CN115919286B - Disease data acquisition and analysis method and system - Google Patents
Disease data acquisition and analysis method and system Download PDFInfo
- Publication number
- CN115919286B CN115919286B CN202211416107.5A CN202211416107A CN115919286B CN 115919286 B CN115919286 B CN 115919286B CN 202211416107 A CN202211416107 A CN 202211416107A CN 115919286 B CN115919286 B CN 115919286B
- Authority
- CN
- China
- Prior art keywords
- nasal
- target patient
- secretion
- nasal cavity
- rhinitis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 83
- 201000010099 disease Diseases 0.000 title claims abstract description 21
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 21
- 210000003928 nasal cavity Anatomy 0.000 claims abstract description 193
- 206010039083 rhinitis Diseases 0.000 claims abstract description 145
- 238000011156 evaluation Methods 0.000 claims abstract description 83
- 230000000241 respiratory effect Effects 0.000 claims abstract description 68
- 230000028327 secretion Effects 0.000 claims description 130
- 206010039101 Rhinorrhoea Diseases 0.000 claims description 120
- 208000010753 nasal discharge Diseases 0.000 claims description 114
- 230000006399 behavior Effects 0.000 claims description 111
- 210000002850 nasal mucosa Anatomy 0.000 claims description 38
- 238000009423 ventilation Methods 0.000 claims description 32
- 230000001815 facial effect Effects 0.000 claims description 26
- 230000002159 abnormal effect Effects 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 9
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000003709 image segmentation Methods 0.000 claims description 3
- 230000010352 nasal breathing Effects 0.000 claims description 3
- 210000001989 nasopharynx Anatomy 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000012544 monitoring process Methods 0.000 abstract description 3
- 230000000007 visual effect Effects 0.000 abstract description 3
- 208000036071 Rhinorrhea Diseases 0.000 description 6
- 238000003745 diagnosis Methods 0.000 description 6
- 230000007547 defect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention relates to the technical field of disease data acquisition and analysis, and particularly discloses a disease data acquisition and analysis method and an acquisition and analysis system. On the one hand, the accuracy and the reliability of the state evaluation result of the nasal cavity corresponding to the target patient are guaranteed to the greatest extent by analyzing the state evaluation index of the nasal cavity corresponding to each acquisition time point of the target patient, the persuasion of the state analysis result of the nasal cavity corresponding to the target patient is improved, and accurate and visual data are provided for the analysis of the severity of rhinitis corresponding to the follow-up target patient. On the other hand, the singleness and one-sided performance of the monitoring and analysis of the severity of the rhinitis corresponding to the patient in the current technology are effectively solved by analyzing the respiratory state evaluation index of the target patient corresponding to each acquisition time point, and the reliability and the accuracy of the analysis of the severity of the rhinitis of the patient are increased.
Description
Technical Field
The invention relates to the technical field of disease data acquisition and analysis, in particular to a disease data acquisition and analysis method and an acquisition and analysis system.
Background
Along with the rapid development of science and technology, the application fields of data acquisition and analysis become wider, wherein the most remarkable is the data acquisition and data analysis of diseases, and the characteristics of intellectualization, high efficiency and the like provide more convenient and effective diagnosis results for patients, thereby becoming an important diagnosis mode for keeping population healthy.
Because of the influence of social working environment and living environment, many patients do not pay attention to the rhinitis of the patients, so that the rhinitis of the patients is aggravated, a series of diseases are caused, and the importance of rhinitis data acquisition and analysis is highlighted.
At present, when rhinitis data of a patient are acquired, the inside of the nasal cavity of the patient is mainly detected, the rhinitis type diagnosis is carried out on the patient based on the nasal cavity internal data of the patient, the inspection process is complicated, the intelligent and high-efficiency analysis of the rhinitis type corresponding to the patient cannot be realized, and the diagnosis period of the patient is further prolonged.
At present, when the severity degree of rhinitis of a patient is analyzed, the nasal cavity data of the patient is mainly diagnosed and analyzed by a doctor, the breathing state data of the patient is ignored, the one-sided and subjective performance of the analysis of the severity degree of rhinitis of the patient are caused, meanwhile, the scientific data support is lacked, and a certain influence is generated for the follow-up treatment of the patient to a certain extent.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a disease data acquisition and analysis method and a disease data acquisition and analysis system, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme: the first aspect of the invention provides a disease data acquisition and analysis method, comprising the following steps: 1. collecting and analyzing nasal secretion information of a patient: the facial state videos of the target patient corresponding to the set period are collected, facial state sub-videos of the target patient corresponding to the nasal discharge behaviors are obtained, and meanwhile nasal secretion information of the target patient corresponding to the nasal discharge behaviors is extracted and analyzed to obtain the specific rhinitis type corresponding to the target patient.
2. Patient nasal mucosa status collection analysis: the method comprises the steps of collecting the internal information of the nasal cavity of each collecting time point in the corresponding setting period of a target patient to obtain a nasal cavity internal information set of the target patient corresponding to each collecting time point, and analyzing a nasal cavity internal state evaluation index of the target patient corresponding to each collecting time point.
3. Patient respiratory information acquisition and analysis: and acquiring the respiratory information of each acquisition time point in the corresponding setting period of the target patient to obtain a respiratory information set of each acquisition time point of the target patient, and analyzing respiratory state evaluation indexes of each acquisition time point of the target patient.
4. Patient rhinitis severity analysis: and analyzing the rhinitis severity evaluation coefficient corresponding to the target patient to obtain the rhinitis severity evaluation coefficient corresponding to the target patient, and analyzing the rhinitis severity level corresponding to the target patient.
5. Rhinitis severity display and treatment: and displaying the rhinitis severity level corresponding to the target patient, and performing corresponding treatment.
As a preferred scheme of the invention, in the first step, nasal secretion information corresponding to each nasal discharge behavior of a target patient is extracted and analyzed, and the specific extraction and analysis steps are as follows: 101: image segmentation is carried out on the facial state sub-video corresponding to each nasal discharge behavior of the target patient, so that a facial state sub-image set corresponding to each nasal discharge behavior of the target patient is obtained, and the nasal cavity part of the facial state sub-image set corresponding to each nasal discharge behavior of the target patient is focused and amplified, so that a nasal cavity part sub-image set corresponding to each nasal discharge behavior of the target patient after focusing and amplifying is obtained.
102: extracting nasal secretion images corresponding to the first nasal discharge behaviors of the target patient from the nasal cavity position sub-image collection corresponding to the first nasal discharge behaviors of the target patient after focusing and amplifying, extracting nasal secretion images corresponding to the first nasal discharge behaviors of the target patient from the nasal secretion images corresponding to the first nasal discharge behaviors of the target patient, matching the nasal secretion images corresponding to the first nasal discharge behaviors of the target patient with the stored nasal secretion images corresponding to the various secretion states to obtain secretion states corresponding to the first nasal discharge behaviors of the target patient, executing 103 if the secretion states corresponding to the first nasal discharge behaviors of the target patient are thick, and executing 104 if the secretion states corresponding to the first nasal discharge behaviors of the target patient are clear water states.
103: and matching the thick state of the first nasal discharge behavior corresponding to the target patient with the specific rhinitis type of the thick state corresponding to the stored first nasal discharge secretion to obtain the specific rhinitis type of the first nasal discharge behavior corresponding to the target patient, and taking the specific rhinitis type corresponding to the target patient as the specific rhinitis type corresponding to the target patient.
104: the nasal secretion image corresponding to the first nasal discharge behavior of the target patient and the nasal secretion image corresponding to each nasal discharge behavior of the target patient are compared with each other, if the nasal secretion image corresponding to a certain nasal discharge behavior of the target patient is inconsistent with the nasal secretion image corresponding to the first nasal discharge behavior, the processing is performed 105, and if the nasal secretion image corresponding to each nasal discharge behavior of the target patient is consistent with the nasal secretion image corresponding to the first nasal discharge behavior, the processing is performed 106.
105: extracting nasal secretion images of the secondary nasal discharge behaviors, recording the nasal secretion images as nasal secretion images of the subsequent nasal discharge behaviors, simultaneously matching the nasal secretion images with the stored nasal secretion images corresponding to various secretion states to obtain secretion states of the subsequent nasal discharge behaviors corresponding to the target patient, and matching the thick state of the subsequent nasal discharge behaviors corresponding to the target patient with the specific rhinitis type of the stored subsequent nasal discharge corresponding to the thick state if the secretion states of the subsequent nasal discharge behaviors corresponding to the target patient are thick, so as to obtain the specific rhinitis type of the subsequent nasal discharge behaviors corresponding to the target patient, and taking the specific rhinitis type as the specific rhinitis type corresponding to the target patient.
106: and (3) marking the nasal secretion image corresponding to each nasal discharge behavior of the target patient as a consistent nasal secretion image, taking the secretion state corresponding to the first nasal discharge behavior of the target patient as the secretion state corresponding to the subsequent nasal discharge behavior of the target patient, and simultaneously matching the secretion state with the stored specified rhinitis type corresponding to the subsequent nasal discharge to the clear water state to obtain the specified rhinitis type corresponding to the subsequent nasal discharge behavior of the target patient as the specified rhinitis type corresponding to the target patient.
As a preferred scheme of the present invention, in the second step, the nasal cavity internal information of each collection time point in the corresponding setting period of the target patient is collected, and the specific collection mode is as follows: the method comprises the steps of acquiring the internal nasal cavity images of all acquisition time points in a corresponding setting period of a target patient through an electronic nasopharynx laryngoscope, and obtaining the internal nasal cavity images of all acquisition time points in the corresponding setting period of the target patient.
Extracting the nasal mucosa shape outline, the nasal secretion distribution area and the abnormal protrusion area in the nasal cavity of each acquisition time point in the corresponding setting period of the target patient from the nasal cavity internal image of each acquisition time point in the corresponding setting period of the target patient, and overlapping and comparing the nasal mucosa shape outline of each acquisition time point in the corresponding setting period of the target patient with the stored reference nasal mucosa shape outline to obtain the nasal mucosa overlapping area of each acquisition time point of the target patient.
The nasal mucosa overlapping area, the nasal secretion distribution area and the nasal cavity internal abnormal bulge area of the target patient corresponding to each acquisition time point form a nasal cavity internal information set of the target patient corresponding to each acquisition time point.
As a preferred embodiment of the present invention, in the second step, the nasal cavity internal state evaluation index of the target patient corresponding to each collection time point is analyzed in the following specific analysis manner: matching the specific rhinitis type corresponding to the target patient with the set allowable nasal cavity secretion distribution area and allowable nasal cavity internal abnormal bulge area corresponding to each specific rhinitis type to obtain the allowable nasal cavity secretion distribution area and allowable nasal cavity internal abnormal bulge area corresponding to the target patient, and respectively marking as S 1 ' and S 2 ′。
And matching the specified rhinitis type corresponding to the target patient with the reference nasal mucosa overlapping area corresponding to each specified rhinitis type, so as to obtain the reference nasal mucosa overlapping area corresponding to the target patient, and marking as S'.
According to the formulaCalculating the nasal cavity internal state evaluation index phi of the target patient corresponding to each acquisition time point i Nasal internal state evaluation index expressed as the target patient corresponding to the i-th collection time point, i expressed as the number of each collection time point, i=1, 2>Respectively expressed as the nasal mucosa superposition area, the nasal secretion distribution area and the abnormal protruding area in the nasal cavity of the target patient corresponding to the ith acquisition time point, a 1 、a 2 、a 3 The values are respectively expressed as the set influence factors corresponding to the nasal mucosa superposition area, the nasal secretion distribution area and the abnormal bulge area in the nasal cavity.
As a preferred scheme of the present invention, in the third step, the respiration information of each acquisition time point in the set period corresponding to the target patient is acquired, and the specific acquisition mode is as follows: the nasal resistance meter is used for collecting the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity and the nasal inhalation resistance value of the right nasal cavity at each collection time point in the corresponding setting period of the target patient, so that the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity and the nasal inhalation resistance value of the right nasal cavity of the target patient at each collection time point are obtained.
And acquiring the nasal cavity ventilation flow of the left nasal cavity and the nasal cavity ventilation flow of the right nasal cavity at each acquisition time point in the corresponding setting period of the target patient through a nasal respiration meter to obtain the nasal cavity ventilation flow of the left nasal cavity and the nasal cavity ventilation flow of the right nasal cavity of the target patient at each acquisition time point.
The respiratory information set of the target patient corresponding to each collecting time point is formed by the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal ventilation flow of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity, the nasal inhalation resistance value of the right nasal cavity and the nasal ventilation flow of the right nasal cavity of the target patient corresponding to each collecting time point.
As a preferred embodiment of the present invention, in the third step, the respiratory state evaluation index of the target patient corresponding to each acquisition time point is analyzed in the following specific analysis manner: matching the specified rhinitis type corresponding to the target patient with the set reference respiratory information set corresponding to each specified rhinitis type to obtain the reference respiratory information set corresponding to the target patient, extracting the nasal exhalation resistance value, the nasal inhalation resistance value, the nasal ventilation flow, the nasal exhalation resistance value, the nasal inhalation resistance value and the nasal ventilation flow corresponding to the target patient, which are respectively recorded as hq' Left side 、xq′ Left side 、tq′ Left side 、hq′ Right side 、xq′ Right side And tq' Right side 。
According to the formulaCalculating the corresponding acquisition time of the target patientPoint left nasal respiratory state assessment index, +.>Left nasal respiratory state evaluation index, expressed as target patient corresponding to the ith acquisition time point,/>The left nasal cavity nasal exhalation resistance value, the nasal inhalation resistance value and the nasal cavity ventilation flow rate corresponding to the ith acquisition time point of the target patient are respectively expressed as a 4 、a 5 、a 6 The set values of the left nasal cavity nasal exhalation resistance, the left nasal cavity nasal inhalation resistance and the influence factors corresponding to the left nasal cavity ventilation flow are respectively expressed.
Calculating the right nasal cavity respiratory state evaluation index of the target patient corresponding to each acquisition time point according to a formula, and recording as
According to the formulaCalculating respiratory state evaluation indexes gamma of the target patient corresponding to each acquisition time period i A respiratory state evaluation index, b, expressed as the target patient corresponding to the ith acquisition period 1 、b 2 The set left nasal cavity respiratory state evaluation index and the right nasal cavity respiratory state evaluation index are respectively expressed as weight factors corresponding to the set left nasal cavity respiratory state evaluation index and the right nasal cavity respiratory state evaluation index.
As a preferred scheme of the present invention, in the fourth step, the rhinitis severity assessment coefficient corresponding to the target patient is analyzed, and a specific analysis formula thereof is as follows:θ is expressed as a rhinitis severity evaluation coefficient corresponding to the target patient, b 3 、b 4 Respectively representing the set nasal cavity internal state evaluation index and the weight factor corresponding to the respiratory state evaluation index.
As a preferred scheme of the present invention, in the fourth step, the severity level of rhinitis corresponding to the target patient is analyzed, and the specific analysis method is as follows: and matching the rhinitis severity evaluation coefficient corresponding to the target patient with the rhinitis severity evaluation coefficient threshold corresponding to the set various rhinitis severity grades to obtain the rhinitis severity grade corresponding to the target patient.
A second aspect of the present invention provides a disease data acquisition analysis system, comprising: the nasal secretion information acquisition and analysis module is used for acquiring facial state videos of the target patient corresponding to a set period, obtaining facial state sub-videos of the target patient corresponding to various nasal discharge behaviors, and analyzing the appointed rhinitis type corresponding to the target patient.
The nasal mucosa state acquisition and analysis module is used for acquiring the internal information of the nasal cavity of each acquisition time point in the corresponding setting period of the target patient, and analyzing the internal state evaluation index of the nasal cavity of the target patient corresponding to each acquisition time point.
And the patient respiratory information acquisition and analysis module is used for acquiring respiratory information of each acquisition time point in the corresponding set period of the target patient and analyzing respiratory state evaluation indexes of the target patient corresponding to each acquisition time point.
And the rhinitis severity analysis module is used for analyzing the rhinitis severity assessment coefficient corresponding to the target patient and analyzing the rhinitis severity level corresponding to the target patient.
And the rhinitis severity display and processing module is used for displaying the rhinitis severity level corresponding to the target patient and performing corresponding processing.
The storage library is used for storing nasal secretion images corresponding to various secretion states, storing appointed rhinitis types corresponding to the first nasal secretion state and the thick state, storing appointed rhinitis types corresponding to the subsequent nasal secretion state and storing reference nasal mucosa shape outlines.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: the invention collects the facial state sub-videos corresponding to each time of nasal discharge behaviors of the target patient, analyzes the appointed rhinitis type corresponding to the target patient, realizes the intellectualization and high efficiency of the rhinitis type analysis corresponding to the patient, and makes up the defect of diagnosis of the rhinitis type corresponding to the patient in the current technology.
According to the invention, the nasal cavity internal information of each acquisition time point in the corresponding setting period of the target patient is acquired, and the nasal cavity internal state evaluation index of each acquisition time point of the target patient is analyzed, so that the accuracy and reliability of the nasal cavity internal state evaluation result of the target patient are ensured to the greatest extent, the persuasion of the nasal cavity internal state analysis result of the target patient is improved, and accurate and visual data are provided for the analysis of the rhinitis severity degree of the subsequent target patient.
According to the invention, the respiratory information of each acquisition time point in the corresponding setting period of the target patient is acquired, and the respiratory state evaluation index of the target patient corresponding to each acquisition time point is analyzed, so that the singleness and one-sided performance of monitoring and analyzing the severity of the rhinitis corresponding to the patient in the prior art are effectively solved, and the reliability and the accuracy of analyzing the severity of the rhinitis of the patient are increased.
According to the rhinitis severity evaluation method, the rhinitis severity evaluation coefficient corresponding to the target patient is analyzed, and the rhinitis severity grade corresponding to the target patient is obtained through analysis, so that the persuasion of the rhinitis severity analysis of the patient is improved, and the follow-up development of targeted treatment on the patient is promoted.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a flow chart of the steps of the method of the present invention.
FIG. 2 is a schematic diagram of the system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a first aspect of the present invention provides a disease data acquisition and analysis method, including the following steps: 1. collecting and analyzing nasal secretion information of a patient: the facial state videos of the target patient corresponding to the set period are collected, facial state sub-videos of the target patient corresponding to the nasal discharge behaviors are obtained, and meanwhile nasal secretion information of the target patient corresponding to the nasal discharge behaviors is extracted and analyzed to obtain the specific rhinitis type corresponding to the target patient.
As a further improvement of the present invention, in the first step, the facial status sub-video of each nasal discharge behavior of the target patient is obtained by the following specific method: and identifying whether the target patient has the runny nose behavior from the facial state video of the corresponding setting period of the target patient, if the target patient has the runny nose behavior in the corresponding setting period, marking the starting runny nose and ending runny nose corresponding to the target patient as the runny nose behavior, and counting the number of times of the runny nose behavior in the corresponding setting period of the target patient.
Extracting facial state sub-videos of the runny behaviors of the target patient in the corresponding setting period from the facial state videos of the target patient in the corresponding setting period, and obtaining the facial state sub-videos of the runny behaviors of the target patient.
As a further improvement of the present invention, in the first step, nasal secretion information corresponding to each nasal discharge behavior of the target patient is extracted and analyzed, and the specific extraction and analysis steps are as follows: 101: image segmentation is carried out on the facial state sub-video corresponding to each nasal discharge behavior of the target patient, so that a facial state sub-image set corresponding to each nasal discharge behavior of the target patient is obtained, and the nasal cavity part of the facial state sub-image set corresponding to each nasal discharge behavior of the target patient is focused and amplified, so that a nasal cavity part sub-image set corresponding to each nasal discharge behavior of the target patient after focusing and amplifying is obtained.
102: extracting nasal secretion images corresponding to the first nasal discharge behaviors of the target patient from the nasal cavity position sub-image collection corresponding to the first nasal discharge behaviors of the target patient after focusing and amplifying, extracting nasal secretion images corresponding to the first nasal discharge behaviors of the target patient from the nasal secretion images corresponding to the first nasal discharge behaviors of the target patient, matching the nasal secretion images corresponding to the first nasal discharge behaviors of the target patient with the stored nasal secretion images corresponding to the various secretion states to obtain secretion states corresponding to the first nasal discharge behaviors of the target patient, executing 103 if the secretion states corresponding to the first nasal discharge behaviors of the target patient are thick, and executing 104 if the secretion states corresponding to the first nasal discharge behaviors of the target patient are clear water states.
103: and matching the thick state of the first nasal discharge behavior corresponding to the target patient with the specific rhinitis type of the thick state corresponding to the stored first nasal discharge secretion to obtain the specific rhinitis type of the first nasal discharge behavior corresponding to the target patient, and taking the specific rhinitis type corresponding to the target patient as the specific rhinitis type corresponding to the target patient.
104: the nasal secretion image corresponding to the first nasal discharge behavior of the target patient and the nasal secretion image corresponding to each nasal discharge behavior of the target patient are compared with each other, if the nasal secretion image corresponding to a certain nasal discharge behavior of the target patient is inconsistent with the nasal secretion image corresponding to the first nasal discharge behavior, the processing is performed 105, and if the nasal secretion image corresponding to each nasal discharge behavior of the target patient is consistent with the nasal secretion image corresponding to the first nasal discharge behavior, the processing is performed 106.
105: extracting nasal secretion images of the secondary nasal discharge behaviors, recording the nasal secretion images as nasal secretion images of the subsequent nasal discharge behaviors, simultaneously matching the nasal secretion images with the stored nasal secretion images corresponding to various secretion states to obtain secretion states of the subsequent nasal discharge behaviors corresponding to the target patient, and matching the thick state of the subsequent nasal discharge behaviors corresponding to the target patient with the specific rhinitis type of the stored subsequent nasal discharge corresponding to the thick state if the secretion states of the subsequent nasal discharge behaviors corresponding to the target patient are thick, so as to obtain the specific rhinitis type of the subsequent nasal discharge behaviors corresponding to the target patient, and taking the specific rhinitis type as the specific rhinitis type corresponding to the target patient.
106: and (3) marking the nasal secretion image corresponding to each nasal discharge behavior of the target patient as a consistent nasal secretion image, taking the secretion state corresponding to the first nasal discharge behavior of the target patient as the secretion state corresponding to the subsequent nasal discharge behavior of the target patient, and simultaneously matching the secretion state with the stored specified rhinitis type corresponding to the subsequent nasal discharge to the clear water state to obtain the specified rhinitis type corresponding to the subsequent nasal discharge behavior of the target patient as the specified rhinitis type corresponding to the target patient.
In a specific embodiment, the facial state sub-videos of the corresponding nasal discharge behaviors of the target patient are collected, and the specific rhinitis types corresponding to the target patient are analyzed, so that the intelligent and efficient analysis of the rhinitis types corresponding to the patient is realized, and the defect of diagnosis of the rhinitis types corresponding to the patient in the current technology is overcome.
2. Patient nasal mucosa status collection analysis: the method comprises the steps of collecting the internal information of the nasal cavity of each collecting time point in the corresponding setting period of a target patient to obtain a nasal cavity internal information set of the target patient corresponding to each collecting time point, and analyzing a nasal cavity internal state evaluation index of the target patient corresponding to each collecting time point.
As a further improvement of the present invention, in the second step, the nasal cavity internal information of each collection time point in the corresponding setting period of the target patient is collected, and the specific collection mode is as follows: the method comprises the steps of acquiring the internal nasal cavity images of all acquisition time points in a corresponding setting period of a target patient through an electronic nasopharynx laryngoscope, and obtaining the internal nasal cavity images of all acquisition time points in the corresponding setting period of the target patient.
Extracting the nasal mucosa shape outline, the nasal secretion distribution area and the abnormal protrusion area in the nasal cavity of each acquisition time point in the corresponding setting period of the target patient from the nasal cavity internal image of each acquisition time point in the corresponding setting period of the target patient, and overlapping and comparing the nasal mucosa shape outline of each acquisition time point in the corresponding setting period of the target patient with the stored reference nasal mucosa shape outline to obtain the nasal mucosa overlapping area of each acquisition time point of the target patient.
The nasal mucosa overlapping area, the nasal secretion distribution area and the nasal cavity internal abnormal bulge area of the target patient corresponding to each acquisition time point form a nasal cavity internal information set of the target patient corresponding to each acquisition time point.
As a further improvement of the present invention, the analysis of the nasal cavity internal state evaluation index of the target patient corresponding to each collection time point in the second step is as follows: matching the specific rhinitis type corresponding to the target patient with the set allowable nasal cavity secretion distribution area and allowable nasal cavity internal abnormal bulge area corresponding to each specific rhinitis type to obtain the allowable nasal cavity secretion distribution area and allowable nasal cavity internal abnormal bulge area corresponding to the target patient, and respectively marking as S 1 ' and S 2 ′。
And matching the specified rhinitis type corresponding to the target patient with the reference nasal mucosa overlapping area corresponding to each specified rhinitis type, so as to obtain the reference nasal mucosa overlapping area corresponding to the target patient, and marking as S'.
According to the formulaCalculating the nasal cavity internal state evaluation index phi of the target patient corresponding to each acquisition time point i Nasal internal state evaluation index expressed as the target patient corresponding to the i-th collection time point, i expressed as the number of each collection time point, i=1, 2>Respectively expressed as the nasal mucosa superposition area, the nasal secretion distribution area and the abnormal protruding area in the nasal cavity of the target patient corresponding to the ith acquisition time point, a 1 、a 2 、a 3 Respectively expressed as the set nasal mucosa overlapping area, nasal secretion distribution area and abnormal bulge area in the nasal cavityInfluence factors.
In a specific embodiment, the method collects the internal information of the nasal cavity at each collection time point in the corresponding setting period of the target patient, and analyzes the internal state evaluation index of the nasal cavity at each collection time point of the target patient, so that the accuracy and the reliability of the internal state evaluation result of the nasal cavity corresponding to the target patient are guaranteed to the greatest extent, the persuasion of the internal state analysis result of the nasal cavity corresponding to the target patient is improved, and accurate and visual data are provided for the analysis of the severity of rhinitis corresponding to the subsequent target patient.
3. Patient respiratory information acquisition and analysis: and acquiring the respiratory information of each acquisition time point in the corresponding setting period of the target patient to obtain a respiratory information set of each acquisition time point of the target patient, and analyzing respiratory state evaluation indexes of each acquisition time point of the target patient.
As a further improvement of the present invention, in the third step, the respiration information of each acquisition time point in the set period corresponding to the target patient is acquired, and the specific acquisition mode is as follows: the nasal resistance meter is used for collecting the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity and the nasal inhalation resistance value of the right nasal cavity at each collection time point in the corresponding setting period of the target patient, so that the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity and the nasal inhalation resistance value of the right nasal cavity of the target patient at each collection time point are obtained.
And acquiring the nasal cavity ventilation flow of the left nasal cavity and the nasal cavity ventilation flow of the right nasal cavity at each acquisition time point in the corresponding setting period of the target patient through a nasal respiration meter to obtain the nasal cavity ventilation flow of the left nasal cavity and the nasal cavity ventilation flow of the right nasal cavity of the target patient at each acquisition time point.
The respiratory information set of the target patient corresponding to each collecting time point is formed by the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal ventilation flow of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity, the nasal inhalation resistance value of the right nasal cavity and the nasal ventilation flow of the right nasal cavity of the target patient corresponding to each collecting time point.
As a further improvement of the present invention, the third step is to analyze the respiratory state evaluation index of the target patient corresponding to each acquisition time point, and the specific analysis mode is as follows: matching the specified rhinitis type corresponding to the target patient with the set reference respiratory information set corresponding to each specified rhinitis type to obtain the reference respiratory information set corresponding to the target patient, extracting the nasal exhalation resistance value, the nasal inhalation resistance value, the nasal ventilation flow, the nasal exhalation resistance value, the nasal inhalation resistance value and the nasal ventilation flow corresponding to the target patient, which are respectively recorded as hq' Left side 、xq′ Left side 、tq′ Left side 、hq′ Right side 、xq′ Right side And tq' Right side 。
According to the formulaCalculating the left nasal cavity respiratory state evaluation index of the target patient corresponding to each acquisition time point, wherein the left nasal cavity respiratory state evaluation index is->Left nasal respiratory state evaluation index, expressed as target patient corresponding to the ith acquisition time point,/>The left nasal cavity nasal exhalation resistance value, the nasal inhalation resistance value and the nasal cavity ventilation flow rate corresponding to the ith acquisition time point of the target patient are respectively expressed as a 4 、a 5 、a 6 The set values of the left nasal cavity nasal exhalation resistance, the left nasal cavity nasal inhalation resistance and the influence factors corresponding to the left nasal cavity ventilation flow are respectively expressed.
According to the formulaCalculating the right nasal cavity respiratory state evaluation index of the target patient corresponding to each acquisition time point, wherein the right nasal cavity respiratory state evaluation index is->Right nasal respiratory state evaluation index expressed as the target patient corresponding to the ith acquisition time point, +.>The right nasal cavity nasal exhalation resistance value, the nasal inhalation resistance value and the nasal cavity ventilation flow rate corresponding to the ith acquisition time point of the target patient are respectively expressed as a 7 、a 8 、a 9 The set values of the right nasal cavity nasal exhalation resistance, the right nasal cavity nasal inhalation resistance and the influence factors corresponding to the right nasal cavity ventilation flow are respectively expressed.
According to the formulaCalculating respiratory state evaluation indexes gamma of the target patient corresponding to each acquisition time period i A respiratory state evaluation index, b, expressed as the target patient corresponding to the ith acquisition period 1 、b 2 The set left nasal cavity respiratory state evaluation index and the right nasal cavity respiratory state evaluation index are respectively expressed as weight factors corresponding to the set left nasal cavity respiratory state evaluation index and the right nasal cavity respiratory state evaluation index.
In a specific embodiment, the invention effectively solves the problems of singleness and unilateralness of monitoring and analyzing the severity of the rhinitis corresponding to the patient in the prior art by acquiring the respiratory information of each acquisition time point in the corresponding setting period of the target patient and analyzing the respiratory state evaluation index of the target patient corresponding to each acquisition time point, and increases the reliability and the accuracy of analyzing the severity of the rhinitis of the patient.
4. Patient rhinitis severity analysis: and analyzing the rhinitis severity evaluation coefficient corresponding to the target patient to obtain the rhinitis severity evaluation coefficient corresponding to the target patient, and analyzing the rhinitis severity level corresponding to the target patient.
As a further improvement of the present invention, in the fourth step, the rhinitis severity assessment coefficient corresponding to the target patient is analyzed, and a specific analysis formula thereof is as follows:θ is expressed as a rhinitis severity evaluation coefficient corresponding to the target patient, b 3 、b 4 Respectively representing the set nasal cavity internal state evaluation index and the weight factor corresponding to the respiratory state evaluation index.
As a further improvement of the present invention, in the fourth step, the severity level of rhinitis corresponding to the target patient is analyzed in the following specific analysis manner: and matching the rhinitis severity evaluation coefficient corresponding to the target patient with the rhinitis severity evaluation coefficient threshold corresponding to the set various rhinitis severity grades to obtain the rhinitis severity grade corresponding to the target patient.
In a specific embodiment, the rhinitis severity assessment coefficient corresponding to the target patient is analyzed, and the rhinitis severity level corresponding to the target patient is obtained through analysis, so that the persuasion of the rhinitis severity analysis of the patient is improved, and the follow-up development of targeted treatment on the patient is promoted.
5. Rhinitis severity display and treatment: and displaying the rhinitis severity level corresponding to the target patient, and performing corresponding treatment.
Referring to fig. 2, a second aspect of the present invention provides a disease data acquisition and analysis system, comprising: the nasal secretion information acquisition and analysis module of the patient, the nasal mucosa state acquisition and analysis module of the patient, the breathing information acquisition and analysis module of the patient, the rhinitis severity display and processing module and the storage library.
The nasal secretion information acquisition and analysis module of the patient is respectively connected with the nasal mucosa state acquisition and analysis module of the patient, the respiratory information acquisition and analysis module of the patient and the storage library, the nasal mucosa state acquisition and analysis module of the patient is respectively connected with the rhinitis severity analysis module of the patient and the storage library, the respiratory information acquisition and analysis module of the patient is connected with the rhinitis severity analysis module of the patient, and the rhinitis severity analysis module of the patient is connected with the rhinitis severity display and processing module.
The nasal secretion information acquisition and analysis module is used for acquiring facial state videos of the target patient corresponding to a set period, obtaining facial state sub-videos of the target patient corresponding to various nasal discharge behaviors, and analyzing the appointed rhinitis type corresponding to the target patient.
The nasal mucosa state acquisition and analysis module is used for acquiring the internal information of the nasal cavity of each acquisition time point in the corresponding setting period of the target patient, and analyzing the internal state evaluation index of the nasal cavity of the target patient corresponding to each acquisition time point.
And the patient respiratory information acquisition and analysis module is used for acquiring respiratory information of each acquisition time point in the corresponding set period of the target patient and analyzing respiratory state evaluation indexes of the target patient corresponding to each acquisition time point.
And the rhinitis severity analysis module is used for analyzing the rhinitis severity assessment coefficient corresponding to the target patient and analyzing the rhinitis severity level corresponding to the target patient.
And the rhinitis severity display and processing module is used for displaying the rhinitis severity level corresponding to the target patient and performing corresponding processing.
The storage library is used for storing nasal secretion images corresponding to various secretion states, storing appointed rhinitis types corresponding to the first nasal secretion state and the thick state, storing appointed rhinitis types corresponding to the subsequent nasal secretion state and storing reference nasal mucosa shape outlines.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (7)
1. A disease data acquisition and analysis system, comprising:
the nasal secretion information acquisition and analysis module is used for acquiring facial state videos of the target patient corresponding to a set period to obtain facial state sub-videos of the target patient corresponding to each nasal discharge behavior, extracting and analyzing nasal secretion information of the target patient corresponding to each nasal discharge behavior to obtain the designated rhinitis type corresponding to the target patient;
the nasal mucosa state acquisition and analysis module is used for acquiring the internal information of the nasal cavity of each acquisition time point in the corresponding setting period of the target patient and analyzing the internal state evaluation index of the nasal cavity of the target patient corresponding to each acquisition time point;
the analysis target patient corresponds to the nasal cavity internal state evaluation index of each acquisition time point, and the specific analysis mode is as follows:
matching the specific rhinitis type corresponding to the target patient with the set allowable nasal cavity secretion distribution area and allowable nasal cavity internal abnormal bulge area corresponding to each specific rhinitis type to obtain the allowable nasal cavity secretion distribution area and allowable nasal cavity internal abnormal bulge area corresponding to the target patient, and respectively marking asAnd->；
Matching the specified rhinitis type corresponding to the target patient with the set reference nasal mucosa overlapping area corresponding to each specified rhinitis type to obtain the reference nasal mucosa overlapping area corresponding to the target patient, and marking as；
According to the formulaCalculating the nasal cavity internal state evaluation index of the target patient corresponding to each acquisition time point, and performing +.>Expressed as the target patient corresponds to the ith acquisition time pointNasal cavity internal state evaluation index, i is expressed as the number of each collection time point,/for each collection time point>E is expressed as a natural constant, ">Respectively expressed as the nasal mucosa superposition area, the nasal secretion distribution area and the nasal cavity internal abnormal bulge area of the target patient corresponding to the ith acquisition time point->The corresponding influence factors are respectively expressed as the set nasal mucosa superposition area, nasal secretion distribution area and abnormal bulge area in the nasal cavity;
the patient breathing information acquisition and analysis module is used for acquiring breathing information of each acquisition time point in the corresponding set period of the target patient and analyzing the breathing state evaluation index of the target patient corresponding to each acquisition time point;
the rhinitis severity analysis module of the patient is used for analyzing the rhinitis severity evaluation coefficient corresponding to the target patient and analyzing the rhinitis severity level corresponding to the target patient;
the rhinitis severity display and processing module is used for displaying the rhinitis severity level corresponding to the target patient and correspondingly processing the rhinitis severity level;
the storage library is used for storing nasal secretion images corresponding to various secretion states, storing appointed rhinitis types corresponding to the first nasal secretion state and the thick state, storing appointed rhinitis types corresponding to the subsequent nasal secretion state and storing reference nasal mucosa shape outlines.
2. A disease data acquisition and analysis system according to claim 1, wherein: the nasal secretion information corresponding to each nasal discharge behavior of the target patient is extracted and analyzed, and the method specifically comprises the following steps:
101: image segmentation is carried out on the facial state sub-video corresponding to each nasal discharge behavior of the target patient, so as to obtain a facial state sub-image set corresponding to each nasal discharge behavior of the target patient, and the nasal cavity part of the facial state sub-image set corresponding to each nasal discharge behavior of the target patient is focused and amplified, so as to obtain a nasal cavity part sub-image set corresponding to each nasal discharge behavior after focusing and amplifying;
102: extracting nasal secretion images corresponding to the first nasal discharge behaviors of the target patient from the nasal cavity position sub-image collection corresponding to the first nasal discharge behaviors of the target patient after focusing and amplifying, extracting nasal secretion images corresponding to the first nasal discharge behaviors of the target patient from the nasal secretion images corresponding to the first nasal discharge behaviors of the target patient, simultaneously matching the nasal secretion images corresponding to the first nasal discharge behaviors of the target patient with the stored nasal secretion images corresponding to the various secretion states to obtain secretion states corresponding to the first nasal discharge behaviors of the target patient, executing 103 if the secretion states corresponding to the first nasal discharge behaviors of the target patient are thick, and executing 104 if the secretion states corresponding to the first nasal discharge behaviors of the target patient are clear water states;
103: matching the thick state of the first nasal discharge behavior corresponding to the target patient with the specific rhinitis type of the thick state corresponding to the stored first nasal discharge secretion to obtain the specific rhinitis type of the first nasal discharge behavior corresponding to the target patient, and taking the specific rhinitis type corresponding to the target patient as the specific rhinitis type;
104: comparing the nasal secretion image of the first nasal discharge behavior corresponding to the target patient with the nasal secretion image of each nasal discharge behavior corresponding to the target patient, executing 105 if the nasal secretion image of a certain nasal discharge behavior corresponding to the target patient is inconsistent with the nasal secretion image of the first nasal discharge behavior corresponding to the target patient, and executing 106 if the nasal secretion image of each nasal discharge behavior corresponding to the target patient is consistent with the nasal secretion image of the first nasal discharge behavior corresponding to the target patient;
105: extracting a nasal secretion image of the secondary nasal discharge behavior, recording the nasal secretion image of the secondary nasal discharge behavior as a nasal secretion image of the subsequent nasal discharge behavior, simultaneously matching the nasal secretion image with the nasal secretion image corresponding to the stored various secretion states to obtain a secretion state of the target patient corresponding to the subsequent nasal discharge behavior, and matching the thick state of the target patient corresponding to the subsequent nasal discharge behavior with a specific rhinitis type of the stored subsequent nasal discharge corresponding to the thick state if the secretion state of the target patient corresponding to the subsequent nasal discharge behavior is the thick state to obtain the specific rhinitis type of the target patient corresponding to the subsequent nasal discharge behavior as the specific rhinitis type corresponding to the target patient;
106: and (3) marking the nasal secretion image corresponding to each nasal discharge behavior of the target patient as a consistent nasal secretion image, taking the secretion state corresponding to the first nasal discharge behavior of the target patient as the secretion state corresponding to the subsequent nasal discharge behavior of the target patient, and simultaneously matching the secretion state with the stored specified rhinitis type corresponding to the subsequent nasal discharge to the clear water state to obtain the specified rhinitis type corresponding to the subsequent nasal discharge behavior of the target patient as the specified rhinitis type corresponding to the target patient.
3. A disease data acquisition and analysis system according to claim 1, wherein: the nasal cavity internal information of each acquisition time point in the corresponding setting period of the target patient is acquired in the following specific acquisition modes:
acquiring the internal nasal cavity images of each acquisition time point in the corresponding setting period of the target patient through the electronic nasopharynx laryngoscope, so as to obtain the internal nasal cavity images of each acquisition time point in the corresponding setting period of the target patient;
extracting the nasal mucosa shape outline, the nasal secretion distribution area and the abnormal protrusion area in the nasal cavity of each acquisition time point in the corresponding setting period of the target patient from the nasal cavity internal image of each acquisition time point in the corresponding setting period of the target patient, and overlapping and comparing the nasal mucosa shape outline of each acquisition time point in the corresponding setting period of the target patient with the stored reference nasal mucosa shape outline to obtain the nasal mucosa overlapping area of each acquisition time point of the target patient;
the nasal mucosa overlapping area, the nasal secretion distribution area and the nasal cavity internal abnormal bulge area of the target patient corresponding to each acquisition time point form a nasal cavity internal information set of the target patient corresponding to each acquisition time point.
4. A disease data acquisition and analysis system according to claim 1, wherein: the respiration information of each acquisition time point in the corresponding setting period of the target patient is acquired in the following specific acquisition modes:
acquiring the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity and the nasal inhalation resistance value of the right nasal cavity of each acquisition time point in the corresponding setting period of a target patient through a nasal resistance meter to obtain the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity and the nasal inhalation resistance value of the right nasal cavity of the target patient corresponding to each acquisition time point;
collecting the nasal cavity ventilation flow of the left nasal cavity and the nasal cavity ventilation flow of the right nasal cavity in the corresponding set period of the target patient through a nasal breathing meter to obtain the nasal cavity ventilation flow of the left nasal cavity and the nasal cavity ventilation flow of the right nasal cavity of the target patient corresponding to the collecting time points;
the respiratory information set of the target patient corresponding to each collecting time point is formed by the nasal exhalation resistance value of the left nasal cavity, the nasal inhalation resistance value of the left nasal cavity, the nasal ventilation flow of the left nasal cavity, the nasal exhalation resistance value of the right nasal cavity, the nasal inhalation resistance value of the right nasal cavity and the nasal ventilation flow of the right nasal cavity of the target patient corresponding to each collecting time point.
5. The disease data acquisition analysis system of claim 4, wherein: the respiratory state evaluation index of the analysis target patient corresponding to each acquisition time point is specifically analyzed as follows:
matching the specified rhinitis type corresponding to the target patient with the set reference respiratory information set corresponding to each specified rhinitis type to obtain the reference respiratory information set corresponding to the target patient, and extracting the nasal exhalation resistance value, the nasal inhalation resistance value and the reference left nasal cavity corresponding to the reference left nasal cavity from the reference respiratory information set corresponding to the target patientThe nasal ventilation flow of the cavity, the nasal exhalation resistance value of the reference right nasal cavity, the nasal inhalation resistance value of the reference right nasal cavity and the nasal ventilation flow of the reference right nasal cavity are respectively recorded asAnd->；
According to the formulaCalculating the left nasal cavity respiratory state evaluation index of the target patient corresponding to each acquisition time point, wherein the left nasal cavity respiratory state evaluation index is->Left nasal respiratory state evaluation index, expressed as target patient corresponding to the ith acquisition time point,/>The left nasal cavity nasal exhalation resistance value, the nasal inhalation resistance value and the nasal cavity ventilation flow rate which are respectively expressed as the target patient corresponding to the ith acquisition time point are +.>Respectively representing the set left nasal cavity nasal exhalation resistance value, the set left nasal cavity nasal inhalation resistance value and the set influence factors corresponding to the left nasal cavity ventilation flow;
calculating the right nasal cavity respiratory state evaluation index of the target patient corresponding to each acquisition time point according to a formula, and recording as；
According to the formulaCalculating respiratory state evaluation index of target patient corresponding to each acquisition time period,/for each acquisition time period>Respiratory state evaluation index, expressed as target patient corresponding to the ith acquisition period,/for>The set left nasal cavity respiratory state evaluation index and the right nasal cavity respiratory state evaluation index are respectively expressed as weight factors corresponding to the set left nasal cavity respiratory state evaluation index and the right nasal cavity respiratory state evaluation index.
6. The disease data collection analysis system of claim 5, wherein: the rhinitis severity evaluation coefficient corresponding to the target patient is analyzed, and a specific analysis formula is as follows:，/>a rhinitis severity assessment coefficient expressed as the corresponding to the target patient, < >>Respectively representing the set nasal cavity internal state evaluation index and the weight factor corresponding to the respiratory state evaluation index.
7. A disease data acquisition and analysis system according to claim 1, wherein: the specific analysis mode of the rhinitis severity level corresponding to the target patient is as follows: and matching the rhinitis severity evaluation coefficient corresponding to the target patient with the rhinitis severity evaluation coefficient threshold corresponding to the set various rhinitis severity grades to obtain the rhinitis severity grade corresponding to the target patient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211416107.5A CN115919286B (en) | 2022-11-12 | 2022-11-12 | Disease data acquisition and analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211416107.5A CN115919286B (en) | 2022-11-12 | 2022-11-12 | Disease data acquisition and analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115919286A CN115919286A (en) | 2023-04-07 |
| CN115919286B true CN115919286B (en) | 2024-02-02 |
Family
ID=86549776
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211416107.5A Active CN115919286B (en) | 2022-11-12 | 2022-11-12 | Disease data acquisition and analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115919286B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017032873A2 (en) * | 2015-08-26 | 2017-03-02 | Resmed Sensor Technologies Limited | Systems and methods for monitoring and management of chronic desease |
| CN109497946A (en) * | 2018-11-14 | 2019-03-22 | 山东师范大学 | A kind of rhinitis parting decision-making system |
| CN111681757A (en) * | 2020-06-03 | 2020-09-18 | 广西壮族自治区人民医院 | 25(OH) D level-based prediction system for severity of new coronary pneumonia disease and construction and use method thereof |
| CN111883253A (en) * | 2020-07-16 | 2020-11-03 | 山东大学 | Disease data analysis method and lung cancer risk prediction system based on medical knowledge base |
| CN112890816A (en) * | 2020-12-11 | 2021-06-04 | 万达信息股份有限公司 | Health index scoring method and device for individual user |
| CN114828743A (en) * | 2019-12-19 | 2022-07-29 | 皇家飞利浦有限公司 | Automated and objective symptom severity scoring |
| KR20220107675A (en) * | 2021-01-26 | 2022-08-02 | 전태준 | Server and Method for Providing Health Condition through Analysis Hair by Using Machine Learning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3659154B1 (en) * | 2017-07-25 | 2022-09-07 | Koninklijke Philips N.V. | A system and method for determining a risk level of a pollen-induced allergy of a user |
-
2022
- 2022-11-12 CN CN202211416107.5A patent/CN115919286B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017032873A2 (en) * | 2015-08-26 | 2017-03-02 | Resmed Sensor Technologies Limited | Systems and methods for monitoring and management of chronic desease |
| CN109497946A (en) * | 2018-11-14 | 2019-03-22 | 山东师范大学 | A kind of rhinitis parting decision-making system |
| CN114828743A (en) * | 2019-12-19 | 2022-07-29 | 皇家飞利浦有限公司 | Automated and objective symptom severity scoring |
| CN111681757A (en) * | 2020-06-03 | 2020-09-18 | 广西壮族自治区人民医院 | 25(OH) D level-based prediction system for severity of new coronary pneumonia disease and construction and use method thereof |
| CN111883253A (en) * | 2020-07-16 | 2020-11-03 | 山东大学 | Disease data analysis method and lung cancer risk prediction system based on medical knowledge base |
| CN112890816A (en) * | 2020-12-11 | 2021-06-04 | 万达信息股份有限公司 | Health index scoring method and device for individual user |
| KR20220107675A (en) * | 2021-01-26 | 2022-08-02 | 전태준 | Server and Method for Providing Health Condition through Analysis Hair by Using Machine Learning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115919286A (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11033221B2 (en) | Method and device for swallowing impairment detection | |
| CN109190540B (en) | Biopsy region prediction method, image recognition device, and storage medium | |
| US20140288440A1 (en) | Systems and methods for quantitative capnogram analysis | |
| CN109035283B (en) | Emphysema accurate detection and quantitative analysis method based on randomly selected partitions | |
| CN107887032A (en) | A kind of data processing method and device | |
| JP7215756B2 (en) | Methods for early identification of exacerbations of chronic obstructive pulmonary disease | |
| CN115862819B (en) | Medical image management method based on image processing | |
| CN117497181B (en) | Chronic disease information management system based on artificial intelligence | |
| CN103735267B (en) | A kind of device based on sound of snoring examination OSAHS | |
| CN115919286B (en) | Disease data acquisition and analysis method and system | |
| CN109065165B (en) | Chronic obstructive pulmonary disease prediction method based on reconstructed airway tree image | |
| US20240090795A1 (en) | Methods for Pulmonary Function Testing With Machine Learning Analysis and Systems for Same | |
| CN115526842A (en) | Nasopharyngeal laryngoscope monitoring method, device, system, computer equipment and storage medium | |
| US11941811B2 (en) | Method for assessing cardiothoracic ratio and cardiothoracic ratio assessment system | |
| KR102501816B1 (en) | A method for providing an automatic lung organ analysis service using artificial intelligence based on a patient's personalized index | |
| KR102501815B1 (en) | Method and apparatus for automatically analyzing a lung organ using artificial intelligence | |
| CN116153517B (en) | Recovery evaluation method and system based on lung function exercise monitoring model | |
| KR102670185B1 (en) | Apparatus and method of providing diagnostic evaluation index capable of diagnosing structural and functional changes in subject using computed tomography image | |
| CN113506296B (en) | Slow obstructive pulmonary disease diagnosis device based on priori knowledge CT (computed tomography) subregion image omics | |
| CN115393353B (en) | Medical image data quality monitoring and management method and system | |
| CN114916953A (en) | Semi-automatic respiratory sound labeling method, system and equipment | |
| Bonthada et al. | An automated deep learning pipeline for detecting user errors in spirometry test | |
| US20220323700A1 (en) | Tracking respiratory mechanics of a patient | |
| CN116563228A (en) | Chronic obstructive pulmonary disease prediction system based on CT image and deep learning | |
| CN117503056A (en) | Multi-scale respiratory event automatic positioning method and device based on bimodal feature learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240102 Address after: Room 2510, East Building, No. 2218 Hunan Road, Pudong New Area, Shanghai, April 2012 Applicant after: Shanghai AIDU Medical Technology Co.,Ltd. Address before: Modern City, Block B, Intersection of Chama Road and Jiefang Avenue (Phase 5), Jiang'an District, Wuhan City, Hubei Province, 430014 Applicant before: Hubei Mingluo Medical Technology Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |