A method for constructing a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision-making support system

Abstract

The present invention relates to the field of medical data processing technology, and discloses a method for constructing a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision-making support system. Based on multiple dimensions of data, including clinical information, motor function scores, mood scores, and cognitive function scores of a target group before and after STN-DBS treatment, a high-precision Parkinson's disease efficacy prediction model is constructed using a machine learning elastic network. This model enables accurate preoperative screening of Parkinson's disease patients for subthalamic nucleus deep brain stimulation (DBS), effectively improving the accuracy and efficiency of individualized surgical decision-making for Parkinson's disease patients with different clinical symptoms.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to a method for constructing a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision-making support system. Background Art

[0002] Subthalamic nucleus deep brain stimulation (STN-DBS) is an emerging treatment for Parkinson's disease (PD). Several randomized controlled trials conducted since the 1990s have shown that STN-DBS offers benefits and advantages over pharmacological interventions, with efficacy typically measured using the Movement Disorder Society-sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Improving the efficacy of STN-DBS requires optimal selection of surgical candidates, accurate target localization, adequate stimulation protocols, and appropriate medication management. However, even for experienced movement disorder specialists, making individualized surgical decisions for patients with varying clinical symptoms remains challenging, and tools are needed to predict potential surgical outcomes based on preoperative evaluation.

[0003] Therefore, there is an urgent need for a method to construct a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision-making support system. Summary of the Invention

[0004] The present invention provides a method for constructing a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision-making support system, which are used to address the defect of lacking auxiliary tools for predicting potential surgical outcomes based on preoperative evaluation.

[0005] The present invention provides a method for constructing a Parkinson's disease efficacy prediction model, comprising:

[0006] Obtain clinical information, motor function scores, mood scores, and cognitive function scores of the target group before and after STN-DBS treatment, including Parkinson's disease patients;

[0007] Based on the clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment, factor regression analysis was used to obtain characteristic variables related to the efficacy of STN-DBS.

[0008] Based on the characteristic variables related to the efficacy of STN-DBS, a Parkinson's disease efficacy prediction model was constructed using the elastic network.

[0009] In one embodiment, the clinical information includes any one of the following or any combination thereof: disease duration, age at surgery, age at onset, levodopa equivalent dose (LED), and levodopa medication improvement rate.

[0010] In one embodiment, the motor function score comprises a motor function score obtained by the Unified Parkinson's Disease Rating Scale (UPDRS).

[0011] In one embodiment, the mood score includes a first mood score obtained by the Hamilton Anxiety Rating Scale (HAMA) and a second mood score obtained by the Hamilton Depression Rating Scale (HAMD).

[0012] In one embodiment, the cognitive function score includes a first cognitive function score obtained by the Mini-Mental State Examination (MMSE) and a second cognitive function score obtained by the Montreal Cognitive Assessment (MoCA).

[0013] In one embodiment, the factor regression analysis method includes univariate COX regression analysis and multivariate COX regression analysis. The factor regression analysis method is used to obtain characteristic variables related to the efficacy of STN-DBS based on the clinical information, motor function score, mood score, and cognitive function score of the target group before and after STN-DBS treatment, including:

[0014] Based on the clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment, the first potential risk factor related to the efficacy of STN-DBS was obtained using univariate COX regression analysis.

[0015] According to the first potential risk factor related to the efficacy of STN-DBS, the second potential risk factor related to the efficacy of STN-DBS was identified using multivariate COX regression analysis as a characteristic variable related to the efficacy of STN-DBS.

[0016] In one embodiment, the first potential risk factor associated with STN-DBS efficacy is obtained using univariate COX regression analysis based on the clinical information, motor function score, mood score, and cognitive function score of the target group, including:

[0017] According to the clinical information, motor function score, emotional score, and cognitive function score of the target group, the univariate COX regression analysis method was used, and the factor corresponding to the probability value P1<0.05 in the univariate COX regression analysis results was regarded as the first potential risk factor related to the efficacy of STN-DBS.

[0018] In one embodiment, the second potential risk factor associated with STN-DBS efficacy is determined using multivariate COX regression analysis based on the first potential risk factor associated with STN-DBS efficacy as a characteristic variable associated with STN-DBS efficacy, including:

[0019] According to the first potential risk factor related to the efficacy of STN-DBS, the multivariate COX regression analysis method was used. The first potential risk factor corresponding to the probability value P2<0.05 in the multivariate COX regression analysis results was used as the second potential risk factor related to the efficacy of STN-DBS, and at the same time as the characteristic variable related to the efficacy of STN-DBS.

[0020] In one embodiment, the method of constructing a Parkinson's disease efficacy prediction model based on characteristic variables related to STN-DBS efficacy comprises:

[0021] Based on the characteristic variables related to the efficacy of STN-DBS, a Parkinson's disease efficacy prediction model was constructed using the elastic network. The Parkinson's disease efficacy prediction model was presented in the form of a nomogram.

[0022] The present invention also provides a system for constructing a Parkinson's disease efficacy prediction model, comprising:

[0023] A data acquisition module is used to obtain clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment, where the target group includes Parkinson's disease patients;

[0024] The preprocessing module is used to obtain characteristic variables related to the efficacy of STN-DBS based on the clinical information, motor function scores, emotional scores, and cognitive function scores of the target group using factor regression analysis;

[0025] The construction module is used to: construct a Parkinson's disease efficacy prediction model through an elastic network based on characteristic variables related to STN-DBS efficacy.

[0026] The invention also provides an STN-DBS surgery decision support system, comprising:

[0027] A data receiving module is configured to receive clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment, sent from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery;

[0028] A prediction module is configured to: determine whether the subject is suitable for STN-DBS surgery based on the subject's clinical information, motor function score, mood score, and cognitive function score before STN-DBS treatment, and a Parkinson's disease efficacy prediction model obtained by any of the above-mentioned methods for constructing a Parkinson's disease efficacy prediction model;

[0029] The data output module is used to send the prediction result of whether the subject is suitable for STN-DBS surgery back to the at least one terminal.

[0030] The present invention also provides an electronic device comprising a processor and a memory storing a computer program, wherein the processor implements any of the above-mentioned methods for constructing a Parkinson's disease efficacy prediction model when executing the computer program.

[0031] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for constructing any of the above-mentioned Parkinson's disease efficacy prediction models is implemented.

[0032] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute any of the above-mentioned methods for constructing a Parkinson's efficacy prediction model.

[0033] The present invention provides a method for constructing a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision-making support system. Based on multiple dimensions of data, including clinical information, motor function scores, mood scores, and cognitive function scores of a target group before and after STN-DBS treatment, a high-precision Parkinson's disease efficacy prediction model is constructed through a machine learning elastic network. This model can accurately screen Parkinson's disease patients for subthalamic nucleus deep brain stimulation before and after surgery, effectively improving the accuracy and efficiency of making individualized surgical decisions for Parkinson's disease patients with different clinical symptoms. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 This is a flow chart of a method for constructing a Parkinson's disease efficacy prediction model provided by the present invention.

[0036] Figure 2 This is a schematic diagram of cross-validation using a training set and a validation set in the process of constructing a Parkinson's disease efficacy prediction model according to an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram of performing specificity and sensitivity tests using a training set and a validation set in the process of constructing a Parkinson's disease efficacy prediction model according to an embodiment of the present invention.

[0038] Figure 4This is a schematic diagram of verifying the effectiveness of the model using a calibration curve and an ROC curve through a training set in the process of constructing a Parkinson's disease efficacy prediction model according to an embodiment of the present invention.

[0039] Figure 5 This is a schematic diagram of verifying the effectiveness of the model using a calibration curve and an ROC curve through a validation set in the process of constructing a Parkinson's disease efficacy prediction model according to an embodiment of the present invention.

[0040] Figure 6 This is a schematic diagram showing a Parkinson's disease efficacy prediction model constructed according to an embodiment of the present invention.

[0041] Figure 7 This is a structural diagram of a system for constructing a Parkinson's disease efficacy prediction model provided by the present invention.

[0042] Figure 8 This is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0044] The following combination Figures 1-8 The present invention describes a method for constructing a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision support system. It should be noted that the method for constructing a Parkinson's disease efficacy prediction model provided by the present invention can be executed by any network-side device or terminal-side device that meets the technical requirements, such as a device for constructing a Parkinson's disease efficacy prediction model.

[0045] Figure 1 Schematic diagram of the method for constructing a Parkinson's disease efficacy prediction model provided by the present invention. Figure 1 The present invention provides a method for constructing a Parkinson's disease efficacy prediction model, which may include:

[0046] Step S110: Obtain clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment, wherein the target group includes a Parkinson's disease patient group. In this embodiment, clinical information, motor function scores, emotional scores, and cognitive function scores of the target group are obtained before STN-DBS treatment, and one year and five years after STN-DBS treatment, which helps the Parkinson's disease efficacy prediction model understand the relationship between clinical information, motor function scores, emotional scores, cognitive function scores, and the long-term and short-term efficacy of STN-DBS treatment.

[0047] This example used 496 PD patients who underwent bilateral DBS as the target population and collected their clinical information, motor function scores, mood scores, and cognitive function scores. Inclusion criteria included: (1) meeting the 2006 diagnostic criteria for primary PD by the Movement Disorders and PD Group of the Chinese Medical Association's Neurology Branch; (2) Parkinson's disease duration >4 years; (3) significant decline in drug efficacy, or symptom fluctuations such as "on-off" phenomena, dyskinesias, and impact on the patient's quality of life; (4) patients and their families signed informed consent. Exclusion criteria included: (1) patients with contraindications to DBS; (2) patients with moderate or severe cognitive impairment or persistent severe mental disorders; (3) patients with severe brain atrophy or diffuse cerebral ischemic lesions as shown by cranial MRI; and (4) patients who were unable to complete postoperative follow-up.

[0048] Specifically, clinical information includes any one of the following or any combination thereof: disease course, age at surgery, age at onset, levodopa equivalent dose (LED), and levodopa medication improvement rate, where levodopa medication improvement rate = (baseline score before medication - lowest score after medication) / baseline score before medication × 100%.

[0049] Motor function scores include those obtained using the Unified Parkinson's Disease Rating Scale (UPDRS). The Unified Parkinson's Disease Rating Scale is an internationally recognized Parkinson's disease symptom rating scale consisting of four subscales (UPDRS-I, II, III, and IV). Higher scores indicate more severe symptoms. UPDRS-I is a mental, behavioral, and emotional score that assesses the patient's mental state. It includes 1 to 4 items with a total score of 0 to 16, with higher scores indicating more severe symptoms. UPDRS-II is a score for activities of daily living, encompassing 13 items such as writing, dressing, personal hygiene, and turning over, with a total score of 0 to 52. Higher scores indicate poorer daily living abilities and a greater inability to care for oneself. UPDRS-III is a motor examination score, encompassing 14 items such as facial expression, tremor, rigidity, bradykinesia, postural disturbances, and gait examination, with a total score of 0 to 56. Higher scores indicate more severe somatic motor symptoms. UPDRS-IV is a motor complication score, assessing complications such as dyskinesia and symptom fluctuations, with a total score of 0 to 23. A score of >1 on item 32 indicates dyskinesia in patients with Parkinson's disease; a score of 1 on item 36 indicates on-off switching. The UPDRS uses a 0-4 scale for a total score of 199. Scores for each component are weighted to create a total score, with higher scores indicating more severe disease.

[0050] The mood score includes the first mood score obtained by the Hamilton Anxiety Rating Scale (HAMA) and the second mood score obtained by the Hamilton Depression Rating Scale (HAMD).

[0051] The primary emotion score includes scores for 14 domains: anxious mood, tension, fear, insomnia, cognitive function (also known as memory and attention impairment), depressed mood, somatic anxiety, cardiovascular symptoms, respiratory symptoms, gastrointestinal symptoms, genitourinary symptoms, autonomic nervous system symptoms, and behavioral performance during the interview. All items in the HAMA are scored on a 5-point scale ranging from 0 to 4, with the following levels being: 0 (no symptoms); 1 (mild); 2 (moderate); 3 (severe); and 4 (extremely severe). The total HAMA score accurately reflects the severity of anxiety symptoms. It can be used to assess the severity of anxiety symptoms in patients with anxiety and depressive disorders and to evaluate the effectiveness of various medications and psychological interventions. According to data provided by the Chinese Scale Collaborative Group: a total score ≥29 indicates possible severe anxiety; ≥21 indicates significant anxiety; ≥14 indicates definite anxiety; ≥7 indicates possible anxiety; and ≤7 indicates no anxiety symptoms.

[0052] The secondary mood score includes 24 items, including depression, guilt, suicidal thoughts, difficulty sleeping, somatic anxiety, general symptoms, and insight. Each item is scored from 0 to 2 or 0 to 4 to indicate severity. The sum of the scores is the HAMD score. A total score of 35 or more indicates severe depressive symptoms; a total score of 30 or more and less indicates moderate depressive symptoms; a total score of 20 or more and less indicates mild or moderate depressive symptoms; a total score of 8 or more and less than 20 indicates no significant depressive symptoms; and a total score of less than 8 indicates no depressive symptoms.

[0053] The cognitive function scores included the first cognitive function score obtained by the Mini-Mental State Examination (MMSE) and the second cognitive function score obtained by the Montreal Cognitive Assessment (MoCA).

[0054] The first cognitive function score includes scores for five domains: orientation, attention, memory, language ability, and executive function.

[0055] MMSE scale criteria: (1) Cognitive dysfunction: The maximum score is 30 points, a score of 27-30 points is normal, and a score of <27 points is cognitive dysfunction; (2) Dementia classification criteria: illiteracy ≤ 17 points, primary school level ≤ 20 points, secondary school level (including technical secondary school) ≤ 22 points, university level (including junior college) ≤ 23 points; (3) Dementia severity grading: mild MMSE ≥ 21 points, moderate MMSE 10-20 points, severe MMSE ≤ 9 points. The MMSE scale examines the patient's cognitive level by evaluating the following five aspects: (1) Orientation: Orientation refers to a person's ability to recognize time, place, people, and their own status. In the scale examination, this item is reflected in questions such as "date, season, province, city". (2) Memory: Memory is the ability to remember, retain, re-recognize, and reproduce the content and experience reflected by objective things. In the memory examination items, the doctor usually tells the subject that several questions will be asked to check the memory. The doctor says the names of three unrelated things and then asks the subject to repeat them. (3) Attention and calculation ability: Attention refers to the ability of a person's mental activities to be directed and focused on something. In this part of the examination, the doctor will ask the subject to subtract 7 from 100, then subtract 7 again, and so on for 5 times. (4) Recall ability: In the examination of recall ability, the doctor will ask the subject to repeat the names of three unrelated things that the doctor had previously said to test the subject's recall ability. (5) Language ability: Language ability refers to the ability to master language. This ability is manifested in the ability to speak or understand unprecedented, grammatical sentences, the ability to distinguish ambiguous sentences, the ability to distinguish sentences with the same surface form but different actual meanings or sentences with different surface forms but similar actual meanings, and the ability to use language skills such as listening, speaking, reading, writing and translation. In the MMSE scale, language ability is evaluated in the following six aspects: (1) Naming ability: the doctor will take out some cards and objects for the subject to see, and ask the subject to say the name of the object shown on the card; (2) Retelling ability: the doctor will say a sentence and ask the subject to pay attention to the sentence and repeat it; (3) Three-step command: the doctor will ask the subject to complete a series of actions according to the doctor's order; (4) Reading ability: the doctor will provide a card and ask the subject to read the text on the card and complete the action according to the text requirements; (5) Writing ability: the doctor will ask the subject to write a complete sentence spontaneously; (6) Structural ability: the doctor will provide a graphic and ask the subject to copy it.

[0056] The second cognitive function score includes scores for eight subdomains: visual-spatial and executive ability, naming ability, attention, calculation ability, language ability, abstract thinking ability, delayed memory, and orientation.

[0057] The MoCA scale has a maximum score of 30, with scores of 26 or higher considered normal, 18-26 mild cognitive impairment (MCI), 10-17 moderate, and less than 10 severe. MCI patients score approximately 22 (19-25), and Alzheimer's patients score between 11 and 21. If the subject has ≤12 years of education (high school level), 1 point can be added to the score, but the total score cannot exceed 30. If the patient is illiterate or has a low level of education, the basic test can be used.

[0058] Step S120: Based on the clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment, characteristic variables related to the efficacy of STN-DBS are obtained using factor regression analysis, where the factor regression analysis method includes univariate COX regression analysis and multivariate COX regression analysis.

[0059] In one embodiment, step S120 may include:

[0060] Based on the clinical information, motor function scores, emotional scores, and cognitive function scores of the target group, univariate COX regression analysis was performed, and the factors corresponding to the probability value P1 < 0.05 in the univariate COX regression analysis results were regarded as the first potential risk factors related to the efficacy of STN-DBS;

[0061] According to the first potential risk factor related to the efficacy of STN-DBS, the multivariate COX regression analysis method was used. The first potential risk factor corresponding to the probability value P2<0.05 in the multivariate COX regression analysis results was used as the second potential risk factor related to the efficacy of STN-DBS, and at the same time as the characteristic variable related to the efficacy of STN-DBS.

[0062] After the univariate Cox regression analysis, the data results may be affected by multiple other factors. Therefore, the present embodiment further performs a multivariate Cox regression analysis to eliminate the influence of multiple factors on the surgical efficacy at the same time.

[0063] Table 1 shows the results of a univariate COX regression analysis of preoperative risk factors (i.e., clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment) in this embodiment, with disease duration, levodopa equivalent dose, motor function score, levodopa medication improvement rate, emotional score, and cognitive function score as the first potential risk factor. Table 2 shows the results of a multivariate COX regression analysis based on the results of the univariate COX regression analysis in this embodiment, with disease duration, motor function score, levodopa medication improvement rate, and emotional score as the second potential risk factor, as characteristic variables related to the efficacy of STN-DBS.

[0064] Table 1

[0065]

[0066] Table 2

[0067]

[0068]

[0069] Step S130: Based on the characteristic variables related to the STN-DBS efficacy, elastic network machine learning is used to construct a Parkinson's disease efficacy prediction model, wherein the Parkinson's disease efficacy prediction model is displayed in the form of a nomogram. The Parkinson's disease efficacy prediction model can be used to obtain the degree of influence of the characteristic variables related to the STN-DBS efficacy on the STN-DBS treatment efficacy.

[0070] In one embodiment, step S130 may include:

[0071] The ROC curve was used to evaluate the prediction accuracy of the Parkinson's disease efficacy prediction model;

[0072] The calibration curve and multiple sampling comparisons were used to evaluate the prediction results of the Parkinson's disease efficacy prediction model and the probability of the actual surgical improvement rate to verify the accuracy and consistency index of the Parkinson's disease efficacy prediction model as conditions to determine whether the predicted effect of the Parkinson's disease efficacy prediction model on the improvement rate is consistent with the actual improvement rate.

[0073] In this example, the disease duration, motor function score, levodopa medication improvement rate, and mood score of the target population are used as characteristic variables related to the efficacy of STN-DBS. A training set and a validation set are divided, and a Parkinson's disease efficacy prediction model is constructed using the elastic network in machine learning. The elastic network has high scalability and elasticity, as well as a flexible network topology, which can ensure the prediction accuracy, high performance and stability, and rapid deployment capability of the Parkinson's disease efficacy prediction model. During the model construction process, cross-validation, specificity and sensitivity testing are performed on the training set and validation set, and the effectiveness of the model is verified using calibration curves and ROC curves (see Figure 2-5 ), in order to improve the prediction accuracy of the Parkinson's efficacy prediction model, and in this embodiment, the constructed Parkinson's efficacy prediction model is finally displayed in the form of a nomogram, such as Figure 6 shown.

[0074] The present invention provides a method for constructing a Parkinson's disease efficacy prediction model and an STN-DBS surgery decision-making support system. Based on clinical information, motor function scores, emotional scores, cognitive function scores and other data from a target group before and after STN-DBS treatment, combined with factor regression analysis, a high-precision Parkinson's disease efficacy prediction model is constructed. This model can achieve accurate preoperative screening of Parkinson's disease patients for deep brain stimulation of the subthalamic nucleus, effectively improving the accuracy and efficiency of making individualized surgical decisions for Parkinson's disease patients with different clinical symptoms.

[0075] The Parkinson's efficacy prediction model obtained by the method for constructing a Parkinson's efficacy prediction model provided by the present invention can be used to assist in STN-DBS surgical decision-making. The clinical information, motor function score, emotional score, cognitive function score, etc. of the subject before STN-DBS treatment are input into the Parkinson's efficacy prediction model. According to the degree of influence of characteristic variables related to STN-DBS efficacy obtained by the Parkinson's efficacy prediction model on the STN-DBS treatment efficacy, the degree of surgical improvement that the subject may obtain after STN-DBS treatment can be calculated based on the clinical information, motor function score, emotional score, and cognitive function score of the subject before STN-DBS treatment, thereby assisting in deciding whether the subject needs to undergo STN-DBS treatment.

[0076] The following describes a system for constructing a Parkinson's disease efficacy prediction model provided by the present invention. The system for constructing a Parkinson's disease efficacy prediction model described below and the method for constructing a Parkinson's disease efficacy prediction model described above can refer to each other.

[0077] The present invention provides a system for constructing a Parkinson's disease efficacy prediction model, which may include:

[0078] A data acquisition module is used to obtain clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment, where the target group includes Parkinson's disease patients;

[0079] The preprocessing module is used to obtain characteristic variables related to the efficacy of STN-DBS based on the clinical information, motor function scores, emotional scores, and cognitive function scores of the target group using factor regression analysis;

[0080] The construction module is used to: construct a Parkinson's disease efficacy prediction model through an elastic network based on characteristic variables related to STN-DBS efficacy.

[0081] See also Figure 7 The present invention also provides an STN-DBS surgery decision support system, which may include:

[0082] A data receiving module is configured to receive clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment, sent from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery;

[0083] A prediction module is configured to: determine whether the subject is suitable for STN-DBS surgery based on the subject's clinical information, motor function score, mood score, and cognitive function score before STN-DBS treatment, and a Parkinson's disease efficacy prediction model obtained by any of the above-mentioned methods for constructing a Parkinson's disease efficacy prediction model;

[0084] The data output module is used to send the prediction result of whether the subject is suitable for STN-DBS surgery back to the at least one terminal.

[0085] Figure 8 An example of a physical structure diagram of an electronic device is shown below. Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to perform the following steps:

[0086] receiving clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery;

[0087] Based on the clinical information, motor function score, mood score, and cognitive function score of the subject before STN-DBS treatment, a Parkinson's disease efficacy prediction model obtained by any of the above-mentioned methods for constructing a Parkinson's disease efficacy prediction model is used to predict whether the subject is suitable for STN-DBS surgery;

[0088] The data output module is used to send the prediction result of whether the subject is suitable for STN-DBS surgery back to the at least one terminal.

[0089] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0090] In another aspect, the present invention further provides a computer program product, comprising a computer program, which may be stored on a non-transitory computer-readable storage medium, and wherein when the computer program is executed by a processor, the computer is capable of performing the following steps:

[0091] receiving clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery;

[0092] Based on the clinical information, motor function score, mood score, and cognitive function score of the subject before STN-DBS treatment, a Parkinson's disease efficacy prediction model obtained by any of the above-mentioned methods for constructing a Parkinson's disease efficacy prediction model is used to predict whether the subject is suitable for STN-DBS surgery;

[0093] The data output module is used to send the prediction result of whether the subject is suitable for STN-DBS surgery back to the at least one terminal.

[0094] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is configured to execute the following steps when executed by a processor:

[0095] receiving clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery;

[0096] Based on the clinical information, motor function score, mood score, and cognitive function score of the subject before STN-DBS treatment, a Parkinson's disease efficacy prediction model obtained by any of the above-mentioned methods for constructing a Parkinson's disease efficacy prediction model is used to predict whether the subject is suitable for STN-DBS surgery;

[0097] The data output module is used to send the prediction result of whether the subject is suitable for STN-DBS surgery back to the at least one terminal.

[0098] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0099] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for constructing a Parkinson's disease efficacy prediction model, characterized in that: include: Obtain clinical information, motor function scores, mood scores, and cognitive function scores of the target group before and after STN-DBS treatment, wherein the target group includes a Parkinson's disease patient group, and the clinical information includes any one of the following or any combination thereof: disease course, age at surgery, age at onset, levodopa equivalent dose, and levodopa medication improvement rate; motor function scores include motor function scores obtained using the Unified Parkinson's Disease Rating Scale; mood scores include a first mood score obtained using the Hamilton Anxiety Rating Scale and a second mood score obtained using the Hamilton Depression Rating Scale; cognitive function scores include a first cognitive function score obtained using the Mini-Mental State Examination and a second cognitive function score obtained using the Montreal Cognitive Assessment; Based on the clinical information, motor function scores, mood scores, and cognitive function scores of the target group before and after STN-DBS treatment, factor regression analysis was used to obtain characteristic variables related to the efficacy of STN-DBS. Among them, characteristic variables related to the efficacy of STN-DBS included: disease duration, motor function scores, levodopa medication improvement rate, and mood scores; Based on the characteristic variables related to the efficacy of STN-DBS, a Parkinson's disease efficacy prediction model was constructed through an elastic network. The Parkinson's disease efficacy prediction model was presented in the form of a nomogram.

2. The method for constructing a Parkinson's disease efficacy prediction model according to claim 1, wherein: The factor regression analysis method includes univariate COX regression analysis and multivariate COX regression analysis. The factor regression analysis method is used based on the clinical information, motor function score, mood score, and cognitive function score of the target group before and after STN-DBS treatment to obtain characteristic variables related to the efficacy of STN-DBS, including: Based on the clinical information, motor function scores, emotional scores, and cognitive function scores of the target group before and after STN-DBS treatment, univariate COX regression analysis was used to obtain the first potential risk factors related to the efficacy of STN-DBS. Among them, the first potential risk factors related to the efficacy of STN-DBS include: disease duration, levodopa equivalent dose, motor function score, levodopa medication improvement rate, emotional score, and cognitive function score; Based on the first potential risk factor related to the efficacy of STN-DBS, the multivariate COX regression analysis method was used to identify the second potential risk factors related to the efficacy of STN-DBS as characteristic variables related to the efficacy of STN-DBS. Among them, the second potential risk factors related to the efficacy of STN-DBS included: disease duration, motor function score, levodopa drug improvement rate, and mood score.

3. The method for constructing a Parkinson's disease efficacy prediction model according to claim 2, wherein: Based on the clinical information, motor function score, mood score, and cognitive function score of the target group, the first potential risk factors related to the efficacy of STN-DBS were obtained using the univariate COX regression analysis method, including: According to the clinical information, motor function score, emotional score, and cognitive function score of the target group, the probability value in the single factor COX regression analysis result was converted into The corresponding factors serve as the first potential risk factors related to the efficacy of STN-DBS.

4. The method for constructing a Parkinson's disease efficacy prediction model according to claim 3, wherein: Based on the first potential risk factor related to the efficacy of STN-DBS, the second potential risk factor related to the efficacy of STN-DBS was analyzed using the multivariate COX regression method as a characteristic variable related to the efficacy of STN-DBS, including: According to the first potential risk factor related to STN-DBS efficacy, the probability values ​​in the multivariate COX regression analysis results were analyzed. The corresponding first potential risk factor serves as the second potential risk factor related to the efficacy of STN-DBS, and also serves as a characteristic variable related to the efficacy of STN-DBS.

5. A system for constructing a Parkinson's disease efficacy prediction model, characterized in that: include: A data acquisition module is used to obtain clinical information, motor function scores, emotional scores, and cognitive function scores of a target group before and after STN-DBS treatment, wherein the target group includes a Parkinson's disease patient group, and the clinical information includes any one of the following or any combination thereof: disease course, age at surgery, age at onset, levodopa equivalent dose, and levodopa medication improvement rate; the motor function score includes a motor function score obtained by the Unified Parkinson's Disease Rating Scale; the emotional score includes a first emotional score obtained by the Hamilton Anxiety Rating Scale and a second emotional score obtained by the Hamilton Depression Rating Scale; and the cognitive function score includes a first cognitive function score obtained by the Mini-Mental State Examination and a second cognitive function score obtained by the Montreal Cognitive Assessment Scale. A preprocessing module is used to obtain characteristic variables related to the efficacy of STN-DBS based on the clinical information, motor function scores, mood scores, and cognitive function scores of the target group using factor regression analysis. The characteristic variables related to the efficacy of STN-DBS include disease duration, motor function scores, levodopa medication improvement rate, and mood scores. The construction module is used to: construct a Parkinson's disease efficacy prediction model based on characteristic variables related to STN-DBS efficacy, wherein the Parkinson's disease efficacy prediction model is displayed in the form of a nomogram.

6. A STN-DBS surgery decision support system, characterized in that: include: A data receiving module is configured to receive clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment, sent from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery; A prediction module, configured to: determine whether the subject is suitable for STN-DBS surgery based on the subject's clinical information, motor function score, mood score, and cognitive function score before STN-DBS treatment, and the Parkinson's disease efficacy prediction model obtained by the method for constructing a Parkinson's disease efficacy prediction model according to any one of claims 1 to 4; The data output module is used to send the prediction result of whether the subject is suitable for STN-DBS surgery back to the at least one terminal.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the following steps are implemented: receiving clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery; A prediction module, configured to: determine whether the subject is suitable for STN-DBS surgery based on the subject's clinical information, motor function score, mood score, and cognitive function score before STN-DBS treatment, and the Parkinson's disease efficacy prediction model obtained by the method for constructing a Parkinson's disease efficacy prediction model according to any one of claims 1 to 4; The prediction result of whether the subject is suitable for STN-DBS surgery is sent back to the at least one terminal.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the following steps are implemented: receiving clinical information, motor function scores, emotional scores, and cognitive function scores of a subject before STN-DBS treatment from at least one terminal, wherein the subject is a Parkinson's disease patient to be tested for suitability for STN-DBS surgery; A prediction module, configured to: determine whether the subject is suitable for STN-DBS surgery based on the subject's clinical information, motor function score, mood score, and cognitive function score before STN-DBS treatment, and the Parkinson's disease efficacy prediction model obtained by the method for constructing a Parkinson's disease efficacy prediction model according to any one of claims 1 to 4; The prediction result of whether the subject is suitable for STN-DBS surgery is sent back to the at least one terminal.

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