CN117198491A - Data analysis system for predicting high risk group of chronic obstructive pulmonary disease - Google Patents
Data analysis system for predicting high risk group of chronic obstructive pulmonary disease Download PDFInfo
- Publication number
- CN117198491A CN117198491A CN202311256847.1A CN202311256847A CN117198491A CN 117198491 A CN117198491 A CN 117198491A CN 202311256847 A CN202311256847 A CN 202311256847A CN 117198491 A CN117198491 A CN 117198491A
- Authority
- CN
- China
- Prior art keywords
- medical
- data
- score
- current
- medical data
- 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.)
- Granted
Links
- 208000006545 Chronic Obstructive Pulmonary Disease Diseases 0.000 title claims abstract description 34
- 238000007405 data analysis Methods 0.000 title claims abstract description 21
- 238000012544 monitoring process Methods 0.000 claims abstract description 165
- 238000004458 analytical method Methods 0.000 claims abstract description 88
- 238000005192 partition Methods 0.000 claims description 68
- 238000012937 correction Methods 0.000 claims description 49
- 201000010099 disease Diseases 0.000 claims description 26
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 26
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000013479 data entry Methods 0.000 claims description 14
- 238000007689 inspection Methods 0.000 claims description 11
- 208000024891 symptom Diseases 0.000 claims description 7
- 230000036541 health Effects 0.000 abstract description 5
- 238000007726 management method Methods 0.000 description 64
- 238000012552 review Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 208000017667 Chronic Disease Diseases 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 206010058314 Dysplasia Diseases 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 235000019504 cigarettes Nutrition 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 230000002685 pulmonary effect Effects 0.000 description 1
- 230000000241 respiratory effect Effects 0.000 description 1
- 208000023504 respiratory system disease Diseases 0.000 description 1
- 239000000779 smoke Substances 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
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the technical field of health management systems, in particular to a data analysis system for predicting high risk groups of chronic obstructive pulmonary diseases, which comprises an acquisition unit, a medical database, a management analysis unit and a reminding unit. According to the invention, the current medical data of the acquisition client is set, the update state of the medical data of the corresponding patient is determined, the current medical data is matched and judged according to the stored medical data packet, the original medical data packet is subjected to data update according to the current medical data, and the corresponding medical score is calculated according to the updated data, so that whether the medical data packet is monitored or not is judged, when the medical data packet belongs to a monitoring storage area, the original monitoring period is updated according to the updated current medical data, the accuracy of the monitoring period of the medical data packet is ensured, the accuracy of prediction of the medical period of the high risk group of chronic obstructive pulmonary diseases is ensured, and the medical reminding effectiveness is improved.
Description
Technical Field
The invention relates to the technical field of health management systems, in particular to a data analysis system for predicting high risk groups of chronic obstructive pulmonary diseases.
Background
Chronic obstructive pulmonary disease is a common, preventable and treatable chronic airway disease, a persistent airflow limitation and corresponding respiratory symptoms, often associated with significant exposure to harmful particles or gases such as cigarette smoke, and host factors can also cause individuals to develop chronic obstructive pulmonary disease, including genetic abnormalities, pulmonary dysplasia, and accelerated aging, most of which occur in middle-aged, and are well developed in cold autumn and winter; the high risk group corresponding to the chronic obstructive pulmonary disease should be reviewed and reminded in time to achieve the effect of preventing or slowing down the onset of disease, but as the number of the high risk group of the chronic obstructive pulmonary disease increases, a large amount of statistics is needed to accurately review and remind, so that the establishment of the data system of the high risk group of the chronic obstructive pulmonary disease is particularly important.
Chinese patent publication No.: CN111554365a discloses a chronic disease comprehensive service platform, which realizes medical reminding and health file management by setting a patient end, but can realize complete medical reminding only by communication between the patient and a doctor; therefore, in the existing medical treatment reminding system for the chronic obstructive pulmonary disease, external human intervention is required, or a fixed reminding time limit is set for medical treatment reminding, and when the number of reminding groups is large, the problem that reminding is not timely or missing occurs, so that intelligent management on health data files of the high-risk group of the chronic obstructive pulmonary disease is needed, and prediction and reminding of accurate review time nodes are carried out on the basis of the health data files.
Disclosure of Invention
Therefore, the invention provides a data analysis system for predicting high risk groups of chronic obstructive pulmonary diseases, which is used for solving the problems that in the prior art, chronic diseases are inadequately reminded to seek medical attention and reminding is not timely or is missed.
In order to achieve the above object, the present invention provides a data analysis system for predicting a high risk group of chronic obstructive pulmonary disease, comprising,
the acquisition unit is connected with the client and is used for acquiring current medical data and corresponding current medical account numbers recorded by the client, wherein the current medical data comprises current case data and current examination data;
the medical data package comprises medical data and medical information, a monitoring storage area and a non-monitoring storage area are arranged in the medical data package, the medical information in the medical data package stored in the monitoring storage area comprises a medical account number, a medical score and a monitoring period, and the medical information in the medical data package stored in the non-monitoring storage area comprises the medical account number and the account number score;
the management analysis unit is respectively connected with the acquisition unit and the medical database, and can acquire the historical medical scores of the matched medical data packets when the medical data packets are matched in the monitoring area, carry out primary correction on the historical medical scores according to the historical medical data and the current medical data, and carry out secondary correction according to the matched monitoring period and the interval duration of the current medical data input so as to judge the medical scores after the secondary correction according to the standard partition scores set in the management analysis unit, so as to determine whether the storage position of the matched medical data packets is adjusted.
Further, the management analysis unit can acquire the current medical account number acquired by the acquisition unit and match the current medical account number with the medical account number corresponding to the medical data packet in the medical database,
if the medical data packet matched with the current medical account number does not exist in the medical data base, the management analysis unit establishes a medical data packet of the current medical data in the medical data base, calculates the medical score of the established medical data packet according to the current medical data, and determines the storage position of the established medical data packet;
if a medical data packet matched with the current medical account exists in the medical database and the matched medical data packet is stored in the monitoring storage area, the management analysis unit corrects the medical score in the medical information for the first time according to the historical medical data and the current medical data, calculates the time interval of data input in the last time of data input distance, and corrects the medical score after the first time for the second time so as to determine whether to adjust the storage position of the matched medical data packet;
if there is a medical data packet matching the current medical account number in the medical database, and the matching medical data packet is stored in the non-monitoring storage area, the management analysis unit corrects the medical score in the matching medical data packet according to the historical medical data and the current medical data to determine whether to adjust the storage position of the matching medical data packet.
Further, the management analysis unit is internally provided with standard partition scores, when the medical data packet matched with the current medical account number does not exist in the medical database, the management analysis unit can acquire the current case data and the current check data in the current medical data, acquire the check scores of the case scores corresponding to the current case data and the current check data, calculate the medical scores of the established medical data packet according to the set case weights and the check weights, judge the medical scores of the established medical data packet according to the set standard partition scores,
if the medical score of the established medical data packet is smaller than the standard partition score, the management analysis unit stores the established medical data packet into the monitoring storage area;
if the medical score of the established medical data packet is greater than or equal to the standard partition score, the management analysis unit stores the established medical data packet into the non-monitoring storage area;
wherein ds=a×c1+b×c2, ds is a medical score of the medical data packet established, a is a case score corresponding to the current case data, c1 is a case weight set in the management analysis unit, B is an examination score corresponding to the current examination data, and c2 is an examination weight set in the management analysis unit.
Further, when the management analysis unit stores the established medical data packet in the monitoring storage area, the management analysis unit acquires the current disease type according to the current case data in the current medical data, selects a corresponding initial monitoring period in a disease type period matrix arranged in the management analysis unit, adjusts the initial monitoring period according to the medical score and the standard partition score of the established medical data packet, and stores the adjusted monitoring period in the established medical data packet;
wherein Ti '=ti× (Db/Ds), ti' is the adjusted monitoring period, ti is the initial monitoring period selected from the condition type period matrix, db is the standard partition score, and Ds is the medical score of the established medical data packet.
Further, standard partition scores are set in the management analysis unit, the management analysis unit can acquire medical scores in medical information in matched medical data packets under a second preset condition, acquire matched medical data packet history medical data, acquire check scores corresponding to case scores corresponding to current case data and current check data, and check scores corresponding to case scores corresponding to history check data, and the management analysis unit corrects the medical scores in the matched medical data packets according to the history medical data and the current medical data;
Wherein Dh '=dh× (a/a')× (B/B '), dh' is the corrected medical score, the medical score in the medical data packet to which Dh matches, a is the case score corresponding to the current case data, B is the examination score corresponding to the current examination data, a 'is the case score corresponding to the historical case data, and B' is the examination score corresponding to the historical examination data;
the second preset condition is that a medical data packet matched with the current medical account number exists in the medical database, and the matched medical data packet is stored in the non-monitoring storage area.
Further, the management analysis unit, after correcting the medical scores in the matched medical data packets, compares the corrected medical scores with standard partition scores,
if the corrected medical score is smaller than the standard partition score, the management analysis unit transfers the matched medical data packet from the non-monitoring storage area to the monitoring storage area, acquires the current symptom type of the current medical data, selects an initial monitoring period from a symptom type period matrix arranged in the management analysis unit, adjusts the selected initial monitoring period according to the corrected medical score and the standard partition score, and stores the adjusted monitoring period into the matched medical data packet;
Wherein, ti '=ti× (Db/Dh'), ti 'is the adjusted monitoring period, the initial monitoring period is selected from the disease type period matrix selected by Ti, db is the standard partition score, dh' is the corrected medical score;
and if the corrected medical score is greater than or equal to the standard partition score, the management analysis unit does not adjust the storage position of the matched medical data packet.
Further, the management analysis unit can acquire medical scores in medical information in the matched medical data packet under a first preset condition, acquire history medical data of the matched medical data packet, acquire check scores corresponding to the current check data and case scores corresponding to the history case data, and check scores corresponding to the history check data, perform primary correction on the medical scores in the medical information according to the history medical data and the current medical data, acquire monitoring periods in the medical information in the matched medical data packet, calculate time intervals of data input in the last time of the data input, and perform secondary correction on the medical scores after primary correction according to the monitoring periods in the medical information;
The medical data packet matched with the current medical account number exists in the medical database, and the matched medical data packet is stored in the monitoring storage area;
wherein Dh '=dh× (a/a')× (B/B '), dh' is a medical score after one correction, dh is a medical score in the medical information, a is a case score corresponding to the current case data, B is an examination score corresponding to the current examination data, a 'is a case score corresponding to the historical case data, and B' is an examination score corresponding to the historical examination data; dh "=dh' × [1+ (|tf-th|/Th) ], dh" is the medical score after the second correction, tf is the time interval of the data entry at the current data entry distance, and Th is the monitoring period in the medical information in the matched medical data packet.
Further, the management analysis unit is internally provided with standard partition scores, and after the medical scores are secondarily corrected, the management analysis unit compares the secondarily corrected medical scores with the standard partition scores,
if the medical score after the secondary correction is smaller than the standard partition score, the management analysis unit does not adjust the storage position of the matched medical data packet,
And if the medical score after the secondary correction is greater than or equal to the standard partition score, the management analysis unit transfers the matched medical data packet from the monitoring storage area to the non-monitoring storage area.
Further, when the medical score after the secondary correction is smaller than the standard partition score, the management analysis unit acquires the monitoring period in the matched medical data packet, adjusts the monitoring period once according to the medical score after the secondary correction, adjusts the monitoring period after the primary adjustment again according to the time interval of the data input on the current data input distance, and stores the monitoring period after the secondary adjustment in the matched medical data packet;
wherein, th ' =thx [1+ (Dh "-Dh)/Dh) ], th" =th ' +|th-tf|, th ' is a monitoring period after primary adjustment, th "is a monitoring period after secondary adjustment, th is a monitoring period in the matched medical data packet, dh" is a medical score after secondary correction, dh is a medical score in medical information, tf is a time interval of data entry on the current data entry distance.
Further, the data analysis system further comprises a reminding unit, the reminding unit is respectively connected with the medical database and the client, the reminding unit can acquire a monitoring period of any medical data packet in a monitoring storage area in the medical database, acquire a real-time interval duration of current last data update of the medical data packet, and send a medical treatment reminding to a corresponding client when the real-time interval duration of current last data update of the medical data packet reaches the monitoring period stored in the medical data packet.
Compared with the prior art, the invention has the beneficial effects that the current medical data recorded by the client and the corresponding current medical account number are acquired in real time through the setting acquisition unit, the medical data of the patient corresponding to the current medical account number is determined, the matching judgment is carried out on the current medical data according to the medical data packet stored in the medical database, the original medical data packet is subjected to data updating according to the current medical data, and the corresponding medical score is calculated according to the updated data, so that whether the medical data packet is monitored or not is judged, when the medical data packet belongs to a monitoring storage area, the original monitoring period is updated in real time according to the updated current medical data, the accuracy of the monitoring period of the medical data packet is ensured, the accuracy of the prediction of the medical period of the high risk group of chronic obstructive pulmonary diseases is ensured, and the effectiveness of medical reminding is improved.
Further, when the acquisition unit acquires real-time medical data, the medical data is used as current medical data, account information corresponding to the current medical data, namely an object of the corresponding medical data, whether the object has historical data or not can be quickly determined by matching in a medical database, and a correction mode of the medical score of the object is determined according to the storage position of the historical data.
In particular, the medical scores of the medical data packets are judged by calculating the medical scores of the medical data packets and setting the standard partition scores, when the medical scores of the medical data packets are smaller than the standard partition scores, the case scores corresponding to the current case data are lower than the examination scores corresponding to the current examination data, the medical data packets reach the standard required to be monitored, so that the medical data packets are divided into the monitoring storage areas, and when the medical scores of the medical data packets are greater than or equal to the standard partition scores, the medical data packets do not reach the standard required to be monitored, so that the medical data packets are divided into the non-monitoring storage areas, the centralized analysis and management of the high-risk group data of the chronic obstructive pulmonary diseases are realized, and the medical reminding is convenient to make.
Furthermore, the initial monitoring period is adjusted according to the medical score of the medical data packet and the standard partition score, so that the consistency of the set monitoring period and the medical score is further ensured, the monitoring period of the specific medical data packet is updated in real time along with the corresponding medical data, invalid review or missing review is avoided, the effectiveness of medical reminding is ensured, the standardized management of the medical data packet is improved, and accurate medical reminding is ensured.
Further, when the current medical account number has historical data and is in a non-monitoring storage area, the historical medical score is corrected according to the current case score and the checking score, the historical case score and the checking score, the updated medical score can be rapidly determined, whether the medical account number is monitored or not is determined according to the medical score, and accurate classified monitoring of the medical data is further guaranteed.
Further, when the corrected medical score is smaller than the standard partition score, the medical score is required to be monitored, so that correction is performed on the basis of the selected initial monitoring period by combining with the actual scoring condition of the data, and the accuracy of the adjusted monitoring period is ensured.
Further, for the medical data packet originally existing in the monitoring storage area, when the existing data is updated, the medical data packet is corrected once by combining with the historical data, accurate prediction and correction of medical scores are realized by combining with actual cases and examination conditions, and secondary correction is performed by combining with the actual medical treatment period, so that the system can accurately remind the patient of medical treatment or remind the patient in advance by combining with the actual conditions of the patient, and the effectiveness of medical treatment reminding is improved.
Further, through setting up and remind the unit and carry out real-time judgement control to each medical data package in the control storage area in the medical database to ensure effective accurate warning of seeking medical advice, when realizing unmanned intervention's warning operation, ensured the timeliness of warning of seeking medical advice, avoided the omission of warning of seeking medical advice simultaneously.
Drawings
FIG. 1 is a schematic diagram of a data analysis system for predicting a high risk group of chronic obstructive pulmonary disease according to the present embodiment;
fig. 2 is a schematic diagram illustrating the storage of the medical database according to the present embodiment.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 and 2, fig. 1 is a schematic diagram of a data analysis system for predicting a high risk group of chronic obstructive pulmonary disease according to the present embodiment, fig. 2 is a storage schematic diagram of a medical database according to the present embodiment, and the present embodiment provides a data analysis system for predicting a high risk group of chronic obstructive pulmonary disease, including,
the acquisition unit is connected with the client and is used for acquiring current medical data and corresponding current medical account numbers recorded by the client, wherein the current medical data comprises current case data and current examination data;
the medical data package comprises medical data and medical information, a monitoring storage area and a non-monitoring storage area are arranged in the medical data package, the medical information in the medical data package stored in the monitoring storage area comprises a medical account number, a medical score and a monitoring period, and the medical information in the medical data package stored in the non-monitoring storage area comprises the medical account number and the account number score;
The management analysis unit is respectively connected with the acquisition unit and the medical database, and can acquire the historical medical scores of the matched medical data packets when the medical data packets are matched in the monitoring area, carry out primary correction on the historical medical scores according to the historical medical data and the current medical data, and carry out secondary correction according to the matched monitoring period and the interval duration of the current medical data input so as to judge the medical scores after the secondary correction according to the standard partition scores set in the management analysis unit, so as to determine whether the storage position of the matched medical data packets is adjusted.
The medical data of the patient corresponding to the current medical account is determined by setting the acquisition unit to acquire the current medical data recorded by the client and the corresponding current medical account in real time, the current medical data is matched and judged according to the medical data packet stored in the medical database, the original medical data packet is subjected to data updating according to the current medical data, and the corresponding medical score is calculated according to the updated data, so that whether the medical data packet is monitored or not is judged, when the medical data packet belongs to a monitoring storage area, the original monitoring period is updated according to the updated current medical data, the accuracy of the monitoring period of the medical data packet is ensured, the accuracy of the prediction of the medical period of the high-risk crowd with chronic obstructive pulmonary diseases is ensured, and the effectiveness of medical reminding is improved.
In particular, the management analysis unit can acquire the current medical account number acquired by the acquisition unit and match the current medical account number with the medical account number corresponding to the medical data packet in the medical database,
if the medical data packet matched with the current medical account number does not exist in the medical data base, the management analysis unit establishes a medical data packet of the current medical data in the medical data base, calculates the medical score of the established medical data packet according to the current medical data, and determines the storage position of the established medical data packet;
if a medical data packet matched with the current medical account exists in the medical database and the matched medical data packet is stored in the monitoring storage area, the management analysis unit corrects the medical score in the medical information for the first time according to the historical medical data and the current medical data, calculates the time interval of data input in the last time of data input distance, and corrects the medical score after the first time for the second time so as to determine whether to adjust the storage position of the matched medical data packet;
if there is a medical data packet matching the current medical account number in the medical database, and the matching medical data packet is stored in the non-monitoring storage area, the management analysis unit corrects the medical score in the matching medical data packet according to the historical medical data and the current medical data to determine whether to adjust the storage position of the matching medical data packet.
When the acquisition unit acquires real-time medical data, the medical data is used as current medical data, account information corresponding to the current medical data, namely an object of the corresponding medical data, whether the object has historical data or not can be quickly determined by matching in a medical database, and a correction mode of the medical score of the object is determined according to the storage position of the historical data.
Specifically, the management analysis unit is internally provided with standard partition scores, when the medical data packet matched with the current medical account number does not exist in the medical database, the management analysis unit can acquire the current case data and the current check data in the current medical data, acquire the check scores of the case scores corresponding to the current case data and the current check data, calculate the medical scores of the established medical data packet according to the set case weights and the check weights, judge the medical scores of the established medical data packet according to the set standard partition scores,
if the medical score of the established medical data packet is smaller than the standard partition score, the management analysis unit stores the established medical data packet into the monitoring storage area;
if the medical score of the established medical data packet is greater than or equal to the standard partition score, the management analysis unit stores the established medical data packet into the non-monitoring storage area;
wherein ds=a×c1+b×c2, ds is a medical score of the medical data packet established, a is a case score corresponding to the current case data, c1 is a case weight set in the management analysis unit, B is an examination score corresponding to the current examination data, and c2 is an examination weight set in the management analysis unit.
The medical scores of the medical data packets are judged by calculating the medical scores of the medical data packets and setting the standard partition scores, when the medical scores of the medical data packets are smaller than the standard partition scores, the case scores corresponding to the current case data are lower than the check scores corresponding to the current check data, the medical data packets reach the standard required to be monitored, so that the medical data packets are divided into the monitoring storage areas, and when the medical scores of the medical data packets are greater than or equal to the standard partition scores, the medical data packets do not reach the standard required to be monitored, so that the medical data packets are divided into the non-monitoring storage areas, the centralized analysis management of the high-risk crowd data of the chronic obstructive pulmonary diseases is realized, and the medical reminding is convenient to make.
In this embodiment, the obtained current case data is actual case information or actual doctor's advice information, the corresponding case score is marked in the actual case information, the obtained current inspection data is an actual inspection image or inspection report, the corresponding inspection score is marked in the inspection report, if no scoring item exists in the actually used case information or inspection report, the semantic recognition function of the AI mode can also be set to automatically extract the setting keywords in the case information or inspection report, and the semantic, the number or the occurrence frequency of the setting keywords are used as the standard to calculate the score; the case weight set in this example is 0.72 and the examination weight is 0.28, and the adaptability can be set according to the specific disease type or specific disease condition of the chronic obstructive pulmonary disease.
In this embodiment, the case score, the examination score and the standard partition score adopted are all percentages, and because this embodiment is specific to the overall situation of chronic obstructive pulmonary disease, the specific proportion of the periodic medical treatment is relatively low, this embodiment sets the standard partition score to 22.54, if the specific situation of the chronic obstructive pulmonary disease is different, the feedback setting can be performed according to the case score and the examination score, combining the manual feedback with the conclusion that the periodic medical treatment is required, and also can utilize the self-learning mode of the AI system to input a plurality of groups of historical data with medical treatment period conclusion for learning, and feedback outputs the standard partition score; meanwhile, the standard partition score can be a specific score, a specific disease type or a specific disease condition, or a specific medical advice conclusion, and if the medical advice conclusion of the medical period exists, the medical advice conclusion can be directly judged to be the data of the monitoring storage area.
Specifically, when the management analysis unit stores the established medical data packet in the monitoring storage area, the management analysis unit acquires the current disease type according to the current case data in the current medical data, selects a corresponding initial monitoring period in a disease type period matrix set in the management analysis unit, adjusts the initial monitoring period according to the medical score and the standard partition score of the established medical data packet, and stores the adjusted monitoring period in the established medical data packet;
Wherein Ti '=ti× (Db/Ds), ti' is the adjusted monitoring period, ti is the initial monitoring period selected from the condition type period matrix, db is the standard partition score, and Ds is the medical score of the established medical data packet.
In this embodiment, a disease type monitoring matrix F-T, (F1-T1, F2-T2, F3-T3 … … Fn-Tn) is provided in the management analysis unit, where F1 is a first disease type, T1 is an initial monitoring period corresponding to the first disease type, F2 is a second disease type, T2 is an initial monitoring period corresponding to the second disease type, F3 is a third disease type, T3 is an initial monitoring period corresponding to the third disease type, … …, fn is an nth disease type, tn is an initial monitoring period corresponding to the nth disease type, the management analysis unit obtains a current disease type Fs according to current case data in current medical data, selects a corresponding initial monitoring period Ti in the disease type monitoring matrix F-T according to the current disease type Fs, i=1, 2, 3 … … n, and simultaneously, can also set a key point corresponding to the monitoring period to select, and actually combine the key point matrix for high risk group data management of chronic obstructive pulmonary disease.
The initial monitoring period is adjusted according to the medical score of the medical data packet and the standard partition score, so that the consistency of the set monitoring period and the medical score is further ensured, the monitoring period of the specific medical data packet is updated in real time along with the corresponding medical data, invalid review or missing review is avoided, the effectiveness of medical reminding is ensured, the standardized management of the medical data packet is improved, and accurate medical reminding is ensured.
Specifically, standard partition scores are set in the management analysis unit, the management analysis unit can acquire medical scores in medical information in matched medical data packets under a second preset condition, acquire history medical data of the matched medical data packets, acquire examination scores corresponding to case scores corresponding to current examination data and examination scores corresponding to history examination data, and correct the medical scores in the matched medical data packets according to the history medical data and the current medical data;
wherein Dh '=dh× (a/a')× (B/B '), dh' is the corrected medical score, the medical score in the medical data packet to which Dh matches, a is the case score corresponding to the current case data, B is the examination score corresponding to the current examination data, a 'is the case score corresponding to the historical case data, and B' is the examination score corresponding to the historical examination data;
The second preset condition is that a medical data packet matched with the current medical account number exists in the medical database, and the matched medical data packet is stored in the non-monitoring storage area.
When the current medical account number has historical data and is in a non-monitoring storage area, the historical medical score is corrected according to the current case score and the checking score, the historical case score and the checking score, the updated medical score can be rapidly determined, whether the medical account number is monitored or not is determined according to the medical score, and the accurate classified monitoring of the medical data is further guaranteed.
Specifically, the management analysis unit, after correcting the medical scores in the matched medical data packets, compares the corrected medical scores with standard partition scores,
if the corrected medical score is smaller than the standard partition score, the management analysis unit transfers the matched medical data packet from the non-monitoring storage area to the monitoring storage area, acquires the current symptom type of the current medical data, selects an initial monitoring period from a symptom type period matrix arranged in the management analysis unit, adjusts the selected initial monitoring period according to the corrected medical score and the standard partition score, and stores the adjusted monitoring period into the matched medical data packet;
Wherein, ti '=ti× (Db/Dh'), ti 'is the adjusted monitoring period, the initial monitoring period is selected from the disease type period matrix selected by Ti, db is the standard partition score, dh' is the corrected medical score;
and if the corrected medical score is greater than or equal to the standard partition score, the management analysis unit does not adjust the storage position of the matched medical data packet.
When the corrected medical score is smaller than the standard partition score, the medical score is required to be monitored, so that correction is performed on the basis of the selected initial monitoring period by combining with the actual scoring condition of the data, and the accuracy of the adjusted monitoring period is ensured.
Specifically, the management analysis unit can acquire medical scores in medical information in a matched medical data packet under a first preset condition, acquire history medical data of the matched medical data packet, acquire check scores corresponding to current check data of case scores corresponding to current case data and check scores corresponding to history check data of case scores corresponding to history case data, perform primary correction on the medical scores in the medical information according to the history medical data and the current medical data, acquire monitoring periods in the medical information in the matched medical data packet, calculate time intervals of data input in the last time of the data input, and perform secondary correction on the medical scores after primary correction according to the monitoring periods in the medical information;
The medical data packet matched with the current medical account number exists in the medical database, and the matched medical data packet is stored in the monitoring storage area;
wherein Dh '=dh× (a/a')× (B/B '), dh' is a medical score after one correction, dh is a medical score in the medical information, a is a case score corresponding to the current case data, B is an examination score corresponding to the current examination data, a 'is a case score corresponding to the historical case data, and B' is an examination score corresponding to the historical examination data; dh "=dh' × [1+ (|tf-th|/Th) ], dh" is the medical score after the second correction, tf is the time interval of the data entry at the current data entry distance, and Th is the monitoring period in the medical information in the matched medical data packet.
For the medical data packet originally existing in the monitoring storage area, when the existing data is updated, the medical data packet is corrected once by combining the historical data, accurate prediction and correction medical score are realized by combining the actual cases and the examination conditions, and secondary correction is performed by combining the actual medical treatment period, so that the system can accurately remind the patient of medical treatment or remind the patient in advance by combining the actual conditions of the patient, and the effectiveness of medical treatment reminding is improved.
Specifically, the management analysis unit is internally provided with standard partition scores, and after the medical scores are secondarily corrected, the management analysis unit compares the secondarily corrected medical scores with the standard partition scores,
if the medical score after the secondary correction is smaller than the standard partition score, the management analysis unit does not adjust the storage position of the matched medical data packet,
and if the medical score after the secondary correction is greater than or equal to the standard partition score, the management analysis unit transfers the matched medical data packet from the monitoring storage area to the non-monitoring storage area.
Specifically, when the medical score after the secondary correction is smaller than the standard partition score, the management analysis unit acquires the monitoring period in the matched medical data packet, adjusts the monitoring period once according to the medical score after the secondary correction, adjusts the monitoring period after the primary adjustment again according to the time interval of the data input on the current data input distance, and stores the monitoring period after the secondary adjustment in the matched medical data packet;
wherein, th ' =thx [1+ (Dh "-Dh)/Dh) ], th" =th ' +|th-tf|, th ' is a monitoring period after primary adjustment, th "is a monitoring period after secondary adjustment, th is a monitoring period in the matched medical data packet, dh" is a medical score after secondary correction, dh is a medical score in medical information, tf is a time interval of data entry on the current data entry distance.
And for the medical data packet originally existing in the monitoring storage area, if the data is updated, the data packet still exists in the monitoring storage area, so that the monitoring period is adjusted by combining with the specific medical grading condition, and the actual data updating period is subjected to secondary correction, thereby further improving the effectiveness of medical reminding.
Specifically, the data analysis system further comprises a reminding unit, the reminding unit is respectively connected with the medical database and the client, the reminding unit can acquire a monitoring period of any medical data packet in a monitoring storage area in the medical database, acquire the current real-time interval duration of the last data update of the medical data packet, and send a medical treatment reminding to the corresponding client when the current real-time interval duration of the last data update of the medical data packet reaches the internally stored monitoring period.
Each medical data packet in the monitoring storage area in the medical database is judged and monitored in real time through the setting reminding unit, so that effective and accurate medical reminding is guaranteed, the timeliness of medical reminding is guaranteed while unmanned intervention reminding operation is realized, and meanwhile omission of medical reminding is avoided.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A data analysis system for predicting a high risk group of chronic obstructive pulmonary disease, comprising,
the acquisition unit is connected with the client and is used for acquiring current medical data and corresponding current medical account numbers recorded by the client, wherein the current medical data comprises current case data and current examination data;
The medical data package comprises medical data and medical information, a monitoring storage area and a non-monitoring storage area are arranged in the medical data package, the medical information in the medical data package stored in the monitoring storage area comprises a medical account number, a medical score and a monitoring period, and the medical information in the medical data package stored in the non-monitoring storage area comprises the medical account number and the account number score;
the management analysis unit is respectively connected with the acquisition unit and the medical database, and can acquire the historical medical scores of the matched medical data packets when the medical data packets are matched in the monitoring area, carry out primary correction on the historical medical scores according to the historical medical data and the current medical data, and carry out secondary correction according to the matched monitoring period and the interval duration of the current medical data input so as to judge the medical scores after the secondary correction according to the standard partition scores set in the management analysis unit, so as to determine whether the storage position of the matched medical data packets is adjusted.
2. The data analysis system for predicting a high risk group of chronic obstructive pulmonary disease according to claim 1, wherein the management analysis unit is configured to obtain a current medical account number collected by the collection unit, and match the current medical account number with a medical account number corresponding to a medical data packet in the medical database,
If the medical data packet matched with the current medical account number does not exist in the medical data base, the management analysis unit establishes a medical data packet of the current medical data in the medical data base, calculates the medical score of the established medical data packet according to the current medical data, and determines the storage position of the established medical data packet;
if a medical data packet matched with the current medical account exists in the medical database and the matched medical data packet is stored in the monitoring storage area, the management analysis unit corrects the medical score in the medical information for the first time according to the historical medical data and the current medical data, calculates the time interval of data input in the last time of data input distance, and corrects the medical score after the first time for the second time so as to determine whether to adjust the storage position of the matched medical data packet;
if there is a medical data packet matching the current medical account number in the medical database, and the matching medical data packet is stored in the non-monitoring storage area, the management analysis unit corrects the medical score in the matching medical data packet according to the historical medical data and the current medical data to determine whether to adjust the storage position of the matching medical data packet.
3. The data analysis system for predicting high risk group of chronic obstructive pulmonary disease according to claim 2, wherein the management analysis unit is provided with standard partition scores, the management analysis unit is capable of acquiring current case data and current examination data in current medical data and acquiring examination scores corresponding to case scores corresponding to the current case data and the current examination data when no medical data packet matched with the current medical account number exists in the medical database, the management analysis unit calculates medical scores of the established medical data packet according to the set case weights and examination weights and determines the medical scores of the established medical data packet according to the set standard partition scores,
if the medical score of the established medical data packet is smaller than the standard partition score, the management analysis unit stores the established medical data packet into the monitoring storage area;
if the medical score of the established medical data packet is greater than or equal to the standard partition score, the management analysis unit stores the established medical data packet into the non-monitoring storage area;
wherein ds=a×c1+b×c2, ds is a medical score of the medical data packet established, a is a case score corresponding to the current case data, c1 is a case weight set in the management analysis unit, B is an examination score corresponding to the current examination data, and c2 is an examination weight set in the management analysis unit.
4. The data analysis system for predicting high risk group of chronic obstructive pulmonary disease according to claim 3, wherein the management analysis unit obtains a current disease type according to current case data in current medical data when storing the established medical data packet in the monitoring storage area, selects a corresponding initial monitoring period in a disease type period matrix set in the management analysis unit, adjusts the initial monitoring period according to a medical score and a standard partition score of the established medical data packet, and stores the adjusted monitoring period in the established medical data packet;
wherein Ti '=ti× (Db/Ds), ti' is the adjusted monitoring period, ti is the initial monitoring period selected from the condition type period matrix, db is the standard partition score, and Ds is the medical score of the established medical data packet.
5. The data analysis system for predicting high risk groups of chronic obstructive pulmonary disease according to claim 2, wherein a standard partition score is set in the management analysis unit, the management analysis unit is capable of acquiring medical scores in medical information in matched medical data packets and acquiring history medical data of the matched medical data packets under a second preset condition, acquiring an examination score corresponding to current examination data of case scores corresponding to current case data and an examination score corresponding to history examination data of case scores corresponding to history case data, and the management analysis unit corrects the medical scores in the matched medical data packets according to the history medical data and the current medical data;
Wherein Dh '=dh× (a/a')× (B/B '), dh' is the corrected medical score, the medical score in the medical data packet to which Dh matches, a is the case score corresponding to the current case data, B is the examination score corresponding to the current examination data, a 'is the case score corresponding to the historical case data, and B' is the examination score corresponding to the historical examination data;
the second preset condition is that a medical data packet matched with the current medical account number exists in the medical database, and the matched medical data packet is stored in the non-monitoring storage area.
6. The system according to claim 5, wherein the management analysis unit, after correcting the medical score in the matched medical data packet, compares the corrected medical score with a standard partition score,
if the corrected medical score is smaller than the standard partition score, the management analysis unit transfers the matched medical data packet from the non-monitoring storage area to the monitoring storage area, acquires the current symptom type of the current medical data, selects an initial monitoring period from a symptom type period matrix arranged in the management analysis unit, adjusts the selected initial monitoring period according to the corrected medical score and the standard partition score, and stores the adjusted monitoring period into the matched medical data packet;
Wherein, ti '=ti× (Db/Dh'), ti 'is the adjusted monitoring period, the initial monitoring period is selected from the disease type period matrix selected by Ti, db is the standard partition score, dh' is the corrected medical score;
and if the corrected medical score is greater than or equal to the standard partition score, the management analysis unit does not adjust the storage position of the matched medical data packet.
7. The data analysis system for predicting high risk group of chronic obstructive pulmonary disease according to claim 2, wherein the management analysis unit is capable of acquiring medical scores in medical information in a matched medical data packet under a first preset condition, acquiring history medical data of the matched medical data packet, acquiring an inspection score corresponding to current inspection data of a case score corresponding to current case data, and an inspection score corresponding to history inspection data of a case score corresponding to history case data, performing primary correction on the medical scores in the medical information according to the history medical data and the current medical data, acquiring a monitoring period in the medical information in the matched medical data packet, calculating a time interval of data entry in a current data entry distance, and performing secondary correction on the medical scores after primary correction according to the monitoring period in the medical information;
The medical data packet matched with the current medical account number exists in the medical database, and the matched medical data packet is stored in the monitoring storage area;
wherein Dh '=dh× (a/a')× (B/B '), dh' is a medical score after one correction, dh is a medical score in the medical information, a is a case score corresponding to the current case data, B is an examination score corresponding to the current examination data, a 'is a case score corresponding to the historical case data, and B' is an examination score corresponding to the historical examination data; dh "=dh' × [1+ (|tf-th|/Th) ], dh" is the medical score after the second correction, tf is the time interval of the data entry at the current data entry distance, and Th is the monitoring period in the medical information in the matched medical data packet.
8. The data analysis system for predicting high risk group of chronic obstructive pulmonary disease according to claim 7, wherein the management analysis unit is provided with standard partition scores, and the management analysis unit compares the medical scores after the second correction with the standard partition scores,
if the medical score after the secondary correction is smaller than the standard partition score, the management analysis unit does not adjust the storage position of the matched medical data packet,
And if the medical score after the secondary correction is greater than or equal to the standard partition score, the management analysis unit transfers the matched medical data packet from the monitoring storage area to the non-monitoring storage area.
9. The data analysis system for predicting high risk group of chronic obstructive pulmonary disease according to claim 8, wherein the management analysis unit obtains the monitoring period in the matched medical data packet when the medical score after the secondary correction is smaller than the standard partition score, adjusts the monitoring period once according to the medical score after the secondary correction, adjusts the monitoring period after the primary adjustment again according to the time interval of the data input on the current data input distance, and stores the monitoring period after the secondary adjustment in the matched medical data packet;
wherein, th ' =thx [1+ (Dh "-Dh)/Dh) ], th" =th ' +|th-tf|, th ' is a monitoring period after primary adjustment, th "is a monitoring period after secondary adjustment, th is a monitoring period in the matched medical data packet, dh" is a medical score after secondary correction, dh is a medical score in medical information, tf is a time interval of data entry on the current data entry distance.
10. The data analysis system for predicting high risk groups of chronic obstructive pulmonary disease according to claim 1, wherein the data analysis system further comprises a reminding unit, the reminding unit is respectively connected with the medical database and the client, the reminding unit can obtain a monitoring period of any medical data packet in a monitoring storage area in the medical database, obtain a real-time interval duration of a current last data update of the medical data packet, and send a medical treatment reminding to a corresponding client when the real-time interval duration of the current last data update of the medical data packet reaches the monitoring period stored in the medical data packet.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311256847.1A CN117198491B (en) | 2023-09-27 | 2023-09-27 | Data analysis system for predicting high risk group of chronic obstructive pulmonary disease |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311256847.1A CN117198491B (en) | 2023-09-27 | 2023-09-27 | Data analysis system for predicting high risk group of chronic obstructive pulmonary disease |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117198491A true CN117198491A (en) | 2023-12-08 |
| CN117198491B CN117198491B (en) | 2024-04-09 |
Family
ID=88984991
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311256847.1A Active CN117198491B (en) | 2023-09-27 | 2023-09-27 | Data analysis system for predicting high risk group of chronic obstructive pulmonary disease |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117198491B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372389A (en) * | 2016-08-25 | 2017-02-01 | 浙江大学 | Mobile internet-based chronic disease active management method |
| CN206711090U (en) * | 2017-04-12 | 2017-12-05 | 电子科技大学成都研究院 | It is a kind of to realize remote monitoring and the rheumatism medical system of the consultation of doctors |
| CN109544372A (en) * | 2018-10-30 | 2019-03-29 | 平安医疗健康管理股份有限公司 | Outpatient service chronic disease qualification monitoring method and relevant apparatus based on artificial intelligence |
| CN111739654A (en) * | 2020-06-23 | 2020-10-02 | 深圳市宝安区人民医院 | Internet of things-based isolated hospital security supervision method and device and storage medium |
| CN113076367A (en) * | 2021-04-19 | 2021-07-06 | 广东技术师范大学 | Medical service information sharing system based on Internet |
| CN113643813A (en) * | 2021-08-30 | 2021-11-12 | 平安医疗健康管理股份有限公司 | Chronic disease follow-up supervision method and device based on artificial intelligence and computer equipment |
| CN115171863A (en) * | 2022-07-22 | 2022-10-11 | 广州医科大学附属第一医院（广州呼吸中心） | Chronic disease management system based on two-dimensional code |
| CN115985523A (en) * | 2023-02-02 | 2023-04-18 | 南京市妇幼保健院 | Digital chronic disease follow-up management system |
| CN116246763A (en) * | 2023-05-12 | 2023-06-09 | 山东维克特信息技术有限公司 | Auxiliary management system for rehabilitation stage of medical patient |
-
2023
- 2023-09-27 CN CN202311256847.1A patent/CN117198491B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372389A (en) * | 2016-08-25 | 2017-02-01 | 浙江大学 | Mobile internet-based chronic disease active management method |
| CN206711090U (en) * | 2017-04-12 | 2017-12-05 | 电子科技大学成都研究院 | It is a kind of to realize remote monitoring and the rheumatism medical system of the consultation of doctors |
| CN109544372A (en) * | 2018-10-30 | 2019-03-29 | 平安医疗健康管理股份有限公司 | Outpatient service chronic disease qualification monitoring method and relevant apparatus based on artificial intelligence |
| CN111739654A (en) * | 2020-06-23 | 2020-10-02 | 深圳市宝安区人民医院 | Internet of things-based isolated hospital security supervision method and device and storage medium |
| CN113076367A (en) * | 2021-04-19 | 2021-07-06 | 广东技术师范大学 | Medical service information sharing system based on Internet |
| CN113643813A (en) * | 2021-08-30 | 2021-11-12 | 平安医疗健康管理股份有限公司 | Chronic disease follow-up supervision method and device based on artificial intelligence and computer equipment |
| CN115171863A (en) * | 2022-07-22 | 2022-10-11 | 广州医科大学附属第一医院（广州呼吸中心） | Chronic disease management system based on two-dimensional code |
| CN115985523A (en) * | 2023-02-02 | 2023-04-18 | 南京市妇幼保健院 | Digital chronic disease follow-up management system |
| CN116246763A (en) * | 2023-05-12 | 2023-06-09 | 山东维克特信息技术有限公司 | Auxiliary management system for rehabilitation stage of medical patient |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117198491B (en) | 2024-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gustafsson et al. | Efficiency of organised and opportunistic cytological screening for cancer in situ of the cervix | |
| US20120265549A1 (en) | System and Computer Readable Medium for Predicting Patient Outcomes | |
| WO2008031208A1 (en) | Modeling and control for highly variable and nonlinear processes | |
| CN110795587A (en) | Medical alarm early warning method, service platform and computer readable storage medium | |
| Sarkis et al. | Bilateral recurrent laryngeal nerve injury in a specialized thyroid surgery unit: would routine intraoperative neuromonitoring alter outcomes? | |
| CN109118081B (en) | Operation safety supervision system and method based on image processing mode | |
| CN109247936A (en) | A kind of abnormal brain electricity behavior monitoring system and method for full night sleep monitor | |
| CN115985523B (en) | Digital chronic disease follow-up management system | |
| CN111695836A (en) | Clinical trial online operation management and control integrated system | |
| CN114021971A (en) | Comprehensive evaluation system, method and storage medium for expressway operation and maintenance management | |
| CN112216380A (en) | Method, device, electronic equipment and storage medium for controlling infectious diseases | |
| CN112751925A (en) | Pet dog management system and method based on big data and Internet of things and intelligent equipment | |
| CN112418513A (en) | Temperature prediction method and device, storage medium, and electronic device | |
| CN117198491B (en) | Data analysis system for predicting high risk group of chronic obstructive pulmonary disease | |
| CN114566283A (en) | Cerebral apoplexy-based patient daily protection system and prevention method | |
| CN107342880A (en) | Abnormal information acquisition method and system | |
| CN108446202A (en) | A kind of judgment method of the safe condition of calculator room equipment | |
| CN114913968B (en) | Medical equipment state monitoring system and method based on artificial intelligence | |
| CN116013508A (en) | Data acquisition system and method for ophthalmology | |
| Chang et al. | A structural design of clinical decision support system for chronic diseases risk management | |
| JP2019101465A (en) | Information processing system, control method thereof, control program, and record medium | |
| CN114121239A (en) | Operating room abnormal condition processing method and device and electronic equipment | |
| CN109903850A (en) | Method of the use information technology to perioperative operation safety management | |
| CN113270180B (en) | Scheduling management system for emergency medical treatment | |
| CN117408342B (en) | Neuron network inference method and system based on neuron spike sequence data |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |