Personalized scheme recommendation method, system and equipment for aphasia and medium

Abstract

The invention provides a personalized scheme recommendation method, system and device for aphasia and a medium, and relates to the technical field of medical information processing, and the method comprises the steps: obtaining current hypothesis information; the current hypothesis information comprises a current evaluation node obtained based on a hypothesis check method; executing an evaluation process corresponding to the current evaluation node to the target patient to obtain an evaluation result; positioning a target damaged node of the target patient based on the evaluation result; the evaluation accuracy and efficiency are improved; according to all the target damaged nodes, a damaged language module is predicted, a personalized training scheme is constructed, and the training pertinence and the training effect are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical information processing technology, and in particular to a method, system, electronic device and readable storage medium for recommending a personalized solution for aphasia. Background Art

[0002] Aphasia is a syndrome of acquired language dysfunction caused by organic damage to the language center of the cerebral hemispheres and its related language network, resulting in damage to the brain. While the patient is conscious, he or she shows impaired or lost ability to receive (understand) and use (express) language symbols.

[0003] Traditional methods of rehabilitation training for aphasia often focus on functional recovery, but lack the precise location of the specific damaged links in the patient's language processing, resulting in poor rehabilitation effects and low efficiency.

[0004] Therefore, it is necessary to propose a personalized solution recommendation method, system, electronic device and readable storage medium for aphasia. Summary of the invention

[0005] This specification provides a personalized program recommendation method, system, electronic device and readable storage medium for aphasia. By acquiring the current hypothesis information and executing the corresponding evaluation process, the damaged language module of the target patient can be accurately located, and then a personalized training program can be constructed; it not only improves the accuracy and efficiency of aphasia evaluation, but also ensures the effectiveness and pertinence of the training program. In addition, by introducing a dialect dictionary library, the evaluation and training process is closer to the patient's actual language environment, which helps to improve the training effect.

[0006] The present application provides a personalized solution recommendation method for aphasia using the following technical solutions, including:

[0007] Acquire current assumption information; the current assumption information includes: a current evaluation node;

[0008] Executing the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result;

[0009] locating a target damaged node of the target patient based on the evaluation result;

[0010] According to all the target damaged nodes, the damaged language modules are predicted and a personalized training plan is constructed.

[0011] Optionally, the obtaining current hypothesis information includes:

[0012] Based on the evaluation instruction of the user, original evaluation information is displayed; the original evaluation information includes: a plurality of evaluation nodes and a plurality of directed connection relationships; each of the connection relationships connects two evaluation nodes;

[0013] Based on the user's selection instruction, a current evaluation node obtained based on a hypothesis testing method is determined as current hypothesis information.

[0014] Optionally, the evaluation result includes: evaluation type;

[0015] The locating the target damaged node of the target patient based on the type includes:

[0016] Based on the evaluation type, determining whether the current evaluation node is a critical evaluation node;

[0017] If the current evaluation node is not a critical evaluation node, re-determine a new current evaluation node; and use the new current evaluation node to execute a corresponding evaluation process for the target patient;

[0018] If the current evaluation node is a critical evaluation node, the current evaluation node is used as a target damaged node.

[0019] Optionally, judging whether the current evaluation node is a critical evaluation node based on the evaluation type includes:

[0020] When the evaluation type is the first evaluation type, it is determined that the current evaluation node is not a critical evaluation node; the first evaluation type is used to indicate that the evaluation is normal;

[0021] When the evaluation type is the second evaluation type, whether the current evaluation node is a critical evaluation node is determined based on a criticality determination strategy; the second evaluation type is used to characterize evaluation anomalies.

[0022] Optionally, if the current evaluation node is not a critical evaluation node, re-determining a new current evaluation node, and using the new current evaluation node to perform a corresponding evaluation process on the target patient includes:

[0023] Identifying the information flow where the current evaluation node is located;

[0024] A new current evaluation node is determined according to the evaluation result of the current evaluation node and the information flow in which the current evaluation node is located.

[0025] Optionally, each evaluation node corresponds to a preset question bank; the preset question bank includes a plurality of test information;

[0026] Optionally, executing the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result further includes:

[0027] Establish a dialect dictionary based on dialect phonetic materials;

[0028] Based on the dialect dictionary library, the test information is replaced with the dialect version of the test information to construct a dialect version of the preset question library.

[0029] Optionally, also include:

[0030] In combination with the training situation of the target patient, the personalized training program is updated to obtain a new personalized training program.

[0031] The personalized solution recommendation system for aphasia provided in this application adopts the following technical solutions, including:

[0032] The information acquisition module is used to acquire current assumption information; the current assumption information includes: a current evaluation node;

[0033] An evaluation module, configured to execute the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result;

[0034] a node positioning module, used for positioning a target damaged node of the target patient based on the evaluation result;

[0035] The personalized program building module is used to predict the damaged language module based on all the target damaged nodes and build a personalized training program.

[0036] Optionally, the information acquisition module includes:

[0037] The assumption submodule is used to display original evaluation information based on the evaluation instruction of the user; the original evaluation information includes: a plurality of evaluation nodes and a plurality of directed connection relationships; each of the connection relationships connects two evaluation nodes;

[0038] The selection module is used to determine, based on the user's selection instruction, a current evaluation node obtained based on a hypothesis testing method as current hypothesis information.

[0039] Optionally, the evaluation result includes: evaluation type;

[0040] Optionally, the positioning module includes:

[0041] A first judgment submodule, configured to judge whether the current evaluation node is a critical evaluation node based on the evaluation type;

[0042] A first positioning submodule is used to re-determine a new current evaluation node if the current evaluation node is not a critical evaluation node; and use the new current evaluation node to execute a corresponding evaluation process for the target patient;

[0043] The second positioning submodule is used to take the current evaluation node as a target damaged node if the current evaluation node is a critical evaluation node.

[0044] Optionally, the first judgment submodule includes:

[0045] A first judgment unit, configured to determine that the current evaluation node is not a critical evaluation node when the evaluation type is a first evaluation type; the first evaluation type is used to indicate that the evaluation is normal;

[0046] The second judgment unit is used to judge whether the current evaluation node is a critical evaluation node based on a critical judgment strategy when the evaluation type is a second evaluation type; the second evaluation type is used to characterize evaluation abnormality.

[0047] Optionally, the first positioning submodule includes:

[0048] An information flow identification unit, used to identify the information flow where the current evaluation node is located;

[0049] The node updating unit is used to determine a new current evaluation node according to the evaluation result of the current evaluation node and the information flow in which it is located.

[0050] Optionally, each evaluation node corresponds to a preset question bank; the preset question bank includes a plurality of test information;

[0051] Optionally, the evaluation module further includes:

[0052] A dialect dictionary building submodule is used to build a dialect dictionary based on dialect speech materials;

[0053] The replacement submodule is used to replace the test information with the test information of the dialect version based on the dialect dictionary library, and construct a preset question bank of the dialect version.

[0054] Optionally, also include:

[0055] The updating module is used to update the personalized training program in combination with the training situation of the target patient to obtain a new personalized training program.

[0056] This specification also provides an electronic device, wherein the electronic device includes:

[0057] processor; and,

[0058] A memory storing computer executable instructions, which when executed cause the processor to perform any of the above methods.

[0059] The present specification also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above methods is implemented.

[0060] This specification also provides a computer program product, wherein the computer program product includes: a computer program / instructions, and when the computer program / instructions are executed by a processor, any of the above methods is implemented.

[0061] In the present application, by obtaining current hypothesis information; the current hypothesis information includes: a current evaluation node obtained based on a hypothesis verification method; executing an evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result; locating the target damaged node of the target patient based on the evaluation result; improving the accuracy and efficiency of the evaluation; predicting the damaged language module based on all the target damaged nodes, constructing a personalized training plan, and improving the training effect and training targeting. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of the principle of a method for recommending a personalized solution for aphasia provided in an embodiment of this specification;

[0063] Figure 2 A flowchart of a method for recommending a personalized solution for aphasia provided in an embodiment of this specification;

[0064] Figure 3 A schematic diagram of the structure of a personalized solution recommendation system for aphasia provided in an embodiment of this specification;

[0065] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification;

[0066] Figure 5 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of the present specification. DETAILED DESCRIPTION

[0067] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0068] Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in a variety of forms, and should not be construed as limiting the present invention to the embodiments set forth herein. On the contrary, providing these exemplary embodiments enables the present invention to be more comprehensive and complete, and is more convenient for fully conveying the inventive concept to those skilled in the art. The same reference numerals in the figures represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0069] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0070] In the description of specific embodiments, the features, structures, characteristics or other details described in the present invention are intended to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can practice the technical solutions of the present invention without one or more of the specific features, structures, characteristics or other details.

[0071] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0072] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0073] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0074] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, the personal information processing rules are notified by obvious signs / information, and the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0075] Figure 1 A schematic diagram of the principle of a personalized solution recommendation method for aphasia provided in an embodiment of this specification, the method comprising:

[0076] S1 obtains current assumption information; the current assumption information includes: a current evaluation node;

[0077] S2 executes the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result;

[0078] S3 locates the target damaged node of the target patient based on the evaluation result;

[0079] S4 predicts the damaged language modules based on all the target damaged nodes and builds a personalized training plan.

[0080] Aphasia refers to the loss or impairment of language ability caused by brain damage, which is a clinical syndrome characterized by functional impairment in one or more aspects of the ability to understand, express, perceive, and organize language symbols. Aphasia is an acquired disorder, which can be caused by cerebrovascular disease, brain trauma, brain tumors, infection, central nervous system degeneration and other diseases.

[0081] As a common complication after brain tissue damage, aphasia seriously affects the patient's language communication ability, significantly limits the patient's social ability, and significantly reduces the patient's quality of life.

[0082] Therefore, rehabilitation training for aphasia should be carried out as early as possible on the basis of actively treating the primary disease, which can promote the patient's language comprehension and expression ability, improve independent application of speech communication skills, and restore the patient's direct verbal communication ability with others. Patients who fail to receive rehabilitation training in time may suffer from complete or partial aphasia, accompanied by neurological deficits such as hemiplegia and hemianopsia.

[0083] Systematic language training is currently considered to be the most effective method for treating aphasia. During the edema period caused by brain injury, some brain cells are stunned and "hibernate", and many synapses tend to be interrupted. As the edema subsides, the cells "gradually wake up". After strengthening language function training, the interrupted synapses are tightly connected and the function is quickly restored. If there is no training, the interrupted synapses may lose connection.

[0084] The prognosis of aphasia is closely related to many factors, including lesion size, location, acute phase treatment, age, education level, timing of rehabilitation intervention, training methods, intensity, cooperation, family consolidation training, etc. Aphasia patients can get different degrees of improvement through speech therapy, and some can even return to work.

[0085] Traditional rehabilitation training methods mainly focus on functional recovery, but lack the precise location of the specific damaged links in the patient's language processing, resulting in poor rehabilitation effects and low efficiency.

[0086] Based on this, the present invention provides a personalized solution recommendation method for aphasia, such as Figure 2 As shown, it specifically includes:

[0087] S1 obtains current hypothesis information;

[0088] S11 builds a case database;

[0089] The case database stores several case information. Each patient corresponds to a case information. Case information includes but is not limited to: attribute information, basic information, all record files, etc.

[0090] Attribute information includes but is not limited to: case number, patient name, and years of education.

[0091] Basic information is used to characterize the patient's basic situation, such as medical history, language background, etc.

[0092] The record file is used to record the patient's medical information, evaluation process, and training process. After each evaluation and / or training, a record file will be generated. In one embodiment of the present specification, the record file includes: the recording time of the time and the evaluation result of the time.

[0093] In one embodiment of the present specification, it includes a case management module; a case management module and a case update module;

[0094] The case management module includes: a retrieval submodule and a case deletion submodule. The case deletion submodule is used to delete the selected case information based on the user's case deletion instruction. The retrieval submodule is used to filter the case information based on the user's case retrieval instruction.

[0095] The case update module includes: a record creation submodule and a record deletion submodule. The record creation submodule is used to generate a blank record file for this evaluation or training; the record deletion submodule is used to delete the selected record file.

[0096] Before examining the target patient, the corresponding case information is retrieved from the case database based on the retrieval submodule. Specifically:

[0097] S12 searches for target case information of a target patient;

[0098] S121 displays a search page to the user and obtains a case search request;

[0099] The user in the present invention may be a person who examines the target patient, a person who operates the system, etc., such as a doctor, a therapist or other relevant professionals.

[0100] S121-1 displays the search page to the user;

[0101] Obtaining a user's request to start searching and generating a first searching instruction;

[0102] Based on the user's first search instruction, display a search page to the user;

[0103] The search page includes: several search bars; the search bar includes: search attributes and search areas; the search attributes correspond to the search areas one by one. The search attributes include but are not limited to: case number, patient name, and years of education. The search area is used for users to fill in / select the search attribute value corresponding to the search attribute. The search area can be an input area to facilitate users to enter the search attribute value; the search area can also be a preset drop-down menu, which automatically generates the search attribute value based on the user's selection, and the specific implementation method is not limited.

[0104] S121-2 collects the search attribute value input by the user and generates a case search request;

[0105] After the user fills in / selects the target search attribute value in the corresponding search area on the first search page, clicks Search to generate and send a case search request;

[0106] The case search request includes the attributes to be searched and the corresponding target search attribute values;

[0107] S122 searching and displaying case information that meets the case search request from the case database;

[0108] S122-1, according to the case search request, searching for case information that matches the target search attribute value from the case database;

[0109] In one embodiment of the present specification, the user enters a partial case number and / or a full case number of a target patient in the search area corresponding to the case number. Since the case number corresponds to the patient one-to-one, when the full case number is entered, the case information of the target patient can be directly located.

[0110] When a partial case number is entered, multiple case information may be queried; in this case, all case information that meets the retrieval attribute value is searched from the case database and returned to the query page for display to the user, so that the user can make a selection. Of course, in order to facilitate user selection, the name of the patient with case information is also displayed.

[0111] In another embodiment of the present specification, the user enters the last name of the target patient in the search area corresponding to the patient name to perform a search; the user's search request is obtained, and the case numbers of the retrieved case information are listed based on the search request for the user to select. Of course, the user enters the full name of the target patient in the search area corresponding to the patient name to perform a search; the case numbers of the retrieved case information are listed for the user to select.

[0112] In another embodiment of the present specification, the user enters the target patient's years of education in the search area corresponding to the years of education to perform a search; the user's search request is obtained, and the case numbers of the retrieved case information are listed based on the search request for the user to select.

[0113] In other embodiments of the present specification, it is also possible to directly search without entering a search attribute value in any search area; in this case, since there is no search item, all case numbers will be listed for user selection.

[0114] S122-2 displays the attribute information of the case information that meets the search requirements to the user on the query page;

[0115] Obtain case information that meets the search requirements and display its corresponding attribute information on the query page.

[0116] S123 determines target case information based on the user's selection instruction;

[0117] The user selects attribute information on the query page; the case information corresponding to the attribute information selected by the user is used as the target case information, and the target case information is displayed in detail.

[0118] In one embodiment of the present specification, when the user cannot retrieve the case information of the target patient, it means that there may be no past record of the target patient. At this time, new case information of the target patient is created; basic information of the target patient is obtained and added to the target case information.

[0119] The present invention is based on a friendly human-computer interaction interface, so that users can operate and use the search interface displayed on the human-computer interaction interface.

[0120] S13 creates a new record file in the target case information;

[0121] Since a new assessment / Nth assessment is required this time, the record creation submodule is called to create a new record file to record this assessment.

[0122] S14 displays original evaluation information based on the evaluation instruction of the user;

[0123] The dual-stream model of language processing is considered to be one of the language neural circuits with clinical application value. As a classic theory of brain language processing, the dual-stream model clearly points out the two information flows of language processing: the ventral stream and the dorsal stream.

[0124] Among them, the ventral stream is responsible for the semantic understanding of language and is used to process the meaning of words and sentences. This pathway involves mapping speech representations to word concept representations, which is usually associated with the left brain area and has a slight advantage in the left hemisphere; the dorsal stream is responsible for speech and repetition, and is a sensory-motor interface. This pathway involves mapping speech representations to articulatory motor representations, which has a strong left advantage, while the Broca area and the anterior part of the insula in the more anterior part of the frontal lobe are the articulatory network.

[0125] In order to provide a more accurate neurological tracing of the language disorders of aphasia patients, aphasia assessment can be performed based on the dual-stream model to analyze which of the two main pathways of language processing has problems, so as to facilitate targeted rehabilitation training.

[0126] S141 constructs original evaluation information based on the dual-stream model of language processing;

[0127] In one embodiment of the present specification, the original evaluation information may be a language function module map. The original evaluation information includes: a plurality of evaluation nodes and a plurality of directed connection relationships; each of the connection relationships connects two evaluation nodes;

[0128] The evaluation nodes represent different language function modules. A language task label is set for each evaluation node. The language task label can be set according to the actual situation and is not specifically limited here.

[0129] In one embodiment of the present specification, the language task tags include but are not limited to: spontaneous speech, auditory comprehension, and retelling. In another embodiment of the present specification, the language task tags include but are not limited to: language comprehension, speech output, vocabulary representation, and retelling ability.

[0130] Based on the directed connection relationship, the order of each evaluation node has a sequence relationship. In the same language task, the earlier the order of the evaluation node, the simpler the language function evaluated by the evaluation node.

[0131] In one embodiment of the present specification, original evaluation information is constructed based on information flow; specifically, based on the first information flow, a plurality of first evaluation nodes and a plurality of first connection relationships are constructed; the first information flow is a ventral flow; based on the second information flow, a plurality of second evaluation nodes and a plurality of second connection relationships are constructed. The second information flow is a dorsal flow; wherein the first evaluation node and the second evaluation node may overlap.

[0132] In one embodiment of the present specification, publicly available literature or research results are obtained to determine the evaluation nodes and connection relationships involved in each information flow, as well as the language task label of each evaluation node. Therefore, the specific contents of the evaluation nodes, connection relationships, and language task labels are not limited here.

[0133] S142 obtains the user's evaluation instruction and displays the original evaluation information.

[0134] When the user wants to evaluate the target patient, the user will create a new record file; after the record file is created, an evaluation instruction is sent; based on the user's evaluation instruction, the original evaluation information is displayed.

[0135] S15 determines, based on the user's selection instruction, a current evaluation node obtained based on a hypothesis testing method as current hypothesis information.

[0136] The current assumption information includes: a current evaluation node;

[0137] In order to evaluate the language abilities of aphasic patients and conduct statistical analysis, different language tasks can be evaluated based on hypothesis testing. Specifically, as a language cognitive processing model developed using cognitive neuropsychology methods, hypothesis testing can help users more accurately identify specific language damage modules and the degree of damage.

[0138] Compared with the traditional scale method, the hypothesis testing method can reveal the patient's language dysfunction more deeply and specifically, check whether the language processing process is damaged, and the logical thinking method of the damage level and cause of damage, and provide a clear direction reference for language training and rehabilitation, so that users can intervene in the patient's specific damaged language module, which can significantly improve the effectiveness of rehabilitation. The hypothesis testing method can not only evaluate the patient's current language damage, but also monitor changes over time, and combine with other clinical rehabilitation training methods for targeted treatment, so as to more effectively promote the patient's language rehabilitation.

[0139] Combined with the patient's medical information, the current evaluation node is determined by hypothesis verification. In one embodiment of the specification, the medical information includes: imaging data, information on the damaged language neural circuit provided by the patient's performance; the user proposes hypotheses of the target damaged nodes and processing modules of the language neural network based on the medical information, and determines the corresponding evaluation node; the user selects the corresponding evaluation node in the original evaluation information, and uses the selected evaluation node as the current evaluation node.

[0140] In another embodiment of the present specification, it includes:

[0141] S151 searches for historical evaluation nodes;

[0142] In one embodiment of the present specification, all record files in the target case information are obtained, and the record files are arranged in descending order according to the recording time; the record file ranked first is obtained, and the target damaged node recorded in the record file is used as the historical evaluation node.

[0143] It is easy to understand that the first record file is the record file most recent to the current time interval.

[0144] S152 combines the original evaluation information and the historical evaluation node to display the target evaluation information;

[0145] Combine the historical evaluation nodes and the connection relationship to determine all evaluation nodes before the historical evaluation node as the target evaluation information.

[0146] When there is no historical evaluation node, the target evaluation information includes: all evaluation nodes in the original evaluation information.

[0147] S153 obtains the user's selection instruction based on the target evaluation information, determines the current evaluation node, and constructs the current hypothesis information;

[0148] The user can select an evaluation node from the target evaluation information and use it as the current evaluation node.

[0149] Of course, the target evaluation information is only a decision-making aid and does not have decision-making capabilities, and the evaluation nodes of the non-target evaluation information are not locked; therefore, the user can select the evaluation node of the non-target evaluation information as the current evaluation node based on actual clinical experience.

[0150] In another embodiment of the present specification, imaging data of the target patient is obtained; based on the imaging data of the target patient, the user combines the entity dual-stream model and the language function module map to find the current evaluation node (language function module) to be evaluated through an offline hypothesis testing method, as a means of obtaining current hypothesis information, so as to facilitate the subsequent evaluation process (steps S2-S4, etc.), determine the damaged location of the target patient, and ultimately provide a personalized training plan for the target patient.

[0151] S2 executes the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result;

[0152] Each evaluation node corresponds to a preset question bank; the preset question bank includes a number of test information, and the test information includes: questions and corresponding original answers.

[0153] In one embodiment of the present specification, a Mandarin dictionary is constructed, and a preset question bank of Mandarin version is constructed by combining the Mandarin dictionary and preset evaluation rules.

[0154] In another embodiment of the present specification, considering that aphasia patients are generally older and have a higher degree of dialectalization, the evaluation results of the Mandarin version of the preset question bank may not be accurate. Therefore, a dialect dictionary library can be established based on dialect voice materials; based on the dialect dictionary library, the test information is replaced with the dialect version of the test information to construct a dialect version of the preset question bank.

[0155] Specifically, taking Shanghainese as an example, the present invention combines the language characteristics, cultural characteristics, and pronunciation characteristics of Shanghai with preset evaluation rules to redesign the question bank corresponding to the evaluation process;

[0156] A large amount of Shanghainese audio materials are collected and recorded, covering daily life scenarios such as daily communication, medical communication, family life, shopping, restaurant ordering, public transportation, work communication and social gatherings, to ensure the comprehensiveness and pertinence of the assessment; incorporating daily life scenarios into the training program can also help patients restore their language ability during the training process while enhancing their communication and social skills in real life.

[0157] Based on Shanghainese phonetic materials, a Shanghainese dictionary database is established.

[0158] Retain the original evaluation logic based on the preset evaluation rules; on this basis, based on the Shanghai dialect dictionary, replace the test information with the Shanghai dialect version of the test information, while ensuring the accuracy and effectiveness of the evaluation results, making it closer to the patient's living environment, considering the language characteristics of Shanghai dialect and the specific needs of the patient, and generating a targeted personalized training plan, thereby improving the training effect.

[0159] In one embodiment of the present specification, in the speech understanding task, design vocabulary understanding and sentence understanding tasks that conform to the pronunciation characteristics of Shanghai dialect. Enable the patient to make semantic judgments based on hearing the vocabulary or sentences in Shanghai dialect. This task can cover the unique vocabulary and syntax of Shanghai dialect. For example, design corresponding judgment tasks for the negative words "呒" and "冇".

[0160] In one embodiment of the present specification, in the speech output task, design naming tasks, repetition tasks, and fluency tasks that conform to the speech characteristics of Shanghai dialect. For example, test whether the patient can accurately repeat the unique vocabulary and sentence patterns of Shanghai dialect.

[0161] In one embodiment of the present specification, in the lexical semantics task, use the vocabulary of Shanghai dialect to design tasks to test the patient's vocabulary generation and understanding abilities in a specific language environment.

[0162] The dialect version of the present invention improves the patient's sense of participation and enthusiasm during the evaluation and training processes by being closer to the patient's actual life, thereby improving the compliance of treatment.

[0163] S21 Randomly extract the test information of the preset question quantity from the preset question bank of the current preset node to construct an evaluation plan;

[0164] S22 Display the evaluation plan to the target patient; obtain the feedback information of the target patient;

[0165] In one embodiment of the present specification, display the first question, and after obtaining the feedback information of the target patient, display the next question; until the feedback information of the target patient for the last question is obtained; or stop after obtaining the abort / termination instruction.

[0166] Among them, when it comes to obtaining the voice information of the target patient, utilize advanced speech recognition and synthesis technologies to achieve the automatic recognition and conversion of Shanghai dialect speech, so as to improve the efficiency and accuracy of the evaluation and provide a more convenient evaluation experience for the patient.

[0167] S23 Compare the feedback information with the original answer to obtain the evaluation score of the target patient.

[0168] Determine the number of correct answers of the target patient by comparing the feedback information and the original answer;

[0169] In one embodiment of the present specification, the ratio of the number of correctly answered questions to the preset number of questions is used as the answer accuracy rate; and the answer accuracy rate is used as the evaluation score.

[0170] In another embodiment of the present specification, the number of correctly answered questions is used as the evaluation score.

[0171] S24 constructs an evaluation result by combining the evaluation score and the evaluation condition;

[0172] The evaluation results include: evaluation score and evaluation type;

[0173] S241 determines whether the evaluation score is within a preset threshold; if so, execute S242; otherwise, execute S243.

[0174] S242 determines that the assessment type is the first assessment type.

[0175] S243 determines that the assessment type is the second assessment type.

[0176] In order to improve the evaluation effect, users can also manually annotate according to the actual expression of the target patient and generate annotation information. For example, annotate whether the target patient has speech disorder, effort, or empty expression. Therefore, the evaluation result also includes annotation information.

[0177] Preferably, the evaluation result also includes status information, which is used to characterize the performance of the target patient when the evaluation node is evaluating the patient.

[0178] In one embodiment of the present specification, the state information includes: feedback information of the target patient. In other embodiments of the present specification, the state information also includes: demeanor information of the target patient when evaluating at the evaluation node, action information of the target patient when evaluating at the evaluation node, etc.

[0179] Subsequently, the present invention determines the degree of injury according to the evaluation results and provides a basis for personalized training of the patient.

[0180] S3 locates the target damaged node of the target patient based on the evaluation result;

[0181] S31, based on the evaluation type, determining whether the current evaluation node is a critical evaluation node;

[0182] S311: when the evaluation type is the first evaluation type, determining that the current evaluation node is not a critical evaluation node;

[0183] The first assessment type is used to indicate that the assessment is normal;

[0184] When the evaluation type is normal, it means that there is no obvious problem with the current evaluation node, and it is necessary to search forward for the evaluation node with problems based on the current evaluation node.

[0185] S312: when the evaluation type is the second evaluation type, judging whether the current evaluation node is a critical evaluation node based on a critical judgment strategy;

[0186] The second assessment type is used to characterize assessment abnormalities.

[0187] S312-1 determines whether the current evaluation node is an original evaluation node; if so, execute S312-2; otherwise, execute S312-3.

[0188] Find the language task label corresponding to the current evaluation node as the current language task label;

[0189] The first evaluation node corresponding to the current language task label is used as the original evaluation node.

[0190] S312-2 uses the current evaluation node as a critical evaluation node;

[0191] If the current evaluation node is an original evaluation node, then determining the current evaluation node as a critical evaluation node;

[0192] Specifically, if the first evaluation node in the language task evaluates abnormally, it basically means that it cannot achieve a simpler language task, and it can be reasonably inferred that the subsequent evaluation nodes are also likely to be abnormal. Therefore, the current evaluation node (i.e., the original evaluation node) is identified as a critical evaluation node.

[0193] S312-3 identifies the evaluation type of the previous evaluation node of the current evaluation node to obtain an identification result; based on the identification result, determines whether the current evaluation node is the critical evaluation node.

[0194] If the current evaluation node is not the original evaluation node, identifying the evaluation type of the previous evaluation node of the current evaluation node to obtain an identification result;

[0195] Based on the identification result, it is determined whether the current evaluation node is the critical evaluation node.

[0196] Specifically, the previous evaluation node directly connected to the current evaluation node in the current language task label is used as the previous evaluation node;

[0197] If the evaluation type of the previous evaluation node is the first evaluation type, the current evaluation node is determined to be a critical evaluation node;

[0198] Specifically, if the previous assessment node corresponds to the first assessment type, and the current assessment node corresponds to the second assessment type, and the patient's performance or ability has significantly declined or become abnormal at the current assessment node, the current assessment node is likely where the patient begins to have problems, and therefore can be identified as a critical assessment node.

[0199] If the evaluation type of the previous evaluation node is the second evaluation type, it is determined that the current evaluation type is not a critical evaluation node.

[0200] If the evaluation types of the previous evaluation node and the current evaluation node are both the second evaluation type, it means that the abnormality of the current evaluation node may be a continuation of the abnormal state of the previous evaluation node, and is not the first problem to occur on the current evaluation node. Therefore, the current evaluation node is not a critical point.

[0201] If the evaluation type of the previous evaluation node is empty, that is, it is necessary to evaluate other evaluation nodes before the current evaluation node or directly evaluate the previous evaluation node to find and determine the critical evaluation node.

[0202] S32: if the current evaluation node is not a critical evaluation node, re-determine a new current evaluation node; and use the new current evaluation node to execute a corresponding evaluation process for the target patient;

[0203] S321 determines a new current evaluation node;

[0204] In one embodiment of the present specification, it includes:

[0205] (A-1) Collect historical case information of historical aphasia patients. The historical case information includes: the evaluation sequence of each evaluation node during the evaluation, and the evaluation results and damaged information flow of each evaluation node.

[0206] (A-2) Construct training samples based on historical case information.

[0207] (A-3) Construct an evaluation node prediction model; input the original evaluation information into the evaluation sequence prediction model for pre-training.

[0208] (A-4) Input the training samples into the evaluation node prediction model to learn how to determine the next evaluation node based on the evaluation type of the current evaluation node; after the learning is completed, the evaluation sequence strategy is obtained.

[0209] (A-5) If the current evaluation node is not a critical evaluation node, a new evaluation node is determined as the current evaluation node based on the evaluation type and evaluation order strategy of the current evaluation node.

[0210] In another embodiment of the present specification, if the current evaluation node is not a critical evaluation node, predicting a new current evaluation node based on the evaluation type of the current evaluation node includes:

[0211] (B-1) determining the prediction object to be selected;

[0212] Among them, when the evaluation type of the current evaluation node is the first evaluation type, it means that the performance of the target patient at the current evaluation node is normal. Therefore, it is necessary to continue to search for the first abnormal evaluation node backward; at this time, the evaluation node after the current evaluation node is used as the candidate evaluation node.

[0213] When the evaluation type of the current evaluation node is the second evaluation type, it means that the performance of the target patient at the current evaluation node is already abnormal, and therefore, it is necessary to continue looking for the first abnormal evaluation node; at this time, the evaluation node before the current evaluation node is used as the candidate evaluation node.

[0214] Sort the candidate evaluation nodes according to the preset order to determine the new current evaluation node; specifically:

[0215] (B-2) Collect historical case information and impaired information flow of patients with aphasia.

[0216] The historical case information includes: the evaluation results of each evaluation node during the evaluation.

[0217] (B-3) Construct training samples based on historical case information.

[0218] (B-4) Construct a probability prediction model; input the original evaluation information into the evaluation sequence prediction model for pre-training.

[0219] (B-5) Input the training samples into the probability prediction model to obtain the selection probability of each evaluation node. The selection probability can be the proportion of the number of selections of each evaluation node; the selection probability can also be the probability that the evaluation node is a critical evaluation node.

[0220] (B-6) Arrange the probabilities of the candidate evaluation nodes in descending order based on the selection probability; and use the candidate evaluation node that ranks first in the order as the new current evaluation node.

[0221] Of course, the probability prediction model of the present invention can be a decision tree. After obtaining the evaluation result of the current evaluation node, the candidate evaluation nodes and their candidate probabilities are determined based on the probability prediction model to facilitate user decision-making.

[0222] In another embodiment of the present specification, all evaluation nodes may be displayed, or the evaluation nodes to be selected may be displayed, and the user may manually determine a new current evaluation node;

[0223] Specifically, the damaged information flow of the target patient is determined according to the information flow where the damaged language module is located;

[0224] The user may determine a new current evaluation node according to the evaluation result of the current evaluation node and the information flow of the evaluation node; wherein the user selects an evaluation node in the evaluation interface; and the evaluation node selected by the user is identified and used as the current evaluation node.

[0225] The original assessment information of the present invention can be navigated by a vivid and intuitive flowchart, combined with speech characteristics and thinking habits, to provide users with accurate, intuitive and effective assessment means, so as to more quickly determine the direct cause of the language barrier and facilitate subsequent targeted training.

[0226] S322 re-executes S2 after determining the new current evaluation node.

[0227] S33: If the current evaluation node is a critical evaluation node, use the current evaluation node as a target damaged node.

[0228] The present invention uses a hypothesis testing method to infer whether aphasic patients have impairments in speech comprehension, speech output, vocabulary semantics, speech-to-graph conversion, etc., so that users can make further decisions.

[0229] S4 predicts the damaged language modules based on all the target damaged nodes and builds a personalized training plan.

[0230] S41, determining a damaged language module based on the language task label of the target damaged node;

[0231] Identify the language task label of the current evaluation node as the target language task label;

[0232] The language module corresponding to the target language task label is used as the damaged language module:

[0233] The patient may have impairments in multiple language tasks respectively, so the number of target damaged nodes is not limited to one, that is, the damaged language module is not limited to one.

[0234] S42 obtains the evaluation score of the target damaged node and determines the severity of the damaged language module;

[0235] The higher the assessment score, the lower the severity of the impaired language module.

[0236] The severity of the damaged language module is quantified based on the evaluation score. In one embodiment of the specification, when the evaluation score is the answer accuracy rate, the answer error rate is used as the severity of the damaged language module; wherein the answer error rate = (1-answer accuracy rate) × 100%.

[0237] In another embodiment of the present specification, when the evaluation score is the number of correctly answered questions, the severity is determined based on the difference between the preset number of questions and the evaluation score.

[0238] In another embodiment of the present specification, the gears are divided according to preset division conditions; the evaluation scores are mapped to evaluation gears; wherein the evaluation gears may be expressed in the form of: normal, near normal, partial impairment, severe impairment; the evaluation gears may be expressed in the form of: better, worse; the evaluation gears may be expressed in the form of: mild, moderate, severe, etc. The evaluation gears may be expressed in the form of: smooth, not smooth, etc.

[0239] The severity can be presented in the form of percentage, score, level, etc., and there is no specific limitation here.

[0240] Specifically, publicly available medical literature and research results may be collected to construct prediction conditions; or the prediction conditions may be independently determined or improved by the user, and no specific limitation is made here.

[0241] In one embodiment of the present specification, the correspondence between the language function modules and their severity levels is shown in Table 1:

[0242]

[0243] (Table 1)

[0244] The damaged language module and the severity of the damaged language module are taken as the evaluation conclusion.

[0245] S43 sends the target evaluation file to the user to obtain the prediction result fed back by the user.

[0246] The target assessment documents include: assessment results and / or assessment conclusions.

[0247] The specific content of the target evaluation file can be set according to the actual situation. The user can set to obtain only the evaluation results; or set to obtain only the evaluation conclusions; or set to obtain both the evaluation results and the evaluation conclusions according to actual needs.

[0248] Of course, the user can set to obtain all or part of the information in the evaluation results and / or all or part of the information in the evaluation conclusion according to actual needs.

[0249] As mentioned above, the evaluation result includes one or more of evaluation score, evaluation type, status information, and annotation information.

[0250] In one embodiment of the present specification, the evaluation results are retrieved; the status information and evaluation scores of the target patient at different evaluation nodes are searched, and sent to the user, who makes a decision to obtain the prediction results. In one embodiment of the present specification, the prediction results include: aphasia type.

[0251] S44 searches for the corresponding original training scheme based on the prediction result.

[0252] Several training programs are pre-constructed, wherein each aphasia type corresponds to a training program; for different aphasia types, corresponding training programs need to be selected in a targeted manner.

[0253] In the present invention, after manual verification, a prediction result is obtained; the prediction result includes: a target aphasia type.

[0254] A training program corresponding to the target aphasia type is obtained as the training program for the target patient.

[0255] The present invention is based on the basic theories of neuropsychology. Through a comprehensive assessment of the patient's cognitive ability, language function and related neural mechanisms, the type and severity of aphasia in the target patient is predicted, so as to facilitate the construction of a targeted training program.

[0256] In order to improve the applicability and accuracy of the training program, it also includes: S45 optimizes the original training program based on the user's supplementary instructions to obtain a personalized training program;

[0257] Personalized training programs usually include language training, cognitive training and other auxiliary rehabilitation methods, aiming to maximize the patient's language ability and promote the recovery of the patient's language function.

[0258] In one embodiment of the present specification, the user modifies the original training program based on actual experience; of course, other rehabilitation methods can also be added to obtain a final personalized training program; so as to make full use of the plasticity of the brain based on the personalized training program to assist in training patients to recover as soon as possible.

[0259] Other rehabilitation methods include but are not limited to: drug therapy, psychotherapy, and auxiliary therapy.

[0260] Psychotherapy can help patients relieve their emotions and enhance their confidence in recovery; by encouraging patients to participate in social activities, their daily communication skills can be improved.

[0261] Auxiliary treatments include acupuncture therapy and modern technology assistance. Among them, acupuncture therapy (such as traditional Chinese medicine acupuncture therapy) can promote the recovery of patients' neurological function. Use modern technology assistance methods such as non-invasive neuromodulation (tDcs) to improve the training effect.

[0262] In a specific scenario, the present invention can also be appropriately adjusted based on new research results and / or clinical needs, so as to improve the adaptability and scalability of the present invention; while reducing the subjectivity in manual evaluation, it improves diagnostic efficiency and accuracy.

[0263] S46, updating the personalized training program in combination with the training situation of the target patient to obtain a new personalized training program;

[0264] S461 obtains a personalized training plan based on the training instructions;

[0265] S462 executes a personalized training program to train the language function of the target patient;

[0266] In one embodiment of the present specification, the personalized training program includes, but is not limited to: speech comprehension training, speech generation training, and vocabulary and semantics training.

[0267] Among them, speech comprehension training is to restore the function of the ventral stream through auditory training and semantic matching tasks, especially for the vocabulary and sentence structure unique to Shanghainese. For example, after hearing words or sentences in Shanghainese, the patient answers related comprehension questions (such as judging the meaning of vocabulary, understanding simple sentences, etc.).

[0268] Speech production training helps restore dorsal stream function through naming and repetition training, especially in Shanghainese speech production tasks, helping patients adapt to the syllable and phoneme structure of Shanghainese.

[0269] Lexical semantic training is designed based on the phonetic characteristics of Shanghainese. Corresponding vocabulary comprehension training, such as picture-vocabulary matching tasks, synonym and antonym training, semantic classification training, semantic association training, etc., helps patients understand and generate vocabulary, and thus improves their communication ability in Shanghainese.

[0270] After the training is completed, S463 performs evaluation according to S1-S3; and optimizes the training plan based on the evaluation results;

[0271] Regular evaluation is performed during the training process to facilitate timely adjustment of the personalized training program. Specifically, after obtaining the patient's consent, the present invention collects the patient's training data, analyzes it, evaluates the patient's progress in real time, and continuously adjusts the personalized training program based on the feedback.

[0272] In one embodiment of the present specification, if the patient responds well to the lexical semantic task, the difficulty of the task can be increased to conduct training on more complex word semantics.

[0273] For patients with poor speech fluency, more speech fluency training can be added to gradually improve the patient's language output speed and accuracy.

[0274] If the patient has difficulty understanding the Shanghainese dialect vocabulary, additional training tasks in local phonetics and semantics can be added to help him or her better understand the dialect.

[0275] The present invention can record the patient's assessment data and treatment progress, and help doctors adjust treatment plans through data analysis to achieve dynamic management. After the treatment, follow-up visits are conducted to understand the patient's recovery status and collect feedback.

[0276] In other embodiments of the present specification, users can also develop personalized home training plans based on the patient's specific situation and family environment; in order to enable them to meet the needs of home training, the present invention can also display educational content, detailed operating guides and video tutorials through an education module.

[0277] Among them, educational content is used to provide information on basic knowledge, communication skills, psychological support, etc. about aphasia, to help family members better understand and support patients.

[0278] Through detailed operating guides and video tutorials, family members can learn how to assist patients in training, so as to improve patient participation and training results.

[0279] Patients can complete home training by simulating actual communication scenarios, participating in interesting training tasks, etc., to improve their daily communication skills and quality of life, thereby reducing the burden on their families and society.

[0280] In order to facilitate training tracking, you can also record training logs based on the log recording module. The training logs include: the patient's training process, training progress and changes. Then, call the new submodule to create a new record file, and record the training log in the record file. In order to facilitate users to understand the progress of home training in a timely manner, you can also call the training log from the log recording module through the feedback module to display the training process of the target patient, so that users can understand the training situation of the target patient in a timely manner and adjust the training plan.

[0281] Figure 3 A schematic diagram of a personalized solution recommendation system for aphasia provided in an embodiment of this specification, the system includes:

[0282] The information acquisition module is used to acquire current assumption information; the current assumption information includes: a current evaluation node;

[0283] An evaluation module, configured to execute the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result;

[0284] a node positioning module, used for positioning a target damaged node of the target patient based on the evaluation result;

[0285] The personalized program building module is used to predict the damaged language module based on all the target damaged nodes and build a personalized training program.

[0286] Optionally, the information acquisition module includes:

[0287] The assumption submodule is used to display original evaluation information based on the evaluation instruction of the user; the original evaluation information includes: a plurality of evaluation nodes and a plurality of directed connection relationships; each of the connection relationships connects two evaluation nodes;

[0288] The selection module is used to determine, based on the user's selection instruction, a current evaluation node obtained based on a hypothesis testing method as current hypothesis information.

[0289] Optionally, the evaluation result includes: evaluation type;

[0290] Optionally, the positioning module includes:

[0291] A first judgment submodule, configured to judge whether the current evaluation node is a critical evaluation node based on the evaluation type;

[0292] A first positioning submodule is used to re-determine a new current evaluation node if the current evaluation node is not a critical evaluation node; and use the new current evaluation node to execute a corresponding evaluation process for the target patient;

[0293] The second positioning submodule is used to take the current evaluation node as a target damaged node if the current evaluation node is a critical evaluation node.

[0294] Optionally, the first judgment submodule includes:

[0295] A first judgment unit, configured to determine that the current evaluation node is not a critical evaluation node when the evaluation type is a first evaluation type; the first evaluation type is used to indicate that the evaluation is normal;

[0296] The second judgment unit is used to judge whether the current evaluation node is a critical evaluation node based on a critical judgment strategy when the evaluation type is a second evaluation type; the second evaluation type is used to characterize evaluation abnormality.

[0297] Optionally, the first positioning submodule includes:

[0298] An information flow identification unit, used to identify the information flow where the current evaluation node is located;

[0299] The node updating unit is used to determine a new current evaluation node according to the evaluation result of the current evaluation node and the information flow in which it is located.

[0300] Optionally, each evaluation node corresponds to a preset question bank; the preset question bank includes a plurality of test information;

[0301] Optionally, the evaluation module further includes:

[0302] A dialect dictionary building submodule is used to build a dialect dictionary based on dialect speech materials;

[0303] The replacement submodule is used to replace the test information with the test information of the dialect version based on the dialect dictionary library, and construct a preset question bank of the dialect version.

[0304] Optionally, also include:

[0305] The updating module is used to update the personalized training program in combination with the training situation of the target patient to obtain a new personalized training program.

[0306] The functions of the system of the embodiment of the present invention have been described in the above method embodiment, so for details not fully described in this embodiment, please refer to the relevant description in the above embodiment, and no further description will be given here.

[0307] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0308] The electronic device embodiment of the present invention is described below, and the electronic device can be regarded as a physical implementation of the method and device embodiments of the present invention described above. The details described in the electronic device embodiment of the present invention should be regarded as a supplement to the above method or device embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above method or device embodiments.

[0309] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Figure 4 The computer device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0310] like Figure 4As shown, the computer device 400 of this exemplary embodiment is in the form of a general data processing device. The components of the computer device 400 may include but are not limited to: at least one processor 410, at least one memory 420, a network interface 430, a display unit 440, an input component 450, etc.

[0311] The memory 420 stores a computer-readable program, which may be a source program or a code of a read-only program. The program may be executed by the processor 410, so that the processor 410 performs the steps of various embodiments of the present invention. For example, the processor 410 may perform the following steps: Figure 1 Steps shown.

[0312] The memory 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) and / or a cache memory unit, and may further include a read-only memory unit (ROM). The memory 420 may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment.

[0313] Also included is a bus (not shown) which may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0314] The computer device 400 may also communicate with one or more external devices (e.g., keyboard, display, network device, Bluetooth device, etc.) so that a user can interact with the computer device 400 via these external devices, and / or so that the computer device 400 can communicate with one or more other data processing devices (e.g., routers, modems, etc.). Such communication may be performed through the network interface 430, and may also be performed through a network adapter with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet). The network adapter may communicate with other modules of the computer device 400 via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in the computer device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0315] Figure 5 Schematic diagram of a computer readable medium embodiment of the present invention. Figure 5As shown, the computer program can be stored on one or more computer-readable media. The computer-readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. When the computer program is executed by one or more data processing devices, the computer-readable medium is enabled to implement the above method of the present invention.

[0316] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation mode of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a data processing device (which can be a personal computer, a server, or a network device, etc.) to perform the above method according to the present invention.

[0317] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0318] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0319] In summary, the present invention can be implemented by a method, apparatus, electronic device or computer-readable medium that executes a computer program. In practice, a general data processing device such as a microprocessor or a digital signal processor (DSP) can be used to implement some or all functions of the present invention.

[0320] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A personalized solution recommendation method for aphasia, characterized in that: include: Get current assumptions; The current assumption information includes: a current evaluation node; Executing the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result; locating a target damaged node of the target patient based on the evaluation result; According to all the target damaged nodes, the damaged language modules are predicted and a personalized training plan is constructed.

2. A personalized solution recommendation method for aphasia according to claim 1, characterized in that: The obtaining of current assumption information includes: Based on the evaluation instruction of the user, original evaluation information is displayed; the original evaluation information includes: a plurality of evaluation nodes and a plurality of directed connection relationships; each of the connection relationships connects two evaluation nodes; Based on the user's selection instruction, a current evaluation node obtained based on a hypothesis testing method is determined as current hypothesis information.

3. A personalized solution recommendation method for aphasia according to claim 1, characterized in that: The evaluation results include: evaluation type; The locating the target damaged node of the target patient based on the type includes: Based on the evaluation type, determining whether the current evaluation node is a critical evaluation node; If the current evaluation node is not a critical evaluation node, re-determine a new current evaluation node; and use the new current evaluation node to execute a corresponding evaluation process for the target patient; If the current evaluation node is a critical evaluation node, the current evaluation node is used as a target damaged node.

4. A personalized solution recommendation method for aphasia according to claim 3, characterized in that: The determining, based on the evaluation type, whether the current evaluation node is a critical evaluation node includes: When the evaluation type is the first evaluation type, it is determined that the current evaluation node is not a critical evaluation node; the first evaluation type is used to indicate that the evaluation is normal; When the evaluation type is the second evaluation type, whether the current evaluation node is a critical evaluation node is determined based on a criticality determination strategy; the second evaluation type is used to characterize evaluation anomalies.

5. A personalized solution recommendation method for aphasia according to claim 3, characterized in that: If the current evaluation node is not a critical evaluation node, a new current evaluation node is re-determined, and a corresponding evaluation process is performed on the target patient using the new current evaluation node, including: Identifying the information flow where the current evaluation node is located; A new current evaluation node is determined according to the evaluation result of the current evaluation node and the information flow in which the current evaluation node is located.

6. A personalized solution recommendation method for aphasia according to claim 1, characterized in that: Each evaluation node corresponds to a preset question bank; the preset question bank includes a number of test information; The step of executing the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result further includes: Establish a dialect dictionary based on dialect phonetic materials; Based on the dialect dictionary library, the test information is replaced with the dialect version of the test information to construct a dialect version of the preset question library.

7. A personalized solution recommendation method for aphasia according to claim 1, characterized in that: Also includes: In combination with the training situation of the target patient, the personalized training program is updated to obtain a new personalized training program.

8. A personalized solution recommendation system for aphasia, characterized in that: include: An information acquisition module, used to obtain current hypothesis information; The current assumption information includes: a current evaluation node; An evaluation module, configured to execute the evaluation process corresponding to the current evaluation node on the target patient to obtain an evaluation result; a node positioning module, used for positioning a target damaged node of the target patient based on the evaluation result; The personalized program building module is used to predict the damaged language module based on all the target damaged nodes and build a personalized training program.

9. An electronic device, wherein: The electronic device includes: processor; and, A memory storing computer executable instructions which, when executed, cause the processor to perform a method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 7 is implemented.

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