CN110660456A - Clinical decision support and model training method, device, terminal and medium thereof - Google Patents
Clinical decision support and model training method, device, terminal and medium thereof Download PDFInfo
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- CN110660456A CN110660456A CN201910865188.9A CN201910865188A CN110660456A CN 110660456 A CN110660456 A CN 110660456A CN 201910865188 A CN201910865188 A CN 201910865188A CN 110660456 A CN110660456 A CN 110660456A
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The application provides a clinical decision support and a method, a device, a terminal and a medium for training a model thereof, which comprises the following steps: acquiring current clinical data of a current medical staff at a pre-designated medical service institution; inputting the obtained current clinical data into a trained clinical decision support model to output a corresponding current clinical decision for determining a final clinical decision applicable to the clinician according to the current clinical decision. The invention can realize the technical scheme of corresponding clinical decision support function according to the self characteristics of each hospital, and greatly improves the support of clinical decision.
Description
Technical Field
The present application relates to the technical field of clinical decision support, and in particular, to a clinical decision support and a model training method, apparatus, terminal, and medium thereof.
Background
The existing clinical decision support system mainly uses some existing clinical paths or existing word lists, such as word lists of drug interaction, to perform corresponding analysis, but because the used data are all known data, the corresponding clinical decision support function can not be realized according to the self characteristics of each hospital.
Therefore, how to provide a technical solution capable of implementing a corresponding clinical decision support function according to the characteristics of each hospital is a problem to be solved urgently by those skilled in the art.
Content of application
In view of the above drawbacks of the prior art, the present application aims to provide a clinical decision support and a method, an apparatus, a terminal, and a medium for training a model thereof, so as to solve the technical problem that a technical scheme of corresponding clinical decision support function cannot be implemented for the characteristics of each hospital in the prior art.
To achieve the above and other related objects, a first aspect of the present application provides a clinical decision support method comprising: acquiring current clinical data of a current medical staff at a pre-designated medical service institution; inputting the obtained current clinical data into a trained clinical decision support model to output a corresponding current clinical decision for determining a final clinical decision applicable to the clinician according to the current clinical decision.
In some embodiments of the first aspect of the present application, the method further comprises: if the clinical decision support model outputs the corresponding current clinical decision, waiting to receive an external final clinical decision; if the clinical decision support model does not output the corresponding current clinical decision, an instruction is sent to the outside to request for the external input of the final clinical decision before waiting for receiving the external final clinical decision; wherein the final clinical decision entered externally is used to train the clinical decision support model.
In some embodiments of the first aspect of the present application, the method further comprises: and under the condition that the cure rate of the pre-designated medical service institution for the disease of the current visit staff is judged to be lower than a preset threshold value according to the clinical decision support model, outputting the current clinical decision of transfer treatment.
In some embodiments of the first aspect of the present application, the method further comprises: and under the condition that the pre-appointed medical service institution has a specific diagnosis and treatment means with higher success rate aiming at the diseases of the current patient according to the clinical decision support model, outputting the current clinical decision adopting the specific diagnosis and treatment means.
In some embodiments of the first aspect of the present application, the method further comprises: acquiring current clinical data at a pre-designated medical service institution of a current medical staff, wherein the current clinical data at least comprises diseased symptom data; the obtained current clinical data is input into a trained clinical decision support model to output a current clinical decision comprising one or more types of diseases with highest association with diseased symptoms.
In some embodiments of the first aspect of the present application, the trained clinical decision support model is trained based on historical clinical data and a data set of historical clinical decision data of all or part of the historical visits of the pre-assigned healthcare facility.
To achieve the above and other related objects, a second aspect of the present application provides a method for training a clinical decision support model, the trained clinical decision support model being used for inputting current clinical data and outputting a corresponding current clinical decision; the method comprises the following steps: acquiring historical clinical data and historical clinical decision data of all or part of historical medical personnel of a medical service institution; and training by taking the historical clinical data and the historical clinical decision data as input data to construct a clinical decision support model for providing clinical decisions for current doctors.
To achieve the above and other related objects, a third aspect of the present application provides a clinical decision support apparatus comprising: the clinical data acquisition module is used for acquiring the current clinical data of the current medical staff at a pre-designated medical service institution; a clinical decision support model module for inputting the acquired current clinical data into the trained clinical decision support model to output a corresponding current clinical decision for determining a final clinical decision applicable to the clinician according to the current clinical decision.
To achieve the above and other related objects, a fourth aspect of the present application provides a clinical decision support model training apparatus, comprising: the training data acquisition module is used for acquiring historical clinical data and historical clinical decision data of all or part of historical visitors of a medical service institution; and the model construction module is used for training by taking the historical clinical data and the historical clinical decision data as input data so as to construct a clinical decision support model for providing clinical decisions for current clinic staff.
To achieve the above and other related objects, a fifth aspect of the present application provides a computer readable storage medium having stored thereon a first computer program and/or a second computer program, which when executed by a processor implements the clinical decision support method; the second computer program, when executed by a processor, implements the clinical decision support model training method.
To achieve the above and other related objects, a sixth aspect of the present application provides a clinical decision support terminal comprising: a processor and a memory; the memory is for storing a computer program and the processor is for executing the computer program stored by the memory to cause the terminal to perform the clinical decision support method.
To achieve the above and other related objects, a seventh aspect of the present application provides a clinical decision support model training terminal, comprising: a processor and a memory; the memory is configured to store a computer program and the processor is configured to execute the computer program stored by the memory to cause the terminal to perform the clinical decision support model training method.
As described above, the clinical decision support and model training method, device, terminal, and medium thereof according to the present application have the following beneficial effects: the invention can realize the technical scheme of corresponding clinical decision support function according to the self characteristics of each hospital, and greatly improves the support of clinical decision.
Drawings
Fig. 1 is a flowchart of a clinical decision support method according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a clinical decision support method according to an embodiment of the present application.
Fig. 3 is a flowchart illustrating a clinical decision support method according to an embodiment of the present application.
Fig. 4 is a flowchart illustrating a clinical decision support method according to an embodiment of the present application.
Fig. 5 is a flowchart illustrating a clinical decision support method according to an embodiment of the present application.
Fig. 6 is a flowchart illustrating a method for training a clinical decision support model according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of a clinical decision support apparatus according to an embodiment of the present application.
Fig. 8 is a schematic structural diagram of a clinical decision support model training device according to an embodiment of the present application.
Fig. 9 is a schematic structural diagram of a clinical decision support terminal according to an embodiment of the present application.
Fig. 10 is a schematic structural diagram of a clinical decision support model training terminal according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It is noted that, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," and/or "comprising," when used in this specification, specify the presence of stated features, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, operations, elements, components, items, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions or operations are inherently mutually exclusive in some way.
Clinical Decision Support (CDS) provides clinicians, staff, patients, or other individuals with knowledge and personal-specific information that is intelligently screened or presented at appropriate times to improve health and medical services. The CDS includes a variety of tools for enhancing decision-making efforts within a clinical workflow, including computerized alert and instructional information for healthcare workers and patients, clinical guidelines, disease-specific order collections, focused patient data reports and summaries, documentation templates, diagnostic support, and context-dependent reference information, among other tools and means.
However, the existing clinical decision support system mainly uses some existing clinical routes or existing word lists, such as word lists for drug interaction, to perform corresponding analysis, but because the used data are all known data, it is not possible to implement corresponding clinical decision support functions according to the characteristics of each hospital.
In view of this, the present invention provides a technical solution that can implement a corresponding clinical decision support function according to the characteristics of each hospital, and greatly improves the support of clinical decisions. Hereinafter, the technical solution of the present invention will be explained in conjunction with a plurality of embodiments.
Example one
Referring to fig. 1, a flow chart of a clinical decision support method in an embodiment of the invention is shown. The clinical decision support method of the present embodiment includes step S101 and step S102.
It should be noted that the methods described in this embodiment and the following embodiments can be applied to various hardware devices. Examples of the hardware devices include an arm (advanced RISC machines) controller, an fpga (field programmable gate array) controller, an soc (system on chip) controller, a dsp (digital Signal processing) controller, or an mcu (micro controller unit) controller; the hardware devices may also be, for example, a computer that includes components such as memory, a memory controller, one or more processing units (CPUs), a peripheral interface, RF circuitry, audio circuitry, speakers, a microphone, an input/output (I/O) subsystem, a display screen, other output or control devices, and external ports; the computer includes but is not limited to Personal computers such as desktop computers, notebook computers, tablet computers, smart phones, smart televisions, Personal Digital assistants (PDAs for short), and the like; the hardware device may also be, for example, a server, and the server may be arranged on one or more entity servers according to various factors such as functions and loads, or may be formed by a distributed or centralized server cluster, which is not limited in this embodiment.
In step S101, current clinical data of the current medical staff at the pre-designated medical service institution is acquired.
It should be noted that the medical service institution includes but is not limited to: hospitals, health homes, nursing homes, clinics, health offices, or emergency stations. In addition, the pre-designated medical service may be any medical service corresponding to a clinical decision support function, and the current clinical data obtained in the present application and the historical clinical data obtained in the subsequent use of training the clinical decision support model are all data related to corresponding clinical patients (or may also be referred to as the total data of the clinical patients).
In some optional implementations of the present embodiment, the current clinical data includes, but is not limited to: basic information data, medication data, medical expense data, surgical condition data, diseased symptom data, postoperative recovery data, and the like. These data are used to reflect all relevant aspects of a clinical patient, such as patient basic information, overall procedure medication, medical costs, surgical conditions, diseased symptoms, post-operative recovery, and the like. Wherein, the corresponding clinical decision (including the historical clinical decision, the current clinical decision and the final clinical decision) under the clinical data (including the current clinical data and the historical clinical data) of the clinical patient may include a diagnosis and treatment plan for the corresponding clinical patient, and the like.
In step S102, the obtained current clinical data is input into the trained clinical decision support model to output a corresponding current clinical decision for determining a final clinical decision applicable to the clinician according to the current clinical decision.
In some optional implementations of this embodiment, the trained clinical decision support model is trained based on historical clinical data of all or part of the historical visits of the pre-assigned healthcare facility and a data set of historical clinical decision data, and the construction of the clinical decision support model will be described below.
Example two
Fig. 2 is a flow chart of a clinical decision support method according to an embodiment of the invention. The clinical decision support method of the present embodiment includes steps S201 to S204.
In step S201, current clinical data of the current medical staff at the pre-designated medical service institution is acquired.
In step S202, the acquired current clinical data is input into the trained clinical decision support model, and it is determined whether there is a decision output in the clinical decision support model.
In step S203, if the clinical decision support model outputs a corresponding current clinical decision, the current clinical decision is outputted to the corresponding medical staff for determining a final clinical decision based on the current clinical decision. That is, if the clinical decision support model is able to output the corresponding current clinical decision, the decision is output, and medical staff may use the decision as auxiliary information to determine the final clinical decision to be finally implemented according to the decision, so that the clinical decision support model implements the clinical decision support function.
In step S204, if the clinical decision support model does not output the corresponding current clinical decision, before waiting to receive an external final clinical decision, an instruction is sent to the outside to request the external input of the final clinical decision; wherein the final clinical decision entered externally is used to train the clinical decision support model.
Specifically, if the clinical decision model does not output the corresponding current clinical decision, an instruction may be issued to the outside to instruct the external medical staff to independently obtain the final clinical decision, and the decision is executed after the final clinical decision input from the outside is received. In addition, in this case, the final clinical decision input from the outside is used to train the clinical decision support model, thereby continuously optimizing and updating the clinical decision support model.
EXAMPLE III
Fig. 3 is a flow chart of a clinical decision support method according to an embodiment of the invention. The clinical decision support method of the present embodiment includes steps S301 to S304.
In step S301, current clinical data of the current medical care provider at the pre-designated medical service institution is acquired.
In step S302, the acquired current clinical data is input into the trained clinical decision support model, and it is determined whether the cure rate of the pre-specified medical service institution for the disease suffered by the current visit staff is lower than a preset threshold.
In step S303, if the cure rate is lower than the preset threshold, the current clinical decision of the transfer therapy is output.
In step S304, if the cure rate is not lower than the preset threshold, a clinical decision corresponding to the current clinical data is output.
In this embodiment, if the cure rate for a certain disease in the historical data of the healthcare institution is low, it can be considered that the facility has a deficiency in the ability to treat the disease, and the current clinical decision output by the clinical decision support model for the disease may be a suggested transfer.
Example four
Fig. 4 is a flow chart of a clinical decision support method according to an embodiment of the invention. The clinical decision support method of the present embodiment includes steps S401 to S404.
In step S401, current clinical data of the current medical staff at the pre-designated medical service institution is acquired.
In step S402, the acquired current clinical data is input into the trained clinical decision support model, and it is determined whether the pre-designated medical service institution has a specific diagnosis and treatment means with a high success rate for the disease of the current visit staff.
In step S403, if the specific diagnosis and treatment means has a high success rate, the current clinical decision using the specific diagnosis and treatment means is output.
In step S404, if there is no specific diagnosis and treatment means with a high success rate, a clinical decision corresponding to the current clinical data is output.
In this embodiment, if the historical data of the medical service organization has a specific diagnosis and treatment means for a certain disease, the current clinical decision output by the clinical decision support model for the certain disease may be to implement diagnosis and treatment by using the specific diagnosis and treatment means. For example: the hospital uses the transverse cutting frequently and has high success rate in the corresponding operation process, so that the hospital is considered to be good at using the transverse cutting to realize the corresponding operation, and the current clinical decision output by the corresponding operation clinical decision support model is that the transverse cutting is recommended to finish the operation.
It should be noted that the specific diagnosis and treatment means with a high success rate in this embodiment mainly refers to some diagnosis and treatment means with a success rate higher than a preset threshold and different from conventional diagnosis and treatment means.
EXAMPLE five
Fig. 5 is a flow chart of a clinical decision support method according to an embodiment of the invention. The clinical decision support method of the present embodiment includes steps S501 to S502.
In step S501, current clinical data including at least diseased symptom data of the current medical care provider at a pre-designated medical service facility is acquired.
In step S502, the acquired current clinical data is input into the trained clinical decision support model to output a current clinical decision including one or more types of diseases with highest association with disease symptoms.
In this embodiment, considering that the disease type can be determined by the disease symptoms of the patient who is seen from the past, after the current disease symptom data of the current patient is obtained, the corresponding disease type is directly output through the clinical decision support model, and the medical staff can perform corresponding treatment for the disease type. For example, if the abdominal pain is mostly corresponding to a patient with uterine atopy, the current clinical decision output by the clinical decision support model is uterine atopy when the current clinical data is abdominal pain. In a word, the trained clinical decision support model can correspond to the characteristics of the hospital, and the output current clinical decision is ensured to be in accordance with the characteristics of the hospital.
It should be noted that some disease types may correspond to the same disease symptoms, so the technical scheme of this embodiment screens out multiple types of diseases with the highest degree of association through a clinical decision support model, and provides multiple references to medical care personnel to avoid misdiagnosis.
EXAMPLE six
Fig. 6 is a flow chart of a method for training a clinical decision support model according to an embodiment of the invention. The clinical decision support model training method of the present embodiment includes step S601 and step S602.
In step S601, historical clinical data and historical clinical decision data of all or part of the historical visits of a medical service institution are obtained.
It should be noted that the historical data used in this embodiment is already existing data that may include historical clinical data, historical clinical decision, and other historical diagnosis and operation conditions of the hospital, so that a clinical decision support model is obtained by training using the historical data as training data based on statistics, a machine learning method, and the like, the model is trained using data of a specific hospital, and a corresponding clinical decision support function can be implemented according to the characteristics of the specific hospital, where the characteristics of the specific hospital may include the capacity and strength of the hospital.
In step S602, the historical clinical data and the historical clinical decision data are used as input data for training to construct a clinical decision support model for providing clinical decisions for the current visit staff.
Optionally, the clinical decision support model employs a neural network model, which includes but is not limited to: a CNN neural network, an RNN neural network, a BP neural network, or an SVM neural network, etc., which are not limited in this embodiment.
Optionally, the historical clinical data and the historical clinical decision data are used as a total sample and are divided into three independent parts, namely, a model training set, a model verification set and a model test set, for example, 50% of the total sample is divided into the model training set, 25% of the total sample of each station in the model verification set and the model test set, and the three parts are randomly extracted from the sample.
Optionally, supervised learning or unsupervised learning is used to train the clinical decision support model, which is not limited in this embodiment. In the example of supervised learning, samples are labeled, each instance is composed of an input object (historical clinical data) and an expected output value (historical clinical decision data), a prototype model capable of mapping out a new instance is generated by analyzing a training data set, the model is verified by a verification set, and then the model is tested by a test set until the model is trained to be a usable model.
It should be noted that, after being trained, the clinical decision support model of this embodiment is used to input current clinical data and output a corresponding current clinical decision, and an implementation manner of the clinical decision support model of this embodiment is similar to that of the clinical decision support method of the above embodiment, and therefore, details are not repeated.
EXAMPLE seven
Fig. 7 is a schematic structural diagram of a clinical decision support apparatus according to an embodiment of the present invention. The clinical decision support apparatus of the present embodiment comprises a clinical data acquisition module 71 and a clinical decision support model module 72.
The clinical data acquisition module 71 is configured to acquire current clinical data of a current medical staff at a pre-designated medical service institution; the clinical decision support model module 72 is used to input the acquired current clinical data into the trained clinical decision support model to output a corresponding current clinical decision for determining a final clinical decision applicable to the clinician according to the current clinical decision.
It should be noted that the implementation of the clinical decision support apparatus of this embodiment is similar to that of the clinical decision support method in the above embodiments, and therefore, the detailed description thereof is omitted.
Example eight
Fig. 8 is a schematic structural diagram of a clinical decision support model training apparatus according to an embodiment of the present invention. The clinical decision support model training apparatus of the present embodiment includes a training data acquisition module 81 and a model construction module 82.
The training data acquisition module 81 is used for acquiring historical clinical data and historical clinical decision data of all or part of historical visitors of a medical service institution; the model building module 82 is used for training with the historical clinical data and the historical clinical decision data as input data to build a clinical decision support model for providing clinical decisions for the current visit staff.
It should be noted that the implementation of the clinical decision support model training apparatus of this embodiment is similar to that of the clinical decision support model training method in the above embodiments, and therefore, the detailed description is omitted.
It should be understood that the division of each module of the devices in the seventh to eighth embodiments is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the clinical data acquisition module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the clinical data acquisition module. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Example nine
Fig. 9 is a schematic structural diagram of a clinical decision support terminal according to an embodiment of the present application. The clinical decision support terminal provided by the present embodiment includes a processor 901, a memory 902, and a communicator 903; the memory 902 is connected to the processor 901 and the communicator 903 via a system bus and is used for communication with each other, the memory 902 is used for storing computer programs, the communicator 903 is used for communication with other devices, and the processor 901 is used for running the computer programs, so that the clinical decision support terminal executes the steps of the clinical decision support method.
Example ten
Fig. 10 is a schematic structural diagram of a clinical decision support model training terminal in an embodiment of the present application. The clinical decision support terminal provided in this embodiment includes a processor 1001, a memory 1002, and a communicator 1003; the memory 1002 is connected to the processor 1001 and the communicator 1003 via the system bus 1005 and performs communication with each other, the memory 1002 is used for storing computer programs, the communicator 1003 is used for communicating with other devices, and the processor 1001 is used for running the computer programs, so that the clinical decision support terminal performs the steps of the clinical decision support method as described above.
The above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
EXAMPLE eleven
The present embodiments also provide a computer storage medium having a first computer program and/or a second computer program stored thereon. The first computer program, when executed by a processor, implements the clinical decision support method; the second computer program, when executed by a processor, implements the clinical decision support model training method.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
In summary, the present application provides clinical decision support and a method, an apparatus, a terminal, and a medium for training a model thereof, and the technical scheme of the present invention can implement a technical scheme corresponding to a clinical decision support function according to the characteristics of each hospital, thereby greatly improving the support of clinical decision. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.
Claims (12)
1. A clinical decision support method, comprising:
acquiring current clinical data of a current medical staff at a pre-designated medical service institution;
inputting the obtained current clinical data into a trained clinical decision support model to output a corresponding current clinical decision for determining a final clinical decision applicable to the clinician according to the current clinical decision.
2. The method of claim 1, further comprising:
if the clinical decision support model outputs the corresponding current clinical decision, waiting to receive an external final clinical decision;
if the clinical decision support model does not output the corresponding current clinical decision, an instruction is sent to the outside to request for the external input of the final clinical decision before waiting for receiving the external final clinical decision; wherein the final clinical decision entered externally is used to train the clinical decision support model.
3. The method of claim 1, further comprising:
and under the condition that the cure rate of the pre-designated medical service institution for the disease of the current visit staff is judged to be lower than a preset threshold value according to the clinical decision support model, outputting the current clinical decision of transfer treatment.
4. The method of claim 1, further comprising:
and under the condition that the pre-appointed medical service institution has a specific diagnosis and treatment means with higher success rate aiming at the diseases of the current patient according to the clinical decision support model, outputting the current clinical decision adopting the specific diagnosis and treatment means.
5. The method of claim 1, further comprising:
acquiring current clinical data at a pre-designated medical service institution of a current medical staff, wherein the current clinical data at least comprises diseased symptom data;
the obtained current clinical data is input into a trained clinical decision support model to output a current clinical decision comprising one or more types of diseases with highest association with diseased symptoms.
6. The method of claim 1, comprising:
the trained clinical decision support model is trained based on a data set of historical clinical data and historical clinical decision data of all or a portion of the historical visits of the pre-assigned healthcare facility.
7. A clinical decision support model training method is characterized in that a trained clinical decision support model is used for inputting current clinical data and outputting a corresponding current clinical decision; the method comprises the following steps:
acquiring historical clinical data and historical clinical decision data of all or part of historical medical personnel of a medical service institution;
and training by taking the historical clinical data and the historical clinical decision data as input data to construct a clinical decision support model for providing clinical decisions for current doctors.
8. A clinical decision support apparatus, comprising:
the clinical data acquisition module is used for acquiring the current clinical data of the current medical staff at a pre-designated medical service institution;
a clinical decision support model module for inputting the acquired current clinical data into the trained clinical decision support model to output a corresponding current clinical decision for determining a final clinical decision applicable to the clinician according to the current clinical decision.
9. A clinical decision support model training apparatus, comprising:
the training data acquisition module is used for acquiring historical clinical data and historical clinical decision data of all or part of historical visitors of a medical service institution;
and the model construction module is used for training by taking the historical clinical data and the historical clinical decision data as input data so as to construct a clinical decision support model for providing clinical decisions for current clinic staff.
10. A computer-readable storage medium on which a first computer program and/or a second computer program is stored, characterized in that:
the first computer program, when executed by a processor, implements the clinical decision support method of any one of claims 1 to 6;
the second computer program, when executed by a processor, implements the clinical decision support model training method of claim 7.
11. A clinical decision support terminal, comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is adapted to execute the memory-stored computer program to cause the terminal to perform the clinical decision support method of any one of claims 1 to 6.
12. A clinical decision support model training terminal, comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the memory-stored computer program to cause the terminal to perform the clinical decision support model training method of claim 7.
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| CN201910865188.9A CN110660456A (en) | 2019-09-12 | 2019-09-12 | Clinical decision support and model training method, device, terminal and medium thereof |
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| CN111951946A (en) * | 2020-07-17 | 2020-11-17 | 合肥森亿智能科技有限公司 | Operation scheduling system, method, storage medium and terminal based on deep learning |
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