A maternity examination auxiliary system

Abstract

The present application provides a maternal prenatal examination auxiliary system, which relates to the field of data processing, including: an information collection robot and an information prompt module; wherein the information collection robot includes an information collection unit, an information analysis unit and an information sending unit; the information collection unit is used to interact with the target maternal woman to obtain the target maternal woman's identity information, historical medical advice cooperation information and user personalized information; the information analysis unit is used to analyze the historical medical advice cooperation information and the user personalized information to obtain the target cooperation degree interval corresponding to the target maternal woman; the information sending unit is used to send the target medical advice instruction method to the information prompt module; the information prompt module is used to display the target medical advice prompt method. The present application improves the overall medical advice compliance of maternal women to ensure that the physical health indicators of each maternal woman throughout the pregnancy and delivery cycle are effectively monitored and timely adjusted.

Description

Background Art

[0002] Pregnant women do need to undergo regular prenatal checkups before giving birth. This is a vital process that aims to monitor the health of mother and baby and promptly detect and address potential problems. Considering the uniqueness of each pregnant woman, including personality differences, language habits, cultural background, educational background, and different levels of understanding of medical knowledge, it will indeed directly affect their compliance with historical medical advice. If the same method is used to arrange medical advice for every pregnant woman, some pregnant women may not comply with the medical advice. This may cause potential risks to the pregnant woman's body. Summary of the invention

[0003] In response to the above technical problems, the present application provides a maternal prenatal examination auxiliary system, which at least partially solves the problems existing in the prior art.

[0004] In a first aspect of the present application, a maternal examination auxiliary system is provided, the system comprising: an information collection robot and an information prompt module; wherein the information collection robot comprises an information collection unit, an information analysis unit and an information sending unit;

[0005] The information collection unit of the information collection robot is used to interact with the target parturient to obtain the target parturient's identity information, historical medical advice cooperation information and user personalized information.

[0006] The information analysis unit of the information collection robot is used to analyze the historical medical order cooperation information and the user personalized information to obtain the target cooperation degree interval corresponding to the target parturient; wherein each cooperation degree interval has a corresponding medical order instruction method; the medical order instruction method corresponding to the target cooperation degree interval is the target medical order instruction method.

[0007] The information sending unit of the information collection robot is used to send the target medical order instruction method to the information prompt module.

[0008] The information prompt module is used to display the target medical order prompt method.

[0009] This application has at least the following beneficial effects:

[0010] The maternal examination auxiliary system provided by the present application includes an information collection robot and an information prompt module, wherein the information collection robot includes an information collection unit, an information analysis unit and an information sending unit. First, the information collection unit interacts with the target maternal woman by voice, text, etc. before the current prenatal examination to obtain the target maternal woman's identity information, the degree of cooperation with historical medical orders, and the user's personalized information (language habits, personality information, etc.). Secondly, the information analysis unit conducts a comprehensive analysis of the information collected by the information collection unit to obtain the target cooperation interval to which the target maternal woman's degree of cooperation with historical medical orders belongs. Here, the user's personalized information can reflect information such as the personality characteristics of the target maternal woman, which will affect the confidence of the user's corresponding historical medical order cooperation information, that is, if the user is a person who likes to exaggerate, his or her self-reported degree of cooperation with historical medical orders may be high. Therefore, it is necessary to conduct a comprehensive analysis based on the historical medical order cooperation information and the user's personalized information to obtain a more realistic and objective degree of cooperation with historical medical orders, and finally determine the corresponding target cooperation interval. Each cooperation degree interval has a corresponding doctor's instruction mode, that is, when the target parturient's cooperation degree for the historical doctor's instructions belongs to different cooperation degree intervals, the corresponding doctor's instruction is different. Finally, the information prompt module is used to display it to the corresponding medical staff for reference.

[0011] This application obtains the user's personality characteristics and other information by analyzing the user's personalized information corresponding to the target parturient, and uses this to correct the target parturient's self-reported degree of cooperation with the historical medical orders, and obtains the corresponding more objective degree of cooperation with the historical medical orders. Finally, the target cooperation interval corresponding to the target parturient is obtained, and the target medical order instruction method is determined according to the medical order instruction method corresponding to each cooperation interval. That is, when parturients with different personality characteristics are undergoing prenatal examinations, medical staff will determine the corresponding medical order issuance method based on their personality characteristics and the degree of cooperation with the historical medical orders, so as to provide a suitable medical order issuance method for each parturient with different personality characteristics, improve the overall medical order cooperation of the parturient, and ensure that the physical health indicators of each parturient throughout the pregnancy and childbirth cycle are effectively monitored and timely adjusted. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a structural block diagram of the maternal prenatal examination assistance system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0016] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0017] Please refer to Figure 1 As shown, an embodiment of the present application provides a maternal prenatal examination auxiliary system 100, the system comprising: an information collection robot 110 and an information prompt module 120; wherein, the information collection robot comprises an information collection unit 111, an information analysis unit 112 and an information sending unit 113.

[0018] Here, the information collection robot 110 may be a movable interactive robot, which can obtain the information of the parturient through various interactive methods such as voice interaction and text interaction.

[0019] The information collection unit 111 of the information collection robot 110 is used to interact with the target parturient to obtain the target parturient's identity information, historical medical advice cooperation information and user personalized information.

[0020] Specifically, the interaction methods include voice interaction, text interaction, and the like. The identity information of the target parturient can be obtained by manual input, face scanning input, ID card input, and the like. In one embodiment, the historical medical order coordination information is the target parturient's self-description of the execution of each medical order item in the most recent historical medical order obtained by the information collection unit 111 of the information collection robot 110 through interaction with the target parturient. The user personalized information can be the target parturient's speaking speed, common words, speaking attitude, and other information obtained by the target robot through several preset questions to interact with the target parturient.

[0021] The information analysis unit 112 of the information collection robot 110 is used to analyze the historical medical order cooperation information and the user personalized information to obtain the target cooperation degree interval corresponding to the target parturient; wherein each cooperation degree interval has a corresponding medical order instruction method; the medical order instruction method corresponding to the target cooperation degree interval is the target medical order instruction method.

[0022] Specifically, the information analysis unit is used to perform the following steps to obtain the target compliance interval corresponding to the target parturient:

[0023] S210, obtaining the initial historical medical advice compliance degree corresponding to the target parturient according to the identity information and historical medical advice compliance information of the target parturient.

[0024] The information collection robot 110 can be connected to the medical record information database of the hospital. The information analysis unit 112 of the information collection robot 110 is used to perform the following steps to obtain the initial historical medical advice compliance degree corresponding to the target parturient:

[0025] S211, according to the identity information of the target parturient, obtain the list of medical orders corresponding to the most recent historical medical orders of the target parturient Y=(Y 1 , Y 2 , …, Y i , …, Y n ); i = 1, 2, ..., n; wherein n is the number of medical order items contained in the medical order item list corresponding to the most recent historical medical order from the current time; Y i is the item identifier corresponding to the ith medical order item contained in the medical order item list corresponding to the most recent historical medical order from the current time; each medical order item in Y has a corresponding importance score.

[0026] Here, the information analysis unit 112 of the information collection robot 110 retrieves the historical medical advice corresponding to the target parturient during the last prenatal checkup based on the identity information of the target parturient, and obtains the corresponding medical advice item list Y. As an example: Y iIt may be "prohibiting strenuous exercise". In the entire medical order, the importance of different medical orders is different. As an example: the medical order list Y corresponding to the historical medical order = (control blood sugar, return for a check-up in 1 week, carry blood sugar outline results + fasting urine routine + diet diary, make an appointment for fetal heart ultrasound, iodine-rich diet, count fetal movements at 28 weeks of pregnancy). According to the current physical health indicators of the target parturient, the importance of each medical order item may be different. If the current blood sugar index of the target parturient is high, then the importance of "control blood sugar" is higher. If the current blood sugar index of the target parturient is slightly high, then the importance of "control blood sugar" is relatively low, and "make an appointment for fetal heart ultrasound" is a necessary examination, and its corresponding importance may be higher. That is, for target parturients with different physical health indicators, the importance of the same medical order item may also be different. In summary, the importance of each medical order item is determined according to the corresponding maternal health indicators and each medical order item corresponding to the historical medical order. Further, it can be obtained according to the preset importance score determination model.

[0027] S212, according to Y and historical medical advice cooperation information, to obtain the item cooperation degree list P corresponding to Y = (P 1 , P 2 ,…,P i ,…,P n ), where P i Y i The degree of cooperation with corresponding matters.

[0028] Here, the degree of compliance with each medical order item is obtained based on the target pregnant woman's self-reported historical medical order compliance information.

[0029] Among them, P i Follow these steps to determine:

[0030] Among them, if the corresponding medical order item is a first-class medical order item, and the first target answer corresponding to the medical order item is a positive answer, then determine P i =1; otherwise, determine P i =0; wherein the first type of medical order item has two first preset answers; and the first target answer is one of the first preset answers.

[0031] In one embodiment, the first type of medical order items are whether or not medical order items, that is, the preset answers of the corresponding medical order items are "yes" or "no". For example, if a medical order item is "perform fetal heart ultrasound examination", if the first target answer corresponding to the medical order item is an affirmative answer, that is, "yes", then P i =1; if the first target answer corresponding to the medical order item is a negative answer, that is, "no", then determine P i =0.

[0032] If the corresponding medical order item is a second-category medical order item, then P is determined based on the item coordination degree corresponding to each second preset answer of the medical order item and the second target answer corresponding to the medical order item. i ; where 0≤P i ≤1; and the second target answer is one of the second preset answers; the second preset answers corresponding to the second category of medical orders are greater than two.

[0033] In one embodiment, the second type of medical order items are selection-type medical order items, that is, the preset answers of the corresponding medical order items are multiple options. For example, a medical order item is "control blood sugar", and the preset answers of the corresponding medical order item are "complete control", "moderate control" and "completely uncontrolled"; if the second target answer corresponding to the medical order item is "complete control", then P i =1; if the answer to the second objective of the doctor's order is "general control", then P i =0.5; if the answer to the second target of the doctor's order is "completely uncontrolled", then P i =0.

[0034] It should be noted that since many medical orders cannot be fully covered by the execution status with "yes or no", in this embodiment, on the one hand, in order to more accurately obtain the cooperation of the mother for each medical order, a corresponding preset option answer is set for each second-category medical order, and a corresponding score is assigned for each preset option answer, so as to obtain the corresponding item's cooperation degree. On the other hand, it can avoid the waste of computing resources caused by natural language processing when the mother answers without options. Simpler, more convenient and efficient.

[0035] S213, according to Y, P and the importance score of each medical order item, the initial historical medical order compliance degree CP corresponding to the target parturient is obtained; wherein CP meets the following conditions:

[0036] CP=Σ n i＝1 P i Z i

[0037] Among them, Z i P i The corresponding normalized importance score.

[0038] Here, according to the importance score of each medical order item and the degree of cooperation of each medical order item, the overall degree of cooperation of the target parturient with the historical medical orders is preliminarily obtained. i Z iis the initial historical medical order compliance degree of the i-th medical order item in the historical medical order. The initial historical medical order compliance degree of each medical order item is added up to obtain the comprehensive initial historical medical order compliance degree of the target parturient for the entire historical medical order.

[0039] It should be noted that, since each medical order item has a corresponding importance score, and different medical orders include different medical orders, it is necessary to normalize the importance score corresponding to each medical order item in the historical medical order. For example: the medical order item list corresponding to the historical medical order Y = (Y 1 , Y 2 , Y 3 , Y 4 ), where Y 1 The corresponding importance is divided into 8; Y 2 The corresponding importance is divided into 5; Y 3 The corresponding importance is divided into 3; Y 4 The corresponding importance is divided into 4; each importance score is normalized to get Z 1 =1 / (8+5+3+4)×8=0.4; Z 2 =100 / (8+5+3+4)×5=0.25; Z 3 =100 / (8+5+3+4)×3=0.15; Z 4 =100 / (8+5+3+4)×4=0.2. That is, the total score is adjusted to 1, and each importance score is adaptively adjusted in proportion, so as to obtain the normalized importance score. This makes the total importance score corresponding to each medical order the same, which is convenient for subsequent data calculation.

[0040] S220, obtaining a language confidence coefficient corresponding to the target pregnant woman according to the identity information of the target pregnant woman and the user personalized information.

[0041] Here, the above-mentioned historical medical advice cooperation information is obtained by the information collection robot interacting with the target parturient according to the target parturient's historical medical advice and the preset question template. It is self-reported by the target parturient, that is, the target parturient's self-assessment of the degree of cooperation with the historical medical advice. Due to the different personality characteristics of each parturient, as an example: some parturients with more cautious personality characteristics may be more conservative in their assessment of their degree of cooperation with the historical medical advice, that is, they may be lower than the actual execution degree; on the contrary, some parturients with more extensive and exaggerated personality characteristics may be more exaggerated in their assessment of their degree of cooperation with the historical medical advice, that is, they may be higher than the actual execution degree. User personalized information reflects the personality characteristics of each user. In this way, the language confidence coefficient corresponding to the target parturient is obtained. The more conservative the user's own assessment of the degree of cooperation with the historical medical advice is, the higher the corresponding confidence coefficient is. On the contrary, the more exaggerated the user's own assessment of the degree of cooperation with the historical medical advice may be, the lower the corresponding confidence coefficient is.

[0042] Furthermore, the information analysis unit is used to perform the following steps to obtain a language confidence coefficient corresponding to the target parturient:

[0043] S221, inputting the user personalized information of the target pregnant woman into the target classification model.

[0044] S222, according to the target classification model, obtain the language confidence coefficient YZ corresponding to the target parturient; wherein YZ∈[SZ min , SZ max ]; SZ min is the minimum value of YZ, -1≤SZ min <0; SZ max is the maximum value of YZ, 0<SZ min ≤1; and |SZ min |=|SZ max |.

[0045] Among them, the user's personalized information is classified according to the target classification model. The target classification model can be a natural language processing model, which can output the language confidence coefficient YZ corresponding to the target parturient by analyzing the user's personalized information (the user's speaking speed, attitude, etc.). When YZ is -1, it means that the target parturient's confidence in speaking is very low, that is, her own assessment of the degree of compliance with historical medical advice may be very exaggerated, and she likes to exaggerate the facts. On the contrary, when YZ is 1, it means that the target parturient's confidence in speaking is very high, that is, her own assessment of the degree of compliance with historical medical advice may be very conservative.

[0046] The target parturient's self-reported historical compliance with medical advice was corrected based on the above-mentioned language confidence coefficient to obtain a more objective historical compliance with medical advice.

[0047] S230, obtaining a target historical medical order compliance degree according to the initial historical medical order compliance degree and the language confidence coefficient.

[0048] Here, the target historical doctor's order compliance degree MD meets the following conditions:

[0049] MD=(1+YZ)×CP.

[0050] That is, when YZ is -1, it means that the target parturient's confidence in what she said is very low, that is, her own assessment of the degree of compliance with historical medical orders may be very exaggerated, and she likes to exaggerate the facts. At this time, the initial historical medical order compliance degree CP corresponding to the target parturient is corrected according to YZ. Since the confidence in what the target parturient said is very low, that is, what the target parturient said is basically unreliable, the target historical medical order compliance degree MD is determined to be 0. On the contrary, when YZ is 1, it means that the target parturient's confidence in what she said is very high, that is, her own assessment of the degree of compliance with historical medical orders may be very conservative, then the initial historical medical order compliance degree is multiplied by 2, and the corresponding initial historical medical order compliance degree is appropriately raised to make it closer to the actual historical medical order compliance degree. The above YZ being -1 and YZ being 1 are two extreme cases. In one embodiment, the target classification model obtains the corresponding language confidence coefficient based on the input user personalized information, which is mostly in the interval of [-1,1]. The initial historical medical order cooperation degree is corrected according to the specific language confidence coefficient output by the target classification model, so that the target historical medical order cooperation degree is as objective and accurate as possible.

[0051] In this embodiment, by analyzing the user personalized information corresponding to the target parturient, information such as the target parturient's personality characteristics is obtained, and the target parturient's self-reported degree of compliance with historical medical orders is corrected to obtain a corresponding more objective degree of compliance with historical medical orders.

[0052] S240, obtaining a target compliance range corresponding to the target parturient according to the target historical medical order compliance.

[0053] Among them, this embodiment presets several cooperation degree intervals, each cooperation degree interval has a corresponding doctor's order instruction method; the doctor's order instruction method corresponding to the target cooperation degree interval is the target doctor's order instruction method; the target historical doctor's order cooperation degree belongs to the historical doctor's order cooperation degree range corresponding to the target cooperation degree interval. That is, when pregnant women with different personality characteristics are examined during the prenatal checkup, medical staff will determine the corresponding doctor's order issuance method based on their personality characteristics and the degree of cooperation with historical doctor's orders, so as to provide each pregnant woman with different personality characteristics with an appropriate doctor's order issuance method, improve the overall doctor's order cooperation degree of the pregnant woman, and ensure that the physical health indicators of each pregnant woman in the whole pregnancy and delivery cycle are effectively monitored and timely adjusted.

[0054] It should be noted that the medical orders here can be issued in a variety of ways: emphasizing key points, being detailed, etc.

[0055] The information sending unit 113 of the information collection robot 110 is used to send the target medical order indication method to the information prompt module.

[0056] The information prompt module 120 is used to display the target medical order prompt method.

[0057] Specifically, the information prompt module 120 can be set on an electronic device in the doctor's office, so that the doctor can obtain the target pregnant woman's execution of historical medical advice and her corresponding personality before diagnosing her this time, and can adapt the corresponding target medical advice prompt method for her according to her corresponding target compliance interval, so that the target pregnant woman has a higher degree of acceptance and execution of medical advice.

[0058] In an exemplary embodiment of the present application, the information collection robot further includes a reminder unit.

[0059] The reminder unit is used to perform the following steps:

[0060] In response to receiving a medical consultation reminder during the interaction with the target mother, the target mother is prompted according to the medical consultation reminder.

[0061] Specifically, if a medical consultation reminder is received during the interaction with the target pregnant woman, since the information collection robot can communicate with the equipment in the doctor's office, the target pregnant woman can be prompted to seek medical consultation based on the medical consultation reminder.

[0062] In an exemplary embodiment of the present application, the information collection unit 111 of the information collection robot 110 is further configured to perform the following steps:

[0063] S001, generating an output information list according to the identity information, historical medical advice information and user personalized information.

[0064] The information analysis unit 112 of the information collection robot 110 is also used to obtain a target output word list based on the output information list; wherein the target output word list includes a first target output word sub-list and a second target output word sub-list; the corresponding occurrence frequency of each of the first target output words in the first target output word sub-list is equal to or greater than a preset frequency threshold; the corresponding occurrence frequency of each second target output word in the second target output word sub-list is less than the preset frequency threshold, and each second target output word is the same as any preset focus word; the preset focus words are focus words corresponding to several historical abnormal pregnant women.

[0065] Specifically, the information analysis unit is further configured to perform the following steps to obtain a first target output word sublist:

[0066] S250, segmenting each output information in the output information list to obtain a number of initial words.

[0067] According to a preset word segmentation method, each output information in the output information list is segmented to obtain a number of initial words. Each output information is a natural sentence.

[0068] S260, obtaining a plurality of intermediate words according to a preset keyword thesaurus; wherein the intermediate words are initial words that are the same as any keyword in the preset keyword thesaurus.

[0069] The preset keyword word library stores several keywords corresponding to parturient women, for example: abdominal pain, amniotic fluid, fetal movement, etc. The purpose of this step is to filter out the words that are not related to parturient women in the initial words, for example: you, me, night, etc.

[0070] S270, obtaining a first target output word sublist YL=(YL 1 , YL 2 , …, YL j , …, YL m );j=1,2,…,m;where m is the number of the first target output words; YL j Output word for the jth first target; YL j The frequency of occurrence in the output information list is equal to or greater than a preset frequency threshold.

[0071] Each intermediate word whose appearance frequency in the output information list is equal to or greater than a preset frequency threshold is determined as a first target output word.

[0072] In this embodiment, first, words irrelevant to the parturient are filtered out to avoid noise interference, and then the words with higher frequency, that is, the words repeatedly mentioned by the target parturient during the interaction with the information collection robot, are determined as the first target output words. The first target output words may represent some questions that the target parturient is confused about, or the corresponding physical abnormalities, which need to be highlighted to the doctor in order to assist the doctor in making a diagnosis.

[0073] Specifically, after step S260, the information analysis unit is used to perform the following steps to obtain a second target output word sublist:

[0074] S280, obtaining a plurality of temporary words according to a plurality of intermediate words and a preset frequency threshold; wherein the temporary words are intermediate words whose appearance frequency in the output information list is less than the preset frequency threshold.

[0075] S290, according to a plurality of temporary words and a preset determination method, obtain a corresponding second target output word sublist EL=(EL 1 , E.L. 2 , …, EL p , …, EL q ); p = 1, 2, ..., q; where q is the number of the second target output words; EL p Output word for the pth second target.

[0076] Among them, after the first target output word sublist is obtained by screening, the frequency of occurrence of each remaining intermediate word (i.e., each temporary word) in the output information list is less than the preset frequency threshold. However, due to the different cultural background, educational background, and degree of understanding of medical knowledge of each parturient, some words corresponding to physical conditions or other related matters may be ignored by them, so there may be some words in some temporary words that need to be highlighted to the doctor.

[0077] In addition, each historical abnormal parturient has a corresponding abnormal parturient portrait; the historical abnormal parturient is a parturient with at least one abnormal physical health indicator in the historical time. The abnormal parturient portrait is the user portrait corresponding to the abnormal parturient, including the historical prenatal examination results corresponding to each historical abnormal parturient, including the descriptive words mapped from the abnormal indicators and normal indicators; as an example: if the weight in the historical prenatal examination results is too high, the descriptive word that may be mapped is obesity, and obesity is a descriptive word in the abnormal parturient portrait corresponding to the historical abnormal parturient. Furthermore, each abnormal parturient portrait is encoded according to the one-hot encoding method to obtain the corresponding abnormal feature vector.

[0078] The preset determination method includes:

[0079] S291, clustering is performed according to the abnormal pregnant women portraits corresponding to each of the several historical abnormal pregnant women, so as to obtain the corresponding cluster list J=(J 1 , J 2 , …, J x , …, J y ); x = 1, 2, ..., y; y is the number of clusters; J x The cluster identifier corresponding to the x-th cluster; each cluster includes at least one abnormal maternal portrait; each abnormal maternal portrait has a corresponding abnormal feature vector; each historical abnormal maternal has a corresponding preset focus word list; each cluster has a corresponding preset focus word list set.

[0080] Among them, the matching degree between the abnormal feature vectors corresponding to the abnormal maternal portraits of any two historical abnormal maternal women in each cluster is less than the preset abnormal matching degree threshold. That is, each cluster represents a type of historical abnormal maternal women. The abnormal conditions between historical abnormal maternal women in the same cluster are similar.

[0081] In addition, each historical abnormal pregnant woman has a corresponding preset attention word list, and each preset attention word in the preset attention word list is a descriptive word mapped from the abnormal situation in the historical prenatal examination results corresponding to the historical abnormal pregnant woman. As an example: the preset attention words may be obesity, gestational hypertension, etc.

[0082] S292, according to the abnormal feature vector corresponding to each abnormal pregnant woman portrait contained in each cluster, obtain the cluster center vector list ZJ=(ZJ 1 , ZJ 2 , …, ZJ x , …, ZJ y );Among them, ZJ x For J x The corresponding center vector.

[0083] Here, according to the abnormal feature vectors corresponding to all abnormal maternal portraits contained in each cluster, the central vector corresponding to each cluster is obtained, and the central vector may be the average vector of each cluster.

[0084] S293, obtaining a target feature vector MT of a target portrait corresponding to the target parturient.

[0085] The target image corresponding to the target parturient is obtained and encoded according to the one-hot encoding method to obtain the MT.

[0086] S294, according to MT and ZJ, obtain the key matching degree list MJ=(MJ 1 , M.J. 2 , …, MJ x , …, MJ y ); among them, MJ x For MT and ZJ x The degree of match between

[0087] MJ x Meet the following conditions:

[0088] MJ x =(MT·ZJ x ) / (|MT|×|ZJ x |).

[0089] S295, if MJ x If it is greater than the preset matching threshold, J x Determine the target cluster.

[0090] S296, will be with J x A temporary word that is identical to any preset focus word in the corresponding preset focus word list set is determined as a second target output word to obtain a second target output word sub-list EL.

[0091] Here, MJ x The larger the value, the closer the target image is to J x The more similar the corresponding historical maternal portraits are, the more likely the overall situation of the historical prenatal examination results of the target maternal women is to be different from J. xThe corresponding type of pregnant women with abnormal history is similar. That is, the target pregnant women may have the same symptoms as J x Therefore, if a temporary word (i.e., a word that appears less frequently, i.e., a word mentioned by the target mother but not frequently) is related to J x If any of the preset attention words in the corresponding preset attention word list set is the same, it means that it may be a signal that the target parturient has hidden dangers and needs special attention, so it is used as the second target output word.

[0092] In an exemplary embodiment of the present application, each keyword in the preset keyword word library has a corresponding importance score; after step S270, the information analysis unit is further used to perform the following steps:

[0093] S2100, according to the importance score and occurrence frequency of the keyword corresponding to each first target output word, obtain the corresponding ranking priority XL=(XL 1 , XL 2 ,…,XL j ,…,XL m ), where XL j For YL j Corresponding ranking priority; XL j Meet the following conditions:

[0094] XL j =YD j *XP j

[0095] Among them, YD j For YL j The importance of the corresponding keywords; XP j For YL j The frequency of occurrence of the corresponding keyword in the output information list.

[0096] Specifically, each keyword in the preset keyword word library is provided with a corresponding importance score according to its importance. For example, the importance score of amniotic fluid is 0.8; the importance score of low back pain is 0.3.

[0097] In this embodiment, the arrangement priority corresponding to each first target output word is obtained according to the importance score and occurrence frequency of the keyword corresponding to each first target output word, that is, when displaying each first target output word, the first target output word with a high arrangement priority is displayed first, so that the doctor can be reminded in time according to the importance of each first target output word.

[0098] In an exemplary embodiment of the present application, the information collection robot further includes an information preprocessing unit, and the information processing unit is used to perform the following steps:

[0099] Perform word standardization conversion processing on several initial words.

[0100] Specifically, after obtaining a number of initial words, since the target mother may use voice input and there are problems with colloquialism, in order to improve the matching degree with the preset keyword vocabulary (each keyword is a standard word), the initial words are firstly processed by word standardization conversion.

[0101] As an example: Convert "stomach ache" to "abdominal pain".

[0102] In an exemplary embodiment of the present application, the information sending unit 113 of the information collection robot 110 is also used to send the target output word list to the information prompt module.

[0103] In an exemplary embodiment of the present application, the information prompt module 120 is further configured to display each target output word in the target output word list.

[0104] In this embodiment, after each output information in the output information list is segmented, the words with high frequency and the words with low frequency but the same attention words corresponding to the historical abnormal parturient are output to the corresponding medical staff. Here, the words with high frequency (first target output words) are the words repeatedly mentioned by the target parturient during the interaction with the information collection robot, which may represent some questions that the target parturient is confused about, or the corresponding physical abnormality, which needs to be highlighted to the corresponding medical staff to assist the doctor in making a diagnosis. As for the words with low frequency but the same attention words corresponding to the historical abnormal parturient, due to the different cultural background, educational background and degree of understanding of medical knowledge of each parturient, some words corresponding to physical conditions or other related matters may be ignored by them. Therefore, among the words that are not mentioned many times, there may also be some words that need to be highlighted to the doctor. Based on the relevant attention words corresponding to the historical abnormal parturient as a reference, the same second target output words are output. In this way, we collected the words that the target pregnant women used during the interaction with the information collection robot, so as to assist the relevant medical staff to determine the content of the prenatal examination and issue medical advice to the pregnant women based on the above words, and to make the relevant medical staff pay attention to some maternal conditions that may be overlooked based on the above words, so as to ensure that the physical health indicators of each pregnant woman are effectively monitored and adjusted in time throughout the pregnancy cycle. In addition, we can also learn about some of the pregnant women's needs based on the above words, and answer their questions within the limited consultation time to avoid missing some of the inquiry content during the consultation.

[0105] In an exemplary embodiment of the present application, after step S212, the information analysis unit 112 of the information collection robot 110 is used to perform the following steps:

[0106] S214, according to Y, P and the importance of each medical order item, obtain the key item coordination degree list GP corresponding to Y = (GP 1 , G.P. 2 ,…,GP i ,…,GP n );GP i Y i Corresponding key matters cooperation degree; GP i Meet the following conditions:

[0107] GP i =P i *Z i

[0108] Among them, Z i P i The corresponding normalized importance score.

[0109] S215, according to GP and YZ, obtain the corresponding item standard coordination degree list BP=(BP 1 , BP 2 ,…,BP i ,…,BP n ); Among them, BP i Y i Corresponding key matters coordination degree; BP i Meet the following conditions:

[0110] BP i =GP i *YZ.

[0111] Specifically, BP i It indicates the more objective degree of cooperation of the target parturient with respect to a certain medical order item in the most recent historical medical order (taking into account the corresponding language confidence coefficient of the target parturient).

[0112] S216, according to BP and the preset standard compliance threshold, obtain the target medical order list MS = (MS 1 , M.S. 2 ,…,MS c ,…,MS d ); among them, MS c is the cth target medical order item; the standard cooperation degree corresponding to each target medical order item is less than the preset standard cooperation degree threshold.

[0113] Specifically, the target medical orders are those with which the target pregnant women have a low degree of cooperation.

[0114] The information collection unit 111 is used to perform the following steps to obtain a body status information list set F:

[0115] S002, interacting with the target parturient according to the preset question list corresponding to each target medical order item of the MS, so as to obtain a list set of physical status information F = (F 1 , F 2 , …, F c , …, F d ).

[0116] Specifically, for medical advice items with low cooperation of the target parturient, the information collection unit 111 of the information collection robot 110 continues to interact with the target parturient, and this interaction is for each of the above-mentioned target medical advice items.

[0117] In this embodiment, target medical order items whose corresponding item standard compliance is less than a preset standard compliance threshold are selected from the most recent historical medical orders. The item standard compliance is obtained based on the item compliance, importance score and language confidence coefficient of the target parturient corresponding to each medical order item, and the item standard compliance is positively correlated with the corresponding item compliance, importance score and language confidence coefficient of the target parturient, respectively, so as to modify the key item compliance of each medical order item according to the personality characteristics of the target parturient, so as to obtain a more objective and accurate item standard compliance.

[0118] This embodiment obtains the standard degree of compliance for each medical order item. If the standard degree of compliance for the corresponding item is low, it means that the actual degree of compliance of the target parturient to the medical order item may be low. At this time, the information collection unit of the information collection robot interacts with the target parturient according to the preset question list corresponding to the medical order item with a low actual degree of compliance, wherein the questions in the preset question list may be indicator change questions, that is, through secondary interaction, the changes brought to the target parturient's physical indicators by the medical order items with a low actual degree of compliance are determined, so that the doctor can obtain as much effective physical condition of the target parturient as possible before the consultation, so as to assist the doctor in diagnosis and prescribing medical orders. On the other hand, it saves the doctor's consultation time, and can preliminarily know the impact of the medical order with a low actual degree of compliance on the target parturient's physical indicators, so that the doctor can adjust and monitor the corresponding treatment plan, and can promptly discover possible problems of the parturient.

[0119] In an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0120] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0121] The electronic device according to this embodiment of the present application is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0122] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0123] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps described in the above “Exemplary Method” section of this specification according to various exemplary embodiments of the present application.

[0124] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0125] The storage 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.

[0126] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0127] The electronic device can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable users to interact with the electronic device, and / or communicate with any device (such as routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. This communication can be carried out through an input / output (I / O) interface. In addition, the electronic device can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs) and / or public networks, such as the Internet) through a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0128] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile 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 computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0129] In an exemplary embodiment of the present application, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Exemplary Method" section of the present specification.

[0130] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) 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.

[0131] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0132] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0133] Program code for performing the operations of the present application 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 the case of 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., via the Internet using an Internet service provider).

[0134] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0135] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0136] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A maternal examination auxiliary system, characterized in that: The system comprises: an information collection robot and an information prompt module; wherein the information collection robot comprises an information collection unit, an information analysis unit and an information sending unit; The information collection unit of the information collection robot is used to interact with the target parturient to obtain the target parturient's identity information, historical medical advice cooperation information and user personalized information; The information analysis unit of the information collection robot is used to analyze the historical medical advice cooperation information and the user personalized information to obtain a target cooperation degree interval corresponding to the target parturient; wherein each cooperation degree interval has a corresponding medical advice instruction mode; the medical advice instruction mode corresponding to the target cooperation degree interval is the target medical advice instruction mode; The information sending unit of the information collection robot is used to send the target medical order indication method to the information prompt module; The information prompt module is used to display the target medical advice prompt mode; The information analysis unit is used to perform the following steps to obtain the target compliance interval corresponding to the target parturient: S210, obtaining the initial historical medical advice compliance degree corresponding to the target parturient according to the identity information and historical medical advice compliance information of the target parturient; S220, obtaining a language confidence coefficient corresponding to the target parturient according to the identity information and user personalized information of the target parturient; wherein the user personalized information is used to characterize the personality characteristics of the target parturient; different personality characteristics lead to different results of the compliance evaluation of historical medical orders; different results of the compliance evaluation of historical medical orders lead to different corresponding language confidence coefficients; S230, obtaining a target historical medical order compliance degree according to the initial historical medical order compliance degree and the language confidence coefficient; S240, obtaining a target cooperation degree interval corresponding to a target parturient according to the target historical medical advice cooperation degree; The information analysis unit is used to perform the following steps to obtain the initial historical medical advice compliance degree corresponding to the target parturient: S211, according to the identity information of the target parturient, obtain the medical order list Y=(Y1, Y2, ..., Y i , …, Y n );i=1,2,…,n;wherein n is the number of medical order items contained in the medical order item list corresponding to the most recent historical medical order from the current time; Y i is the item identifier corresponding to the i-th medical order item contained in the medical order item list corresponding to the most recent historical medical order at the current time; each medical order item in Y has a corresponding importance score; S212, according to Y and historical medical advice cooperation information, to obtain the item cooperation degree list P=(P1, P2, ..., P i ,…,P n ), where P i Y i The degree of cooperation with the corresponding matters; S213, according to Y, P and the importance score of each medical order item, the initial historical medical order compliance degree CP corresponding to the target parturient is obtained; wherein CP meets the following conditions: CP=Σ n i=1 P i Z i Among them, Z i P i The corresponding normalized importance score.

2. The maternal examination auxiliary system according to claim 1, characterized in that: The information analysis unit is used to perform the following steps to obtain a language confidence coefficient corresponding to the target parturient: S221, inputting the user personalized information of the target pregnant woman into the target classification model; S222, according to the target classification model, obtain the language confidence coefficient YZ corresponding to the target parturient; wherein YZ∈[SZ min , S.Z. max ]; SZ min is the minimum value of YZ, -1≤SZ min <0; SZ max is the maximum value of YZ, 0<SZ max ≤1; and |SZ min |=|SZ max |.

3. The maternal examination auxiliary system according to claim 2, characterized in that: The target historical doctor's order compliance degree MD meets the following conditions: MD=(1+YZ)×CP.

4. The maternal examination auxiliary system according to claim 1, characterized in that: P i Follow these steps to determine: If the corresponding medical order item is a first-class medical order item, and the first target answer corresponding to the medical order item is a positive answer, then determine P i =1; otherwise, determine P i =0; wherein the first type of medical order has two first preset answers; and the first target answer is one of the first preset answers.

5. The maternal examination auxiliary system according to claim 4, characterized in that: P i Follow these steps to determine: If the corresponding medical order item is a second-category medical order item, P is determined according to the item coordination degree corresponding to each second preset answer of the medical order item and the second target answer corresponding to the medical order item. i ; where 0≤P i ≤1; and the second target answer is one of the second preset answers; the second preset answers corresponding to the second category of medical orders are greater than two.

6. The maternal examination auxiliary system according to claim 1, characterized in that: The information collection robot also includes a reminder unit, The reminder unit is used to perform the following steps: In response to receiving a medical consultation reminder during the interaction with the target mother, the target mother is prompted according to the medical consultation reminder.

7. The maternal examination auxiliary system according to claim 1, characterized in that: The interaction includes voice interaction and text interaction.

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