CN115565673A - Medical prediction system for postoperative wound infection based on big data - Google Patents
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Abstract
The invention discloses a medical prediction system for postoperative wound infection based on big data, belonging to the technical field of big data; data acquisition and data processing are carried out from two dimensions of the object per se and the operation, the processed data are integrated to obtain a push coefficient, the overall state of the object subjected to lumbar fusion internal fixation is evaluated based on the push coefficient, and dynamic push display reminding is carried out on different objects in a self-adaptive manner according to the evaluation result, so that a doctor can carry out targeted dynamic patrol and the patrol effect of patients in different postoperative states is improved; meanwhile, the display reminding result is traced and corrected according to the medical big data, so that the display reminding accuracy can be improved; the invention is used for solving the technical problem that the whole effect of the display reminding of the wound infection condition after the lumbar fusion internal fixation operation is poor in the existing scheme.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a medical prediction system for postoperative wound infection based on big data.
Background
Lumbar fusion has been widely developed in hospitals at all levels as an effective treatment means for lumbar instability, but incision infection after lumbar fusion occurs sometimes due to various reasons.
After the lumbar vertebrae fuses internal fixation, the doctor need rely on the experience to carry out different frequent patrols and inquiries to the patient of difference to whether there is the symptom of infection according to the information that receives to come preliminary judgement patient, can't demonstrate the warning to the doctor based on current medical big data, and come to trace back and revise the result that the show was reminded according to medical big data, the overall effect that the show that has the lumbar vertebrae to fuse the internal fixation wound infection condition was reminded is not good.
Disclosure of Invention
The invention aims to provide a medical prediction system for postoperative wound infection based on big data, which is used for solving the technical problem of poor overall effect of displaying and reminding the wound infection condition after lumbar fusion internal fixation operation in the existing scheme.
The purpose of the invention can be realized by the following technical scheme:
a big data based medical prediction system for post-operative wound infection, comprising:
the object monitoring module is used for acquiring case data of an object, and performing digital processing and integration on the case data to obtain portrait basic data containing key evaluation values;
the image analysis module is used for evaluating the influence of the health aspect of the object before the image of the object is performed according to the key value;
if the healthy value is not less than K; if K is a real number larger than zero, judging that the influence of the object on the self aspect is large, generating a second body estimation signal, and matching the body estimation value with a body estimation threshold value according to the second body estimation signal to obtain image data containing a body light level, a body middle level and a body high level;
the operation monitoring module is used for carrying out data statistics and data processing on the operation process of different objects to obtain operation processing data;
the operation evaluation module is used for carrying out simultaneous integration on the operation processing data and the image data to evaluate the overall state of the object to obtain operation evaluation data comprising a first push set and a second push set;
and the display reminding module is used for pushing and displaying the reminding to the target according to the operation evaluation data self-adaptive dynamic state.
Preferably, the step of obtaining the portrait base data includes:
counting case data of a subject, and screening whether diabetes and hypoproteinemia exist in the case data; setting the weight B1 and B2 of a case corresponding to diabetes and hypoproteinemia respectively;
acquiring disease degrees corresponding to diabetes or hypoproteinemia, and respectively marking the corresponding disease degrees as C1 and C2; extracting numerical values of all data, and simultaneously integrating to obtain a key evaluation value JG of the object;
the key value and the marked data constitute the image base data.
Preferably, the step of operatively handling data acquisition comprises:
counting the starting time point of the movable knife for the operation and the finishing time point of the suture, obtaining the time length between the two time points and setting the time length as the operation time length S1; counting the number of the fusion segments and marking as S2;
and combining and numbering the marked data to obtain operation processing data.
Preferably, the step of operating the assessment data acquisition comprises:
extracting numerical values of all data, and integrating and connecting the numerical values with a robust value JG in the image data to obtain a pushing coefficient TSX of an object; and evaluating the overall state of the object according to the pushing coefficient TSX so as to perform pushing display on the target in an adaptive dynamic mode.
Preferably, the plurality of pushing coefficients TSX are arranged in a descending order, and the ordered plurality of pushing coefficients are divided according to a preset division threshold to obtain a first pushing set and a second pushing set; the push coefficients and the first and second push sets constitute operation evaluation data.
Preferably, the working steps of the display reminding module comprise:
and acquiring a first push set and a second push set in the operation evaluation data, displaying and reminding the target of increasing the frequency of patrolling the object corresponding to the sorting push coefficient in the first push set, and displaying and reminding the target of maintaining the existing frequency of patrolling the object corresponding to the sorting push coefficient in the second push set.
Preferably, the system further comprises a tracing evaluation module, which is used for tracing and evaluating the inspection result of the object and the infection result of the object, and correcting the display reminding scheme according to the evaluation result; the method comprises the following steps:
acquiring the total push population of the push coefficients in the first push set and the second push set, and respectively marking the total push population as TR1 and TR2; monitoring infection conditions of objects in the first push set and the second push set, counting the total number of infection people in the first push set and the second push set, and respectively marking as GR1 and GR2;
respectively integrating the total number of pushed people and the total number of infected people in the first pushing set and the second pushing set to obtain an evaluation coefficient PX; and when the evaluation coefficient PX is analyzed, matching the evaluation coefficient PX with a preset evaluation threshold value to obtain an evaluation result.
Preferably, the obtaining of the evaluation result includes: if the evaluation coefficient is smaller than the evaluation threshold value, generating an adjusting signal; if the evaluation coefficient is not less than the evaluation threshold value, generating a maintaining signal; the evaluation coefficients and the corresponding adjustment signals and maintenance signals constitute the evaluation result.
Preferably, when the scheme for displaying the reminder is modified according to the evaluation result, the preset division threshold is reduced by using the adjustment signal in the evaluation result.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, data acquisition and data processing are carried out from two dimensions of the object per se and the operation aspect, the processed data are integrated to obtain a push coefficient, the overall state of the object subjected to lumbar fusion internal fixation is evaluated based on the push coefficient, and dynamic push display reminding is carried out on different objects in a self-adaptive manner according to the evaluation result, so that a doctor can carry out targeted dynamic patrol and the patrol effect of patients in different postoperative states is improved; meanwhile, the display reminding result is traced and corrected according to the medical big data, so that the display reminding accuracy can be improved.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of a medical prediction system for big data based post-operative wound infection according to the present invention.
Fig. 2 is a schematic diagram of an apparatus for implementing a medical prediction system for post-operative wound infection based on big data.
Detailed Description
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.
Example one
As shown in fig. 1, the present invention is a medical prediction system for postoperative wound infection based on big data, comprising an object monitoring module, a figure analysis module, a surgery monitoring module, an operation evaluation module and a display reminding module;
the object monitoring module is used for acquiring case data of the object, and performing digital processing and integration on the case data to obtain the portrait basic data of the object; the method comprises the following steps:
counting the case data of the object, and screening whether diabetes and hypoproteinemia exist in the case data; setting the weight of a case B1 and the weight of a case B2 corresponding to diabetes and hypoproteinemia respectively;
it should be noted that the subject can be a patient who performs lumbar fusion internal fixation, when the subject has diabetes or hypoproteinemia, the infection probability after lumbar fusion internal fixation is higher than that of a subject without the two diseases, and the accuracy of data statistics can be effectively improved by portraying the subject from the aspect of disease characteristics;
acquiring disease degrees corresponding to diabetes or hypoproteinemia, and respectively marking the corresponding disease degrees as C1 and C2;
the disease degree can be in a first level, a second level and a third level, the corresponding severity can be expressed as severe degree, severe degree and mild degree, and the accuracy of portrait analysis can be improved by further mining and counting the disease characteristics;
extracting numerical values of all data, integrating the numerical values simultaneously, and calculating and obtaining a key value JG of the object through a formula; the key value JG is calculated by the formula:
in the formula, j1 and j2 are different preset proportionality coefficients, and the value ranges are (0, 3), the preset proportionality coefficients in the formula are set by a person skilled in the art according to actual conditions or obtained through simulation of a large amount of data, for example, j1 may be 1.431, j2 may be 2.137, j2 is greater than j1, and it indicates that the importance of the data item corresponding to j2 is greater than that of the data item corresponding to j 1;
the key value and the marked data form the image basic data of the object;
in the embodiment of the invention, the healthy value is a numerical value used for integrating various data of the disease aspect of a subject to integrally evaluate the influence of the health of the subject; by integrating various data influencing postoperative infection on the aspect of the object, the accuracy of data analysis on the aspect of the object can be effectively improved, so that reliable data support can be provided for subsequent dynamic pushing display reminding;
the portrait analysis module is used for evaluating the influence of the health of the object before the portrait of the object is made according to the healthy value;
if the healthy estimation value is smaller than K, judging that the influence of the object on the self aspect is small and generating a first body estimation signal; k is a real number greater than zero;
if the healthy value is not less than K, judging that the influence of the object on the self aspect is large, generating a second healthy value signal, and matching the healthy value with a healthy value threshold value according to the second healthy value signal to obtain the image grade corresponding to the object;
if the healthy value is smaller than the healthy value threshold, judging that the object belongs to the body lightness grade;
if the healthy value is not less than the healthy value threshold and not more than M% of the healthy value threshold, and M is a real number more than one hundred, judging that the object belongs to the in-vivo grade;
if the healthy value is larger than M% of the healthy value threshold, judging that the object belongs to a body height grade; wherein, the influence degrees corresponding to the body light level, the body middle level and the body height level are sequentially increased;
the healthy value, the first body estimation signal, the second body estimation signal, the body lightness grade, the body middle grade and the body high grade form the image data of the object;
in the embodiment of the invention, the influence and the corresponding influence level in the self aspect are judged by carrying out data statistics and data analysis on the self aspect of the object, so that different objects can be subjected to differential monitoring analysis in a targeted manner;
the operation monitoring module is used for carrying out data statistics and data processing on the operation processes of different objects to obtain operation processing data;
counting the starting time point of the movable knife for the operation and the finishing time point of the suture, acquiring the time length between the two time points and setting the time length as the operation time length S1; the unit of the operation duration can be minutes;
counting the number of the fusion segments and marking as S2;
it should be noted that, in the aspect of surgery, the longer the surgery time and the larger the number of fusion segments, the greater the corresponding risk of postoperative infection, and by performing data acquisition and data analysis in the aspect of surgery, different dimensional data support can be provided for subsequent different subjects to implement differentiated monitoring and analysis;
combining and numbering the marked data to obtain operation processing data;
the operation evaluation module is used for integrating the operation processing data and the image data in a simultaneous manner to evaluate the overall state of the object to obtain operation evaluation data;
extracting numerical values of all data, integrating and combining the numerical values with a key evaluation value JG in the image data, and calculating to obtain a push coefficient TSX of an object; the calculation formula of the push coefficient TSX is:
in the formula, g1 and g2 are different preset proportionality coefficients, g2 is more than 0 and less than g1, g1 can be 1.658, and g2 can be 0.783; s10 is preset standard operation duration, and S20 is the preset standard fusion segment number; alpha is a preset compensation factor, the value range is (0, 3), and the value can be 1.0627;
in the embodiment of the invention, the push coefficient is a numerical value used for integrating various data of the self aspect and the operation aspect of the object to evaluate the overall state of the object; by integrating, analyzing and evaluating various data influencing postoperative infection in different dimensions and carrying out dynamic pushing, displaying and reminding on different objects in a self-adaptive manner according to an evaluation result, a doctor can carry out dynamic patrol in a targeted manner, and the patrol effect of patients in different postoperative states is improved;
evaluating the overall state of the object according to the pushing coefficient TSX so as to be conveniently and adaptively and dynamically pushed and displayed to the target; the target here is a doctor;
arranging a plurality of pushing coefficients TSX in a descending order, and dividing the plurality of ordered pushing coefficients according to a preset division threshold value to obtain a first pushing set and a second pushing set;
the preset division threshold is used for dividing the plurality of pushing coefficients into two parts, one part needs to pay attention in a focused manner, the other part can pay attention according to the conventional scheme, and compared with the existing scheme that doctors need to perform different degrees of patrol on different postoperative patients through experience, the embodiment of the invention can realize more efficient reminding and guiding effects; the specific numerical value of the division threshold can be set based on the big data of the number of normal people and the number of infected people after the existing lumbar fusion internal fixation;
the pushing coefficient and the first pushing set and the second pushing set form operation evaluation data;
the display reminding module is used for pushing and displaying a reminder to a target according to the self-adaptive dynamic state of the operation evaluation data; the method comprises the following steps:
acquiring a first push set and a second push set in operation evaluation data, displaying and reminding a target of increasing the frequency of patrolling the object corresponding to the sorting push coefficient in the first push set, and displaying and reminding the target of maintaining the existing frequency of patrolling the object corresponding to the sorting push coefficient in the second push set;
it should be noted that the display reminder only plays a role as a reminder reference, but not an absolute role, that is, the initiative for performing the patrol is still on the hand of the target (doctor), and the display reminder can make the doctor perform the dynamic patrol more timely and efficiently.
Example two
On the basis of the first embodiment, the embodiment of the invention further comprises a tracing evaluation module, which is used for tracing and evaluating the inspection result of the object and the infection result of the object, and correcting the scheme for displaying the prompt according to the evaluation result; the method comprises the following steps:
acquiring the total push population of the push coefficients of the first push set and the second push set, and respectively marking the total push population as TR1 and TR2;
monitoring infection conditions of objects in the first pushing set and the second pushing set, counting the total number of infection people in the first pushing set and the second pushing set, and respectively marking as GR1 and GR2;
integrating the total number of pushed people and the total number of infected people in the first pushing set and the second pushing set respectively to obtain an evaluation coefficient PX; the calculation formula of the evaluation coefficient PX is:
when analyzing the evaluation coefficient PX, matching the evaluation coefficient PX with a preset evaluation threshold value;
if the evaluation coefficient is smaller than the evaluation threshold, an adjustment signal is generated, the adjustment signal represents that the preset division threshold is high, so that the display reminding accuracy is poor due to the fact that the display reminding needs to be reminded by focusing attention and is divided into a common attention reminding scheme for reminding, and the display reminding accuracy is improved by perfecting and correcting based on the adjustment signal;
if the evaluation coefficient is not less than the evaluation threshold value, generating a maintaining signal;
the evaluation coefficient and the corresponding adjustment signal and the maintenance signal form an evaluation result;
and when the scheme for displaying the reminding is corrected according to the evaluation result, reducing the preset division threshold value by using the adjusting signal in the evaluation result.
In the embodiment of the invention, when the division threshold value is too high, part of objects needing important attention are ignored and not displayed and reminded in time, and the specific numerical value for reducing the preset division threshold value can be set based on the real-time updated big data of the number of normal people and the number of infected people after the lumbar fusion internal fixation;
in addition, the formulas involved in the above are all numerical calculations by removing dimensions, and are one formula which is closest to the real situation and obtained by collecting a large amount of data and performing software simulation.
EXAMPLE III
Referring to fig. 2, a schematic structural diagram of an apparatus for implementing a medical prediction system for post-operative wound infection based on big data according to an embodiment of the present invention is shown. In an embodiment of the present invention, an apparatus for implementing a medical prediction system for post-operative wound infection based on big data may include a processor, a memory, a communication bus, and a communication interface, and may further include a computer program stored in the memory and executable on the processor.
In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor is a control unit (control unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (such as a medical prediction program for post-operative wound infection based on big data and the like) stored in the memory and calling the data stored in the memory.
The memory includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, and the like. The memory may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory may also be an external storage device of the electronic device in other embodiments, such as a plug-in removable hard drive, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device. The memory may also include both internal storage units and external storage devices of the electronic device. The memory may be used not only to store application software installed in the electronic device and various types of data, such as codes of medical prediction programs for post-operative wound infection based on big data, etc., but also to temporarily store data that has been output or will be output.
The communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. A bus is arranged to enable connection communication between the memory and at least one processor or the like.
The communication interface is used for communication between the electronic equipment and other equipment, and comprises a network interface and a user interface. Alternatively, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 2 shows only an electronic device with components, and those skilled in the art will appreciate that the structure shown in fig. 2 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to the various components, and preferably, the power supply may be logically connected to the at least one processor through the power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, etc., which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures. The memory in the electronic device stores a medical prediction program based on big data post-operation wound infection, which is a combination of a plurality of signals, and when the program runs in the processor, the implementation and the running of each step of the medical prediction system based on big data post-operation wound infection can be realized.
Specifically, the specific implementation method of the signal by the processor may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
The electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the above-described modules is only one logical functional division, and other divisions may be realized in practice.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Claims (9)
1. Big data based medical prediction system for post-operative wound infection, comprising:
the object monitoring module is used for acquiring case data of an object, and performing digital processing and integration on the case data to obtain portrait basic data containing key evaluation values;
the portrait analysis module is used for evaluating the influence of the health of the object before the portrait of the object is made according to the healthy value;
if the healthy value is not less than K; if K is a real number larger than zero, judging that the influence of the object on the self-aspect is large, generating a second personal estimation signal, and matching the personal estimation value with a personal estimation threshold value according to the second personal estimation signal to obtain image data including a body light level, a body middle level and a body height level;
the operation monitoring module is used for carrying out data statistics and data processing on the operation processes of different objects to obtain operation processing data;
the operation evaluation module is used for carrying out simultaneous integration on the operation processing data and the portrait data to evaluate the overall state of the object to obtain operation evaluation data comprising a first push set and a second push set;
and the display reminding module is used for pushing and displaying the reminding to the target according to the self-adaptive dynamic state of the operation evaluation data.
2. The big data based medical prediction system of post-operative wound infection according to claim 1, wherein the step of obtaining the profile base data comprises:
counting case data of a subject, and screening whether diabetes and hypoproteinemia exist in the case data; setting the weight of a case B1 and the weight of a case B2 corresponding to diabetes and hypoproteinemia respectively;
acquiring disease degrees corresponding to diabetes or hypoproteinemia, and respectively marking the corresponding disease degrees as C1 and C2; extracting numerical values of all data, and simultaneously integrating to obtain a key evaluation value JG of the object;
the key value and the marked data constitute the image base data.
3. The big data based medical prediction system for post-operative wound infection according to claim 1, wherein the step of operational process data acquisition comprises:
counting the starting time point of the movable knife for the operation and the finishing time point of the suture, acquiring the time length between the two time points and setting the time length as the operation time length S1; counting the number of the fusion segments and marking as S2;
and combining and numbering the marked data to obtain operation processing data.
4. The big-data based medical prediction system for post-operative wound infection according to claim 1, wherein the step of operational assessment data acquisition comprises:
extracting numerical values of all data and integrating the numerical values with key evaluation values JG in the image data to obtain a push coefficient TSX of an object; and evaluating the overall state of the object according to the pushing coefficient TSX so as to perform pushing display on the target in an adaptive dynamic mode.
5. The big-data-based medical prediction system for post-operative wound infection according to claim 4, wherein the plurality of pushing coefficients TSX are arranged in a descending order, and the ordered plurality of pushing coefficients are divided according to a preset division threshold value to obtain a first pushing set and a second pushing set; the push coefficients and the first and second push sets constitute operation evaluation data.
6. The big data based medical prediction system of post-operative wound infection according to claim 1, wherein the working step of displaying the reminder module comprises:
and acquiring a first push set and a second push set in the operation evaluation data, displaying and reminding the target of increasing the frequency of patrolling the object corresponding to the sorting push coefficient in the first push set, and displaying and reminding the target of maintaining the existing frequency of patrolling the object corresponding to the sorting push coefficient in the second push set.
7. The medical prediction system for postoperative wound infection based on big data according to claim 1, further comprising a retroactive evaluation module for retroactive and evaluating the inspection result of the subject and the infection result of the subject, and correcting the scheme of the display reminder according to the evaluation result; the method comprises the following steps:
acquiring the total push population of the push coefficients in the first push set and the second push set, and respectively marking the total push population as TR1 and TR2; monitoring infection conditions of objects in the first pushing set and the second pushing set, counting the total number of infection people in the first pushing set and the second pushing set, and respectively marking as GR1 and GR2;
respectively integrating the total number of pushed people and the total number of infected people in the first pushing set and the second pushing set to obtain an evaluation coefficient PX; and when analyzing the evaluation coefficient PX, matching the evaluation coefficient PX with a preset evaluation threshold value to obtain an evaluation result.
8. The big data based medical prediction system for post-operative wound infection according to claim 7, wherein the obtaining of the assessment result comprises: if the evaluation coefficient is smaller than the evaluation threshold value, generating an adjusting signal; if the evaluation coefficient is not less than the evaluation threshold value, generating a maintaining signal; the evaluation coefficients and the corresponding adjustment and maintenance signals constitute the evaluation result.
9. The big data based medical prediction system for postoperative wound infection according to claim 8, wherein when the scheme for displaying the reminder is modified according to the evaluation result, the preset partition threshold is reduced by using the adjustment signal in the evaluation result.
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CN117524464B (en) * | 2024-01-04 | 2024-04-05 | 北京和兴创联健康科技有限公司 | Method and system for calculating postoperative target hemoglobin based on big data |
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