be processed in the second stage; determining the number of report reviewers that need to be added in the third stage and the increased time of the report reviewers based on the average work efficiency and the number of patients to be processed in the third stage.
[0011] In one embodiment, the patient information includes at least one of the patient's gender, patient age, patient's initial diagnosis result, patient's emergency level and the department the patient requested to be examined; the examination information includes at least one of the examination type, type item, examiner, report writer, report reviewer, examination machine, examination hospital area and examination time.
[0012] According to a second aspect of the present disclosure, there is provided an information processing system, the system comprising: an information collection module for collecting patient information and examination information; an information storage module for storing historical patient information, historical examination information and historical examination time nodes, the historical examination time nodes being used to characterize the completion time of the patient in each examination stage; a model training module for training a pre-prepared initial model based on the historical patient information, historical examination information and historical examination time nodes in the information storage module to obtain a prediction model; an information processing module for inputting the patient information and examination information into the prediction model, the prediction model outputting the examination time required for the patient in each examination stage; the information processing module is further used to determine a scheduling scheme corresponding to the examination stage in response to the examination time required for the examination stage being greater than a target threshold, based on the average work efficiency of the examination stage and the number of patients to be processed, the scheduling scheme characterizing other resources required to complete the examination stage within the time indicated by the target threshold; an information display module for displaying the scheduling scheme.
[0013] According to a third aspect of the present disclosure, there is provided an electronic device, including:
[0014] at least one processor; and
[0015] a memory communicatively connected to the at least one processor; wherein,
[0016] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.
[0017] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure.
[0018] The present invention discloses an information processing method, system, device and storage medium, which predicts the examination time required for patients in each examination stage based on a prediction model, and in response to the examination time required for the examination stage being greater than a target threshold, determines a scheduling plan corresponding to the examination stage based on the average work efficiency of the examination stage and the number of patients to be processed. The scheduling plan represents other resources required to complete the examination stage within the time indicated by the target threshold. Thus, the emergency radiology examination flow time can be accurately predicted in advance, and a reasonable and effective early warning and scheduling plan can be determined based on the prediction results, thereby improving the efficiency of emergency radiology examinations and reducing the time for emergency patients to undergo radiology examinations.
[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:
[0021] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.
[0022] Figure 1 A schematic diagram showing a process of an information processing method according to an embodiment of the present disclosure Figure 1 ;
[0023] Figure 2 A schematic diagram showing a process of an information processing method according to an embodiment of the present disclosure Figure 2 ;
[0024] Figure 3 A schematic diagram showing a process of an information processing method according to an embodiment of the present disclosure Figure 3 ;
[0025] Figure 4 A schematic diagram of the structure of an information processing system according to an embodiment of the present disclosure is shown;
[0026] Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0027] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.
[0028] Figure 1 A schematic diagram showing a process of an information processing method according to an embodiment of the present disclosure Figure 1 ,like Figure 1 As shown, an information processing method includes:
[0029] Step S101, obtaining patient information and examination information.
[0030] In this embodiment, patient information is used to characterize information about the patient himself, including at least one of the patient's gender, patient age, patient's initial diagnosis result, patient's emergency level, and the department the patient requested to be examined; examination information is used to characterize information related to the examination conducted on the patient, including at least one of the examination type, type item, examiner, report writer, report reviewer, examination machine, examination hospital area, and examination time.
[0031] In one embodiment, the patient information and the examination information can be obtained from systems that can store patient information and examination information, such as an electronic medical record system (EMR), a laboratory information system (LIS), and a radiology information system (RIS).
[0032] Step S102, inputting patient information and examination information into a prediction model, and the prediction model outputs the examination time required for the patient at each examination stage.
[0033] In this embodiment, the prediction model is trained based on the historical patient information, historical examination information and historical examination time required for each examination stage. The prediction model can predict the examination time required for the patient in each examination stage based on the patient information and examination information. Therefore, the patient information and examination information are input into the prediction model, and the prediction model can output the examination time required for the patient in each examination stage.
[0034] In one embodiment, the examination stages include a first stage from completion of registration to completion of the examination, a second stage from completion of the examination to completion of report writing, and a third stage from completion of report writing to completion of report review. The sum of the examination durations of the first, second, and third stages is the total time required for the patient to undergo the examination.
[0035] Step S103, in response to the inspection time required for the inspection stage being greater than the target threshold, a scheduling plan corresponding to the inspection stage is determined based on the average work efficiency of the inspection stage and the number of patients to be processed.
[0036] In this embodiment, the inspection time required for the response inspection stage is greater than the target threshold, including that the inspection time required for the response inspection stage is greater than the first threshold, and the sum of the inspection times required for responding to all inspection stages is greater than the second threshold. If the inspection time required for the response inspection stage is greater than the target threshold, it proves that the time required for the patient's examination is too long, which may delay the best time for patient treatment. Therefore, the scheduling scheme corresponding to the inspection stage can be determined based on the average work efficiency of the inspection stage and the number of patients to be processed, wherein the average work efficiency of the inspection stage is the average value of the historical work efficiency of the inspection stage, the number of patients to be processed can be determined based on the number of patients processed in the history of the inspection stage, and can also be determined based on the number of patients to be processed currently registered for the inspection stage. The scheduling scheme represents other resources required to complete the inspection stage within the time indicated by the target threshold. Scheduling other resources according to the scheduling scheme can improve the inspection efficiency of the inspection stage.
[0037] In the present disclosure, the examination time required for patients in each examination stage is predicted based on the prediction model, and in response to the examination time required for the examination stage being greater than the target threshold, the scheduling plan corresponding to the examination stage is determined based on the average work efficiency of the examination stage and the number of patients to be processed. The scheduling plan represents other resources required to complete the examination stage within the time indicated by the target threshold. As a result, the emergency radiology examination flow time can be accurately predicted in advance, and a reasonable and effective early warning and scheduling plan can be determined based on the prediction results, thereby improving the efficiency of emergency radiology examinations and reducing the time for emergency patients to undergo radiology examinations.
[0038] Figure 2 A schematic diagram showing a process of an information processing method according to an embodiment of the present disclosure Figure 2 ,like Figure 2 As shown, before "inputting patient information and examination information into the prediction model" in step S102, an information processing method further includes:
[0039] Step S201, obtaining multiple pieces of historical data.
[0040] In this embodiment, historical data includes historical patient information, historical examination information and historical examination time nodes. The historical examination time nodes are used to characterize the completion time of the patient in each examination stage, wherein the historical patient information includes at least one of the historical patient's gender, patient age, patient's initial diagnosis result, patient's emergency level and the patient's examination department; the historical examination information includes at least one of the examination type, type item, examination personnel, report writer, report reviewer, examination machine, examination hospital area and examination time of the historical patient's examination. The historical examination time nodes include the time when the historical patient registration was completed, the time when the examination was completed, the time when the report writing was completed and the time when the report review was completed.
[0041] Step S202: Determine training data for a pre-prepared initial model based on historical data.
[0042] In this embodiment, the training data includes the historical inspection time required for each inspection stage, the historical number of patients to be processed, the historical average work efficiency and the influencing parameters of the historical inspection time, wherein the historical inspection time can be calculated based on the historical inspection time nodes, the historical number of patients to be processed can be obtained based on the number of historical patient information corresponding to the inspection stage, the historical average work efficiency can be calculated based on the historical number of patients to be processed in the inspection stage and the total time to process all historical patients to be processed, and the influencing parameters of the historical inspection time are parameters that may affect the inspection time of the inspection stage, including patient age, patient gender, inspection district, inspection type, inspection items, patient emergency level, inspection time during the day or at night, whether the inspection time is a holiday and the historical number of patients to be processed.
[0043] Step S203: input the training data into the initial model for training to obtain a prediction model.
[0044] In this embodiment, the initial model is an adaptive boosting regression algorithm (Adaboost Regression), and the weak regressor of the initial model is at least one of decision tree regression, K-nearest neighbor regression and linear regression. The training data can be input into the initial model for training to obtain a prediction model.
[0045] In one implementation, the training data can be divided into a training set, a validation set, and a test set, and data cleaning is performed to process missing values and outliers. For a given training set D, D = {(X1, Y1), ..., (X m ,Y m )}; where X i =(x1,x2,x3,x4,x5,x6,x7,x8,x9); Y i (1≤i≤m) is the historical inspection time of each stage, X i are the influencing parameters of historical examination duration, and x1, x2, x3, x4, x5, x6, x7, x8, and x9 can be patient age, patient gender, examination hospital area, examination type, examination items, patient emergency level, examination time during the day or at night, whether the examination time is a holiday, and the number of historical patients to be processed, respectively.
[0046] In one embodiment, Adaboost Regression iteratively trains multiple weak regressors and obtains the final prediction result by weighted combination. Adaboost Regression is trained and the number of iterations (that is, the number of weak regressors) is set. In each iteration, Adaboost Regression adjusts the weight of the sample to better fit the data. The appropriate number of iterations can be selected by cross-validation.
[0047] In one embodiment, a validation set can be used to evaluate the performance of Adaboost Regression, and common indicators of regression problems, such as root mean square error (RMSE), mean absolute error (MAE), etc., can be used to determine the number of iterations that may need to be adjusted, the parameters of the weak regressor, etc.
[0048] In one implementation, the model can be finally evaluated based on the test set to ensure that the model performs well on unseen data. Based on the test results, the model may need to be further optimized, and the performance of the model can be regularly evaluated based on actual application situations to make improvements and optimizations.
[0049] In the present disclosure, the basic model of the prediction model is the Adaboost Regression model. The Adaboost Regression model continuously adjusts the sample weights during the iteration process, focusing on instances of previous incorrect or inaccurate predictions, thereby improving the prediction ability for difficult or abnormal data points and ensuring the accuracy of the predicted examination time required for patients at each examination stage.
[0050] In one embodiment, “the inspection time required for the response inspection phase is greater than the target threshold” in step S103 includes:
[0051] If the inspection time required for the response inspection phase is greater than n times the historical average inspection time, a first-level alarm is displayed; if the inspection time required for the response inspection phase is greater than m times the historical average inspection time, a second-level alarm is displayed, where m is greater than n.
[0052] In this embodiment, the historical average examination time for each examination stage can be calculated first. If the predicted examination time required for the patient in each examination stage is greater than n times the historical average examination time, it is considered that an early warning is needed, and a first-level alarm is displayed at this time; if the predicted examination time required for the patient in each examination stage is greater than m times the historical average examination time, it is considered that an early warning is needed, and a first-level alarm is displayed at this time. The first-level alarm and the second-level alarm can be displayed in a visual manner, which is convenient for the staff to perform corresponding operations according to the alarm.
[0053] In one embodiment, if the predicted examination time required for the patient at each examination stage is less than or equal to 130% of the historical average examination time, it is considered that no warning is needed and a green light can be displayed; if the predicted examination time required for the patient at each examination stage is between 130% and 160% of the historical average examination time, it is considered that a warning is needed, and a first-level alarm is displayed, which can be a yellow light; if the predicted examination time required for the patient at each examination stage is greater than 160% of the historical average examination time, it is considered that a warning is needed, and a second-level alarm is displayed, which can be a red light, that is, n is 130% and m is 160%.
[0054] Figure 3 A schematic diagram showing a process of an information processing method according to an embodiment of the present disclosure Figure 3 ,like Figure 3 As shown, step S103 "in response to the inspection time required for the inspection stage being greater than the target threshold, determining a scheduling scheme corresponding to the inspection stage based on the average work efficiency of the inspection stage and the number of patients to be processed" includes:
[0055] Step S301, based on the average work efficiency and the number of patients to be processed in the first stage, determine the number of examination machines that need to be added and the increase time of the examination machines in the first stage.
[0056] In this embodiment, the first stage is the stage from registration completion to examination completion. If the examination time required for the first stage is greater than the target threshold, the number of examination machines that need to be added and the increase time of the examination machines are determined based on the average work efficiency and the number of patients to be processed in the first stage. The average work efficiency of the examination stage is the average value of the historical work efficiency of the examination stage, and the number of patients to be processed can be determined based on the number of patients processed in the history of the examination stage, or based on the number of patients to be processed currently registered in the examination stage.
[0057] Step S302, based on the average work efficiency and the number of patients to be processed in the second stage, determine the number of report writers that need to be added in the second stage and the additional time required for the report writers.
[0058] In this embodiment, the second stage is the stage from the completion of the examination to the completion of the report writing. If the examination time required for the second stage is greater than the target threshold, the number of report writers to be added and the increased time of the report writers are determined based on the average work efficiency of the second stage and the number of patients to be processed. The average work efficiency of the examination stage is the average value of the historical work efficiency of the examination stage, and the number of patients to be processed can be determined based on the number of patients processed in the history of the examination stage, or based on the number of patients to be processed currently registered in the examination stage.
[0059] Step S303, based on the average work efficiency and the number of patients to be processed in the third stage, determine the number of report reviewers that need to be added in the third stage and the increase time of the report reviewers.
[0060] In this embodiment, the third stage is the stage from the completion of report writing to the completion of report review. If the inspection time required for the third stage is greater than the target threshold, the number of report reviewers to be added and the increase in the time of the report reviewers are determined based on the average work efficiency and the number of patients to be processed in the third stage. The average work efficiency of the inspection stage is the average value of the historical work efficiency of the inspection stage, and the number of patients to be processed can be determined based on the number of patients processed in the history of the inspection stage, or based on the number of patients to be processed currently registered in the inspection stage.
[0061] In one implementation method, the scheduling plans corresponding to the first stage, the second stage, and the third stage can all be displayed visually, making it convenient for staff to schedule resources based on the scheduling plans.
[0062] In the present disclosure, the scheduling plan represents other resources required to complete the examination stage within the time indicated by the target threshold. After adjusting the resource arrangement according to the scheduling plan, the efficiency of emergency radiological examinations is improved, the time for patients to undergo radiological examinations is reduced, and the actual examination time required for patients in each examination stage can reach a reasonable threshold.
[0063] Figure 4 A schematic diagram of the structure of an information processing system according to an embodiment of the present disclosure is shown. Figure 4 As shown, an information processing system includes:
[0064] The information collection module 10 is used to collect patient information and examination information; the information storage module 11 is used to store historical patient information, historical examination information and historical examination time nodes, and the historical examination time nodes are used to represent the completion time of the patient in each examination stage; the model training module 12 is used to train the pre-prepared initial model based on the historical patient information, historical examination information and historical examination time nodes in the information storage module to obtain a prediction model; the information processing module 13 is used to input patient information and examination information into the prediction model, and the prediction model outputs the examination time required for the patient in each examination stage; the information processing module 13 is also used to respond to the examination time required for the examination stage being greater than the target threshold, and determine the scheduling plan corresponding to the examination stage based on the average work efficiency of the examination stage and the number of patients to be processed, and the scheduling plan represents other resources required to complete the examination stage within the time indicated by the target threshold; the information display module 14 is used to display the scheduling plan.
[0065] In one embodiment, the model training module 12 is also used to: obtain multiple historical data, the historical data includes historical patient information, historical examination information and historical examination time nodes, the historical examination time nodes are used to characterize the completion time of the patient in each examination stage; determine the training data for the pre-prepared initial model based on the historical data, the training data includes the historical examination time required for each examination stage, the historical number of patients to be processed, the historical average work efficiency and the influencing parameters of the historical examination time; input the training data into the initial model for training to obtain a prediction model; wherein the initial model is an adaptive boosting regression algorithm, and the weak regressor of the initial model is at least one of decision tree regression, K-nearest neighbor regression and linear regression.
[0066] In one embodiment, the information processing module 13 is also used to: display a first level alarm when the inspection time required for the response inspection phase is greater than n times the historical average inspection time; display a second level alarm when the inspection time required for the response inspection phase is greater than m times the historical average inspection time, where m is greater than n.
[0067] In one embodiment, the inspection stage includes a first stage from completion of registration to completion of inspection, a second stage from completion of inspection to completion of report writing, and a third stage from completion of report writing to completion of report review. The information processing module 13 is also used to: determine the number of inspection machines that need to be added and the increased time of the inspection machines in the first stage based on the average work efficiency and the number of patients to be processed in the first stage; determine the number of report writers that need to be added and the increased time of the report writers in the second stage based on the average work efficiency and the number of patients to be processed in the second stage; determine the number of report reviewers that need to be added and the increased time of the report reviewers in the third stage based on the average work efficiency and the number of patients to be processed in the third stage.
[0068] In one embodiment, the patient information includes at least one of the patient's gender, the patient's age, the patient's initial diagnosis result, the patient's emergency level, and the department the patient requested to be examined; the examination information includes at least one of the examination type, type item, examiner, report writer, report reviewer, examination machine, examination hospital area, and examination time.
[0069] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.
[0070] Figure 5A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.
[0071] like Figure 5 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.
[0072] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0073] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as an information processing method. For example, in some embodiments, an information processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of an information processing method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform an information processing method in any other appropriate manner (e.g., by means of firmware).
[0074] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0075] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0076] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0077] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0078] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.
[0079] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.
[0080] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0081] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.
[0082] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. An information processing method, characterized in that: The method comprises: Obtain patient and examination information; The patient information and examination information are input into a prediction model, and the prediction model outputs the examination time required for the patient at each examination stage; In response to the inspection time required for the inspection stage being greater than a target threshold, a scheduling plan corresponding to the inspection stage is determined based on the average work efficiency of the inspection stage and the number of patients to be processed, and the scheduling plan represents other resources required to complete the inspection stage within the time indicated by the target threshold.
2. The method according to claim 1, characterized in that Before inputting the patient information and examination information into the prediction model, the method further includes: Acquire multiple pieces of historical data, wherein the historical data includes historical patient information, historical examination information, and historical examination time nodes, wherein the historical examination time nodes are used to represent the completion time of each examination stage of the patient; Determining training data for a pre-prepared initial model based on the historical data, the training data including historical examination time required for each examination stage, historical number of patients to be processed, historical average work efficiency, and influencing parameters of historical examination time; The training data is input into the initial model for training to obtain the prediction model.
3. The method according to claim 2, characterized in that The initial model is an adaptive boosting regression algorithm, and the weak regressor of the initial model is at least one of decision tree regression, K-nearest neighbor regression and linear regression.
4. The method according to claim 1, characterized in that: The inspection time required for the response inspection phase is greater than a target threshold, including: If the inspection time required to respond to the inspection stage is greater than n times the historical average inspection time, a first-level alarm is displayed; In response to the inspection time required for the inspection phase being greater than m times the historical average inspection time, a second level alarm is displayed, where m is greater than n.
5. The method according to claim 1, characterized in that The inspection stages include a first stage from completion of registration to completion of inspection, a second stage from completion of inspection to completion of report writing, and a third stage from completion of report writing to completion of report review.
6. The method according to claim 5, characterized in that Determining a scheduling scheme corresponding to the inspection stage based on the average work efficiency of the inspection stage and the number of patients to be processed includes: Based on the average work efficiency and the number of patients to be processed in the first stage, determining the number of examination machines that need to be added and the increase time of the examination machines in the first stage; Based on the average work efficiency and the number of patients to be processed in the second phase, determine the number of additional report writers and the additional hours of report writers required in the second phase; Based on the average work efficiency and the number of patients to be processed in the third stage, the number of report reviewers that need to be added in the third stage and the increase time of the report reviewers are determined.
7. The method according to any one of claims 1 to 6, characterized in that: The patient information includes at least one of the patient's gender, age, initial diagnosis, emergency level and the department the patient requested to be examined; the examination information includes at least one of the examination type, type item, examiner, report writer, report reviewer, examination machine, examination hospital area and examination time.
8. An information processing system, characterized in that: The system comprises: Information collection module, used to collect patient information and examination information; An information storage module, used to store historical patient information, historical examination information and historical examination time nodes, wherein the historical examination time nodes are used to represent the completion time of each examination stage of the patient; A model training module is used to train a pre-prepared initial model based on historical patient information, historical examination information and historical examination duration nodes in the information storage module to obtain a prediction model; An information processing module, used for inputting the patient information and examination information into a prediction model, wherein the prediction model outputs the examination time required for the patient at each examination stage; The information processing module is further configured to, in response to the inspection time required for the inspection stage being greater than a target threshold, determine a scheduling scheme corresponding to the inspection stage based on an average work efficiency of the inspection stage and a number of patients to be processed, wherein the scheduling scheme represents other resources required to complete the inspection stage within the time indicated by the target threshold; The information display module is used to display the scheduling plan.
9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1-7.