CN115798662A - Case transport analysis system for hospital case management based on artificial intelligence algorithm - Google Patents
Case transport analysis system for hospital case management based on artificial intelligence algorithm Download PDFInfo
- Publication number
- CN115798662A CN115798662A CN202211508254.5A CN202211508254A CN115798662A CN 115798662 A CN115798662 A CN 115798662A CN 202211508254 A CN202211508254 A CN 202211508254A CN 115798662 A CN115798662 A CN 115798662A
- Authority
- CN
- China
- Prior art keywords
- case
- patient
- hospital
- medical
- analysis system
- 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.)
- Pending
Links
Images
Landscapes
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention provides a case transport analysis system for hospital case management based on an artificial intelligence algorithm, which is characterized in that: the data of each medical diagnosis and treatment device is collected by a 5G internet of things terminal data collection device connected with the medical diagnosis and treatment device, the 5G internet of things terminal data collection device transmits the data to a medical institution to which the medical institution belongs through a 5G base station, the medical institution uploads the data to a cloud server through the Internet, and a case delivery analysis system for hospital case management in the cloud server processes the data; the case transport analysis system for hospital case management comprises a plurality of modules; the artificial intelligence algorithm is a neighbor algorithm. The invention changes the medical record of the patient from paper to electronization and informatization, thereby improving the medical efficiency and the medical quality.
Description
Technical Field
The invention relates to the field of case transportation analysis systems for hospital case management, in particular to a case transportation analysis system for hospital case management based on an artificial intelligence algorithm.
Background
In the current paper patient case form, a patient who goes to a hospital to see a doctor usually finds that a case needs to be refilled every time a hospital is changed, and a plurality of examinations need to be repeated, so that a great amount of time and money are wasted, and meanwhile, the medical resources of the whole medical system are greatly wasted.
When a doctor diagnoses a patient, the doctor only judges the current symptoms of the patient, and the trial and error process of the headache and foot pain of the headache doctor increases the time and money cost of the patient.
Disclosure of Invention
The invention provides a case transport analysis system for hospital case management based on an artificial intelligence algorithm, which aims to overcome the defects of the prior art, change the case of a patient from paper to electronic and information, improve the medical efficiency and improve the medical quality.
The technical scheme for solving the technical problem is as follows:
case transport analysis system for hospital case management based on artificial intelligence algorithm, its characterized in that:
the data of each medical diagnosis and treatment device is collected by a 5G internet of things terminal data collection device connected with the medical diagnosis and treatment device, the 5G internet of things terminal data collection device transmits the data to a medical institution to which the medical institution belongs through a 5G base station, the medical institution uploads the data to a cloud server through the Internet, and a case delivery analysis system for hospital case management in the cloud server processes the data;
the case transport analysis system for hospital case management comprises the following modules:
a: case home page module:
when a patient is hospitalized and treated, a social security card or an identity card is used for registration operation, after the registration action is completed, the hospital case management case delivery analysis system can automatically retrieve the historical case information of the patient according to the social security card or the identity card of the patient, and a treatment record is created in the hospital case management case delivery analysis system; the patient enters a case home page module from the terminal to check the information related to the current treatment and the historical case information belonging to the patient;
a doctor uses a social security card or an identity card of a patient to perform card reading operation of hardware equipment, and calls historical case information of the patient; meanwhile, an artificial intelligence algorithm is involved, and according to the current registered department of the patient, the artificial intelligence algorithm is adopted to extract the historical case information which is the same as or similar to the current clinic, and then the extracted historical case information is provided for the doctor in a streaming data display mode;
b, a data module:
the patient obtains a permanent and unique case file belonging to the patient according to the social security card or the identity card of the patient;
the outpatient medical record recording module:
when a patient is in a visit, a doctor can input relevant medical record information of the visit into a case transport analysis system for hospital medical record management according to a diagnosis result;
d: inpatient case input module:
aiming at the disease condition change and diagnosis and treatment stage information of inpatients, a doctor inputs the relevant medical record information of the inpatient in the hospital in a case transport analysis system for hospital medical record management;
the disease course management module:
carrying out whole-course management on the patient;
f: the diagnosis and treatment equipment access module:
through 5G internet of things terminal data acquisition equipment attached to each diagnosis and treatment equipment of a hospital, diagnosis and treatment data which are generated by a patient during diagnosis and need to be uploaded are collected into individual cases of a case transport analysis system for hospital case management through a social security card or an identity card of the patient;
g: the medical record classification statistical module:
using an artificial intelligent algorithm to analyze, classify and file the historical case information of the patient through multidimensional parameters such as the treatment department, the disease condition content, the treatment time and the like;
h: the medical record conveying and analyzing module:
data intercommunication is realized among multiple medical institutions in the region, and the data intercommunication is called among all large medical institutions in real time;
the artificial intelligence algorithm is a neighbor algorithm.
Further: the case delivery analysis system for hospital case management was run as Tensorflow.
Furthermore, the terminal in the case home page module is a self-service machine or a computer client terminal of a hospital, a webpage terminal, a mobile phone APP or a WeChat applet.
Further, the specific expression form of the whole disease course management of the patient is as follows:
the method comprises the steps of pre-diagnosis instruction, adding an online registration function in a case transport analysis system for hospital case management, and recommending patients to carry out corresponding department registration according to an artificial intelligence algorithm and body abnormal conditions selected by the patients;
the change trend condition of a single disease species and the current disease information of the single disease species are obtained after the disease condition of a patient is intelligently screened and selected in the diagnosis;
the physical signs and the medication condition of the patient are tracked after the patient is diagnosed, when the course of the medicine treatment is about to expire, the information is pushed in advance in the modes of short messages, APP or WeChat and the like, meanwhile, the information of the patient needing to be reviewed or retested is submitted to a service department of a medical institution in a report form periodically, and the service department timely informs the patient to review the patient periodically.
Further: the case transport analysis system for hospital case management comprises a regional medical center cloud system;
medical instrument data continuously acquire vital signs of a patient, and are remotely transmitted to a cloud system of a regional medical center through a 5G internet of things terminal data acquisition device and respectively displayed on a large screen for remote consultation of a specialist and a screen of a site medical workstation;
the cloud system of the regional medical center performs real-time analysis, and the analysis result is transmitted to a large remote consultation screen and a screen of a site medical workstation of an expert doctor.
Further: the 5G internet of things terminal data acquisition device transmits data to a department to which the terminal belongs through a 5G base station, and then transmits the data to a medical institution to which the terminal belongs through the 5G base station.
Further: and the 5G data acquisition equipment of the IOT terminal loads a 5G wireless signal transmission module by taking a universal PCI-E interface and a USB interface as references.
The invention has the advantages that:
according to the invention, in the existing medical diagnosis and treatment equipment, a 5G internet of things terminal data acquisition device is added in a mode of expanding and adding a module, diagnosis and treatment data of the diagnosis and treatment equipment is uploaded to a case transport analysis system for hospital case management in real time, high flexibility, high transportability and high performance support are provided based on a Tensorflow deep learning framework, and the system is combined with an artificial intelligent neighbor algorithm.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a diagram of a hardware architecture according to the present invention.
Detailed Description
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained according to the drawings without inventive labor. In order to facilitate an understanding of the invention, the invention is described in more detail below with reference to the accompanying drawings and specific examples.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present. As used in this specification, the terms "upper," "lower," "inner," "outer," "bottom," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the invention and simplicity in description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
As shown in fig. 1:
the medical diagnosis and treatment equipment 1 is additionally provided with a 5G Internet of things data acquisition device 2 to realize data acquisition and real-time uploading.
Aiming at the real-time data acquisition of the medical diagnosis and treatment equipment 1, the 5G internet of things terminal data acquisition equipment 2 is connected with the medical diagnosis and treatment equipment 1, the 5G internet of things terminal data acquisition equipment 2 takes a universal PCI-E interface and a USB interface as references, a 5G wireless signal transmission module is loaded, and under the condition of matching with a 5G base station 3, the requirements of larger broadband, higher network speed and lower time delay can be realized, and the scene requirements of large access amount, timely feedback, stability and reliability of the internet of things can be perfectly met.
The data (on-off and diagnosis and treatment data of the diagnosis and treatment equipment) of each medical treatment and treatment equipment 1 is acquired by the 5G internet of things terminal data acquisition equipment 2 connected with the medical treatment and treatment equipment, the 5G internet of things terminal data acquisition equipment 2 meets the condition that the delay time is not more than 1 millisecond, the downlink peak value of the transmission rate is not less than 1.54Gbps, the uplink peak value of the transmission rate is not less than 308Mbps, the throughput is 500 responses per second, 100 transactions per second are supported, and 8 second/G data transmission is supported. The 5G internet of things terminal data acquisition equipment 2 transmits data to a department 1 'to which the terminal belongs through a 5G base station 3, and then transmits the data to a medical institution 4 (a county-level hospital, a town health hospital and a community health service center) to which the terminal belongs through the 5G base station 3', the medical institution 4 transmits the data to the cloud server 5 through the Internet, and a case transport analysis system 6 for hospital case management in the cloud server 5 processes the data.
The case transport analysis system 6 for hospital case management mainly includes the following modules:
a: case home page module:
the case front page will be designed from both the point of view of the care giver as well as the patient himself.
When a patient goes to a doctor for a doctor, a social security card or an identity card is used for registration operation, after the registration action is completed, the hospital case delivery analysis system can automatically retrieve the historical case information of the patient according to the social security card or the identity card of the patient, and a current doctor record is created in the hospital case delivery analysis system. The patient can enter a case home page module from a self-service machine or a computer client terminal, a webpage terminal, a mobile phone APP or a WeChat applet and other terminals of a hospital according to the identity card number to check the relevant information of the patient at the time, such as registration time, a department of the patient, relevant examination items, detailed charges, a state of a to-be-checked report (whether the report is issued) and the like. Similarly, based on the digitally stored medical record information, the patient can also view the historical medical record information attributed to the patient.
For medical care personnel, the patient case information in a longer time dimension is more beneficial for doctors to make reasonable diagnosis on the patient condition, when the patient enters a relevant department for treatment, the doctors use the social security card or the identity card of the patient to carry out card reading operation of hardware equipment, and the historical case information of the patient is quickly called. Meanwhile, an artificial intelligence algorithm is involved, and according to the current registered department of the patient, a neighbor algorithm (KNN, K-nearest neighbor) is adopted to extract the historical case information which is the same as or similar to the current department of the patient, and then the historical case information is provided for the doctor in a streaming data display mode according to the time line dimension.
The doctor is when diagnosing patient, not only judge to the symptom that patient shows at present, can also be according to historical case information, similar symptom that appears to patient in the past is examined, simultaneously can also be according to the relevant information of record in the historical case, like what type of medicine has been used in the past, use what instrument of diagnosing, get what treatment, etc., make things convenient for the doctor to more clearly and accurately issue treatment instruction, the trial-and-error process of headache doctor foot has been avoided, patient's time and money cost have also been reduced to a great extent.
B, a data module:
a medical record management system based on big data cloud integration can realize comprehensive sharing of data in a medical system under the precondition of ensuring data security.
Patients often find that a patient needs to be refilled in every hospital, and a plurality of examinations need to be repeated, so that a great amount of time and money are wasted, and meanwhile, the medical resources of the whole medical system are greatly wasted.
When the big data intercommunication is achieved by the case delivery analysis system for hospital case management, a patient can obtain a permanent and unique case file belonging to the patient according to the unique ID (identity card or social security card) of the patient. For patients, when a medical institution is replaced, the patients do not need to fill in cases repeatedly and a large number of paper report documents do not need to be stored, all the information for seeing a doctor, the case information, the diagnosis information, the medication information, the examination information and the image information can be inquired quickly and in real time through a case transport analysis system for hospital case management, and the embarrassment that the patients look like a sky book because the cases are only seen by doctors is solved to a certain extent based on the characteristic that a standardized system has the requirement on the accuracy of information input.
For medical institutions and medical personnel, patients can provide perfect case information, past illness state information, historical diagnosis information, medication information, image pictures and the like during treatment, the medical personnel also play a very important guiding role in controlling the illness state of the patients, and can reduce repeated examination behaviors to a certain extent, so that the efficiency of medical institutions for receiving and treating the patients is greatly improved. In the process of analyzing the historical case information, the doctor can also avoid trial and error operation on repeated diagnosis and treatment means, and the doctor can conveniently and quickly find out the cause of disease and give medicines according to the symptoms.
The outpatient medical record recording module:
when a patient is in a doctor, a doctor can input the relevant medical record information of the doctor in a case transport analysis system for hospital medical record management according to a diagnosis result, the system usability is improved for saving the income time of the doctor, a large number of pull-down menus are provided for the doctor to conveniently and quickly input in a medical record input interface, and continuous input can be realized by clicking a plus button or an increase button.
D: inpatient case input module:
similar with outpatient service medical record input, to inpatient's state of an illness change and diagnosis and treatment stage information, also provided the medical record input module of the type of being in hospital in the case transport analytic system for the medical record management of hospital, the doctor inputs this relevant medical record information of being in hospital in the case transport analytic system for the medical record management of hospital.
The disease course management module:
the Internet-based case transport analysis system for big-data hospital case management can manage the whole course of a disease of a patient, and an online and offline integrated medical service mode before, during and after the patient is treated is realized.
The concrete expression form is as follows:
the method comprises the steps of pre-diagnosis guidance, adding an online registration function in a case delivery analysis system for hospital case management, and recommending the patient to carry out corresponding department registration according to the near neighbor algorithm (KNN, K-nearest neighbor) of an artificial intelligence algorithm in combination with the abnormal body condition of the patient selected by the patient;
the change trend condition of a single disease species and the current disease information of the single disease species are obtained after the disease condition of a patient is intelligently screened and selected in the diagnosis;
the physical signs and medication conditions of the patient are tracked after the patient is diagnosed, for example, when the course of treatment of the medicine is about to expire, the information is pushed in advance through short messages, APP or WeChat and the like, meanwhile, the medical record system submits the information of the patient needing to be reexamined or reexamined to a service department of a medical institution in a report form, and the service department timely informs the patient of the reexamination at the fixed period, so that the medical service level of a hospital is improved, the reasonable treatment rhythm of the illness state of the patient is also ensured, and the existing 'slow medical treatment' phenomenon is solved.
F: the diagnosis and treatment equipment access module:
the case transport analysis system for hospital case management based on the artificial intelligence algorithm is not only used as a notebook of cases, but also used for collecting diagnosis and treatment data which are generated by patients during diagnosis and are required to be uploaded through 5G internet of things terminal data acquisition equipment attached to each diagnosis and treatment equipment of the hospital, and collecting the data into the case transport analysis system for hospital case management through unique ID retrieval conditions (social security cards or identity cards) of the patients, so that the patients can see the relevant diagnosis and treatment data or imaging examination data.
For medical care personnel, after the examination of the diagnosis and treatment equipment is finished, data such as examination results are immediately synchronized to personal cases of patients in a case delivery analysis system for hospital case management, doctors can quickly check the data on a computer, and the image-type examination result data can be enlarged and checked on the basis of the characteristics of the computer, so that the possibility that deviation may occur in information transmission of paper reports is avoided.
G: the medical record classification statistical module:
from the perspective of patients, not only a single weapon can appear in a long time range, which puts forward the requirements of classification statistics on the case transportation analysis system for hospital case management. In the classification statistics, the artificial intelligent KNN nearest neighbor algorithm is combined, the history medical record information of the patient is subjected to nearest neighbor case analysis, classification and filing through multidimensional parameters such as the clinic of seeing a doctor, the content of the state of an illness, the time of seeing a doctor and the like. The patient can quickly check the current and historical clinic information, diagnosis and treatment means, medicines and the like of a certain type of case according to the classification information, and can also obtain the detailed expenditure condition of the patient on the state of an illness according to the historical charging detail of each type of case. The taxonomic aggregation of medical record information has helped physicians analyze patient etiology more accurately in medical institutions and outpatients or hospitalized physicians.
H: the medical record conveying and analyzing module: when enough medical institutions access to the case transport analysis system for hospital case management, internet hospital clustering can be realized in an area or even a larger range.
The medical fields in which each medical institution excels are different, and when the medical record system can perform data intercommunication in multiple medical institutions, the possibility that the difficult and complicated diseases are treated in more specialized way can be realized. Among each medical institution, can exchange study to the case information data of the same type, the information transfer of paper case is not easy, even very loaded down with trivial details and easy destruction, in the case transport analytic system for the case management of hospital, case information can be real-timely called at each big medical institution, very big improvement the transmission efficiency of data, when the medical institution organizes to exchange study, also can reference professional medical institution in the mode of disposition of some aspect's case and promote self medical service level according to the result of disposition, optimize the utilization efficiency of medical resource.
By combining the modules, the medical record is taken as a technical file, records related original data of diagnosis, treatment, nursing, examination and the like of a patient in a hospital, is an effective carrier for smoothly spreading various works in the hospital, and simultaneously, the digital medical record system provides great support for the development of communication learning and medical technology. Based on the artificial intelligence neighbor algorithm (KNN, K-nearest neighbor), a very strong algorithm support is provided for the arrangement, induction and analysis of massive medical record data. The information management of the medical records realizes the computer network management of a plurality of links of patients from admission to discharge, subsequent management and the like, shares resources, saves a large amount of manpower, material resources and financial resources, improves the working efficiency and has great influence on the improvement of management benefits, economic benefits and social benefits.
The neighbor algorithm (KNN algorithm, K-nearest neighbor) outlines:
in the case transport analysis system for hospital case management of the present invention, the present invention selects the KNN algorithm in terms of the selection of the artificial intelligence algorithm, that is, the artificial intelligence algorithm is the KNN algorithm.
KNN Algorithm overview:
the guiding idea of the kNN algorithm is "red for julient, black for julient", and the class of you is inferred by your neighbors. Firstly, calculating the distance between a sample to be classified and a training sample of a known class, and finding k neighbors with the nearest distance to the sample data to be classified; and judging the category of the sample data to be classified according to the categories of the neighbors. Measured by the distance of two points in space. The greater the distance, the less similar the two points are. The distance is chosen many, usually with a simpler euclidean distance:
other distance algorithms: the march distance can mitigate distance distortion due to linear combination of attributes, which is a covariance matrix of data:
manhattan distance:
chebyshev distance:
d che (x，y)＝max j (|x j -y j |)
chebyshev distance:
d che (x，y)＝max j (|x j -y j |)
the KNN algorithm is classified by measuring the distance between different feature values. The idea is as follows: a sample belongs to a class if the majority of the K most similar samples in feature space (i.e., the nearest neighbors in feature space) belong to the class, where K is typically an integer no greater than 20. In the KNN algorithm, the selected neighbors are all objects that have been correctly classified. The method only determines the category of the sample to be classified according to the category of one or a plurality of samples of the nearest neighbor in the classification decision.
The KNN algorithm makes decisions by relying on the dominant class among the k objects, rather than a single object class decision. These two points are the advantages of the KNN algorithm. The KNN algorithm is that under the condition that the data and the labels in the training set are known, test data are input, the features of the test data are compared with the corresponding features in the training set, and the first K data most similar to the test data in the training set are found, so that the class corresponding to the test data is the class with the largest occurrence frequency in the K data, and the description of the algorithm is as follows:
1) Calculating the distance between the test data and each training data;
2) Sorting according to the increasing relation of the distances;
3) Selecting K points with the minimum distance;
4) Determining the occurrence frequency of the category where the first K points are located;
5) And returning the category with the highest frequency of occurrence in the former K points as the prediction classification of the test data.
The case transport analysis system for hospital case management of the present invention operates with Tensorflow.
Google open-source Tensorflow is open-source mathematical computation software developed by using C + + language and is computed in a Data Flow Graph (Data Flow Graph) form. The nodes in the graph represent mathematical operations, while the lines in the graph represent interactions between multidimensional data arrays (tensors). The Tensorflow flexible architecture can be deployed in one or more CPUs, desktops and servers of GPUs, or in mobile devices using a single API application. Tensorflow was originally developed by researchers and the Google Brain team for machine learning and deep neural networks and was after sourcing it has been applied in almost every field.
Tensorflow is a framework which is most used and most huge in community all over the world, maintenance and updating are frequent due to the fact that Google corporation produces the framework, a Python and C + + interface is arranged, a course is complete, and meanwhile the first version of reproduction of many papers is written based on Tensorflow and is a default leader of a deep learning world framework.
The TensorFlow workflow is easy to understand. Its API remains highly consistent and stable, and maintainers are constantly striving to ensure that each change is downward compatible.
The seamless integration of TensorFlow with NumPy allows most data scientists who know Python to get water like fish.
Tensorflow runs on a CPU and a GPU, such as a desktop, a server, a mobile device of a mobile phone, and the like.
Further, the case transport analysis system for hospital case management comprises a regional medical center cloud system (5G + medical Internet of things regional medical center), index data of patients in severe monitoring (including mobile 5G + ICU) are analyzed in real time through a 5G remote acquisition technology and an existing medical software integrated system, timely, accurate and efficient judgment is provided for disease condition diagnosis and treatment, disease condition grading information is provided for medical staff in real time, and manual diagnosis and treatment intervention is implemented in real time after the medical staff verify the information. The 5G + ICU platform has the functions of real-time analysis, prediction, auxiliary decision and the like of single disease species by gradually using an artificial algorithm.
The ICU ward 9 kinds of medical apparatus are installed with 5G internet of things terminal data acquisition equipment 2 to form a 5G + ICU data ward which can acquire and transmit patient sign data to a regional medical center cloud system (5G + medical internet of things regional medical center).
Medical instrument data continuously collects vital signs (urine flow, or electrocardio, or respiration rate, or blood oxygen content and the like) of a patient, and the vital signs are remotely transmitted to a cloud system (5G + medical Internet of things regional medical center) of a regional medical center through a 5G Internet of things terminal data collection device 2 and are respectively displayed on a large remote consultation screen of a specialist doctor and a wall screen of a site medical workstation.
The cloud system (5G + medical Internet of things) of the regional medical center carries out real-time analysis, and the analysis result is transmitted to a large screen for remote consultation of doctors and specialists, a wall screen of a site medical work station and the like, so that the doctor and specialist remote consultation center can carry out real-time diagnosis and carry out real-time intervention on site medical workers.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. Case transport analysis system for hospital case management based on artificial intelligence algorithm, its characterized in that:
the data of each medical diagnosis and treatment device is collected by a 5G internet of things terminal data collection device connected with the medical diagnosis and treatment device, the 5G internet of things terminal data collection device transmits the data to a medical institution to which the medical institution belongs through a 5G base station, the medical institution uploads the data to a cloud server through the Internet, and a case delivery analysis system for hospital case management in the cloud server processes the data;
the case transport analysis system for hospital case management comprises the following modules:
a: case home page module:
when a patient is hospitalized and treated, a social security card or an identity card is used for registration operation, after the registration action is completed, the hospital case management case delivery analysis system can automatically retrieve the historical case information of the patient according to the social security card or the identity card of the patient, and a treatment record is created in the hospital case management case delivery analysis system; the patient enters a case home page module from the terminal to check the information related to the current treatment and the historical case information belonging to the patient;
a doctor uses a social security card or an identity card of a patient to perform card reading operation of hardware equipment, and calls historical case information of the patient; meanwhile, an artificial intelligence algorithm is involved, and according to the current registered department of the patient, the artificial intelligence algorithm is adopted to extract the historical case information which is the same as or similar to the current clinic, and then the extracted historical case information is provided for the doctor in a streaming data display mode;
b, a data module:
the patient obtains a permanent and unique case file belonging to the patient according to the social security card or the identity card of the patient;
the outpatient medical record recording module:
when a patient is in a visit, a doctor can input relevant medical record information of the visit into a case transport analysis system for hospital medical record management according to a diagnosis result;
d: inpatient case input module:
aiming at the disease condition change and diagnosis and treatment stage information of inpatients, a doctor inputs the relevant medical record information of the inpatient in the hospital in a case transport analysis system for hospital medical record management;
the disease course management module:
performing whole-course management on the patient;
f: the diagnosis and treatment equipment access module:
through 5G internet of things terminal data acquisition equipment attached to each diagnosis and treatment equipment of a hospital, diagnosis and treatment data which are generated by a patient during diagnosis and need to be uploaded are collected into individual cases of a case transport analysis system for hospital case management through a social security card or an identity card of the patient;
g: the medical record classification statistical module:
using an artificial intelligent algorithm to analyze, classify and archive the historical medical record information of the patient through multidimensional parameters such as the clinic of seeing a doctor, the content of the state of an illness and the time of seeing a doctor;
h: the medical record conveying and analyzing module:
carrying out data intercommunication among multiple medical institutions in the region, and calling the medical institutions in real time;
the artificial intelligence algorithm is a neighbor algorithm.
2. The case delivery analysis system for hospital case management based on artificial intelligence algorithm as claimed in claim 1, characterized in that: the case delivery analysis system for hospital case management was run as Tensorflow.
3. The case delivery analysis system for hospital case management based on artificial intelligence algorithm as claimed in claim 1, characterized in that: the terminal in the case home page module is a self-service machine or a computer client terminal of a hospital, a webpage terminal, a mobile phone APP or a WeChat applet.
4. The case delivery analysis system for hospital case management based on artificial intelligence algorithm as claimed in claim 1, characterized in that: the specific expression form of the overall disease course management of the patient is as follows:
the method comprises the steps of pre-diagnosis instruction, adding an online registration function in a case transport analysis system for hospital case management, and recommending patients to carry out corresponding department registration according to an artificial intelligence algorithm and body abnormal conditions selected by the patients;
the change trend condition of a single disease species in time dimension and current disease information are obtained after the illness state of a patient is intelligently screened and selected in a clinic;
the physical signs and medication conditions of the patient are tracked after the patient is diagnosed, when the course of treatment of the medicine is about to expire, the information is pushed in advance in the modes of short messages, APP or WeChat and the like, meanwhile, the information of the patient needing to be reexamined or reexamined is submitted to a service department of a medical institution in a report form regularly, and the service department timely informs the patient of the regular reexamination.
5. The case delivery analysis system for hospital case management based on artificial intelligence algorithm as claimed in claim 1, characterized in that: the case transport analysis system for hospital case management comprises a regional medical center cloud system;
medical instrument data continuously collects vital signs of a patient, is remotely transmitted to a cloud system of a regional medical center through 5G internet of things terminal data collection equipment, and is displayed on a large screen for remote consultation of a specialist doctor and a screen of a site medical workstation respectively;
the cloud system of the regional medical center performs real-time analysis, and the analysis result is transmitted to a large remote consultation screen and a screen of a site medical workstation of an expert doctor.
6. The case delivery analysis system for hospital case management based on artificial intelligence algorithm as claimed in claim 1, characterized in that: the 5G internet of things terminal data acquisition device transmits data to a department to which the terminal belongs through a 5G base station, and then transmits the data to a medical institution to which the terminal belongs through the 5G base station.
7. The case delivery analysis system for hospital case management based on artificial intelligence algorithm as claimed in claim 1, characterized in that: and the 5G data acquisition equipment of the IOT terminal loads a 5G wireless signal transmission module by taking a universal PCI-E interface and a USB interface as references.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211508254.5A CN115798662A (en) | 2022-11-28 | 2022-11-28 | Case transport analysis system for hospital case management based on artificial intelligence algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211508254.5A CN115798662A (en) | 2022-11-28 | 2022-11-28 | Case transport analysis system for hospital case management based on artificial intelligence algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115798662A true CN115798662A (en) | 2023-03-14 |
Family
ID=85442789
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211508254.5A Pending CN115798662A (en) | 2022-11-28 | 2022-11-28 | Case transport analysis system for hospital case management based on artificial intelligence algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115798662A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116978507A (en) * | 2023-08-02 | 2023-10-31 | 深圳市鹏川科技有限公司 | Medical information prepositive acquisition system based on big data |
| CN117079831A (en) * | 2023-10-17 | 2023-11-17 | 中国人民解放军总医院第六医学中心 | Medical records statistics management method and system based on big data analysis |
| CN116978507B (en) * | 2023-08-02 | 2024-05-10 | 深圳市鹏川科技有限公司 | Medical information prepositive acquisition system based on big data |
-
2022
- 2022-11-28 CN CN202211508254.5A patent/CN115798662A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116978507A (en) * | 2023-08-02 | 2023-10-31 | 深圳市鹏川科技有限公司 | Medical information prepositive acquisition system based on big data |
| CN116978507B (en) * | 2023-08-02 | 2024-05-10 | 深圳市鹏川科技有限公司 | Medical information prepositive acquisition system based on big data |
| CN117079831A (en) * | 2023-10-17 | 2023-11-17 | 中国人民解放军总医院第六医学中心 | Medical records statistics management method and system based on big data analysis |
| CN117079831B (en) * | 2023-10-17 | 2023-12-22 | 中国人民解放军总医院第六医学中心 | Medical records statistics management method and system based on big data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10937164B2 (en) | Medical evaluation machine learning workflows and processes | |
| Souri et al. | A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment | |
| US10867696B2 (en) | Data processing systems and methods implementing improved analytics platform and networked information systems | |
| US20030233250A1 (en) | Systems and methods for managing biological data and providing data interpretation tools | |
| DE112014000897T5 (en) | Learning health systems and procedures | |
| WO2022060949A1 (en) | Systems and methods for automatically identifying a candidate patient for enrollment in a clinical trial | |
| US20210398650A1 (en) | Medical imaging characteristic detection, workflows, and ai model management | |
| CN113808738B (en) | Disease identification system based on self-identification image | |
| KR20200128241A (en) | Method for providing osteoporosis diagnosis and treatment service based on bigdata and artificial intelligence | |
| US20040030586A1 (en) | System and method for patient clinical data management | |
| US20230005151A1 (en) | Methods and systems for performing real-time radiology | |
| CN115798662A (en) | Case transport analysis system for hospital case management based on artificial intelligence algorithm | |
| CN102103734A (en) | Medical information integration system and method | |
| CN115862818A (en) | Medical image equipment data analysis system based on deep learning algorithm | |
| JP7278256B2 (en) | Devices, systems and methods for optimizing image acquisition workflows | |
| Megalooikonomou et al. | Medical data fusion for telemedicine | |
| CN114678126A (en) | Disease tracking and predicting system | |
| KR20230021665A (en) | Systems and methods for processing electronic images to determine salient information in digital pathology | |
| Demigha | Mining Knowledge of the Patient Record | |
| CN111128330A (en) | Automatic entry method and device for electronic case report table and related equipment | |
| Patil et al. | Tuberculosis Detection Using a Deep Neural Network | |
| RU2761518C1 (en) | Digital platform for providing medical diagnoses based on artificial intelligence with possibility of verification by doctor | |
| Ma et al. | Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients | |
| US20230238096A1 (en) | Method and system for providing a medical report | |
| US20230389878A1 (en) | Methods, systems, and frameworks for debiasing data in drug discovery predictions |
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 |