KR102150602B1 - Method, apparatus and computer program for postoperative diagnosis - Google Patents

Abstract

The present invention relates to a method of estimating the likelihood of recovery of a user after surgery, wherein step a of collecting state information and symptom information of a user through a user device in a service server, swelling of the state information of the user in the service server Or state change information of the state parameter for pain, a risk class determined based on the state parameter at a certain point in time, a symptom parameter for lumps among the symptom information, and a detailed parameter for at least one of a feeling of heat and erythema accompanying the lumps In step b of estimating the likelihood of occurrence of inflammation using and in the service server, a recovery score is assigned for each state parameter using the user's state information and the risk level, and an average score for each of the state parameters is used. Step c, which estimates the recovery rate.

Description

Postoperative condition diagnosis method, device and computer program {METHOD, APPARATUS AND COMPUTER PROGRAM FOR POSTOPERATIVE DIAGNOSIS}

The present invention relates to a method and apparatus for diagnosing a postoperative condition. More specifically, the present invention relates to a system for monitoring a user after plastic surgery, estimating a recovery degree and a recovery rate of an individual user using user data, and analyzing a risk situation.

With the recent development of computing technology, the medical paradigm is changing to data-based. It is a trend to diagnose a patient's condition and provide personalized treatment based on accumulated medical information without expert intervention.

Korean Patent Publication 10-2018-0059715 (published on June 6, 2018)

The present invention provides a system that collects state and symptom information of a user who has undergone a surgical operation, estimates the likelihood of occurrence of inflammation, recovery rate, and recovery ambience of the patient based on user data, and provides a notification on a risk situation. It aims to do.

The method for estimating the possibility of recovery of a user after surgery according to an embodiment of the present invention includes step a of collecting the user's state information and symptom information through the user device in the service server, in the service server, among the user's state information. Details of at least one of state change information of state parameters for swelling or pain, risk rating determined based on the state parameter at a certain point in time, symptom parameters for lumps among the symptom information, and hot sensation and erythema accompanying the lumps Step b of estimating the likelihood of occurrence of inflammation using parameters, and in the service server, a recovery score is assigned for each state parameter using the user's state information and the risk level, and an average score for each of the state parameters is used. And the step c of estimating the recovery rate.

delete

According to the present invention, status information and symptom information of a user who has undergone a surgical operation may be collected in arbitrary time units, and the likelihood of occurrence of inflammation, recovery rate, and recovery ambience of a specific patient may be estimated based on user data. It works. Furthermore, according to the present invention, there is an effect of estimating a risk for any condition and/or symptom occurring after a surgical operation, and providing a notification to the user.

1 is a flowchart illustrating a method of diagnosing a user's recovery state after a surgical operation according to an embodiment of the present invention
2 is an example of a user interface that collects user's state information and symptom information according to an embodiment of the present invention and displays the likelihood of occurrence of inflammation, recovery rate, and recovery atmosphere

The present invention is not limited to the description of the embodiments to be described below, and it is obvious that various modifications may be made without departing from the technical gist of the present invention. Further, in describing the embodiments, descriptions of technical contents that are widely known in the technical field to which the present invention pertains and are not directly related to the technical subject matter of the present invention will be omitted.

Meanwhile, in the accompanying drawings, the same elements are represented by the same reference numerals. In addition, some components in the accompanying drawings may be exaggerated, omitted, or schematically illustrated. This is to clarify the gist of the present invention by omitting unnecessary description not related to the gist of the present invention.

In the medical field, especially in surgical operations, it takes time to recover after surgery. During the recovery period, the patient suffers from swelling, bruising, pain, bleeding, and inflammation at the surgical site, and abnormal symptoms may occur. The patient may wonder if his condition is a temporary condition accompanying the operation or an emergency that requires an immediate visit. Furthermore, the patient may wonder if he or she is recovering from the surgery, is the recovery rate normal, and how long the recovery period is expected.

For example, in the case of eye plastic surgery, it is common for dark bruising, bleeding, and swelling at the surgical site to disappear within a month after surgery. In other words, it can be diagnosed as a dangerous situation if dark bruising, bleeding, and swelling do not decrease after a month.

The present invention aims to solve the above problems. That is, according to an embodiment of the present invention, it is possible to collect user state information and symptom information in units of time, and estimate a user's recovery speed and recovery atmosphere based on the user information. Further, according to an embodiment of the present invention, based on the collected data, the surgical site and the surgical method are classified, and the recovery grade and the risk grade are classified by time for each specified surgical operation, and the recovery grade and the risk grade are used as user condition parameters and symptoms. Can be distinguished by parameters.

A system for diagnosing a user condition according to an embodiment of the present invention may include a user device and a service server.

The user device may be equipped with an application for diagnosing a user's postoperative state according to an embodiment of the present invention. The application may perform a function of providing a user interface for collecting user state information and symptom information, and providing the collected state information and symptom information to a service server. Furthermore, the application may perform a function of receiving and displaying information on a user's recovery rate and recovery atmosphere from the service server.

Furthermore, the service server according to the embodiment of the present invention collects the patient's biometric information from before and after surgery until the patient is completely recovered, applies the collected biometric information to the machine learning framework to learn, and learns the body from surgery to recovery. Information can be modeled. Furthermore, the service server may estimate the likelihood of occurrence of inflammation, recovery rate, and recovery ambience of the patient by applying biometric information of a patient at a specific time point to the machine learning framework.

For example, the service server may collect bioelectric signals of a patient from before surgery until the patient is completely recovered after surgery. The bioelectrical signal may be collected from an electrode sensor attached to the patient's skin. For example, a wearable device in contact with a patient's skin may include an electrode sensor capable of measuring a bioelectrical signal, and digitally convert the bioelectrical signal collected through the electrode sensor and transmit it to a service server.

The service server may generate a safety level vector, an electrocardiogram vector, an electromyogram vector, and an electroencephalogram vector by classifying the safety level, electrocardiogram, electromyogram, or electroencephalogram signal from the received bioelectrical signal. Alternatively, the service server may reduce the multidimensional bioelectrical signal vector received from the wearable device to a lower dimension within a range including information on safety, electrocardiogram, electromyography, and electroencephalogram signals.

As another example, the service server may collect biometric information from image data photographing a surgical site. For example, when data obtained by photographing the surgical site in an arbitrary time unit is received from the user device, the service server extracts feature points for the surgical site, bruise, and scar from the image data, and selects an arbitrary feature point, for example, Based on the feature points, the scale and RGB values of image data over time can be corrected and synchronized.

Furthermore, the service server calculates a feature descriptor for information such as brightness, color, sharpness, gradient, scale, pattern value, and change value distribution value of the feature point from the synchronized image data, and provides images for the surgical site, bruise, and scar. You can create vectors. The image vector includes, for example, information on the size of the surgical site at a specific time point, the range of the scar, color, ridge, and erythema, and may further include information on the range, color, and color distribution of the bruise. Alternatively, the service server may process the image vector to include information on changes in the surgical site, bruise, and scar images that change over time.

As another example, the service server may collect biometric information such as score data for pain, bleeding, and swelling input by a patient or medical staff.

Furthermore, the service server aggregates bioelectrical signal data such as safety level, electrocardiogram, electromyography, electroencephalogram, etc., image data for surgical sites, bruises, and scars, and score data for pain, bleeding, and swelling based on the time axis to provide biometric information. Can be integrated into vectors. Furthermore, the service server may generate a database for bioelectrical signal data, image data for surgery sites, bruises, scars, score data for pain, bleeding, and swelling, and integrated biometric information vectors.

Afterwards, the service server can model the biometric information over time until recovery after surgery by learning a large amount of biometric information in the machine learning framework.

For example, the service server may generate a label for estimating the occurrence of inflammation from biometric information of any patient with inflammation in the surgical site after the surgical operation. As another example, the service server may generate a label for estimating a recovery ambience from biometric information of a patient following a normal healing process after a specific surgery. Thereafter, the service server may model the biometric information over time until recovery after the surgical operation using the biometric information database and the label.

Thereafter, the service server may apply the biometric information of an arbitrary user to the biometric information model of the machine learning framework to estimate a recovery state such as inflammation occurrence probability, recovery speed, and recovery atmosphere. Furthermore, when the estimated recovery state of the user exceeds a preset range, the service server may determine that it is an abnormal symptom and notify it.

Meanwhile, the service server according to an embodiment of the present invention may classify the stages of healing after a surgical operation according to the passage of time.

A wound in the human body means that the skin or other tissues are cut off or a part of the tissue is damaged due to physical damage caused by an external force. There are abrasions, bruises, lacerations, and cutouts caused by a knife blade, and in the form of wounds, there are epidermal detachment windows, subcutaneous bleeding, left wounds, acne, and renal wounds, but largely divided into closed windows and open windows.

In this case, the wound accompanying the surgical operation can be regarded as an incision caused by a blade (mass) artificially generated in a normal body. In order to heal these wounds, individualized processes do not proceed in sequence, but each process proceeds simultaneously.

The wound healing process and duration vary depending on the degree of tissue damage, but in general, damage caused by a clean incision can follow the predicted pattern under normal circumstances. Therefore, the service server can classify the recovery period after surgery into inflammatory, proliferative, and maturity. In this case, each period is not independent and may overlap with other periods.

More specifically, the service server may match symptoms such as fever, tumors, swelling, dysfunction, and scab formation that may occur up to 48 hours after surgery and the inflammatory phase. Furthermore, the service server can match symptoms such as disappearance of inflammation signs, swelling, reduction of wound size, reduction of gaps in the wound, and itchiness, which can occur from 2 days to 6 weeks after surgery, with the proliferative period. Furthermore, the service server can match symptoms in which the feeling of pulling is reduced, red or protruding scars are reduced, and the function is normalized with maturity, that is, skin remodeling.

The stage of healing after surgery may be set differently according to the patient's age, race, type of wound, body part, and duration of the inflamed area.

Furthermore, the service server can check data on typical patient conditions, symptoms and atypical conditions, and symptoms in the categorized healing stage, and classify the risk level at that time. Furthermore, the service server may check whether a state or symptom at any time after surgery of a specific patient is normal, needs attention, or is abnormal. For example, the service server may collect user state information and symptom information from the user device and classify the risk level of the state or symptom at the time by referring to the database.

After that, the service server can estimate the likelihood of occurrence of inflammation, the recovery rate, and the recovery ambience of the user by using the risk level.

For example, the service server may determine the healing state of the wound by comparing information on changes in parameters such as bleeding, pain, bruising, swelling, and scar accompanying the surgical operation with data on the current state of the user. That is, it is possible to infer the healing rate of the user by comparing the timing of the clinical sign and the stage of the user's condition.

A more specific method of diagnosing a user in the service server will be described later in the description of FIG. 1.

1 is a flowchart illustrating a process of diagnosing a user's condition after a surgical operation according to an embodiment of the present invention.

In step 130, the service server may classify the recovery period of the surgical operation according to a predetermined criterion.

For example, the recovery period after surgery can be classified into an early inflammatory phase, a late inflammatory phase, an early proliferative phase, a late proliferative phase, an early maturity phase, and a late maturity phase. In this case, each period is not independent and may overlap with other periods.

For example, the initial inflammatory phase is from the day of surgery to 3 days after the operation, the late inflammatory phase is from 3 days to 7 days after the operation, the early proliferative phase is from 3 days after the operation to 21 days after the operation, the late proliferative phase is from 21 days after the operation to 42 days after the operation, and the early maturity phase. 14 days after surgery ~ 184 days after surgery ~ Late maturity can be classified as 184 days after surgery.

On the other hand, it should be noted that the present invention suffices to classify the recovery period of the surgical operation according to a predetermined criterion in step 130, and is limited to the above example and cannot be interpreted. That is, the classification of the recovery period may be changed according to the setting, and may be classified into, for example, an inflammatory phase, a proliferative phase, and a maturity phase.

In step 135, the service server may define a state parameter over time. This is to quantitatively evaluate the patient's condition by using common symptoms accompanying surgery such as bruising, pain, bleeding, swelling, and scarring. For example, a bruise after surgery is bright red-bluish dark red-a light green thin border around a dark bruise-a yellow bruise around a dark bruise-a dark bruise becomes smaller and a widespread yellow bruise comes down -If a little light yellow bruise remains-If a little light brown bruise remains-If all the bruises are gone, it can be divided into 8 stages. (H1~H8)

As another example, the pain after surgery continues to be very painful, even if it is still, to the extent that it is difficult to tolerate-the degree to which it continues to hurt quite a lot even when it is still-the degree to which it continues to be a little painful even when it is still-the degree to which it is temporarily painful when touching or moving-the degree to which it is not painful, but to the degree that it often throbs-does not hurt to the touch It can be divided into six levels, such as degree. (P1~P6)

As another example, bleeding can be divided into three stages: the degree to which the blood does not stop so that several sheets of gauze are drenched-the degree to which the blood flows out a little-the degree to which there is no bleeding. (B1~B3)

As another example, if the swelling after surgery is widely swollen to the periphery-If only the surgery area is swollen a lot-If the swelling is lower than the beginning and does not swell any more-There is a noticeable small swelling all day long enough to be recognized by others -If there is a small swelling that makes a difference in the morning and afternoon-If there is a small swelling that can be felt by yourself-There is no swelling. It can be classified into 7 stages. (E1~E7)

As another example, if the scar after surgery is not scarred-If the wound is sutured and the area around the wound is red-If the wound has a scab-A bumpy red scar-A light pink scar that protrudes slightly-A scar with inconspicuous skin color -It can be divided into 7 stages, such as enlarged or depressed scars. (S1~S7)

Furthermore, the service server can model the distribution of state parameters over time after surgery. This can then be used to classify the risk rating for the condition at a specific point in time or to estimate the recovery rate.

For example, condition parameters may be distributed based on H1 or H2 for bruises on the 2nd day after surgery, P2 to P5 for pain, B2 for bleeding, E1 or E2 for swelling, and S2 for scars, and the 7th day after surgery, the bruise is H4. To H8, pain P2 to P6, bleeding B3, swelling E1 or E2, scars may be distributed based on S2, S4, S5.

Meanwhile, the service server can define symptom parameters over time for each type of surgery. This is to quantitatively evaluate the symptoms generally associated with the surgery.

For example, in the case of eye-related surgical operations, the symptom parameters can be defined as a foreign body sensation in the eye, subconjunctival bleeding, lumps, pulling sensation, mucous membrane lift, dry eye syndrome, itching at the surgical site, conjunctival swelling, sensation dull, and asymmetry. .

As another example, in the case of a nose-related surgical operation, a lot of runny nose, nasal congestion, subconjunctival bleeding, lumps, pulling sensation, protruding flesh or bones, itching at the surgical site, sensational depression, asymmetry, etc. can be defined as symptom parameters. I can.

As another example, in the case of a surgical operation involving both eyes and nose, a foreign body sensation in the eye, subconjunctival bleeding, lumps, pulling sensation, mucous membrane lifting, dry eye syndrome, itching, conjunctival edema, and sensation may be defined as symptom parameters.

On the other hand, among the various symptoms that can accompany a specific surgery, “monochrome” can be classified as an important index related to the occurrence of inflammation. Accordingly, according to an exemplary embodiment of the present invention, the symptom for lumps may be defined together with detailed parameters for the occurrence of heat sensation and skin redness.

Furthermore, the service server can model the distribution of symptom parameters over time after surgery. This can then be used to classify the risk rating for the condition at a specific point in time.

For example, the feeling of pulling on the 11th day after surgery on the eye area or itching at the surgery site is normal, but a feeling of foreign body in the eye, subconjunctival bleeding, mucous membrane lift, dry eye, conjunctival edema, sensational depression, and asymmetry are all symptoms that require attention. Can be classified. In particular, in the case of lumpy symptoms, it can be classified as an abnormal symptom as an indicator related to inflammation.

After that, the service server can classify the risk level of the status parameter by recovery period. The risk rating can be assigned as normal, caution, or abnormal, which can then be used to estimate the likelihood of developing inflammation, the rate of recovery, and the extent of recovery. (Step 140)

For example, in the case of a bruise parameter, if H1, which means a bright red bruise, appears within 3 days after surgery, it is normal, if it appears within 3 to 6 days, it can be given a caution, and if it appears after that, an abnormal grade can be assigned. . Furthermore, if H2, which means a bluish dark red bruise, appears before 5 days after surgery, it is normal, if it appears after 5 days, it can be given an abnormal grade if it appears after 15 days.

As another example, in the case of the swelling parameter, E1, which means swelling wide to the periphery, is assigned a normal level if it appears 7 days before surgery, caution when it appears after 7 days, and abnormal grade when it appears even after 14 days. can do.

Furthermore, the service server can classify the risk level of symptom parameters by recovery period. The risk rating can be assigned as normal, caution, or abnormal, which can then be used to estimate the likelihood of developing inflammation, the rate of recovery, and the extent of recovery. (Step 145)

For example, in the case of an eye-related surgical operation, if the symptom of the pulling sensation appears within 2 months after the operation, it is normal, and if it occurs after 2 months after the operation, a caution grade can be given. In addition, if the symptom of a foreign body sensation in the eye appeared immediately after surgery and within 14 days after surgery, caution may be given, and if it appeared after 14 days, an abnormal grade may be assigned.

As another example, in the case of a nose-related surgical operation, the symptoms of nasal congestion may be assigned a normal grade if within one month after the operation, a caution grade after a month, and a risk grade after two months. Furthermore, asymmetry symptoms can be given a normal grade if within 10 days after surgery, a caution grade if within 6 months after 10 days, and an abnormal grade after 6 months.

Particularly, among the various symptoms that can accompany surgical operations, the “muddy” symptom can be classified as a very important index related to the occurrence of inflammation. Therefore, the lumpy symptom can be given an abnormal grade at any time after surgery.

Meanwhile, in steps 110 to 125, the user device may collect state information of a patient after surgery.

More specifically, in step 110, the user device may collect basic information of the user about age, age, sex, body mass index, constitution, skin type, and number of operations. This is for specifying the user and generating statistical information.

In step 115, the user device may collect hospital information through a user input. For example, it is possible to collect identification information about the hospital visiting the hospital, the purpose of the visit, and identification information of the operation. This is an arbitrary step for generating statistical information about hospitals and doctors and may be omitted depending on implementation.

In step 120, the user device may collect surgical information through a user input. For example, it is possible to collect information on the surgical site, surgical method, and timing.

<A> of FIG. 2 is an example of a user interface for collecting hospital information or surgery information. When a user input for a surgery hospital, surgeon, operation day, operation site, operation method, and number of operations is received, hospital information and surgery information may be generated through a user interface as illustrated in <A> of FIG. 2.

Surgery information and/or hospital information may be collected through a user device as illustrated in FIG. 1, but may be collected from a server of a hospital performing surgery according to an embodiment of the present invention. For example, the service server may collect hospital information and/or surgery information by interlocking with the hospital system to query the waiting list for surgery.

In step 125, the user device may collect user status and symptom information for each time period. For example, it is possible to collect information on conditions such as bruising, swelling, pain, bleeding, and scars, and information on discomfort symptoms. According to an embodiment of the present invention, the state information and symptom information may be collected through a user interface displaying the parameter defined in step 135.

According to a preferred embodiment of the present invention, state information and symptom information of a user may be collected for each time period. In order to estimate the patient's recovery rate and recovery ambience, it is necessary to change the user's condition and symptoms over time after surgery.

Because.

Furthermore, the user device may classify a risk level for the collected status information and/or symptom information according to a preset criterion, and display the risk level through the user interface.

For example, the user device may divide the risk level into upper, middle, and lower levels, and match each level with red, yellow, and blue to display the risk level with a color value.

For example, if the user's swelling or pain has increased compared to the past, or if a bright red bruise continues, if severe pain continues, if bleeding continues, if the swelling continues after one month after surgery, the risk rating for the condition is classified into the upper grade. In addition, the risk level for the user status can be displayed by displaying a red icon in the corresponding status item of the user status list.

As another example, when a user experiences symptoms of mucous membrane lifting or sensational depression, the risk level for the symptom is divided into medium, and a yellow icon is displayed in the corresponding symptom item in the user symptom list to indicate the risk level for the user symptoms. can do.

An example of a user interface for collecting patient status and symptom information from a user device and displaying a risk level will be described later in the description of FIG. 2.

<B, C> of FIG. 2 is an example of a user interface for collecting patient status information. The user can designate any one of bruise, swelling, pain, bleeding, and scar through the user interface as shown in <B, C> of FIG. 2, and quantitatively measure the state at the time point through the view of the designated index. .

In particular, according to an embodiment of the present invention, a user may select state change information of a corresponding indicator through a user interface such as 210. Even when the user selects the same view for his or her state at the previous point in time and at the corresponding point in time, the state may be changed finely over time, and 220 is for checking such state change information.

According to an embodiment of the present invention, state change information may be classified into decrease, maintenance, increase, and the like, and may be collected by providing a user selectable view such as the example of 210.

delete

<D> of FIG. 2 is an example of a user interface for collecting patient symptom information. The user may select a symptom corresponding to himself from among symptoms that may accompany the operation through the user interface as shown in <D> of FIG. 2.

Particularly, among the various symptoms that can accompany the surgical operation, the “monochrome” symptom is an important index related to the possibility of inflammation. When the user selects “monguri” (Fig. 2D, 215), “heat sensation” or “skin An additional user interface may be provided for entering symptoms for “redness”.

Meanwhile, <E> of FIG. 2 is an example of a user interface in which a risk level for a user's condition and symptoms is displayed. The user may be provided with a notice on a risk condition and/or symptom item as a color value through a user interface such as <E> of FIG. 2.

More specifically, 222 of <E> of FIG. 2 is a list of the user's condition and symptoms. An example of 222 is a case in which a red icon is displayed in the swelling increase and bleeding increase item in the user's condition list, a red icon is displayed in the foreign body sensation in the user's symptom list, a red icon in the mucous membrane lifting item, and a yellow icon is displayed in the sensory deterioration item. .

As described above, according to an embodiment of the present invention, information on a risk level in a specific state is displayed as a color icon, so that a user can intuitively recognize the risk level in a corresponding state.

Returning to the description of FIG. 1 again, in step 150, user information may be transmitted from the user device to the service server. The user device may provide basic information of the user, hospital information, surgery information, user's state information and symptom information over time to the service server.

In step 160, the service server may estimate the possibility of occurrence of inflammation of the corresponding user based on the collected user information.

It is appropriate to determine the likelihood of occurrence of inflammation after 5 days or more after surgery, and parameters for swelling or pain among the user's condition information, condition change information, swelling or pain risk level, and parameters for lumps among the user's symptom information. , It may be calculated using user information about heat sensation or skin redness.

For example, if a specific user's swelling state change information at a specific point in time is “increased” or if the risk level for the swelling state at that point is “abnormal”, the likelihood of inflammation may be increased by 5%.

As another example, when the information about the change in the state of pain at a specific time of a specific user is “increased” or the risk level for the pain state at that time is “abnormal”, the likelihood of occurrence of inflammation may be increased by 5%.

As another example, when “mongwoori” is selected among symptom information of a specific user at a specific time point, the likelihood of occurrence of inflammation may be increased by 50%. Further, if the user has a symptom of “monogori”, since the possibility of occurrence of inflammation is very high, according to the exemplary embodiment of the present invention, when the symptom is “heat sensation” or “skin redness” together with the “monogori” symptom, Each can increase the likelihood of developing inflammation by 20%.

In step 170, the service server may estimate the recovery speed of the user based on the collected user information.

The recovery speed can be estimated by assigning a recovery score for each state parameter by using the user's state information and the risk level of the state information, and matching the average score for the state parameter with the classification of recovery speed from very slow to very fast.

For example, the recovery score may be assigned 5 points for normal risk level, 2 points for caution, and 0 points for abnormal. Furthermore, 10 points may be given if an arbitrary condition exceeds the normal grade and is a very good condition (out of grade). For example, on the 4th day after surgery, if the user's bruise condition is H4 (if the yellow bruise is widely spread around the dark bruise), it is a very good condition that exceeds the normal grade, and a recovery score for the bruise parameter can be given 10 points. .

Further, the recovery rate is, for example, if the average recovery score is less than 2, it is very slow, if it is 2 or more and less than 3, it is very slow, if it is 3 or more and less than 4, it is slow, if it is 4 or more and less than 5, it is slightly slow, and if it is 5 or more and less than 6, it is usually, 6 or more. The recovery score can be qualitatively expressed by matching a little faster if it is less than 7, fast if it is 7 or more, very fast if it is 8 or more, and very fast if it is 9 or more.

For example, a specific user may consider a case where the condition for bruising is H4, the condition for pain is P3, the condition for bleeding is B2, the condition for swelling is E4, and the condition for scar is S4 on the 21st day after surgery. .

According to an embodiment of the present invention, on the 21st day after surgery, H4 is caution, P3 is caution, B2 is risk, E4 is normal, and S4 is normal. In this case, the user's recovery score at the point in time may be given 2 points for bruises, 2 points for pain, 0 points for bleeding, 5 points for swelling, and 5 points for scars. The total number of recovery scores is 14 points, and dividing this by 5, which is the number of state parameters, the average value of the recovery scores using the state parameters can be calculated as 2.8. In this case, since the average value of the recovery score is matched as much slow, the user's recovery speed can be determined as "very slow".

In step 170, the service server may estimate the recovery atmosphere of the corresponding user based on the collected user information.

The user's recovery atmosphere may be calculated using state change information about swelling and pain, symptom information, etc. based on the recovery score for the state parameter. For example, an additional score of 0.1 when swelling and pain is reduced at a certain point in time, an additional score of 0 when maintained, and an additional score of -0.1 when it is increased can be given. In the case of YES, 0 points, -1.5 for caution grade, and -3 points for abnormal grade.

In the previous example in which a specific user was 21 days after surgery, the condition for bruising was H4, the condition for pain was P3, the condition for bleeding was B2, the condition for swelling was E4, and the condition for scar was S4, at that point in time. Swelling is reduced, pain is maintained, and mucous membrane lift and dry eye symptoms develop can be considered.

According to an embodiment of the present invention, the user's state recovery score at the time point may be given 2 points for bruises, 2 points for pain, 0 points for bleeding, 5 points for swelling, and 5 points for scars. . (Total 14 points)

Furthermore, 0.1 as an additional score for swelling and 0 as an additional score for pain may be given by reflecting state change information for swelling and pain. (Total 0.1 points)

Furthermore, considering the symptoms of mucous membrane lift and dry eye syndrome, on the 15th day after surgery, mucous membrane lift and dry eye symptoms can be given -1.5 points for each symptom as a caution grade. (Total -3 points) In this case, the total recovery score is 11.1 points, which can be estimated as 11.1% recovery.

Meanwhile, when the probability of occurrence of the user's condition or symptom is less than or equal to a preset range, the service server may separately determine the risk of the user's condition or symptom. (Step 172)

For example, if the user's swelling or pain has increased compared to the past, or if a bright red bruise continues, if severe pain continues, if bleeding continues, if the swelling continues after one month after surgery, the probability of occurrence is very low and the risk is estimated to be high. can do.

In step 175, the service server may transmit information on the probability of occurrence of inflammation, the recovery rate, the estimated value of the recovery atmosphere, and the estimated risk value to the user device, and in step 180, the user device may notify the user.

<E, F> of FIG. 2 is an example of a user interface that provides information on the likelihood of occurrence of inflammation, a recovery rate, and a recovery atmosphere.

According to an embodiment of the present invention, the likelihood of occurrence of inflammation can be quantitatively displayed as shown in 220 and provided to the user, and an estimated value for the recovery rate can be displayed as shown in 230 and provided to the user. Furthermore, information on the recovery atmosphere can be quantitatively displayed, such as 240, and provided to the user.

The embodiments of the present invention disclosed in the present specification and drawings are provided only to provide specific examples to easily explain the technical content of the present invention and to aid understanding of the present invention, and are not intended to limit the scope of the present invention. It is apparent to those of ordinary skill in the art that other modifications based on the technical idea of the present invention can be implemented in addition to the embodiments posted here.

Claims (4)

In the method of estimating the possibility of recovery of the user after surgery,
In the service server, a step of collecting the state information and symptom information of the user through the user device,
In the service server, state change information of a state parameter for swelling or pain among the user's state information, a risk level determined based on the state parameter at a certain point in time, a symptom parameter for a lump in the symptom information, and a symptom parameter for a lump in the symptom information. A step b of estimating the likelihood of occurrence of inflammation using a detailed parameter for at least one of the accompanying heat sensation and erythema; And
In the service server, the step c is to assign a recovery score for each state parameter using the user's state information and the risk level, and estimate a recovery rate using an average score for each of the state parameters. How to estimate the likelihood of recovery.
The method of claim 1, wherein after step c,
Checking state change information based on a recovery score assigned to each state parameter for the swelling and pain, and giving an additional score to the state change information;
Assigning a risk level to the symptom information and assigning a recovery score for each risk level; And
And estimating a recovery ambience of a corresponding user by aggregating a recovery score for the condition parameter and a recovery score for the symptom parameter.
The method of claim 2,
In step c, in the service server, when the risk level is abnormal, 0 points, caution, 2 points, normal, 5 points, and 10 points are assigned to each symptom parameter, and the symptom And estimating a recovery rate using an average value of recovery scores for each parameter.
The method of claim 3, wherein step a,
In the user device, a view capable of specifying a user's state is provided, the state information is collected by receiving a user selection input for the view, and the risk level for the state information is classified according to a preset criterion Displaying the risk level; And
In the user device, a view for symptoms accompanying any surgery is provided, the symptom information is collected by receiving a user selection input for the view, and the risk level for the symptom information is classified according to a preset criterion And displaying the risk level.

Images