An evaluation prediction system

Abstract

The present invention discloses an evaluation and prediction system, comprising a clinical data acquisition module, an imaging data acquisition module, a motor symptom evaluation module, a non-motor symptom evaluation module, a symptom classification module, a parameter selection module and an exit module, which is suitable for treating Parkinson's syndrome. The prediction and evaluation system of the present invention acquires the patient's clinical data, imaging data, motor symptom evaluation results and non-motor symptom evaluation results, and through the system's built-in classification types and corresponding treatment parameters, can easily realize the classification evaluation and prediction of PDS symptoms and the selection of rTMS treatment parameters, greatly reducing the workload and difficulty required for symptom evaluation and treatment plan determination for such diseases.

Description

Technical Field

[0001] The invention belongs to the technical field of medical equipment, and in particular is an evaluation and prediction system. Background Art

[0002] Parkinson's syndrome (PDS) refers to a general term for diseases with Parkinson-like clinical manifestations, mainly including primary Parkinson's disease, multiple system atrophy, progressive supranuclear palsy, vascular Parkinson's syndrome, Lewy body dementia, etc. Common clinical manifestations include motor symptoms such as bradykinesia, muscle rigidity, tremor, abnormal posture and gait, as well as non-motor symptoms such as cognitive impairment, depression, and sleep disorders. The prevalence of Parkinson's disease in people over 65 years old is about 1.7%, and it is a common neurodegenerative disease in middle-aged and elderly people. PDS is a chronic progressive disease that seriously endangers people's health. A considerable number of patients have poor response to drugs, and the disease progresses very rapidly, seriously affecting the work and quality of life of patients.

[0003] Repeated Transcranial Magnetic Stimulation (rTMS) is a new cortical stimulation technology developed in recent years. It is non-invasive and fast-acting. Repeated stimulation of specific parts of the human skull is of great significance in the clinical treatment of PDS. It can regulate the excitability of the motor cortex and play an important role in improving motor function and non-motor symptoms. It is especially suitable for PDS patients who do not respond well to drugs and can serve as an effective supplement to drug treatment. rTMS treatment parameters include: stimulation site, stimulation intensity, stimulation frequency, daily treatment duration, number of treatments in a course of treatment, etc. Different rTMS parameters are required to improve different motor symptoms and non-motor symptoms of PDS patients at the same time.

[0004] However, currently, the symptom assessment and prediction of PDS patients and the selection of rTMS treatment parameters need to be done manually, and there is still a lack of an rTMS parameter assessment and prediction system that can simply achieve objective and quantitative results for PDS patients. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide an evaluation prediction system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] An evaluation and parameter prediction system for treating Parkinson's syndrome patients, comprising a clinical data acquisition module, an image data acquisition module, a motor symptom evaluation module, a non-motor symptom evaluation module, a symptom classification module, a parameter selection module and an exit module; wherein:

[0008] A clinical data acquisition module, connected to the image data acquisition module and the exit module, for acquiring the patient's clinical data and jumping to the image data acquisition module or the exit module according to the clinical data and a preset jump rule;

[0009] An image data acquisition module, connected to the clinical data acquisition module, the motor symptom assessment module and the exit module, for acquiring the patient's image data and jumping to the motor symptom assessment module and the exit module according to the image data and a preset jump rule;

[0010] A motor symptom assessment module, connected to the image data acquisition module and the non-motor symptom assessment module, for acquiring the motor symptom information of the patient, evaluating the acquired motor symptom information, obtaining an evaluation result, and jumping to the non-motor symptom assessment module according to a preset jump rule;

[0011] A non-motor symptom assessment module, connected to the motor symptom assessment module, is used to obtain the patient's non-motor symptom information, evaluate the acquired non-motor symptom information, obtain an evaluation result, and jump to the symptom classification module according to a preset jump rule;

[0012] A symptom classification module, connected to the non-motor symptom assessment module, for determining the symptom classification of the patient according to the assessment results of the motor symptom information and the non-motor symptom information;

[0013] A parameter selection module, connected to the symptom classification module, for obtaining a treatment parameter selection instruction for a patient and determining treatment parameters according to the treatment parameter selection instruction;

[0014] The exit module is connected to the clinical data acquisition module and the image data acquisition module, and is used to obtain the exit reason information, and determine the diagnosis result and exit the system according to the exit reason information.

[0015] In a possible implementation, the clinical data acquisition module includes:

[0016] A clinical data acquisition unit, used to acquire gender information, age information, clinical movement disorder information, and clinical previous surgery information;

[0017] A clinical data jump unit is connected to the information acquisition unit. If the age information acquired by the information acquisition unit is outside the preset age range, the unit jumps to the exit module, otherwise it jumps to the image data module; if all the selected items of the acquired clinical movement disorder information are no, the unit jumps to the exit module, otherwise it jumps to the image data module; if any item of the acquired clinical previous surgery information is yes, the unit jumps to the exit module, otherwise it jumps to the image data module.

[0018] In a possible implementation, the image data acquisition module includes:

[0019] An image data acquisition unit, used for acquiring head CT image information and head MR image information;

[0020] The image data jump unit is connected to the image data acquisition unit. If any one of the head CT image information and the head MR image information acquired by the image data acquisition unit is selected as yes, it jumps to the exit module, otherwise it jumps to the motor symptom assessment module.

[0021] In a possible implementation, the motor symptom assessment module includes:

[0022] A motor symptom data acquisition unit, used to acquire ataxia information, freezing gait information, and comprehensive motor symptom information;

[0023] a motor symptom assessment unit connected to the motor symptom data acquisition unit, and configured to respectively determine an ataxia score, a freezing gait score, a tremor score, and a posture and gait abnormality score according to the ataxia information, the freezing gait information, the tremor information, and the posture and gait information;

[0024] The motor symptom jump unit is connected to the motor symptom assessment unit and the non-motor symptom assessment module. If the ataxia score is greater than the preset score, it jumps to the non-motor symptom assessment module; if the freezing gait score is greater than the preset score, it jumps to the non-motor symptom assessment module; after the motor symptom assessment unit completes the tremor score and the abnormal posture and gait score, it jumps to the non-motor symptom assessment module.

[0025] In a possible implementation, the non-motor symptom assessment module includes:

[0026] A non-motor symptom data acquisition unit, used to acquire cognitive impairment information, depression status information, and sleep disorder information;

[0027] a non-motor symptom assessment unit, connected to the non-motor symptom data acquisition unit, for determining a cognitive impairment score, a depressive state score, and a sleep disorder score, respectively, based on the cognitive impairment information, the depressive state information, and the sleep disorder information;

[0028] The non-motor symptom jump unit is connected to the non-motor symptom assessment unit and the symptom classification module. If the cognitive impairment score is less than the preset score, it jumps to the symptom classification module; if the depression state score is greater than the preset score, it jumps to the symptom classification module; after the non-motor symptom assessment unit completes the sleep disorder score, it jumps to the symptom classification module.

[0029] In a possible implementation, the symptom classification module includes:

[0030] A classification unit, for determining a corresponding symptom classification code according to the ataxia score, freezing gait score, tremor score and abnormal posture and gait score determined by the motor symptom assessment unit, the cognitive impairment score, depression state score and sleep disorder score determined by the non-motor symptom assessment unit, and a preset symptom classification rule;

[0031] A jump unit is connected to the typing unit and the parameter selection module, and is used to jump to the parameter selection module after the typing unit determines the symptom typing code.

[0032] In a possible implementation, the parameter selection module includes:

[0033] An instruction receiving unit, used for receiving a patient's instruction for selecting treatment parameters;

[0034] A parameter determination unit is connected to the instruction receiving unit and is used to determine a treatment parameter scheme according to the treatment parameters received by the instruction receiving unit.

[0035] In a possible implementation, the exit module includes:

[0036] An exit reason acquisition unit, connected to the clinical data acquisition module and the image data acquisition module, for acquiring exit reason information when the clinical data acquisition module and the image data acquisition module jump to the exit module;

[0037] An exit unit is connected to the exit reason acquisition unit and is used to determine the diagnosis result and exit the system according to the exit reason information.

[0038] The advantages of the present invention are:

[0039] The predictive evaluation system of the present invention acquires the patient's clinical data, imaging data, motor symptom evaluation results and non-motor symptom evaluation results, and through the system's built-in classification types and corresponding treatment parameters, can easily realize the classification evaluation prediction of PDS symptoms and the selection of rTMS treatment parameters, greatly reducing the workload and difficulty required for symptom evaluation and treatment plan determination for this type of disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0041] The following is combined with Figure 1, further illustrating a specific implementation of the evaluation and parameter prediction system for treating Parkinson's syndrome patients of the present invention. The evaluation and parameter prediction system for treating Parkinson's syndrome patients of the present invention is not limited to the description of the following embodiments.

[0042] Embodiment 1:

[0043] This embodiment provides a specific structure of an evaluation and parameter prediction system for treating Parkinson's syndrome patients, such as Figure 1 As shown, it includes a clinical data acquisition module, an imaging data acquisition module, a motor symptom assessment module, a non-motor symptom assessment module, a symptom classification module, a parameter selection module and an exit module; wherein,

[0044] A clinical data acquisition module, connected to the imaging data acquisition module and the exit module, is used to acquire the patient's clinical data and jump to the imaging data acquisition module or the exit module according to the clinical data and a preset jump rule;

[0045] An image data acquisition module, connected to the clinical data acquisition module, the motor symptom assessment module and the exit module, is used to acquire the patient's image data and jump to the motor symptom assessment module and the exit module according to the image data and a preset jump rule;

[0046] The motor symptom assessment module is connected to the image data acquisition module and the non-motor symptom assessment module, and is used to obtain the patient's motor symptom information, evaluate the acquired motor symptom information, obtain the evaluation result, and jump to the non-motor symptom assessment module according to a preset jump rule;

[0047] The non-motor symptom assessment module is connected to the motor symptom assessment module, and is used to obtain the patient's non-motor symptom information, evaluate the obtained non-motor symptom information, obtain the evaluation result, and jump to the symptom classification module according to the preset jump rule;

[0048] A symptom classification module, connected to the non-motor symptom assessment module, is used to determine the patient's symptom classification based on the assessment results of the motor symptom information and the non-motor symptom information;

[0049] A parameter selection module, connected to the symptom classification module, is used to obtain a treatment parameter selection instruction of the patient and determine the treatment parameters according to the treatment parameter selection instruction;

[0050] The exit module is connected to the clinical data acquisition module and the image data acquisition module, and is used to obtain the exit reason information, and determine the diagnosis result and exit the system according to the exit reason information.

[0051] The clinical data acquisition module includes:

[0052] A clinical data acquisition unit, used to acquire gender information, age information, clinical movement disorder information, and clinical previous surgery information;

[0053] The clinical data jump unit is connected to the information acquisition unit. If the age information acquired by the information acquisition unit is outside the range of 18-80 years old, it will jump to the exit module, otherwise it will jump to the image data module; if all the clinical movement disorder information acquired is selected as no, it will jump to the exit module, otherwise it will jump to the image data module, wherein the acquired clinical movement disorder information includes: whether there is bradykinesia (slow movement, manifested as a decrease in the amplitude or speed of movement during continuous movement), muscle rigidity (when in a relaxed position, the main joints of the limbs and neck will feel uniform resistance during passive movement), tremor (unconscious, rhythmic oscillatory movement produced by alternating or synchronous contraction of agonist and antagonist muscles, clinically manifested as uncontrolled rhythmic shaking or trembling of the head / mandible / limbs), abnormal posture and gait (Gait disorders include panic gait, which is manifested by flexion of the trunk, neck and limbs, moving forward in small steps faster and faster, with the center of gravity forward. Freezing gait is manifested when starting to walk, turning, passing through narrow passages or approaching the destination, and may also be temporarily unable to walk when walking in a straight line), ataxia (it is clumsy and uncoordinated movement caused by cerebellar, proprioception and vestibular dysfunction. If the limbs, trunk and pharyngeal muscles are involved, it may manifest as posture, gait and language disorders); if any of the options in the obtained clinical previous surgical information is yes, jump to the exit module, otherwise jump to the image data acquisition module. The obtained clinical previous surgical information includes: whether there is a history of cranial surgery, skull defect, cardiac pacemaker implantation, brain pacemaker implantation, catheter stent intervention surgery, cochlear implant, history of epilepsy, and history of increased intracranial pressure.

[0054] The specific interface for data collection and the jump rules can be shown in the following table:

[0055]

[0056]

[0057] Table 1

[0058] The image data acquisition module includes:

[0059] An image data acquisition unit, used for acquiring head CT image information and head MR image information;

[0060] The image data jump unit is connected to the image data acquisition unit. If any one of the head CT image information and the head MR image information acquired by the image data acquisition unit is selected as yes, it jumps to the exit module, otherwise it jumps to the motor symptom assessment module, wherein the head CT image information and the head MR image information include: whether there is an intracranial space-occupying lesion, the presence of a metallic foreign body in the skull, obvious hydrocephalus, obvious brain atrophy, and cerebral hemorrhage.

[0061] The specific interface for data collection and the jump rules can be shown in the following table:

[0062]

[0063] Table 2

[0064] The motor symptom assessment module includes:

[0065] A motor symptom data acquisition unit, used to acquire ataxia information, freezing gait information, and comprehensive motor symptom information;

[0066] a motor symptom assessment unit connected to the motor symptom data acquisition unit, and used to determine an ataxia score, a freezing gait score, a tremor score, and a posture and gait abnormality score, respectively, based on the ataxia information, the freezing gait information, the tremor information, and the posture and gait information;

[0067] The motor symptom jump unit is connected to the motor symptom assessment unit and the non-motor symptom assessment module. If the ataxia score is greater than the preset score, it jumps to the non-motor symptom assessment module. The specific interface for implementing the motor symptom assessment and the jump rules can be shown in the following table:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] Table 3.

[0075] The non-motor symptom assessment module includes:

[0076] A non-motor symptom data acquisition unit, used to acquire cognitive impairment information, depression status information, and sleep disorder information;

[0077] a non-motor symptom assessment unit, connected to the non-motor symptom data acquisition unit, for determining a cognitive impairment score, a depressive state score, and a sleep disorder score, respectively, based on the cognitive impairment information, the depressive state information, and the sleep disorder information;

[0078] The non-motor symptom jump unit is connected to the non-motor symptom assessment unit and the symptom classification module. If the cognitive impairment score is less than the preset score, it jumps to the symptom classification module. The specific interface for implementing the non-motor symptom assessment and the jump rules can be shown in the following table:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Table 4

[0089] The symptom classification module includes:

[0090] The classification unit is used to determine the corresponding symptom classification code according to the ataxia score, freezing gait score, tremor score and abnormal posture and gait score determined by the motor symptom assessment unit, the cognitive impairment score, depression state score and sleep disorder score determined by the non-motor symptom assessment unit, and the preset symptom classification rules. The specific classification rules can be as follows:

[0091]

[0092] Table 5

[0093] In the classification unit, the weight levels of motor symptom assessment for classification are: 1. ataxia type, 2. freezing gait type, 3. tremor type or intermediate type or abnormal posture and gait type. For example, patients who meet the ataxia score standard are classified as ataxia type, and no further assessment is made for the presence of freezing gait or tremor. Patients who do not meet the ataxia score standard are further assessed for the presence of freezing gait. If the patient meets the freezing gait score standard, he or she is classified as freezing gait type, and no further assessment is made for the tremor type or intermediate type. Patients who do not meet the freezing gait score standard are further assessed for the tremor type or intermediate type or abnormal posture and gait type.

[0094] The weight levels for classification of non-motor symptom assessment are: 1 cognitive impairment type, 2 depression type, 3 sleep disorder type. For example, patients who meet the cognitive impairment type score criteria are classified as cognitive impairment type, and no further assessment is required for depression or sleep disorder. Patients who do not meet the cognitive impairment type score criteria are further assessed for depression. If the patient meets the depression score criteria, they are classified as depression type, and no further assessment is required for sleep disorder. Patients who do not meet the depression score criteria are further assessed for sleep disorder type.

[0095] Each PDS patient has one motor symptom classification and at most one non-motor symptom classification;

[0096] The jump unit is connected to the typing unit and the parameter selection module, and is used to jump to the parameter selection module after the typing unit determines the symptom typing code.

[0097] The parameter selection module includes: an instruction receiving unit, which is used to receive the patient's treatment parameter selection instruction; a parameter determination unit, which is connected to the instruction receiving unit and is used to determine the treatment parameters received by the instruction receiving unit. The interface and specific treatment parameters displayed by the module can be shown in the following table:

[0098]

[0099]

[0100] Table 6.

[0101] In the parameter selection module, select the morning time for rTMS treatment of motor symptoms. The afternoon time for rTMS treatment of non-motor symptoms. For example, if the PDS is ataxia, the classification code is a, and the number of the selected stimulation parameter is A1. If the PDS is ataxia and also a depressive patient, the classification code is a and g, and the selected stimulation parameter is A1 in the morning and B2 in the afternoon.

[0102] The exit module includes:

[0103] The exit reason acquisition unit is connected to the clinical data acquisition module and the imaging data acquisition module, and is used to obtain the exit reason information when the clinical data acquisition module and the imaging data acquisition module jump to the exit module. The exit reason information includes age <18 years old or age >80 years old, no typical clinical manifestations of PDS (bradykinesia, muscle rigidity, tremor, abnormal posture and gait, ataxia), past medical history of rTMS contraindications (history of head surgery, skull defect, cardiac pacemaker implantation, brain pacemaker implantation, catheter stent intervention surgery, cochlear implant, history of epilepsy, history of increased intracranial pressure), imaging examinations indicate the presence of rTMS contraindications (intracranial space-occupying lesions, metal foreign bodies in the skull, obvious hydrocephalus, obvious brain atrophy, cerebral hemorrhage);

[0104] The exit unit is connected to the exit reason acquisition unit and is used to determine the diagnosis result and exit the system according to the exit reason information.

[0105] The display interface of the exit unit and the specific reason information can be shown in the following table:

[0106]

[0107] Table 7

[0108] Through the mutual compatibility and cooperation of the clinical data acquisition module, the imaging data acquisition module, the motor symptom assessment module, the non-motor symptom assessment module, the PDS symptom classification module and the rTMS parameter selection module, firstly, by collecting clinical data and imaging data, it is determined whether the Parkinson's syndrome patient can enter the PDS-rTMS evaluation and parameter prediction system. After the patient enters the PDS-rTMS evaluation and parameter prediction system, the Parkinson's syndrome patient is symptom-typed through the motor symptom and non-motor symptom evaluation system. Finally, different stimulation parameters are selected for rTMS treatment according to the symptom classification of the Parkinson's syndrome patient. In this way, the severity of the patient's symptoms can be quickly determined, thereby facilitating the customization of the best treatment plan.

[0109] The PDS-rTMS parameter prediction and evaluation system must first collect the patient's clinical data and imaging data to screen PDS patients to see if they are suitable for entering the evaluation system. PDS patients are evaluated for motor symptoms and non-motor symptoms. According to the scores and score ratios of different evaluation items, the patient's clinical motor symptoms are classified, such as ataxia type, freezing gait type, tremor type, intermediate type, and abnormal posture and gait type. At the same time, patients with non-motor symptoms are classified for non-motor symptoms, such as cognitive impairment type, depression type, and sleep disorder type. This application selects different parameters for individualized treatment of PDS patients with different types to maximize the clinical benefits of patients. The PDS-rTMS parameter prediction and evaluation system provides an effective, mature, and operational rTMS evaluation and parameter selection process for PDS patients.

[0110] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An evaluation prediction system, characterized in that: include: Clinical data acquisition module, imaging data acquisition module, motor symptom assessment module, non-motor symptom assessment module, symptom classification module, parameter selection module and exit module; among them, A clinical data acquisition module, connected to the image data acquisition module and the exit module, for acquiring the patient's clinical data and jumping to the image data acquisition module or the exit module according to the clinical data and a preset jump rule; An image data acquisition module, connected to the clinical data acquisition module, the motor symptom assessment module and the exit module, for acquiring the patient's image data and jumping to the motor symptom assessment module and the exit module according to the image data and a preset jump rule; A motor symptom assessment module, connected to the image data acquisition module and the non-motor symptom assessment module, for acquiring the motor symptom information of the patient, evaluating the acquired motor symptom information, obtaining an evaluation result, and jumping to the non-motor symptom assessment module according to a preset jump rule; A non-motor symptom assessment module, connected to the motor symptom assessment module, is used to obtain the patient's non-motor symptom information, evaluate the acquired non-motor symptom information, obtain an evaluation result, and jump to the symptom classification module according to a preset jump rule; A symptom classification module, connected to the non-motor symptom assessment module, for determining the symptom classification of the patient according to the assessment results of the motor symptom information and the non-motor symptom information; A parameter selection module, connected to the symptom classification module, for obtaining a treatment parameter selection instruction for a patient and determining treatment parameters according to the treatment parameter selection instruction; The exit module is connected to the clinical data acquisition module and the image data acquisition module, and is used to obtain the exit reason information, and determine the diagnosis result and exit the system according to the exit reason information.

2. The evaluation prediction system according to claim 1, characterized in that: The clinical data acquisition module comprises: A clinical data acquisition unit, used to acquire gender information, age information, clinical movement disorder information, and clinical previous surgery information; A clinical data jump unit is connected to the information acquisition unit. If the age information acquired by the information acquisition unit is outside the preset age range, the unit jumps to the exit module, otherwise it jumps to the image data module; if all the selected items of the acquired clinical movement disorder information are no, the unit jumps to the exit module, otherwise it jumps to the image data module; if any item of the acquired clinical previous surgery information is yes, the unit jumps to the exit module, otherwise it jumps to the image data module.

3. The evaluation prediction system according to claim 1, characterized in that: The image data acquisition module comprises: An image data acquisition unit, used for acquiring head CT image information and head MR image information; The image data jump unit is connected to the image data acquisition unit. If any one of the head CT image information and the head MR image information acquired by the image data acquisition unit is selected as yes, it jumps to the exit module, otherwise it jumps to the motor symptom assessment module.

4. The evaluation prediction system according to claim 1, characterized in that: The motor symptom assessment module includes: A motor symptom data acquisition unit, used to acquire ataxia information, freezing gait information, and comprehensive motor symptom information; a motor symptom assessment unit connected to the motor symptom data acquisition unit, and configured to respectively determine an ataxia score, a freezing gait score, a tremor score, and a posture and gait abnormality score according to the ataxia information, the freezing gait information, the tremor information, and the posture and gait information; The motor symptom jump unit is connected to the motor symptom assessment unit and the non-motor symptom assessment module. If the ataxia score is greater than the preset score, it jumps to the non-motor symptom assessment module; if the freezing gait score is greater than the preset score, it jumps to the non-motor symptom assessment module; after the motor symptom assessment unit completes the tremor score and the abnormal posture and gait score, it jumps to the non-motor symptom assessment module.

5. The evaluation prediction system according to claim 1, characterized in that: The non-motor symptom assessment module includes: A non-motor symptom data acquisition unit, used to acquire cognitive impairment information, depression status information, and sleep disorder information; a non-motor symptom assessment unit, connected to the non-motor symptom data acquisition unit, for determining a cognitive impairment score, a depressive state score, and a sleep disorder score, respectively, based on the cognitive impairment information, the depressive state information, and the sleep disorder information; The non-motor symptom jump unit is connected to the non-motor symptom assessment unit and the symptom classification module. If the cognitive impairment score is less than the preset score, it jumps to the symptom classification module; if the depression state score is greater than the preset score, it jumps to the symptom classification module; after the non-motor symptom assessment unit completes the sleep disorder score, it jumps to the symptom classification module.

6. The evaluation prediction system according to claim 4 or 5, characterized in that: The symptom classification module includes: A classification unit, for determining a corresponding symptom classification code according to the ataxia score, freezing gait score, tremor score and abnormal posture and gait score determined by the motor symptom assessment unit, the cognitive impairment score, depression state score and sleep disorder score determined by the non-motor symptom assessment unit, and a preset symptom classification rule; A jump unit is connected to the typing unit and the parameter selection module, and is used to jump to the parameter selection module after the typing unit determines the symptom typing code.

7. The evaluation prediction system according to claim 1, characterized in that: The parameter selection module comprises: An instruction receiving unit, used for receiving a patient's instruction for selecting treatment parameters; A parameter determination unit is connected to the instruction receiving unit and is used to determine a treatment parameter scheme according to the treatment parameters received by the instruction receiving unit.

8. The evaluation prediction system according to claim 1, characterized in that: The exit module includes: An exit reason acquisition unit, connected to the clinical data acquisition module and the image data acquisition module, for acquiring exit reason information when the clinical data acquisition module and the image data acquisition module jump to the exit module; An exit unit is connected to the exit reason acquisition unit and is used to determine the diagnosis result and exit the system according to the exit reason information.

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