CN112201339A - Auxiliary diagnostic system for psychology - Google Patents
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- 208000024891 symptom Diseases 0.000 claims abstract description 66
- 238000003745 diagnosis Methods 0.000 claims abstract description 31
- 208000019901 Anxiety disease Diseases 0.000 claims abstract description 13
- 230000036506 anxiety Effects 0.000 claims abstract description 13
- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 208000028017 Psychotic disease Diseases 0.000 claims abstract description 7
- 201000006152 substance dependence Diseases 0.000 claims abstract description 7
- 208000011117 substance-related disease Diseases 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 19
- 230000008451 emotion Effects 0.000 claims description 17
- 230000008909 emotion recognition Effects 0.000 claims description 16
- 206010012374 Depressed mood Diseases 0.000 claims description 6
- 208000004547 Hallucinations Diseases 0.000 claims description 6
- 230000008921 facial expression Effects 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 6
- 206010062519 Poor quality sleep Diseases 0.000 claims description 4
- 206010038743 Restlessness Diseases 0.000 claims description 4
- 206010010144 Completed suicide Diseases 0.000 claims description 3
- 206010049976 Impatience Diseases 0.000 claims description 3
- 208000001431 Psychomotor Agitation Diseases 0.000 claims description 3
- 208000027418 Wounds and injury Diseases 0.000 claims description 3
- 230000036528 appetite Effects 0.000 claims description 3
- 235000019789 appetite Nutrition 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000006378 damage Effects 0.000 claims description 3
- 208000014674 injury Diseases 0.000 claims description 3
- 230000002040 relaxant effect Effects 0.000 claims description 3
- 230000003989 repetitive behavior Effects 0.000 claims description 3
- 208000013406 repetitive behavior Diseases 0.000 claims description 3
- 230000003252 repetitive effect Effects 0.000 claims description 3
- 239000000779 smoke Substances 0.000 claims description 3
- 230000003340 mental effect Effects 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000003058 natural language processing Methods 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 2
- 206010013954 Dysphoria Diseases 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000001544 dysphoric effect Effects 0.000 description 1
- 230000002996 emotional effect Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 206010022437 insomnia Diseases 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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Abstract
The invention discloses a psychiatric auxiliary diagnosis system, comprising: the information acquisition module is used for acquiring symptom information of the patient; the preprocessing module is used for carrying out standardization processing on the symptom information to obtain input information conforming to a preset format; the symptom classification module is used for carrying out symptom classification on the input information which accords with the preset format to obtain a symptom classification result; the symptom classification result comprises: normal, depression, anxiety, psychotic, substance dependence, obsessive-compulsive; the degree classification module is used for carrying out degree classification on the symptom classification result to obtain a degree classification result; the degree classification result comprises: normal, mild, moderate, severe. The technical scheme provided by the invention can be used for efficiently and intelligently detecting the mental condition of the patient, thereby assisting the diagnosis of a doctor.
Description
Technical Field
The invention relates to the technical field of medical intelligent diagnosis, in particular to a psychiatric auxiliary diagnosis system.
Background
The existing mental medical main diagnosis method is a manual scale examination method, preset questions and various preset scores corresponding to the questions are given in a scale, when the mental medical main diagnosis method is used, a doctor can compare and score according to answers of a patient, and the patient can perform self-test through the scale. Obviously, the existing diagnosis method is complicated to operate and does not meet the requirement of intelligent development of modern psychology medicine.
Disclosure of Invention
The invention aims to provide a psychiatric assistant diagnosis system which can efficiently and intelligently detect the mental condition of a patient so as to assist a doctor to diagnose.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a psychiatric assisted diagnosis system comprising: the information acquisition module is used for acquiring symptom information of the patient; the preprocessing module is used for carrying out standardization processing on the symptom information to obtain input information conforming to a preset format; the symptom classification module is used for carrying out symptom classification on the input information which accords with the preset format to obtain a symptom classification result; the symptom classification result comprises: normal, depression, anxiety, psychotic, substance dependence, obsessive-compulsive; the degree classification module is used for carrying out degree classification on the symptom classification result to obtain a degree classification result; the degree classification result comprises: normal, mild, moderate, severe.
Further, still include: and the emotion recognition module is used for acquiring data of the facial expression of the patient and acquiring the emotion information of the patient according to the acquired facial expression information.
Further, still include: and the diagnosis suggestion module is used for outputting diagnosis suggestions according to the degree classification result output by the degree classification module and the emotion information output by the emotion recognition module.
Further, the information acquisition module includes: the questioning module is used for outputting questioning content corresponding to the emotion information according to the emotion information of the patient acquired by the emotion recognition module; the input module is used for the patient to input the response to the questioning content; the questioning module is also used for adjusting questioning content according to the emotion information of the patient acquired by the emotion recognition module and the response of the patient acquired by the input module.
Further, the input module comprises a text input module and/or a voice input module.
Preferably, the preprocessing module, the symptom classification module and the degree classification module are pre-trained BERT models.
Preferably, the preprocessing module comprises: the word removing module is used for removing the tone words and stop words from the symptom information and acquiring first processing information; and the sentence dividing module is used for dividing sentences of the first processing information to acquire input information conforming to a preset format.
Preferably, the method for determining that the symptom classification result is depression includes at least one of the following items in the input information: low mood/depression, no interest in doing things, poor sleep, feeling tired/tired, poor appetite/too much eating, feeling that oneself is bad/disappointed to oneself, difficulty in concentrating attention, slow/slow reaction, suicide/oneself injury; the method for determining that the symptom classification result is anxiety includes the steps that the input information includes at least one of the following items: stress, anxiety, worry, difficulty in relaxing, restlessness, impatience, fear, pain in body parts; the method for judging whether the symptom classification result is the psychotic, wherein the input information comprises at least one of the following items: auditory hallucination and visual hallucination; the method for determining that the symptom classification result is substance dependence includes the steps of, in the input information, including at least one of: want to smoke, want to drink; the method for judging the symptom classification result is forced, wherein the input information comprises at least one item of the following items: repetitive thoughts occur and repetitive behaviors occur.
According to the psychiatric assistant diagnosis system provided by the embodiment of the invention, the information acquisition module, the preprocessing module, the symptom classification module and the degree classification module are arranged, so that the symptom information of the patient can be automatically identified and preprocessed, and the classification of the diagnosis result and the judgment of the severity degree can be automatically carried out, and therefore, a doctor can be assisted to carry out efficient diagnosis. In addition, the invention also combines an emotion recognition module to acquire the current emotion information of the patient so as to carry out multi-dimensional diagnosis on the patient and acquire more accurate diagnosis results.
Drawings
FIG. 1 is a first block diagram of a system according to an embodiment of the present invention;
FIG. 2 is a second block diagram of the system according to the embodiment of the present invention;
FIG. 3 is a diagram illustrating a symptom classification module performing symptom classification according to an embodiment of the present invention;
fig. 4 is a schematic diagram of degree classification performed by the degree classification module in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
Fig. 1 is a first system structure diagram according to an embodiment of the present invention, which includes: the information acquisition module is used for acquiring symptom information of the patient; the preprocessing module is used for carrying out standardization processing on the symptom information to obtain input information conforming to a preset format; the symptom classification module is used for carrying out symptom classification on the input information which accords with the preset format to obtain a symptom classification result; the symptom classification result comprises: normal, depression, anxiety, psychotic, substance dependence, obsessive-compulsive; the degree classification module is used for carrying out degree classification on the symptom classification result to obtain a degree classification result; the degree classification result comprises: normal, mild, moderate, severe.
The preprocessing module, the symptom classification module and the degree classification module in this embodiment are pre-trained bert (bidirectional Encoder retrieval from transformations) models. The BERT model is a natural language preprocessing model that can classify text.
In this embodiment, the information obtaining module includes: the questioning module is used for outputting questioning content corresponding to the emotion information according to the emotion information of the patient acquired by the emotion recognition module; the input module is used for the patient to input the response to the questioning content; the questioning module is also used for adjusting questioning content according to the emotion information of the patient acquired by the emotion recognition module and the response of the patient acquired by the input module. The invention adopts a virtual psychologist mode to ask questions of the patient, and the patient answers the questions of the virtual psychologist through a human-computer interaction mode. Specifically, NLP (Natural Language processing) technology and S2A (Speech To Animation) technology are combined, voice-form inquiry examination is driven through NLP, emotion recognition of a patient is carried out according To answers of the patient, inquiry content and expressions of a virtual doctor are controlled through S2A technology, an inquiry flow is optimized, and inquiry and examination with temperature are carried out by the virtual doctor.
In this embodiment, the input module includes a text input module and/or a voice input module. That is, the patient can input his or her symptoms by text input or oral mode, so that the system can obtain the symptom information.
In this embodiment, the preprocessing module includes: the word removing module is used for removing the tone words and stop words from the symptom information and acquiring first processing information; and the sentence dividing module is used for dividing sentences of the first processing information to acquire input information conforming to a preset format.
For example, the user inputs: you are good and I feel very restless recently and have a very depressed mood! It is difficult to fall asleep at night, and it is not easy to sleep and wake up early in the morning. The system firstly carries out word removing processing to remove the stop words preset by the system, such as 'hello', 'I' and the like in the text; then, the space and the non-Chinese comma in the text are replaced by the Chinese comma, and the result after replacement is as follows: recently, people feel very dysphoria and have a very depressed mood, the people have a little difficulty sleeping at night, and the people are not easy to sleep in the morning and wake up early. And finally, taking Chinese commas as separators, segmenting sentences, wherein the result after segmentation is the following 4 short sentences:
(1) recently feel very dysphoric
(2) Depressed mood
(3) It is somewhat difficult to fall asleep at night
(4) Is not easy to sleep and wake up in the morning
The 4 phrases are input information conforming to a predetermined format. The 4 phrases are input into a symptom classification module, so that the symptoms can be automatically classified, as shown in fig. 3. In the symptom classification module, the judgment method for the symptom classification result to be depression is that the input information comprises at least one item of the following items: low mood/depression, no interest in doing things, poor sleep, feeling tired/tired, poor appetite/too much eating, feeling that oneself is bad/disappointed to oneself, difficulty in concentrating attention, slow/slow reaction, suicide/oneself injury; the method for determining that the symptom classification result is anxiety includes the steps that the input information includes at least one of the following items: stress, anxiety, worry, difficulty in relaxing, restlessness, impatience, fear, pain in body parts; the method for judging whether the symptom classification result is the psychotic, wherein the input information comprises at least one of the following items: auditory hallucination and visual hallucination; the method for determining that the symptom classification result is substance dependence includes the steps of, in the input information, including at least one of: want to smoke, want to drink; the method for judging the symptom classification result is forced, wherein the input information comprises at least one item of the following items: repetitive thoughts occur and repetitive behaviors occur. The above 25 sections can comprehensively detect the current mental condition of the patient.
For the symptom classification task, as shown in fig. 3, the BERT model inserts a [ CLS ] symbol before inputting information, and uses an output vector corresponding to the symbol as a semantic representation of the whole text for text classification. It can be understood that: this symbol without explicit semantic information fuses the semantic information of each word/word in the text more "fairly" than other words/words already in the text.
After obtaining the symptom classification result, further, the severity of the corresponding symptom needs to be judged/classified, especially for patients with depression and anxiety as the symptom classification result. At this point, the system automatically throws the question and the user needs to re-enter text or speech. For example, the system throws the question: how long did this symptom of poor sleep persist? And (3) user input: perhaps one or two days. The system makes a determination of the severity of depression based on the user's input at that time. In this embodiment, the degree classification result includes: normal, mild, moderate, severe 4 grades are indicated by the numbers 0, 1, 2, 3, respectively, as shown in fig. 4. It should be noted that, in the patients who have a depression or anxiety in the symptom classification result in the previous step, the output result in the module may be normal because the degree of depression or anxiety is slight and is within the normal range of emotional fluctuation.
In this embodiment, as shown in fig. 2, the method further includes: and the emotion recognition module is used for acquiring data of the facial expression of the patient and acquiring the emotion information of the patient according to the acquired facial expression information.
Further, still include: and the diagnosis suggestion module is used for outputting diagnosis suggestions according to the degree classification result output by the degree classification module and the emotion information output by the emotion recognition module.
The emotion recognition module and the diagnosis suggestion module are arranged, so that the system can carry out multi-dimensional diagnosis on the patient to obtain more accurate diagnosis results.
According to the psychiatric assistant diagnosis system provided by the embodiment of the invention, the information acquisition module, the preprocessing module, the symptom classification module and the degree classification module are arranged, so that the symptom information of the patient can be automatically identified and preprocessed, and the classification of the diagnosis result and the judgment of the severity degree can be automatically carried out, and therefore, a doctor can be assisted to carry out efficient diagnosis. In addition, the invention also combines an emotion recognition module to acquire the current emotion information of the patient so as to carry out multi-dimensional diagnosis on the patient and acquire more accurate diagnosis results.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (8)
1. A psychiatric assisted diagnosis system, comprising:
the information acquisition module is used for acquiring symptom information of the patient;
the preprocessing module is used for carrying out standardization processing on the symptom information to obtain input information conforming to a preset format;
the symptom classification module is used for carrying out symptom classification on the input information which accords with the preset format to obtain a symptom classification result; the symptom classification result comprises: normal, depression, anxiety, psychotic, substance dependence, obsessive-compulsive;
the degree classification module is used for carrying out degree classification on the symptom classification result to obtain a degree classification result; the degree classification result comprises: normal, mild, moderate, severe.
2. The psychiatric medical aid diagnostic system of claim 1, further comprising:
and the emotion recognition module is used for acquiring data of the facial expression of the patient and acquiring the emotion information of the patient according to the acquired facial expression information.
3. The psychiatric medical aid diagnostic system of claim 2, further comprising:
and the diagnosis suggestion module is used for outputting diagnosis suggestions according to the degree classification result output by the degree classification module and the emotion information output by the emotion recognition module.
4. The psychiatric medical aid diagnostic system according to claim 2, wherein the information acquisition module comprises:
the questioning module is used for outputting questioning content corresponding to the emotion information according to the emotion information of the patient acquired by the emotion recognition module;
the input module is used for the patient to input the response to the questioning content;
the questioning module is also used for adjusting questioning content according to the emotion information of the patient acquired by the emotion recognition module and the response of the patient acquired by the input module.
5. The psychiatric medical aid diagnosis system according to claim 4, wherein said input module comprises a text input module and/or a voice input module.
6. The psychiatric medical aid diagnosis system of claim 1, wherein the preprocessing module, the symptom classification module, and the degree classification module are pre-trained BERT models.
7. The psychiatric medical aid diagnostic system of claim 6, wherein the preprocessing module comprises:
the word removing module is used for removing the tone words and stop words from the symptom information and acquiring first processing information;
and the sentence dividing module is used for dividing sentences of the first processing information to acquire input information conforming to a preset format.
8. The system according to claim 6, wherein the symptom classification result is depression, and the input information comprises at least one of the following items: low mood/depression, no interest in doing things, poor sleep, feeling tired/tired, poor appetite/too much eating, feeling that oneself is bad/disappointed to oneself, difficulty in concentrating attention, slow/slow reaction, suicide/oneself injury;
the method for determining that the symptom classification result is anxiety includes the steps that the input information includes at least one of the following items: stress, anxiety, worry, difficulty in relaxing, restlessness, impatience, fear, pain in body parts;
the method for judging whether the symptom classification result is the psychotic, wherein the input information comprises at least one of the following items: auditory hallucination and visual hallucination;
the method for determining that the symptom classification result is substance dependence includes the steps of, in the input information, including at least one of: want to smoke, want to drink;
the method for judging the symptom classification result is forced, wherein the input information comprises at least one item of the following items: repetitive thoughts occur and repetitive behaviors occur.
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| CN118016250A (en) * | 2024-04-09 | 2024-05-10 | 北京智精灵科技有限公司 | Mental disorder rehabilitation training system based on virtual person |
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