CN114465828B - Case data processing method for medical system - Google Patents
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Abstract
The invention provides a case data processing method for a medical system, which comprises the steps of determining target case data of a target child node in a case decision tree through input data of a patient end based on a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient; sending a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on the sending request; obtaining target case data corresponding to child nodes in a case decision tree; the physician side updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient side, so that the safety of the case data is improved, and data sharing is realized.
Description
Technical Field
The invention relates to a data processing technology, in particular to a case data processing method for a medical system.
Background
With the continuous progress of science and technology and the continuous improvement of the living standard of people, the existing medical system is continuously abundant, for example: provincial, urban, regional and rural hospitals, each hospital system storing abundant case data.
However, in existing medical systems, individual case data cannot be shared, for example: the case data of a plurality of hospitals still use case records, and every time a patient goes to the hospital to make an inquiry, a doctor updates the case data on the case records correspondingly, but if the patient transfers to the hospital, the corresponding case records are not applicable any more, and cannot be further updated on the basis of the past; some hospitals have developed electronic information systems to record personal data of patients, but the sharing of personal case data of patients cannot be realized, and part of the reasons are that the personal privacy of patients cannot be guaranteed after the patients access to a network, and in addition, because daily case data is huge, network paralysis can be easily caused, once a data server is paralyzed, the hospital cannot normally operate, and the problem that the past case data needs to be recovered is solved.
Therefore, how to realize secure sharing of personal case data becomes an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a case data processing method for a medical system, which can realize sharing of case data under the condition of ensuring safety of the case data of a patient, can enable the patient to select and display the type of a relevant case which the patient wants to ask based on personal intention of the patient, and can hide the type of the case which is irrelevant to the inquiry.
In a first aspect of the embodiments of the present invention, a method for processing case data for a medical system is provided, where a patient transmits case data to a physician end of a physician based on a patient end, and the method specifically includes:
the patient end determines target case data of a target child node in a case decision tree based on input data of a patient, generates a child node key of the target child node according to the target case data, and generates a parent node key of the case decision tree according to patient attribute data of the patient;
sending a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on the sending request;
the physician side decrypts the case decision tree for the first time based on the parent node key to obtain the patient attribute data corresponding to the parent node in the case decision tree, and decrypts the case decision tree for the second time based on the child node key to obtain the target case data corresponding to the child node in the case decision tree;
and the physician end updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient end.
Optionally, in a possible implementation manner of the first aspect, initializing the case decision tree by the following steps, specifically including:
the patient end receives historical case data input by a user, and sequentially extracts patient attribute data, case type data and case data corresponding to each case type data in the historical case data;
constructing parent nodes of a case decision tree according to the patient attribute data;
constructing a corresponding number of decision tree child nodes according to the case category data, wherein each decision tree child node is connected with the decision tree parent node;
and constructing decision tree grandchild nodes in a corresponding number according to the case data corresponding to each case category data, wherein each decision tree grandchild node is connected with a corresponding decision tree child node.
Alternatively, in one possible implementation form of the first aspect,
the steps of determining target case data of a target child node in a case decision tree based on input data of a patient from a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient specifically include:
generating a target child node by a patient based on input data of the patient, and acquiring target case data at a target grandchild node connected with the target child node in a case decision tree;
generating a first character string based on the acquired node number of the target grandchild node, the occupied capacity of each target grandchild node, the current inquiry initial time and a first random number, calculating the first character string based on a hash algorithm to obtain a first hash value, and taking the first hash value as a first child node key, wherein the first child node key is the key of the target child node;
generating a second character string according to the patient age data, the patient height data, the patient communication data and a second random number, calculating the second character string based on a Hash algorithm to obtain a second Hash value, and taking the second Hash value as a parent node key;
a first child node key and a parent node key are generated by the following formulas,
wherein the content of the first and second substances,is a first sub-node key that is,is a key of a parent node, and is,in order to be a function of the hash function,the number of nodes that are the target grandchild node,in order to examine the initial time of the diagnosis,is a first random number that is a random number,is as followsThe capacity of the target grandchild node is occupied,is the data of the age of the patient,is the height data of the patient and is the height data of the patient,an upper limit value for the patient communication data,for the patient at firstThe value to which the communication data corresponds,is a second random number.
Optionally, in a possible implementation manner of the first aspect, in the step of determining, by the patient, target case data of a target child node in the case decision tree based on input data of the patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient, the method specifically includes:
determining all child nodes by the patient based on the input data of the patient, and acquiring all case data at all grandchild nodes connected with all child nodes in the case decision tree;
generating a third character string based on the acquired node numbers of all grandchild nodes corresponding to all child nodes, the occupied capacity of all grandchild nodes, the current inquiry initial time and the first random number, calculating the third character string based on a hash algorithm to obtain a third hash value, and taking the third hash value as a second child node key, wherein the second child node key is the key of all child nodes;
the second child node key is generated by the following formula,
wherein the content of the first and second substances,is the key of the second child node and,as the number of nodes of all the grandchild nodes,in order to address the initial moment of the next inquiry,in order to be a function of the hash function,is a first random number that is a random number,for the patientAnd the individual grandchild nodes occupy the capacity.
Optionally, in a possible implementation manner of the first aspect, the method further includes:
the patient end determines a viewing sub-node that needs to be viewed by a physician based on the input data of the patient;
traversing all child nodes in the case decision tree, and if the target child node corresponding to the viewing child node does not exist, generating a target child node corresponding to the viewing child node;
connecting the newly generated target child node as a new added child node with the parent node of the decision tree, and updating the case decision tree;
and configuring an initial key for the newly added child node.
Optionally, in a possible implementation manner of the first aspect, the method further includes:
after receiving a case decision tree sent by a patient end, a doctor end generates a new sub-node of a corresponding case type based on case type data input by the doctor;
generating an additional grandchild node corresponding to the additional child node according to case data input by a doctor end to obtain an updated case decision tree of the doctor end;
after receiving the case decision tree updated by the doctor, the patient extracts case data corresponding to the new child node to generate a new child node key if judging that the new child node exists in the case decision tree.
Optionally, in a possible implementation manner of the first aspect, the method further includes:
initializing the pre-allocation capacity of each child node in the decision tree according to the initial number of the child nodes in the decision tree;
after judging the newly added child nodes, acquiring the pre-distribution capacity of the child nodes of each child node in the case decision tree and the current occupied capacity of each child node to obtain the residual capacity of each child node, and obtaining the residual total capacity according to the residual capacities of all the child nodes;
acquiring case category data corresponding to each child node, and determining a first distribution weight corresponding to each child node according to the case category data;
acquiring case data corresponding to all grandchild nodes of each child node, determining the last inquiry time closest to the current time in the case data, and determining the second distribution weight corresponding to each child node according to the last inquiry time;
and determining the newly-added capacity of the newly-added child node based on the first distribution weight, the second distribution weight, the node number of the grandchild node and the remaining total capacity.
Optionally, in a possible implementation manner of the first aspect, in the step of determining, based on the first allocation weight, the second allocation weight, the number of nodes in the grandchild node, and the remaining total capacity, a newly added capacity of a newly added child node specifically includes:
the newly added capacity is calculated by the following formula,
wherein the content of the first and second substances,in order to newly increase the capacity of the child node,the first assigned weight of the child node is newly added,weights are assigned to the second of the newly added child nodes,for an upper bound on the number of nodes of the case decision tree child node,is the number of nodes of the child node,is as followsThe number of nodes of the grandchild node to which the child node is connected,is as followsA first assigned weight for a child node,is as followsThe second assignment of weights to the child nodes,the number of nodes that are standard grandchild nodes,is as followsThe child nodes of the child nodes pre-allocate capacity,is as followsThe current occupied capacity of the child node(s),in order to have a remaining total capacity,the value is adjusted for the newly added capacity.
Alternatively, in one possible implementation form of the first aspect,
generating an inquiry frequency of each child node according to the number of nodes of grandchild nodes connected with each child node of the case decision tree, the inquiry initial time corresponding to the initial grandchild node connected with each child node and the inquiry initial time corresponding to the final grandchild node connected with each child node;
generating a child node portrait according to the inquiry frequency of each child node and the number of child nodes of the case decision tree;
generating a decision tree portrait according to the child node portrait and the frequency weight of the child node;
obtaining a first integrated inspection time period according to the decision tree image;
the decision tree portrayal and the first volume examination time period are calculated by the following formula,
wherein the content of the first and second substances,in order to make a decision tree representation,is the initial moment of the next inquiry corresponding to the initial grandchild node connected with the first child node,is as followsCurrent time corresponding to the terminal grandchild node connected with the child nodeThe initial time of the inquiry is set,is as followsThe number of nodes of the grandchild node to which the child node is connected,is a firstThe frequency weight of a sub-node,for the adjustment value of the image of the decision tree,for the reference value of the decision tree image,is a first examination time period of the body examination,the value is adjusted for the physical examination period,for an upper bound on the number of nodes of the case decision tree child node,the number of nodes that are child nodes.
Alternatively, in one possible implementation form of the first aspect,
acquiring a second physical examination time period when the patient actually performs physical examination;
obtaining a time adjustment trend according to the first volume inspection time period and the second volume inspection time period;
correcting the adjustment value of the physical examination time period according to the time adjustment trend to obtain a corrected adjustment value of the physical examination time period;
the corrected adjustment value of the physical examination time period is obtained by the following formula,
wherein the content of the first and second substances,for the second examination period of time,the adjusted value is the adjusted value of the physical examination time period after the correction,the trend correction value is increased for the physical examination period,the trend correction value is reduced for the physical examination period.
In a second aspect of the embodiments of the present invention, a storage medium is provided, in which a computer program is stored, which, when being executed by a processor, is adapted to carry out the method according to the first aspect of the present invention and various possible designs of the first aspect of the present invention.
In a third aspect of the embodiments of the present invention, a readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the first aspect of the present invention and the methods according to the first aspect of the present invention.
According to the medical system case data processing method provided by the invention, the sharing of case data is realized through the interaction of the patient side and the doctor side, so that the doctor can refer to the past case data, and the acquired reference information is more comprehensive; according to the invention, the parent node key is used for protecting the personal attribute data, the basic information of the patient is protected, the information stealing is prevented, the protection of the privacy data of the patient is enhanced through the dynamically set child node key, the patient can select the corresponding case data under the case type to be displayed in the inquiry, the safety of the personal case data is improved, and the privacy function is increased.
The technical scheme provided by the invention can generate a corresponding parent node key and a corresponding child node key by utilizing a hash function based on a case decision tree generated before, wherein the parent node key can be generated based on the attribute data of each patient, such as: a random mother key is generated according to the height, the age, the mobile phone number and the like, the mother keys of all people are different, and the mother keys generated each time are different due to the addition of random numbers, so that the safety of basic data of a patient is greatly improved; the child keys are generated according to the number of the grandchild nodes, the memory occupied by each child node, the inquiry time and the like, at the moment, the child node keys added with random numbers have randomness, and meanwhile, the memory capacity of information such as medical advice and the like added by a doctor each time is also random, so that the child node keys of patients after each visit can be refreshed, and the safety of case data is greatly improved.
According to the technical scheme provided by the invention, a plurality of scenes shared with a doctor end are provided, a patient can only display case data of a case type corresponding to the inquiry through the patient end, the keys of corresponding child nodes are different, and other part of case data are hidden to protect the privacy of the patient; many times, some diseases are complications caused by the simultaneous existence of several types of case data, and patients can also share the whole case decision tree, and all corresponding child nodes can generate the same child node key, so that doctors can conveniently check the case data, 2 different case data sharing modes are provided, the method is more practical, and the case data interaction efficiency is improved.
The technical scheme provided by the invention can obtain the residual capacity of each sub-node through the number of the sub-nodes of the case type, the initial memory capacity pre-allocated before and the current occupied capacity, so as to obtain the total residual capacity, and according to the total residual capacity and each case type, for example: the method comprises the steps of carrying out dynamic allocation on residual capacity, selecting a plurality of common diseases to allocate a plurality of memories, selecting a plurality of recently seen diseases to allocate a plurality of memories for storing the follow-up diagnoses and the like, carrying out intelligent capacity allocation according to the types of the cases, carrying out zoning according to the types of the cases, and reducing the time for retrieving and reading data when extracting and using case data.
According to the technical scheme provided by the invention, the decision tree representation is generated according to the patient's seeing frequency and the number of the types of the cases, a time period for automatically reminding the patient of carrying out physical examination for a long time is generated according to the decision tree representation, and the adjustment is carried out according to the time period of the patient according to the actual need of the physical examination list, so that the result is more accurate.
Drawings
Fig. 1 is a schematic view of an application scenario of the technical solution provided by the present invention;
FIG. 2 is a flow chart of a first embodiment of a method for case data processing for a medical architecture;
FIG. 3 is a flow chart of a second embodiment of a method for medical system case data processing;
FIG. 4 is a schematic diagram of a medical data processing system for a medical system;
fig. 5 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that A, B, C all comprise, "comprises A, B or C" means comprise one of A, B, C, "comprises A, B and/or C" means comprise any 1 or any 2 or 3 of A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
As shown in fig. 1, for a scene schematic diagram of the technical solution provided by the present invention, a patient selects case category data under a case data decision tree to be displayed by using a patient end, and sends the case decision tree, a parent node key and a child node key to a physician end, the patient end is connected with the physician end in a wired or wireless connection, which is not limited herein, the physician decrypts the case data decision tree twice based on the parent node key and the child node key, respectively obtains patient attribute data at the parent node and case data at a grandchild node under target case category data of the child node, the physician adds present case data content to generate a new grandchild node, correspondingly generates a new case decision tree, and sends the new case decision tree to the patient end through the physician end for updating, which can be understood, the patient end may be a mobile device such as a mobile phone and a tablet, but is not limited thereto, and the doctor end may be a processor device such as a laptop and a desktop.
According to the technical scheme provided by the invention, personal case data sharing is realized through the patient end carried by the patient, the safety of the patient case data is ensured through a dynamic encryption mode, and the patient can select the data to be displayed in each inquiry so as to ensure personal privacy.
The invention provides a case data processing method for a medical system, as shown in fig. 2, a patient transmits case data to a doctor end of a doctor based on a patient end, and the method specifically comprises the following steps:
step S110, the patient end determines target case data of a target child node in the case decision tree based on input data of the patient, generates a child node key of the target child node according to the target case data, and generates a parent node key of the case decision tree according to patient attribute data of the patient.
According to the technical scheme provided by the invention, a patient end actively inputs data to be displayed to determine target case data of a target child node in a case data case decision tree based on the patient, generates a child node key corresponding to the target child node according to the target case data to be displayed by the patient, and generates a parent node key according to the age, the height and communication data of the patient end; for example: after the patient arrives at the consulting room, the patient operates the patient end to search and select the data to be displayed, and then target disease case data under the child nodes are used, for example: there are four cases of data under the fracture case category, that is, the patient is fractured four times, and the child node key is generated according to the time, the number of the four times and the occupied capacity of the four times, and the data is obtained by the patient attribute data, for example: the age data of the patient, the height data of the patient and the communication data of the patient are not limited, and a master key is generated, so that the privacy of the personal case data is improved.
In a possible implementation manner of the technical solution provided by the present invention, step S110 specifically includes:
generating a target child node by a patient based on input data of the patient, and acquiring target case data at a target grandchild node connected with the target child node in a case decision tree;
generating a first character string based on the acquired node number of the target grandchild node, the occupied capacity of each target grandchild node, the current inquiry initial time and a first random number, calculating the first character string based on a hash algorithm to obtain a first hash value, and taking the first hash value as a first child node key, wherein the first child node key is the key of the target child node; according to the technical scheme provided by the invention, the attribute data of the target grandchild node is encrypted by using the hash function, the encryption process of the hash function is irreversible, namely, what the original plaintext is cannot be deduced backwards through the output scattered data, and meanwhile, the data security is greatly improved by using random numbers, node numbers, the occupied capacity of each target grandchild node and the like to carry out random encryption.
Generating a second character string according to the patient age data, the patient height data, the patient communication data and a second random number, calculating the second character string based on a hash algorithm to obtain a second hash value, and taking the second hash value as a parent node key; the technical scheme provided by the invention multiplies the patient age data and the patient height data to process the communication data of the patient, such as: the mobile phone, the telephone and the like add the communication data of the patient, multiply the random number and the height correspondingly and respectively to obtain a random number according to the age, then carry out encryption processing by utilizing the hash function, and improve the safety of the data by utilizing the irreversible encryption of the hash function.
The first child node key and the parent node key are generated by the following formulas,
wherein the content of the first and second substances,is a first sub-node key that is,is a key of a parent node, and is,in order to be a function of the hash function,the number of nodes that are the target grandchild node,in order to address the initial moment of the next inquiry,is a first random number that is a random number,is as followsThe capacity of the target grandchild node is occupied,is the data of the age of the patient,is the height data of the patient and is the height data of the patient,an upper limit value for the patient communication data,for the patientA numerical value corresponding to the communication data, wherein the communication data may be a mobile phone number or a telephone number without limitation,is a second random number that is a function of,capacity is occupied for all the target grandchild nodes,a numerical sum of the patient communication data, such as: 12345678912 if the patient communication data is the patient mobile phone number, corresponding toIs 48.
According to the technical scheme provided by the invention, the parent node and the child node are encrypted by using the hash function, and other nodes do not provide password locking, so that a doctor can only look up the content of the case type relevant to the inquiry, and the privacy data of a patient is protected, for example: the patient has had the disease history of haemorrhoids, feels extremely private and just do not demonstrate to falling down the fracture state of an illness and do not help, has guaranteed that patient's privacy case data can not be looked into, has also promoted the security of case data simultaneously.
In another possible implementation manner of the technical solution provided by the present invention, step S110 specifically includes:
determining all child nodes by the patient end based on the input data of the patient, and acquiring all case data at all grandchild nodes connected with all child nodes in the case decision tree; according to the technical scheme provided by the invention, all child nodes are determined by a patient end based on input data of the patient, and all case data at grandchild nodes connected with all child nodes in a case decision tree are obtained, such as: the patient has hyperglycemia, hypertension and hyperlipidemia, three child nodes of hyperglycemia, hypertension and hyperlipidemia and case data of all grandchild nodes connected under hyperglycemia, hypertension and hyperlipidemia are correspondingly obtained, for example, a consultation experience is provided at grandchild nodes under hyperglycemia child nodes: 3/2022, 25/15: 00, carrying out detection once, wherein the blood sugar detection value is 7.1 millimole/liter, and a consultation experience exists at a grandchild node below a hypertension node: 3/2022, 25/25 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; the grandchild node under the hyperlipemia node has an inquiry experience: 26/3/2022 15: 00, carrying out one-time detection, wherein the blood fat detection value is 6.2 mmol/L.
Generating a third character string based on the acquired node numbers of all grandchild nodes, the occupied capacity of all grandchild nodes, the initial time of next inquiry and the first random number, calculating the third character string based on a hash algorithm to obtain a third hash value, and taking the third hash value as a second child node key, wherein the second child node key is the key of all child nodes; according to the technical scheme provided by the invention, the node numbers of the descendant nodes of all the child nodes are obtained, and it can be understood that the assumption that a patient only sees one disease type every time the patient goes to a hospital, and the corresponding number of the descendant nodes is the total number of times of seeing a disease for the patient, namely three inquiry experiences under hyperglycemia, hypertension and hyperlipidemia; obtaining the inquiry experience at the grandchild node: year 2022, 3 month 25 day 15: 00, carrying out detection once, wherein the blood sugar detection value is 7.1mmol/L, and the grandchild node under the hypertension node has an inquiry experience: year 2022, 3 month 25 day 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; the grandchild node under the hyperlipemia node has an inquiry experience: 26/3/2022 15: 00, carrying out one-time detection, wherein the blood fat detection value is 6.2mmol/L occupied capacity, and the corresponding: year 2022, 3 month 25 day 15: 00. year 2022, 3 month 25 day 13: 00. the method comprises the following steps 26/2022 15: 00, combining a random number at the initial moment to form a dynamic value, and performing full encryption by using a hash function, wherein all the child nodes correspond to the same secret key.
The second child node key is generated by the following formula,
wherein the content of the first and second substances,is a second sub-node key that is,as the number of nodes of all the grandchild nodes,in order to address the initial moment of the next inquiry,in order to be a function of the hash function,for the patientThe capacity is occupied by the individual grandchild node,is a first random number that is a random number,and occupying capacity for all the grandchild nodes.
The technical scheme provided by the invention provides another scene, and a plurality of cases are related in reality, for example, hyperglycemia, if the blood sugar is not well controlled, the metabolism is abnormal, cardiovascular diseases are likely to occur, and finally cardiovascular diseases are caused, for example, coronary heart disease is common; cataract: if the blood sugar of the diabetic is not well controlled, the crystal in the eye can be diseased, and the longer the disease is, the turbid crystal can be caused, so that the vision is finally reduced, and the blindness can be caused in serious cases; the difference from another scheme is that the present scheme can perform information sharing of multiple child nodes, even all of them, and a physician can view case data at a grandchild node under multiple child nodes according to his own will, because different from another scheme, in the present scheme, all child nodes generate a child node key, it can be understood that the keys of all child nodes are the same, for example: the node key for hyperglycemia is: 111, the node key for the fracture is: 111, the node key of hypertension is: 111, therefore, all case data of the patient are displayed, and the keys of all the child nodes are a key, so that the doctor can conveniently look up the case data.
And step S120, sending the case decision tree, the parent node key and the child node key which are preset at the patient side to the doctor side based on the sending request.
According to the technical scheme provided by the invention, a patient sends a sending request through a patient end, and a case decision tree, a parent node key and a child node key which are preset at the patient end are sent to a doctor end based on the sending request. The doctor end can receive the whole decision tree, but all the nodes are in a locked state, and meanwhile, the doctor end can conveniently operate the doctor end by a follow-up doctor to unlock the data displayed by the patient by receiving the parent node key and the child node key.
Step S130, the physician side decrypts the case decision tree for the first time based on the parent node key to obtain the patient attribute data corresponding to the parent node in the case decision tree, and decrypts the case decision tree for the second time based on the child node key to obtain the target case data corresponding to the child node in the case decision tree.
According to the technical scheme provided by the invention, a doctor receives a decision tree, a parent node key and a child node key of a patient end, decrypts the case decision tree once by using the parent node key, and obtains patient attribute data corresponding to the parent node in the case decision tree after decryption, such as: patient name, height, weight; and carrying out secondary decryption on the corresponding child nodes in the case decision tree through the child node keys to obtain case data after decryption, protecting the privacy of patients, realizing the sharing of personal case data and facilitating reference of doctors. It can be understood that the patient obtains the hemorrhoids, but the department of inquiry is ophthalmology, the corresponding patient only needs to display the data of the ophthalmology child node, and only transmits the key of the ophthalmology child node, so that the data sharing is realized, the personal privacy is protected, and the data security is improved.
Step S140, the physician side updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient side.
According to the technical scheme provided by the invention, a doctor updates target case data under a child node based on newly-added case data added by the doctor, correspondingly updates to form a new grandchild node, adds the new grandchild node under the child node, correspondingly grows to form a new case decision tree, the doctor sends the updated case decision tree to a patient, replaces the case decision tree before updating in the patient with the new case decision tree, and the continuous updating of the decision tree at the patient realizes the growth of the decision tree, so that the case data of the patient can be more accurately reflected, meanwhile, the key of the corresponding child node also dynamically changes, and the safety of the case data of the patient is improved.
In a possible implementation manner, the technical solution provided by the present invention further includes, as shown in fig. 3, initializing a case decision tree by the following steps, specifically including:
step S210, a patient end receives historical case data input by a user, and sequentially extracts patient attribute data, case type data and case data corresponding to each case type data in the historical case data; according to the technical scheme provided by the invention, a patient end receives historical case data input by a user, wherein the case data comprises case types, inquiry time and case data contents, such as: obtaining the inquiry experience at the grandchild node: 3/2022, 25/15: 00, carrying out one-time detection, wherein the blood sugar detection value is 7.1mmol/L, the corresponding case type is hyperglycemia, the inquiry time is 3 months, 25 days, 15 days in 2022 years: 00, case data content 3, month, 25, day 15 in 2022: 00, carrying out one-time detection, wherein the blood sugar detection value is 7.1mmol/L, the memory occupied by the corresponding case data content is 480B, and dividing historical case data into patient attribute data, case category data and case data corresponding to each case category data to prepare for subsequently establishing a case decision tree.
And S220, constructing parent nodes of the case decision tree according to the patient attribute data. According to the technical scheme provided by the invention, a parent node of a decision tree is constructed according to information such as patient attribute data, for example, height data 175cm of a patient, age data 27 year of the patient, communication data (mobile phone number) 12345678912 of the patient and the like, the content corresponding to the parent node for constructing the decision tree is the height data of the patient, the age data of the patient, the communication data of the patient and the like, and the parent node is constructed to facilitate the generation of a case type child node corresponding to each person in subsequent corresponding generation.
And step S230, constructing a corresponding number of decision tree child nodes according to the case category data, wherein each decision tree child node is connected with the decision tree parent node. According to the technical scheme provided by the invention, according to the case type data of the patient, for example: the case type data of hypertension, hyperglycemia, hyperlipidemia, fracture and the like form case tree child nodes, it can be understood that 4 cases correspondingly form 4 child nodes, N cases correspondingly form N child nodes, the child nodes are connected with the parent nodes, and the rear of the child nodes is constructed so that the grandchild nodes under the child nodes can be constructed later.
Step S240, building decision tree grandchild nodes of a corresponding number according to the case data corresponding to each case category data, where each decision tree grandchild node is connected to a corresponding decision tree child node. According to the technical scheme provided by the invention, a corresponding number of decision tree grandchild nodes are constructed according to the case data corresponding to each case category data, for example, the case category data only has one hypertension, namely, the patient only has too high blood pressure, the corresponding visit record according to the hypertension case category data corresponds, for example, 2 visits are made, and the grandchild nodes under the hypertension node have secondary inquiry experience: 3/2022, 25/25 13:00, performing one-time detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; 3/2022, 26/13: 00, when the blood pressure detection value is 130 systolic pressure and 85 diastolic pressure, 2 grandchild nodes are generated correspondingly, and the two grandchild nodes are connected to the child node of the hypertension case type of the child node.
According to the technical scheme provided by the invention, a case decision tree is generated according to past case data of a patient, a basis is established for the growth of a subsequent case decision tree, and reference is also made for subsequent doctors to consult through establishing a past case decision tree.
In a possible embodiment, the technical solution provided by the present invention further includes:
the patient end determines a viewing sub-node that needs to be viewed by a physician based on the input data of the patient; according to the technical scheme provided by the invention, the patient determines the viewing sub-node to be viewed by the doctor based on the input data of the patient, for example, the patient inputs the type of the hypertension case, and the doctor wants to view the type of the hypertension case.
Traversing all child nodes in the case decision tree, and if the target child node corresponding to the viewing child node does not exist, generating a target child node corresponding to the viewing child node; according to the technical scheme provided by the invention, based on input data of a patient, for example, a doctor wants to check the type of a hypertension case, and traverses all child nodes in a case decision tree based on the type of the hypertension case, if the child nodes without the type of the hypertension case are found to indicate that the patient does not have past medical history of hypertension before, the patient directly generates child nodes of the type of the hypertension case, and corresponding to the fact that no grandchild node exists under the child nodes of the hypertension, the child nodes can be directly sent to the doctor to add grandchild nodes to the doctor.
Connecting the newly generated target child node as a new added child node with the parent node of the decision tree, and updating the case decision tree; according to the technical scheme provided by the invention, a newly generated target child node, such as the newly generated case type child node of hypertension, is connected with the parent node of the case data of the patient and becomes a new case decision tree after being newly added, and compared with the previous case type child node with hypertension, the new case decision tree has one more child node, is updated, and the whole decision tree is conveniently sent to a doctor end in the follow-up process.
The technical scheme provided by the invention is that the key of the newly added child node each time is the initial key, and the initial key can be: 111, 123, the number and the mode of setting the initial key are not limited, for example, a fixed key may be set by the patient as the initial key.
According to the technical scheme provided by the invention, a patient establishes a new case type sub-node by himself through a patient side; if there is no corresponding case type under the case decision tree, that is, there is no corresponding child node, the patient adds the child node of the case data decision tree through the patient end, it can be understood that the patient never has hypertension before, and the blood pressure is found to be high by measuring at home on a certain day, so the patient himself adds the child node in the hospital, so that the case decision tree grows dynamically instead of being fixed, the autonomy of the patient is increased, the patient can carry out follow-up physical examination observation by the newly built node and add data through the doctor.
In another possible implementation manner, the technical solution provided by the present invention further includes:
after receiving a case decision tree sent by a patient end, a doctor end generates a new sub-node of a corresponding case type based on case type data input by the doctor; according to the technical scheme provided by the invention, after the doctor end receives the case decision tree sent by the patient end, when the data of the doctor end is searched and increased by the doctor, for example, the doctor searches the child nodes of the hypertension case type and does not search the child nodes, the doctor directly inputs the child nodes of the hypertension case type to newly add the child nodes of the case decision tree.
Generating an additional grandchild node corresponding to the additional child node according to case data input by a doctor end to obtain an updated case decision tree of the doctor end; according to the technical scheme provided by the invention, according to case data input by a doctor end, for example: 3/2022, 25/25 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, the blood pressure detection value is diastolic pressure of 80, the content of the lower grandchild node of the child node corresponding to the type of the hypertension case is generated, the doctor directly adds the grandchild node to complete the updating of the decision tree, the doctor carries out the updating of the decision tree, and the corresponding case decision tree is more accurate.
After receiving the case decision tree updated by the doctor, the patient extracts case data corresponding to the new child node to generate a new child node key if judging that the new child node exists in the case decision tree. According to the technical scheme provided by the invention, after the patient end receives the case decision tree updated by the doctor end,
the technical scheme provided by the invention provides another scheme for adding the child nodes of the case by the doctor in another scene, if the case decision tree has no corresponding case type, namely has no corresponding child node, the doctor adds the child nodes of the case data decision tree through the doctor end, and can understand that the patient never has too high blood sugar before, but finds the high blood sugar after the hospital detects the high blood sugar, so that the doctor adds the child nodes of the case type, the case decision tree is in an increased state, and the doctor adds the child node data more accurately according to the diagnosis result.
In a possible embodiment, the technical solution provided by the present invention further includes:
initializing the pre-allocation capacity of each child node in the decision tree according to the initial number of the child nodes in the decision tree; according to the technical scheme provided by the invention, before memory allocation is carried out on a new child node, for example, a patient sees 2 different disease types before according to the initial number of the child nodes in a decision tree, such as: hypertension and fracture are 10 GB in total, and 5GB are respectively distributed to 2 nodes.
After judging the newly added child nodes, acquiring the pre-distribution capacity of the child nodes of each child node in the case decision tree and the current occupied capacity of each child node to obtain the residual capacity of each child node, and obtaining the residual total capacity according to the residual capacities of all the child nodes; according to the technical scheme provided by the invention, the capacity of the newly-added child node and the capacity of the newly-added child node can be conveniently distributed in the follow-up process by acquiring the residual total capacity. According to the technical scheme provided by the invention, after the new adding child node is judged to be generated, the child node pre-allocation capacity of each child node in the case decision tree is acquired, for example, 10 GB and 2 child nodes correspond to two points, the pre-allocation capacity is 5GB, the current occupied capacity of the first node for hypertension is assumed to be 2GB, the current occupied capacity of the second node for fracture is assumed to be 1GB, and the corresponding residual total capacity is 7 GB.
Acquiring case category data corresponding to each child node, and determining a first distribution weight corresponding to each child node according to the case category data; the technical scheme provided by the invention quantizes the case type data, respectively quantizes the weight of each case type, and judges the subsequent memory capacity needing to be increased through the quantized weight, for example: since hypertension and the like need to be detected frequently, the data is updated frequently with a corresponding weight of 100, for example: the probability that a fracture due to a fall is an accidental recurrence is not high, and the corresponding weight may be 10.
Acquiring case data corresponding to all grandchild nodes of each child node, determining the last inquiry time closest to the current time in the case data, and determining the second distribution weight corresponding to each child node according to the last inquiry time; according to the technical scheme provided by the invention, the time closest to the current time in each case data is checked as the second weight, so that the occurrence duration of the case is measured. For example: today's visit times are 3 months, 27 days 1: 50, there are 2 cases under the corresponding sub-nodes of hypertension, which are 3 months, 22 days and 2 days: 50 and 3 months, 26 days 3: 50, 1 case data under the child nodes of the fracture is 1 month, 22 days and 2 days: 50, it will be appreciated that the fracture cases are separated by too long a period of time, and the correspondence also represents a case that is a small probability event or has recovered without further treatment, and the second weight ratio for hypertension is greater than the fracture, which may be, for example, 200 for hypertension and 10 for fracture.
Determining the newly increased capacity of the newly increased child node based on the first distribution weight, the second distribution weight, the node number of the grandchild node and the remaining total capacity;
in a possible implementation manner, in the step of determining the newly added capacity of the newly added child node based on the first allocation weight, the second allocation weight, the node number of the grandchild node, and the remaining total capacity, the technical solution provided by the present invention specifically includes:
the newly added capacity is calculated by the following formula,
wherein, the first and the second end of the pipe are connected with each other,in order to newly increase the capacity of the child node,the first assigned weight of the child node is newly added,weights are assigned to the second of the newly added child nodes,for an upper bound on the number of nodes of the case decision tree child node,is the number of nodes of the child node,is as followsThe number of nodes of the grandchild node to which the child node is connected,is as followsA first assigned weight for a child node,is as followsThe second assignment of a child node is a weight,the number of nodes that are standard grandchild nodes,is as followsThe child nodes of the child nodes pre-allocate capacity,is as followsThe current occupied capacity of the child node(s),in order to have a remaining total capacity,in order to newly increase the capacity adjustment value,the proportion value of the new added child node to all child nodes is obtained, wherein,andin a direct proportion to each other, the reaction temperature is controlled,andinversely, it can be understood that the capacity to be allocated can be obtained by multiplying the weight ratio of the child node by the remaining capacity, and meanwhile, other child nodes can also be calculated by using the formula, for example: calculating the allocated capacity of the fracture sub-node, calculating the allocated capacity of the hypertensive sub-node, and so on, again without limitation, wherein the first allocated weight of the new sub-node is addedDetermined quantitatively according to case category data, and second distribution weight of new child nodeCan be artificially set and quantitatively determined according to the last inquiry time closest to the current time of historical case dataFirst assigned weight of child nodeIs quantitatively determined according to case category dataSecond distribution weight of child nodesIs quantitatively determined according to the last interrogation moment closest to the current moment.
According to the technical scheme provided by the invention, the weighted values are obtained by quantifying the type and duration of the case, the capacities of all the child nodes are respectively distributed, and the capacity partition is very quick when the data is called correspondingly. It can be understood that the distribution volume corresponding to common cases such as hypertension, diabetes, cold, etc. is large, and the distribution volume corresponding to unusual diseases such as fracture, etc. is small.
The technical solution provided by the present invention, in a possible implementation manner, further includes
Generating an inquiry frequency of each child node according to the number of nodes of grandchild nodes connected with each child node of the case decision tree, the inquiry initial time corresponding to the initial grandchild node connected with each child node and the inquiry initial time corresponding to the final grandchild node connected with each child node; according to the technical scheme provided by the invention, the node of the grandchild node, for example, the node of the grandchild node under the hypertension node is utilized, for example, the hypertension has 2 inquiries, which correspond to 3 months and 25 days in 2022 and 7 days in 25: 00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; 3/2022, 25/25 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 85; the corresponding initial grandchild node is the earliest node sorted from morning to evening, the terminal grandchild node is the latest node sorted from morning to evening, the corresponding time period is 7 × 3600-13 × 3600=21600, and the time period is divided by the number of times to obtain the interval time of each inquiry, namely the inquiry frequency, wherein if only one grandchild node exists, the time period is set as a default value, and the default value can be 100 or 1000.
Generating a child node portrait according to the inquiry frequency of each child node and the number of child nodes of the case decision tree; according to the technical scheme provided by the invention, the sub-node portrait is generated through the inquiry frequency of each sub-node and the number of the sub-nodes, and the inquiry frequency and the number of the sub-nodes reflect the current health condition of a patient.
Generating a decision tree portrait according to the child node portrait and the frequency weight of the child node; the image value is adjusted by the frequency weight of the sub-node, the image value is combined with the decision tree image benchmark value, the case data condition of the patient is reflected, the health degree of the patient is correspondingly reflected, the decision tree image benchmark value is the average value of daily health case data, the frequency weight of the sub-node can be set manually and correspondingly and is mainly determined according to the inquiry frequency of a certain case of the patient, the higher the inquiry frequency is, the larger the frequency weight value of the corresponding sub-node is, the lower the inquiry frequency is, the smaller the frequency weight value of the corresponding sub-node is, for example: the data cases of the patient under the orthopedic sub-nodes are 4 times, the frequency of the orthopedic inquiry is obtained by dividing the application time by the times, and the frequency weight value of the sub-nodes is correspondingly set according to the frequency of the orthopedic inquiry.
Obtaining a first integrated inspection time period according to the decision tree image; according to the technical scheme provided by the invention, by adjusting the average degree of health, if the number of child nodes is too large, the disease is more, the corresponding constitution is worse, the corresponding user portrait is larger, the time interval is smaller, the too small value is adjusted through the adjustment value of the physical examination time period, for example, the value is smaller than 1, and a minimum standard physical examination time period is set, such as: one physical examination per week, or one physical examination every 3 days. Meanwhile, the inquiry frequency is that the inquiry time interval is larger, the corresponding image value is smaller, the inquiry time interval is larger, the good health state is realized without carrying out a plurality of physical examinations, and the corresponding first physical examination time period is larger.
The decision tree portrayal and the first volume examination time period are calculated by the following formula,
wherein, the first and the second end of the pipe are connected with each other,in order to make a decision tree representation,is as followsWhen the initial inquiry time corresponding to the initial grandchild node connected with the child node is up,is as followsWhen the initial inquiry moment of the current inquiry is corresponding to the terminal grandchild node connected with the child node,is as followsThe number of nodes of the grandchild node to which the child node is connected,is as followsThe frequency weight of a sub-node,for the decision tree image adjustment value,for the reference value of the decision tree image,is a first examination time period of the body examination,the value is adjusted for the physical examination period,for an upper bound on the number of nodes of the case decision tree child node,is the number of nodes of the child node, 365 is the time corresponding to one year,time intervals for the interrogation, for example: the hypertension sub-nodes are correspondingly provided with 2 grandchild nodes, the 1 st inquiry initial time is 7:00 at 1 month and 10 days 2022, the 2 nd inquiry initial time is 13:00 at 1 month and 10 days 2022, correspondinglyIs 7 × 3600=25200, corresponding toIs 13 x 3600=46800,for the frequency of the interrogation of each sub-node,andin a direct proportion to each other, the reaction temperature is controlled,and withIn inverse proportion, it can be understood that the larger the number of child nodes, the shorter the time interval corresponding to the physical examination,andin the inverse proportion,andinversely proportional, it can be understood that the larger the interval of inquiry, the larger the corresponding interval of physical examination, wherein the decision tree representation adjustment valueAnd adjustment value of physical examination time periodCan be set manually, and the corresponding decision tree portrait adjusting value and the corresponding physical examination time period adjusting value are adjusted and determined according to the physical constitution change process of the patient. Wherein the adjustment value of the physical examination time periodThe setting can be manually set, for example: because the patient physique is relatively poor, the actual output time interval is too short, the patient can not go to the physical examination according to the output frequency, and the adjustment value of the physical examination time period can be manually adjusted to meet the actual requirement.
According to the technical scheme provided by the invention, a physical examination time period is automatically generated according to the number of the types of the cases of the patient and the frequency of inquiry, and is used for reminding the patient how often the patient should go to physical examination or inquiry, and the function of automatically reminding the physical examination is realized by processing the case data.
The technical solution provided by the present invention, in a possible implementation manner, further includes
A second physical examination period is obtained when the patient is actually physically examined. Generally, the physical examination of the patient is regular, the second physical examination time period may be the actual physical examination time period of the patient, the shorter the second physical examination time period is, the higher the actual physical examination frequency of the patient is proved, and the longer the second physical examination time period is, the lower the actual physical examination frequency of the patient is proved.
Obtaining a time adjustment trend according to the first volume inspection time period and the second volume inspection time period; the system obtains an adjustment trend value with time increasing or decreasing according to the actual physical examination time period (second physical examination time) of the patient and the first physical examination time period automatically output by the system.
Correcting the adjustment value of the physical examination time period according to the time adjustment trend to obtain a corrected adjustment value of the physical examination time period;
the corrected adjustment value of the physical examination time period is obtained by the following formula,
wherein the content of the first and second substances,for the second examination period of time,the adjusted value is the adjusted value of the physical examination time period after the correction,the trend correction value is increased for the physical examination period,the trend correction value is reduced for the physical examination period,is the absolute value of the difference between the second volume detection time period and the first volume detection time period,is the difference between the second examination time period and the first examination time period. The technical scheme provided by the invention is divided into 2 cases, namely, the actual physical examination time period is greater than the time period output by the system, the difference value of the corresponding physical examination time period is positive and the adjustment value of the physical examination time period is correspondingly increased; the other situation is that the actual physical examination time period is less than the time period output by the system, and at the moment, if the difference value corresponding to the physical examination time period is a negative number, the absolute value is correspondingly taken and the adjustment value of the physical examination time period is correspondingly reduced, wherein during physical examinationInterval increase trend correction valueCan be a range set by people according toThe error is adjusted by the difference value of the first physical examination time period, and the correction value of the increasing trend of the physical examination time periodCan be a range set by people according toThe absolute value of the difference between the second volume inspection time period and the first volume inspection time period is adjusted.
According to the technical scheme provided by the invention, the automatically generated physical examination time period is corrected according to the physical examination order value, the actual physical examination time period and the actual time period of the patient after the patient actually goes to physical examination, so that the actual condition of the patient is better met, and the data is more accurate. The invention has the advantages that the autonomous learning and adjusting process is realized, the system output is more in line with the actual situation, and the output result is more accurate and practical.
In order to implement the medical system case data processing method provided by the present invention, the present invention further provides a medical system case data processing system, wherein a patient transmits case data to a physician end of a physician based on a patient end, as shown in fig. 4, the method specifically includes:
the generation module is used for determining target case data of a target child node in a case decision tree by a patient based on input data of the patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient;
the transmitting module is used for transmitting a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on a transmitting request;
the decryption module is used for decrypting the case decision tree for the first time by a doctor end based on the parent node key to obtain patient attribute data corresponding to the parent node in the case decision tree, and decrypting the case decision tree for the second time based on the child node key to obtain target case data corresponding to the child node in the case decision tree;
and the updating module is used for updating the target case data to obtain updated case data by the doctor end based on the case adding data added by the doctor, updating the case decision tree to obtain an updated case decision tree according to the updated case data, and sending the updated case decision tree to the patient end by the doctor end.
Referring to fig. 5, which is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention, the electronic device 50 includes: a processor 51, a memory 52 and computer programs; wherein
A memory 52 for storing the computer program, which may also be a flash memory (flash). The computer program is, for example, an application program, a functional module, or the like that implements the above method.
A processor 51 for executing the computer program stored in the memory to implement the steps performed by the apparatus in the above method. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory 52 may be separate or integrated with the processor 51.
When the memory 52 is a device independent of the processor 51, the apparatus may further include:
a bus 53 for connecting the memory 52 and the processor 51.
The present invention also provides a readable storage medium, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the methods provided by the various embodiments described above.
The present invention also provides a readable storage medium, in which a computer program is stored, and the computer program is used for implementing the method provided by the above-mentioned various embodiments when being executed by a processor.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to a processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the apparatus, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A case data processing method for a medical system is characterized in that a patient transmits case data to a doctor end of a doctor based on a patient end, and the method specifically comprises the following steps:
the patient end determines target case data of a target child node in a case decision tree based on input data of a patient, generates a child node key of the target child node according to the target case data, and generates a parent node key of the case decision tree according to patient attribute data of the patient;
sending a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on the sending request;
the physician side decrypts the case decision tree for the first time based on the parent node key to obtain patient attribute data corresponding to the parent node in the case decision tree, and decrypts the case decision tree for the second time based on the child node key to obtain target case data corresponding to the child node in the case decision tree;
and the physician end updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient end.
2. The method of claim 1,
initializing a case decision tree by the following steps, specifically comprising:
the patient end receives historical case data input by a user, and sequentially extracts patient attribute data, case type data and case data corresponding to each case type data in the historical case data;
constructing parent nodes of a case decision tree according to the patient attribute data;
constructing a corresponding number of decision tree child nodes according to the case category data, wherein each decision tree child node is connected with the decision tree parent node;
and constructing decision tree grandchild nodes in a corresponding number according to the case data corresponding to each case category data, wherein each decision tree grandchild node is connected with a corresponding decision tree child node.
3. The method of claim 1,
the steps of determining target case data of a target child node in a case decision tree based on input data of a patient from a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient specifically include:
generating a target child node by a patient based on input data of the patient, and acquiring target case data at a target grandchild node connected with the target child node in a case decision tree;
generating a first character string based on the acquired node number of the target grandchild node, the occupied capacity of each target grandchild node, the current inquiry initial time and a first random number, calculating the first character string based on a hash algorithm to obtain a first hash value, and taking the first hash value as a first child node key, wherein the first child node key is the key of the target child node;
generating a second character string according to the patient age data, the patient height data, the patient communication data and a second random number, calculating the second character string based on a Hash algorithm to obtain a second Hash value, and taking the second Hash value as a parent node key;
the first child node key and the parent node key are generated by the following formulas,
wherein the content of the first and second substances,is a first sub-node key that is,is a key of a parent node, and is,in order to be a function of the hash function,is the number of nodes of the target grandchild node,in order to determine the initial moment of the current inquiry,is a first random number that is a random number,is as followsThe capacity is occupied by the individual target grandchild node,is the data of the age of the patient,is the height data of the patient and is,an upper limit value for the patient communication data,for the patientThe value to which the piece of communication data corresponds,is a second random number.
4. The method of claim 1,
the steps of determining target case data of a target child node in a case decision tree based on input data of a patient from a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient specifically include:
determining all child nodes by the patient based on the input data of the patient, and acquiring all case data at all grandchild nodes connected with all child nodes in the case decision tree;
generating a third character string based on the acquired node numbers of all grandchild nodes corresponding to all child nodes, the occupied capacity of all grandchild nodes, the current inquiry initial time and the first random number, calculating the third character string based on a hash algorithm to obtain a third hash value, and taking the third hash value as a second child node key, wherein the second child node key is the key of all child nodes;
the second child node key is generated by the following formula,
wherein the content of the first and second substances,is the key of the second child node and,as the number of nodes of all the grandchild nodes,in order to address the initial moment of the next inquiry,in order to be a function of the hash function,is a first random number that is a random number,for the patient at firstAnd the individual grandchild nodes occupy the capacity.
5. The method of claim 2, further comprising:
the patient end determines a view sub-node to be viewed by the physician based on the patient's input data;
traversing all child nodes in the case decision tree, and if the target child node corresponding to the viewing child node does not exist, generating a target child node corresponding to the viewing child node;
connecting the newly generated target child node as a new added child node with the parent node of the decision tree, and updating the case decision tree;
and configuring an initial key for the newly added child node.
6. The method of claim 2, further comprising:
after receiving a case decision tree sent by a patient end, a doctor end generates a new child node of a corresponding case type based on case type data input by the doctor;
generating an additional grandchild node corresponding to the additional child node according to case data input by a doctor end to obtain an updated case decision tree of the doctor end;
after receiving the case decision tree updated by the doctor, the patient extracts case data corresponding to the new child node to generate a new child node key if judging that the new child node exists in the case decision tree.
7. The method of claim 5 or 6, further comprising:
initializing the pre-allocation capacity of each child node in the decision tree according to the initial number of the child nodes in the decision tree;
after judging the newly added child nodes, acquiring the child node pre-distribution capacity of each child node in the case decision tree and the current occupied capacity of each child node to obtain the residual capacity of each child node, and obtaining the residual total capacity according to the residual capacities of all the child nodes;
acquiring case category data corresponding to each child node, and determining a first distribution weight corresponding to each child node according to the case category data;
acquiring case data corresponding to all grandchild nodes of each child node, determining the last inquiry time closest to the current time in the case data, and determining the second distribution weight corresponding to each child node according to the last inquiry time;
and determining the newly-added capacity of the newly-added child node based on the first distribution weight, the second distribution weight, the node number of the grandchild node and the remaining total capacity.
8. The method of claim 7,
in the step of determining the newly added capacity of the newly added child node based on the first distribution weight, the second distribution weight, the node number of the grandchild node, and the remaining total capacity, the method specifically includes:
the newly added capacity is calculated by the following formula,
wherein the content of the first and second substances,in order to newly increase the capacity of the child node,the first assigned weight of the child node is newly added,weights are assigned to the second of the newly added child nodes,an upper limit value for the number of nodes of a case decision tree child node,is the number of nodes of the child node,is a firstThe number of nodes of the grandchild node to which the child node is connected,is as followsA first assigned weight for a child node,is a firstThe second assignment of weights to the child nodes,the number of nodes that are standard grandchild nodes,is as followsThe child nodes of the child nodes pre-allocate capacity,is a firstThe current occupied capacity of the child node(s),in order to have the remaining total capacity,the value is adjusted for the new increased capacity.
9. The method of claim 1,
generating an inquiry frequency of each child node according to the number of nodes of grandchild nodes connected with each child node of the case decision tree, the inquiry initial time corresponding to the initial grandchild node connected with each child node and the inquiry initial time corresponding to the final grandchild node connected with each child node;
generating a child node portrait according to the inquiry frequency of each child node and the number of child nodes of the case decision tree;
generating a decision tree portrait according to the sub-node portrait and the frequency weight of the sub-node;
obtaining a first integrated inspection time period according to the decision tree image;
the decision tree portrayal and the first volume examination time period are calculated by the following formula,
wherein the content of the first and second substances,in order to render a tree of decisions,is as followsWhen the initial inquiry time corresponding to the initial grandchild node connected with the child node is up,is as followsWhen the initial inquiry moment of the current inquiry is corresponding to the terminal grandchild node connected with the child node,is a firstThe number of nodes of the grandchild node to which the child node is connected,is as followsThe frequency weight of the child node(s),for the adjustment value of the image of the decision tree,is used as the reference value of the decision tree portrait,is a first examination time period of the body examination,the value is adjusted for the physical examination period,for an upper bound on the number of nodes of the case decision tree child node,the number of nodes that are child nodes.
10. The method of claim 9,
acquiring a second physical examination time period when the patient actually performs physical examination;
obtaining a time adjustment trend according to the first volume inspection time period and the second volume inspection time period;
correcting the physical examination time period adjustment value according to the time adjustment trend to obtain a corrected physical examination time period adjustment value;
the corrected adjustment value of the physical examination time period is obtained by the following formula,
wherein the content of the first and second substances,for the second examination period of time,the adjusted value is the adjusted value of the physical examination time period after the correction,the trend correction value is increased for the physical examination period,the trend correction value is reduced for the physical examination period.
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