CN111477332A - Blood donator identification and recruitment method based on machine learning - Google Patents
Blood donator identification and recruitment method based on machine learning Download PDFInfo
- Publication number
- CN111477332A CN111477332A CN202010266660.XA CN202010266660A CN111477332A CN 111477332 A CN111477332 A CN 111477332A CN 202010266660 A CN202010266660 A CN 202010266660A CN 111477332 A CN111477332 A CN 111477332A
- Authority
- CN
- China
- Prior art keywords
- blood
- donation
- donator
- model
- blood donation
- 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
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Biomedical Technology (AREA)
- Evolutionary Biology (AREA)
- Artificial Intelligence (AREA)
- Pathology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses a blood donator identification and recruitment method based on machine learning, which comprises the following steps: (1) establishing a blood donator feature library, acquiring the blood donation condition of each blood donator over the years, and designing and calculating the features of each blood donator according to the blood donation condition; (2) constructing an xgboost model, wherein the model input is the characteristics of the blood donator and the model output is the expected output of whether the blood donator is an effective blood donator; (3) training the xgboost model by using the training data obtained by the recruitment data of the past short messages; (4) and for qualified blood donors, giving reference blood donor recommendations according to the characteristic data of the qualified blood donors by using the xgboost model. By means of machine learning and a large amount of historical data, the blood donator can give effective and reasonable blood donators, and therefore efficiency and quality of blood donation recruitment are improved.
Description
Technical Field
The invention relates to a method for identifying and recruiting blood donators, in particular to a machine learning-based method for identifying and recruiting blood donators, and belongs to the technical field of intelligent blood donation management methods.
Background
Blood is a source of life and a very important and scarce medical resource. Modern medical development to date, blood transfusion as an irreplaceable medical means for saving death and supporting injuries, whether planned treatment or emergency intervention, is free from the support of blood which cannot be synthesized and stored for a long time and cannot be replaced by other medicines. Blood transfusions, widely used in surgery, trauma, malignancy, pregnancy, hemophilia, etc., are vital components in saving life and improving health, and can help millions of patients suffering from life-threatening conditions extend life and improve their quality of life each year. Since the development of the uncompensated blood donation work in 1998, the number of the national uncompensated blood donors keeps continuously increasing for 20 years through years of effort, and the number of the national uncompensated blood donors is increased from about 30 ten thousand in 1998 to nearly 1500 ten thousand in 2018. Although the total amount of blood collected nationally is increased to over 2500 million units in 2018 from less than 500 million units in 1998 by more than 4 times, the increase speed of clinical blood demand cannot be adapted, the contradiction between the rigidity demand of clinical blood and insufficient blood supply still stands out, particularly, the contradiction becomes normal in large cities with developed economy and rich medical resources, the wide and continuous spread of the large cities is caused by local, seasonal and structural properties, the shortage of blood supply causes greater and greater contradiction between doctors and patients, and the blood safety risk still exists.
At present, the collection mode of the domestic uncompensated blood donation is mainly realized in a passive mode that a blood supply mechanism waits for blood donation of a blood donor in a street, even if a part of uncompensated blood donations adopt active recruitment modes such as short messages, telephones, mails and the like, scientific analysis on the health dynamics and the blood donation willingness of historical blood donators is lacked, and the efficiency of the blood donation recruitment is generally low. Therefore, on the basis of establishing and perfecting government-dominated and multi-department coordinated uncompensated blood donation guarantee and incentive mechanisms, technical attack based on big blood donation data is developed, a high-efficiency donor recruitment technology adaptive to uncompensated blood donation work is established and optimized, the efficiency and quality of blood donation recruitment are improved, and more donors are recruited, so that the method is an important path for meeting the current 'tight balance' current situation of blood supply and ensuring the requirement and safety of clinical blood. The present invention aims to solve this technical problem.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a blood donator identification and recruitment method based on machine learning.
In order to achieve the purpose, the invention adopts the following technical scheme:
a blood donator identification and recruitment method based on machine learning comprises the following steps:
(1) establishing a blood donator feature library, collecting the blood donation condition of each blood donator over the years, and designing and calculating the feature of each blood donator according to the blood donation condition;
(2) constructing an xgboost model, wherein the model input is the characteristics of the blood donator and the model output is the expected output of whether the blood donator is an effective blood donator;
(3) acquiring data of available/invalid blood donators by using past short messages, and training the xgboost model;
(4) and for qualified blood donators, making effective blood donator ranking by using the xgboost model according to the characteristic data of the qualified blood donators, and recommending the blood donators.
Preferably, in the step (1), the designed and calculated characteristics include age, sex, blood type, recent donation amount, total donation amount, number of donations, interval of donations, frequency of donations, qualification of blood test, education level, living condition, occupation and reaction of donation, wherein the frequency of donation is calculated by formula (1).
Preferably, in the step (1), when some attribute values are missing, default values are adopted for complementing.
Preferably, in the step (2), the basic model parameters of the xgboost model use the tree model as a base classifier, the iteration number, that is, the number of used trees, is set to be 500, and the maximum depth of the tree is set to be 12.
Preferably, in the step (2), the training parameters of the xgboost model are L objective functions for classifying blood donors, the model evaluation function is set to be a two-classification error rate, the learning rate is 0.3, and in order to avoid the influence caused by overfitting during training of each piece, the sampling rate of the data set is set to be 90%, and the selected feature proportion is also 90%.
Preferably, in the step (3), the training step is as follows:
(3.1) tracking the personnel receiving the short messages by means of the previous short message recruitment information, and if blood donation records exist within seven days, determining the personnel as effective blood donation personnel, otherwise, determining the personnel as ineffective blood donation personnel;
(3.2) constructing the collected characteristics of the effective blood donators and the ineffective blood donators when sending short messages to obtain training data of the model;
(3.3) training the xgboost model with the training data as model input and whether the person is a valid donor as expected output.
Preferably, the step (4) comprises the following sub-steps:
(4.1) the blood collection and donation interval period is in accordance with the regulation (the interval period between two times of whole blood/single erythrocyte and whole blood donation is not less than 6 months, and the interval period between two times of single blood platelet/plasma/single granulocyte and whole blood donation is not less than 4 weeks), and the blood donor characteristics of healthy citizens aged from eighteen years to fifty-five years or multiple blood donors who have no blood donation reaction and are in accordance with the health examination requirements and are not more than sixty years old;
(4.2) obtaining a feature set of the subject from a blood donator feature library;
and (4.3) inputting the basic attribute of each blood donation person into the trained xgboost model to obtain the expected output of the effective blood donation persons, sequencing according to the output result, and recommending the persons with higher rank.
Compared with the prior art, the invention utilizes the annual blood donation data of the blood center to construct a basic attribute library of blood donators, obtains training data through the past short message recruitment data, and obtains an xgboost model through training. After the model training is completed, when new short messages need to be issued for recruitment, the blood donator data meeting the conditions can be correspondingly screened, then the xgboost model can be used for giving reference recognition results, the reference recognition results are ranked, and corresponding recruitment objects are selected according to the required number of people. The method is realized by adopting an xgboost model, and effectively excavates the importance degree of the attribute importance, and effectively learns the internal relation of the attribute of the blood donation object. Experimental results show that the classification precision achieved by the method can reach more than 80%, and the method is obviously superior to the prior art.
Drawings
Fig. 1 is a flow chart of a conventional blood center for selecting suitable recruiting subjects;
note: 1. age regulation: healthy citizens aged eighteen to fifty-five or multiple blood donors who have no blood donation reaction and meet the requirement of health examination and do not exceed sixty years old;
2. blood donation interval regulation: the interval between two times of blood collection/single erythrocyte collection and whole blood donation is not less than 6 months, and the interval between two times of single blood collection platelet/plasma/single granulocyte collection and whole blood donation is not less than 4 weeks;
FIG. 2 is a schematic diagram of the xgboost model used in the present invention.
Detailed Description
The technical contents of the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
The invention provides a method for improving short message recruitment precision of blood donators by means of a machine learning method. The following are specifically described below:
a method for recruiting a blood donor by a short message commonly used in a blood center is shown in fig. 1, and blood donors with a specified blood donation interval and age are mainly selected from blood donors in the last year. The part of subjects who have participated in blood donation activities recently shows that the subjects have willingness to donate blood recently, and is a reasonable recruitment target, so that the blood donation time is an effective characteristic for whether to participate in blood donation. Meanwhile, according to the past experience of recruiting blood donations in the blood center, it is important to find out whether the occupation and education degree is important for a person to donate blood. Table 1 shows a blood donation record of a blood donor, which contains basic characteristics of the individual, such as age, sex, residence condition, blood donation response, and the like, and the basic information also has influence on the individual's blood donation. After research, the inventor finds that the willingness of a blood donor is not only dependent on one blood donation, but is often related to the record of multiple blood donations, such as the number of blood donations, the total amount of blood donations, the number of blood donations, the frequency of blood donations and the like. According to the combination of experience and experimental study, a characteristic library containing 13 items of age, sex, blood type, recent blood donation amount, total blood donation amount, blood donation times, blood donation interval, blood donation frequency, whether blood detection is qualified or not, education degree, living condition, occupation and blood donation reaction is designed, wherein the blood donation frequency is calculated by adopting a formula (1).
TABLE 1 recording of blood donation by a blood donor
The blood donation records often have certain missing values, such as education degree missing, occupation missing and the like, in order to deal with the situation, default values are adopted to replace the missing values, the missing education degree is replaced by education degrees of junior high and below, the missing occupation is mainly replaced by other occupation due to the fact that the occupation is not classified in a blood donation system, and the missing living state is replaced by temporary living.
In the embodiment of the present invention, the xgboost model shown in fig. 2 is used, which uses a decision tree model as a base classifier and uses 500 trees, wherein the model input is the characteristic of the blood donator, each characteristic data is firstly classified in a first tree and finally falls into one leaf node, the value of the obtained leaf node is the output value of the first tree, then the same operation is performed by using a second tree, and the output values of all the trees are added, and the expected output of the effective blood donator is output after calculation by using L g donations function, so that the sampling rate of the fitting data set is 90% in order to avoid the situation that the fitting data set occupies 90% in each tree.
In one embodiment of the present invention, the input to the xgboost model is assumed to be
According to the xgboost model, using the first tree we obtain:
wherein the content of the first and second substances,
the weight of the leaf node of the model input x falling into the tree is obtained, the second tree also adopts the same operation as the first tree, and the following analogy is carried out to obtain:
…
produced for each treeWeight set
Add them to obtain the final predicted target value
The goal of the final enrollment is to derive a expectation of whether the donor is an effective blood donation goal
Therefore, when outputting, it needs to be adjusted by using L g-logic function, which is expressed as:
before recruiting using the xgboost model, the model must be iteratively learned, i.e. each tree is generated, and the generation of each tree mainly selects the best splitting characteristics and leaf weights. The model determines the splitting characteristics and the leaf node weight of the tree model according to the loss function, and the calculation only depends on the first derivative and the second derivative of the loss function, and the current optimal splitting characteristics and weight can be directly obtained, so the calculation speed is high. The training process can be realized by using a Python third-party library xgboost, the loss function is defaulted in the third-party library, and the loss function can be designed by self. In this case, the default loss function is better. It should be noted that the hyper-parameters such as the number of decision trees and the learning rate used in the experiment are obtained when the optimization is obtained from the verification set.
In the invention, the model training process is a process for determining how the tree model in the formula is divided and the leaf node weight, and the specific training steps are as follows:
(1) and tracking the personnel receiving the short messages by means of the previous short message recruitment information according to the date of sending the short messages, and if the personnel have blood donation records within seven days, namely participate in blood donation, the personnel are considered as effective blood donation personnel, otherwise, the personnel are ineffective blood donation personnel.
(2) And establishing a blood donator feature library when the collected effective blood donators and the collected ineffective blood donators send short messages, and selecting the features of the corresponding effective blood donators and the ineffective blood donators to obtain a data set of the model.
(3) And (3) taking the training data in the step (2) as model input, using a predefined loss function, and taking whether the person is an effective blood donor as expected output to train the xgboost model and determine the optimal segmentation characteristics and the leaf node weight of the tree model.
In the embodiment of the invention, 95476 short message recruitings are implemented from 2016 to 2019 in total according to the long-term short message recruiting records, wherein 56026 effective blood donator data and 39450 ineffective blood donator data are provided. For these data, to keep the ratio of valid blood donations to invalid blood donations the same, the same amount of data as the number of invalid blood donations is selected from the valid blood donations to construct a data set. And (3) dividing the data set into a training set, a verification set and a test set, training and learning the hyper-parameters and the parameters of the model, and determining the optimal tree model parameters and splitting characteristics.
After the model training is completed, the model can give the importance degree of different features to the classification, and table 2 gives the four most important features and the importance values thereof. Once model training is complete, it can be used in a process for efficient donor identification.
TABLE 2 top four tables of importance of model features
Next, the specific steps of using the trained xgboost model to recruit and recommend blood donation subjects are introduced:
(1) collecting blood donation personnel whose blood donation age and blood donation interval are in accordance with blood donation related regulations.
(2) The characteristics of the subject are obtained from a blood donator basic characteristic library.
(3) And inputting the basic attribute of each blood donator into the trained xgboost model to obtain the expected output of the effective blood donators, sequencing according to the output result, and recommending the personnel with higher rank.
Compared with the prior art, the invention utilizes the annual blood donation data of the blood center to construct the blood donator feature library, obtains training data through the past short message recruitment data, and obtains the xgboost model through training. After the model training is completed, when new short messages need to be issued for recruitment, the blood donator data meeting the conditions can be correspondingly screened, then the xgboost model can be used for giving reference recognition results, the reference recognition results are ranked, and corresponding recruitment objects are selected according to the required number of people. The method is realized by adopting an xgboost model, and effectively excavates the importance degree of the attribute importance, and effectively learns the internal relation of the attribute of the blood donation object. Experimental results show that the classification precision achieved by the method can reach more than 80%, the method is obviously superior to the prior art, and the accuracy of donation recruitment is improved, so that more manpower and material resources are saved, and the efficiency and quality of recruitment are improved.
The blood donator identification and recruitment method based on machine learning provided by the invention is explained in detail above. It will be apparent to those skilled in the art that any obvious modifications thereof can be made without departing from the spirit of the invention, which infringes the patent right of the invention and bears the corresponding legal responsibility.
Claims (7)
1. A blood donation person identification and recruitment method based on machine learning is characterized by comprising the following steps:
(1) establishing a blood donator feature library, collecting the blood donation condition of each blood donator over the years, and designing and calculating the feature of each blood donator according to the blood donation condition;
(2) constructing an xgboost model, wherein the model input is the characteristics of the blood donator and the model output is the expected output of whether the blood donator is an effective blood donator;
(3) acquiring data of available/invalid blood donators by using past short messages, and training the xgboost model; (ii) a
(4) And for qualified blood donators, making effective blood donator ranking by using the xgboost model according to the characteristic data of the qualified blood donators, and recommending the blood donators.
2. The machine learning-based blood donor identification and recruitment method of claim 1, wherein:
in the step (1), the designed and calculated characteristics comprise age, sex, blood type, recent blood donation amount, total blood donation amount, blood donation times, blood donation interval, blood donation frequency, whether blood detection is qualified or not, education degree, living condition, occupation and blood donation reaction, wherein the calculation of the blood donation frequency adopts a formula (1):
3. the machine learning-based blood donor identification and recruitment method of claim 1, wherein: and when some attribute values have missing conditions, complementing the missing conditions by adopting default values.
4. The machine learning-based blood donor identification and recruitment method of claim 1, wherein:
in the step (2), the basic model parameters of the xgboost model take the tree model as a base classifier, the iteration number is set to be 500, and the maximum depth of the tree is set to be 12.
5. The machine learning-based blood donor identification and recruitment method of claim 1, wherein:
in the step (2), training parameters of the xgboost model are that an objective function of the model is an L objective function, a model evaluation function is set to be a two-classification error rate, a learning rate is 0.3, and when each piece of data is trained, in order to avoid the influence caused by overfitting, a sampling rate of a data set is set to be 90%, and a selected feature proportion is also 90%.
6. The machine learning-based blood donor identification and recruitment method of claim 1, wherein:
in the step (3), the training step is as follows:
(3.1) tracking the personnel receiving the short messages by means of the previous short message recruitment information, if blood donation records exist in seven days, the personnel are considered as effective blood donation personnel, and if the blood donation records do not exist, the personnel are invalid;
(3.2) obtaining training data of the model by using the collected characteristics of the effective blood donators and the ineffective blood donators when sending short messages;
(3.3) training the xgboost model with the training data as model input and whether the person is a valid donor as expected output.
7. The method of blood-donating person identification and recruitment according to claim 1, wherein: the step (4) comprises the following substeps:
(4.1) collecting blood donors of healthy citizens who meet the requirement of health examination and do not exceed sixty years and have no blood donation reaction in the past and are aged from eighteen years to fifty-five years and have no blood donation reaction for a plurality of times when the interval period of two times of whole blood/single erythrocyte and whole blood donation is not less than 6 months and the interval period of two times of single platelet/plasma/single granulocyte/whole blood donation is not less than 4 weeks;
(4.2) obtaining a feature set of the subject from a blood donator feature library;
and (4.3) inputting the basic attribute of each blood donation person into the trained xgboost model to obtain the expected output of the effective blood donation persons, sequencing according to the output result, and recommending the persons with higher rank.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010266660.XA CN111477332A (en) | 2020-04-07 | 2020-04-07 | Blood donator identification and recruitment method based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010266660.XA CN111477332A (en) | 2020-04-07 | 2020-04-07 | Blood donator identification and recruitment method based on machine learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111477332A true CN111477332A (en) | 2020-07-31 |
Family
ID=71750229
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010266660.XA Pending CN111477332A (en) | 2020-04-07 | 2020-04-07 | Blood donator identification and recruitment method based on machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111477332A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496209A (en) * | 2022-02-18 | 2022-05-13 | 青岛市中心血站 | Blood donation intelligent decision method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170053067A1 (en) * | 2015-08-18 | 2017-02-23 | International Business Machines Corporation | Cloud-based blood bank collaborative communication and recommendation |
| CN107944913A (en) * | 2017-11-21 | 2018-04-20 | 重庆邮电大学 | High potential user's purchase intention Forecasting Methodology based on big data user behavior analysis |
| CN108549973A (en) * | 2018-03-22 | 2018-09-18 | 中国平安人寿保险股份有限公司 | Identification model is built and method, apparatus, storage medium and the terminal of assessment |
| CN108830409A (en) * | 2018-05-31 | 2018-11-16 | 中国科学技术大学 | The donations behavior of platform is raised towards crowd and contributor keeps prediction technique |
| CN110335057A (en) * | 2019-04-30 | 2019-10-15 | 广发证券股份有限公司 | A kind of fund Precision Marketing Method that machine learning is merged with artificial rule |
-
2020
- 2020-04-07 CN CN202010266660.XA patent/CN111477332A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170053067A1 (en) * | 2015-08-18 | 2017-02-23 | International Business Machines Corporation | Cloud-based blood bank collaborative communication and recommendation |
| CN107944913A (en) * | 2017-11-21 | 2018-04-20 | 重庆邮电大学 | High potential user's purchase intention Forecasting Methodology based on big data user behavior analysis |
| CN108549973A (en) * | 2018-03-22 | 2018-09-18 | 中国平安人寿保险股份有限公司 | Identification model is built and method, apparatus, storage medium and the terminal of assessment |
| CN108830409A (en) * | 2018-05-31 | 2018-11-16 | 中国科学技术大学 | The donations behavior of platform is raised towards crowd and contributor keeps prediction technique |
| CN110335057A (en) * | 2019-04-30 | 2019-10-15 | 广发证券股份有限公司 | A kind of fund Precision Marketing Method that machine learning is merged with artificial rule |
Non-Patent Citations (3)
| Title |
|---|
| 张静航;张健;杨土保;: "中国人群无偿献血参与率及其影响因素的Meta分析" * |
| 谢薇;王美容;: "某市初次无偿献血者再次献血意愿及影响因素调查分析" * |
| 谭咏恩;林绮文;吴岚;张婷;何彦霖;黎世杰;陈锦艳;: "基于角色理论的广州高校学生机采血小板淡季招募策略研究" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496209A (en) * | 2022-02-18 | 2022-05-13 | 青岛市中心血站 | Blood donation intelligent decision method and system |
| CN114496209B (en) * | 2022-02-18 | 2022-09-27 | 青岛市中心血站 | Intelligent decision-making method and system for blood donation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chae et al. | Data mining approach to policy analysis in a health insurance domain | |
| ȚĂRANU | Data mining in healthcare: decision making and precision | |
| Riley et al. | Determinants of children's health care use: an investigation of psychosocial factors | |
| Carmona et al. | Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department | |
| Chang et al. | A new hybrid XGBSVM model: application for hypertensive heart disease | |
| Lin et al. | Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital | |
| Mendenhall et al. | A national study of internal medicine and its specialties: II. Primary care in internal medicine | |
| US20180211727A1 (en) | Automated Evidence Based Identification of Medical Conditions and Evaluation of Health and Financial Benefits Of Health Management Intervention Programs | |
| Grieco et al. | Performance assessment using mixed effects models: a case study on coronary patient care | |
| CN107767960A (en) | Data processing method, device and the electronic equipment of clinical detection project | |
| Sudeshna et al. | Identifying symptoms and treatment for heart disease from biomedical literature using text data mining | |
| CN110033191B (en) | Business artificial intelligence analysis method and system | |
| Roback et al. | Evaluation of health research: measuring costs and socioeconomic effects | |
| Lim | The electoral consequences of international migration in sending countries: Evidence from Central and Eastern Europe | |
| CN111477332A (en) | Blood donator identification and recruitment method based on machine learning | |
| CN117035368B (en) | Doctor dispatching method based on Internet | |
| CN113628744A (en) | Quantitative evaluation system and method for body health | |
| Jiang et al. | Using Data Mining to Analyze Patient Discharge Data for an Urban Hospital. | |
| US20230068453A1 (en) | Methods and systems for determining and displaying dynamic patient readmission risk and intervention recommendation | |
| Nasrabadi et al. | Predicting heart attacks in patients using artificial intelligence methods | |
| CN116598012A (en) | Chronic disease medical care method and system based on federal learning value evaluation | |
| CN115691820A (en) | Automatic construction system for multi-typing standard diagnosis and treatment scheme of disease types | |
| McGuire | An evaluation of diagnosis-related group severity and complexity refinement | |
| JP2003526137A (en) | Method and apparatus for automatically allocating staffing | |
| Rahman et al. | Factors affecting profit efficiency of private hospitals in Bangladesh: are urban hospitals more profit-efficient? |
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 |