Method, device and equipment for processing medical data

Abstract

The present invention provides a method, device, and equipment for processing pharmaceutical data, the method comprising: obtaining pharmaceutical procurement data within a preset area; determining pharmaceutical procurement combination data and pharmaceutical purchase frequency data based on the pharmaceutical procurement data within the preset area; determining the quantity of pharmaceutical distribution stock required by a pharmaceutical agency based on the pharmaceutical procurement combination data, pharmaceutical purchase frequency data, and disease occurrence probability data; determining a target type of pharmaceutical based on the quantity of pharmaceutical distribution stock required by the pharmaceutical agency; and determining the supply demand quantity of the target type of pharmaceutical within the preset area based on the inventory quantity of the target type of pharmaceutical in a database and the health monitoring data of target subjects within the preset area. The solution of the present invention achieves real-time scheduling of pharmaceutical quantities by deeply mining and analyzing pharmaceutical data.

Description

Technical Field

[0001] The present invention relates to the technical field of computer information processing, and in particular to a method, device and equipment for processing medical data. Background Art

[0002] In today's digital age, data in the pharmaceutical sector is experiencing explosive growth. However, because most pharmaceutical distributors only reach customers in a single scenario, they are unable to conduct comprehensive early warning analysis of drug data. This leads to imbalances in drug supply and demand, potentially preventing real-time drug quantity scheduling in areas with high health risks. Summary of the Invention

[0003] The present invention provides a method, device and equipment for processing medical data, which realizes real-time scheduling of drug quantities by deeply mining and analyzing medical data.

[0004] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0005] A method for processing medical data, comprising:

[0006] Obtain drug procurement data within a preset area;

[0007] Determining drug purchasing combination data and drug purchasing frequency data based on the drug purchasing data within the preset area;

[0008] Determine the quantity of drugs that the pharmaceutical agency needs to prepare for distribution based on the drug purchasing combination data, drug purchase frequency data, and disease occurrence probability data;

[0009] Determine the target type of drugs based on the quantity of drugs that the pharmaceutical institution needs to prepare for distribution;

[0010] The supply demand quantity of the target type of medicine in the preset area is determined according to the inventory quantity of the target type of medicine in the database and the health monitoring data of the target objects in the preset area.

[0011] Optionally, determining the drug procurement combination data based on the drug procurement data within the preset area includes:

[0012] Determining various combinations of drugs based on the drug purchasing data and the purchasing combination data analysis model within the preset area;

[0013] Based on the multiple matching combination relationships, the optimal drug procurement combination data is determined.

[0014] Optionally, determining the drug purchase frequency data within the preset area based on the drug purchase data within the preset area includes:

[0015] The drug purchase frequency data within the preset area is determined based on the number of drug purchases within a preset time period and the quantity of drugs purchased each time in the drug procurement data within the preset area.

[0016] Optionally, the quantity of drugs that a pharmaceutical organization needs to prepare for distribution is determined based on the drug purchasing combination data, drug purchase frequency data, and disease occurrence probability data, including:

[0017] According to Q = α1(S1(1+r)P(D)+α2S2fP(D))-I+γL+δ*W, the number of drugs distributed is obtained;

[0018] Among them, Q is the number of drugs that pharmaceutical institutions need to prepare for distribution, α1 is the weight coefficient of commonly used drugs in hospitals, α2 is the weight coefficient of commonly used over-the-counter drugs, S1 is the historical sales volume of business to business, S2 is the historical sales volume of business to individuals, r is the sales growth rate on the enterprise side, P(D) is the probability data of disease occurrence, f is the purchase frequency on the individual side, I is the inventory quantity, γ is the adjustment coefficient related to the expiration date, L is the expiration date of the drug, δ is the weight coefficient of the impact of historical weather on disease incidence, and W is the disease incidence affected by historical weather.

[0019] Optionally, disease occurrence probability data is determined by the following process:

[0020] Acquire multiple infection indicator data within a preset time period in the preset area;

[0021] Disease occurrence probability data is obtained based on the multiple infection indicator data and the disease probability prediction model; wherein the disease probability prediction model is determined based on the comprehensive weather, humidity, air quality, population age distribution, and population density in a preset area.

[0022] Optionally, based on the quantity of drugs that the pharmaceutical institution needs to prepare for distribution, target types of drugs are determined, including:

[0023] Identifying the types of drugs in the drug distribution quantity that the drug agency needs to prepare, and obtaining multiple types of drugs;

[0024] At least one type of drug whose quantity is greater than a preset value is determined as a target type of drug.

[0025] Optionally, determining the supply demand quantity of the target type of medicine in the preset area based on the inventory quantity of the target type of medicine in the database and the health monitoring assessment data of the target subjects in the preset area includes:

[0026] Querying the database for the inventory quantity of the target type of medicine;

[0027] Dynamically assess the health risk of the target object based on the historical health data of the target object in the preset area and the health monitoring data model to obtain health monitoring assessment data of the target object;

[0028] The supply demand quantity of the target type of medicine in the preset area is determined based on the inventory quantity of the target type of medicine and the health monitoring assessment data of the target object.

[0029] An embodiment of the present invention further provides a medical data processing device, comprising:

[0030] An acquisition module is used to obtain drug procurement data in a preset area;

[0031] The processing module is used to determine the drug procurement combination data and the drug purchase frequency data based on the drug procurement data in the preset area; determine the quantity of drugs that the drug agency needs to prepare for distribution based on the drug procurement combination data, the drug purchase frequency data and the disease occurrence probability data; determine the target type of drugs based on the quantity of drugs that the drug agency needs to prepare for distribution; and determine the supply demand quantity of the target type of drugs in the preset area based on the inventory quantity of the target type of drugs in the database and the health monitoring data of the target objects in the preset area.

[0032] An embodiment of the present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the method described above when executed by the processor.

[0033] An embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0034] The above solution of the present invention includes at least the following beneficial effects:

[0035] The above-mentioned scheme of the present invention obtains the drug procurement data in a preset area; determines the drug procurement combination data and drug purchase frequency data based on the drug procurement data in the preset area; determines the number of drugs that the drug agency needs to prepare for distribution based on the drug procurement combination data, drug purchase frequency data and disease occurrence probability data; determines the target type of drugs based on the number of drugs that the drug agency needs to prepare for distribution; determines the supply demand quantity of the target type of drugs in the preset area based on the inventory quantity of the target type of drugs in the database and the health monitoring data of the target objects in the preset area; thereby realizing real-time scheduling of the drug quantity based on the supply demand quantity of the target type of drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1is a flowchart of a method for processing medical data provided by an embodiment of the present invention;

[0037] Figure 2 It is a module diagram of a medical data processing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0039] like Figure 1 As shown, an embodiment of the present invention provides a method for processing medical data, including:

[0040] Step 11, obtaining drug procurement data within a preset area; here, the drug procurement data may include: various types of drug procurement data recorded in the drug agency system; including drug name, procurement time, procurement quantity, procurement amount, supplier and other information.

[0041] Step 12: determining drug purchasing combination data and drug purchasing frequency data based on the drug purchasing data in the preset area;

[0042] Step 13: Determine the quantity of medicines that the pharmaceutical organization needs to prepare for distribution based on the medicine purchasing combination data, the medicine purchasing frequency data, and the disease occurrence probability data;

[0043] Step 14: Determine the target type of drugs based on the quantity of drugs that the drug agency needs to prepare for distribution;

[0044] Step 15: Determine the supply demand quantity of the target type of medicine in the preset area according to the inventory quantity of the target type of medicine in the database and the health monitoring data of the target objects in the preset area.

[0045] This embodiment of the present invention obtains drug procurement data within a preset area; determines drug procurement combination data and drug purchase frequency data based on the drug procurement data within the preset area; determines the number of drugs that a drug agency needs to prepare for distribution based on the drug procurement combination data, drug purchase frequency data and disease occurrence probability data; determines the target type of drugs based on the number of drugs that the drug agency needs to prepare for distribution; determines the supply demand quantity of the target type of drugs within the preset area based on the inventory quantity of the target type of drugs in the database and the health monitoring data of the target objects within the preset area; thereby achieving real-time scheduling of the drug quantity based on the supply demand quantity of the target type of drugs.

[0046] In an optional embodiment of the present invention, in step 12, determining the drug procurement combination data based on the drug procurement data in the preset area includes:

[0047] Step 121, determining a plurality of collocation combinations between a plurality of different medicines based on the medicine purchasing data in the preset area and the purchasing combination data analysis model;

[0048] Step 122: Determine the optimal drug purchasing combination data based on the multiple matching combination relationships.

[0049] In this embodiment, the purchase combination data analysis model can rely on massive user purchase data to analyze the frequency of use and matching rules of conventional drug combinations and explore potential correlation patterns. For example, analyze the purchase of cold medicines and antipyretics in the cold season to determine the best matching plan. Refined analysis of special drug categories can be achieved: for chronic disease drugs, combined with long-term treatment data of patients, analyze the differences in the efficacy of drug combinations at different stages of the disease and their association with weather factors (seasonal changes, temperature changes, etc.). For example, the optimized combination of hypertension drugs and other cardiovascular drugs in the cold winter to cope with blood pressure fluctuations. For children's drugs, combined with their special physiological characteristics and medication needs, analyze the common drug combination patterns of children of different age groups in different seasons (affected by weather), such as the combination use of related drugs during the high incidence of intestinal diseases in children in summer.

[0050] Specifically, the procurement combination data analysis model can analyze the various combination relationships between different drugs through the following steps:

[0051] Enter drug procurement data, including drug name, procurement time, procurement quantity, procurement amount, supplier and other information.

[0052] Organize drug purchase data into a structured table format for subsequent analysis. For example, each row represents a purchase record, and each column corresponds to a different drug attribute.

[0053] Based on the analysis objectives and drug characteristics, relevant features are selected for clustering. For example, drug purchase frequency, purchase amount, co-occurrence with other drugs (i.e., number of times purchased simultaneously), drug category (e.g., cardiovascular, antibiotics), and supplier.

[0054] Feature quantification: Some non-numeric features, such as drug category and supplier, need to be quantified. Methods such as one-hot encoding can be used to convert them into numerical data for model calculations.

[0055] Data normalization: All selected features are normalized to form standard normally distributed data with a mean of 0 and a standard deviation of 1. For example, each drug is converted into a Boolean feature, with a value of 1 if present and 0 otherwise. This eliminates the influence of different dimensions and numerical ranges between features and ensures that each feature has equal weight and influence in the clustering process. Drug combinations are treated as "sentences" and low-dimensional vectors are generated.

[0056] Determining the number of clusters: The optimal number of clusters can be determined using methods such as the elbow rule and the silhouette coefficient. The elbow rule plots the sum of squared errors (SSE) curve for different numbers of clusters, finds the point where the slope changes the most, calculates the SSE (Sum of Squared Errors) for different values ​​of K, and selects the inflection point as a reference for determining the number of clusters. The silhouette coefficient is a measure of clustering quality, ranging from -1 to 1. A larger value indicates better clustering. The optimal value can be determined by calculating the silhouette coefficient for different numbers of clusters.

[0057] SSE represents the sum of the squares of the distances from all sample points to the cluster center to which they belong, and is used to measure the tightness of the cluster. Specifically: Among them, K is the number of clusters, C i is the sample set of the i-th cluster, μ i is the centroid (mean point) of the ith cluster. x is the sample point.

[0058] Model training: The processed data is input into the selected clustering algorithm model for training to obtain different drug clustering results. Each cluster can be regarded as a combination of drugs with similar characteristics.

[0059] Results analysis and potential association pattern mining: Each cluster is analyzed and characterized. For example, the average purchase frequency, average purchase amount, and major drug categories of each cluster are calculated. It can be found that drugs in certain clusters primarily treat specific diseases or come from the same supplier, which may indicate a potential relationship between these drugs.

[0060] Clustering result analysis, statistical characteristics of each cluster: high-frequency drug combinations within the cluster.

[0061] Comparison of drug distribution between clusters (e.g., cluster 0 is mainly composed of cardiovascular drugs, while cluster 1 is mainly composed of hypoglycemic drugs).

[0062] Visualization: Use t-SNE or UMAP to reduce high-dimensional data to 2D and label the clusters.

[0063] Discovering pairing patterns: Observe the co-occurrence of drugs within clusters and analyze which drugs frequently appear together in the same cluster. This indicates a high correlation between them in procurement and the possibility of their combination. For example, a cluster of cold medications might include antipyretics, cough suppressants, and cold medicines. This indicates that these drugs are often purchased together in actual procurement, reflecting their combination patterns in cold treatment.

[0064] Discover potential associations: By comparing the differences and similarities between different clusters, potential association patterns are mined. For example, it is found that the drugs in a certain cluster are complementary or alternative drugs to the drugs in another cluster, or that the drugs in the two clusters have a sequential relationship in the treatment process. For example, the drugs in one cluster are first-line drugs for treating a certain disease, while the drugs in the other cluster are second-line drugs used when the first-line drugs are ineffective. This reveals the potential treatment associations and procurement order between the drugs. High-frequency drug combination patterns, example output: Cluster 0: {Aspirin, Amlodipine} (support 15%), often used in combination with metformin (confidence 80%). Cluster 1: {Insulin, Metformin} (support 10%), reflecting the combination of diabetes drugs. Cluster 2: {Omeprazole, Amoxicillin} (support 8%), suggesting a Helicobacter pylori treatment plan;

[0065] Verification with business knowledge: Cluster analysis results are validated against pharmaceutical expertise and practical business experience. This ensures that the discovered potential association patterns are clinically plausible and have practical application value. For example, medical knowledge can be used to determine whether certain drug combinations comply with treatment guidelines, or by communicating with physicians, pharmacists, and other professionals to understand the use of these drug combinations in clinical practice.

[0066] Through the above steps, the cluster analysis model can help drug purchasers discover potential correlation patterns between drugs, and provide valuable reference for optimizing drug procurement strategies, rationally arranging inventory, and improving drug supply efficiency.

[0067] In an optional embodiment of the present invention, in step 12, determining the drug purchase frequency data in the preset area based on the drug purchase data in the preset area includes:

[0068] Step 123 , determining the drug purchase frequency data within the preset area based on the number of drug purchases within the preset time period and the quantity of drugs purchased each time in the drug purchase data within the preset area.

[0069] In this embodiment, the user's purchase frequency of various medications is monitored in real time by analyzing the number of purchases within a preset time period and the quantity of each purchase. Once an abnormal fluctuation is detected (such as a sharp increase in purchase frequency within a short period of time, or a sudden drop after a long period of stability), an in-depth health risk assessment is immediately triggered. Combining the user's historical purchase data with a drug knowledge base, the system accurately infers changes in the user's health status and provides appropriate early intervention plans.

[0070] In an optional embodiment of the present invention, in step 13, determining the quantity of medicines that the pharmaceutical organization needs to prepare for distribution based on the medicine purchasing combination data, the medicine purchasing frequency data, and the disease occurrence probability data includes:

[0071] According to Q = α1(S1(1+r)P(D)+α2S2fP(D))-I+γL+δ*W, the number of drugs distributed is obtained;

[0072] Among them, Q is the quantity of drugs that the pharmaceutical organization needs to prepare for distribution, that is, the number of drugs to be prepared;

[0073] α1 is the weight coefficient of commonly used drugs in hospitals, and α2 is the weight coefficient of commonly used over-the-counter drugs, which are adjusted according to the drugs and market conditions. For example, for commonly used drugs in hospitals, α1 has a high weight; for commonly used over-the-counter drugs, α2 has a high weight;

[0074] S1 is the historical sales volume of business-to-business, and the historical sales volume of the B-end (business-to-business), reflecting the purchase volume of large customers such as medical institutions;

[0075] S2 is the historical sales volume of business to individual and C-end (business to consumer), showing the purchase situation of consumers;

[0076] r is the sales growth rate of the enterprise side and the B-side, reflecting the growth or decline trend of the B-side market demand;

[0077] P(D) is the probability data of disease occurrence;

[0078] f is the purchase frequency of the individual end, the purchase frequency of the C end, for example, the purchase frequency of cold medicine is high during the flu season;

[0079] I is the inventory quantity. When calculating the distribution quantity, the existing inventory should be taken into account to avoid overstocking or stockouts.

[0080] γ is the adjustment coefficient related to the expiration date. For drugs with short expiration dates, the γ value affects the distribution volume adjustment. For example, for drugs nearing their expiration date, γ can reduce the distribution volume.

[0081] L is the expiration date of the drug. Be cautious when distributing drugs with short expiration dates to prevent losses caused by expiration.

[0082] δ is the weight coefficient of the impact of historical weather on disease incidence. The weight coefficient of the impact of historical weather on disease incidence is determined based on historical data and drug characteristics, reflecting the degree of impact of disease incidence on distribution quantity;

[0083] W is the disease incidence rate affected by historical weather. This is calculated by analyzing historical weather data (such as temperature, precipitation, and humidity) and the incidence of diseases during the corresponding period. It reflects the degree to which weather factors affect the occurrence of diseases. For example, the incidence of intestinal diseases increases during periods of persistent high temperatures.

[0084] In an optional embodiment of the present invention, the disease occurrence probability data is determined by the following process:

[0085] Step 1211, obtaining multiple infection indicator data within a preset time period in the preset area;

[0086] Step 1212: Obtain disease occurrence probability data based on the multiple infection indicator data and the disease probability prediction model;

[0087] Among them, the disease probability prediction model is:

[0088] \(P(D)=\frac{1}{1+e^{-(\beta_0+\beta_1T+\beta_2H+\beta_3AQ+\beta_4A+\beta_5D+\cdots)}}\),

[0089] Among them, P(D) is the predicted value of disease probability, which is calculated based on comprehensive factors such as weather (temperature T, humidity H, air quality (AQ)), population (age distribution (A), population density (D)), etc. The closer the value is to 1, the greater the possibility of disease outbreak, and the demand for related drugs may increase.

[0090] Of course, the disease probability prediction model can also be carried out using the following model, and the training process of the model includes:

[0091] Step 12121: Acquire a first data set, a second data set, and a third data set, wherein the first data set represents the number of susceptible individuals within a preset time period within a preset area, the second data set represents the number of infected individuals within a preset time period within a preset area, and the third data set represents the number of recovered individuals within a preset time period within a preset area; the number of susceptible individuals is determined based on first-type drug purchase data, and the number of infected individuals is determined based on second-type drug data; the first-type and second-type drugs may be of the same, different, or partially different types;

[0092] Step 12122: determining a first parameter based on the first data set and the second data set;

[0093] Step 12123: Determine a second parameter based on the second data set and the third data set;

[0094] Step 12124: Obtain a regional epidemic prediction model based on the first parameter, the second parameter, and a preset equation.

[0095] Furthermore, the trained regional epidemic prediction model is used to extract features from the input population data to obtain feature data;

[0096] Extracting first sub-feature data and second sub-feature data from the feature data, wherein the first sub-feature data represents a feature value of the number of susceptible persons, and the second sub-feature data represents a feature value of the number of infected persons;

[0097] The first sub-feature data and the second sub-feature data are input into a regional epidemic prediction model to perform risk prediction and obtain a disease probability prediction value.

[0098] In specific implementation, let A(t), B(t) and C(t) represent the number of susceptible people, infected people and recovered people at time t, respectively, and A(t)+B(t)+C(t)=N.

[0099] In the SIR model, there are two transitions between the three groups of people: susceptible, infected, and recovered: the infection rate β for the transition from susceptible to infected, and the recovery rate γ for the transition from infected to recovered.

[0100] Assume that after a susceptible person comes into contact with an infected person, the probability of the susceptible person being infected per unit time is b. Since the ratio of susceptible individuals is A / N, there are a total of B(t) infected individuals in the model at time t. Therefore, the number of susceptible individuals decreases with the following rate of change:

[0101]

[0102] Accordingly, the number of infected people increases at the following rate, and at the same time, they transform into the removed state with a unit time probability γ:

[0103]

[0104] The number of removers changes from the infected group to the remover state with probability γ:

[0105]

[0106] The infection rate β and the recovery rate γ are used to represent the behaviors of disease transmission and disease recovery.

[0107] In the early stage of disease transmission, A≈N. Substituting A≈N into formula (2), we get:

[0108]

[0109] It is easy to see that the general solution of this differential equation is: B(t)=Ce (β―γ)t

[0110] Substituting the initial condition B(t=0)=1 into the above equation, we get C=1, so:

[0111] B(t)=e (β―γ)t =e (rb―γ)t ;

[0112] The data fitting yields β = 0.1827 (0.95 confidence interval: (0.176, 0.1895))

[0113] When C = 1, it is the threshold for whether an infectious disease will die out. When C < 1, during the infection process, the maximum number of infections that each infected person can transmit per unit time is less than 1. At this point, even without any prevention and control measures, the infectious disease will gradually die out naturally. When C > 1, the infectious disease will always persist and form an endemic epidemic. At this time, only by implementing certain measures can C be reduced to less than 1, and the infectious disease epidemic can be effectively controlled and gradually eliminated.

[0114] In an optional embodiment of the present invention, in step 14, determining the target type of drugs based on the quantity of drugs that the drug agency needs to prepare for distribution includes:

[0115] Step 141, identifying the types of medicines in the quantity of medicines that the pharmaceutical institution needs to prepare for distribution, and obtaining multiple types of medicines;

[0116] Step 142 : Determine at least one type of drug whose quantity is greater than a preset value as a target type of drug.

[0117] In this example, we identify drugs with a sharp increase in sales in a specific region or nationwide (i.e., blockbuster drugs). We combine the drug knowledge base and clinical research data to conduct an in-depth analysis of the efficacy, applicable population, and therapeutic effects of blockbuster drugs, and determine the reasons for their becoming blockbuster drugs (e.g., increased demand due to public health emergencies, increased demand for drugs due to the promotion of new treatment options, etc.).

[0118] Establish a rapid response cooperation mechanism with pharmaceutical companies. Once a blockbuster drug is detected, immediately contact the relevant pharmaceutical company to obtain more detailed drug information (such as drug ingredients, production process, quality control standards, etc.) and supply status (such as inventory quantity, production capacity, distribution plan, etc.). At the same time, the push system will promptly push blockbuster drug information to the company's internal procurement department, sales department, and medical institutions, ensuring that the company can promptly adjust procurement strategies, reserve sufficient inventory, optimize sales channels, meet market demand, and ensure the timeliness and stability of drug supply.

[0119] In an optional embodiment of the present invention, in step 15, determining the supply demand quantity of the target type of medicine in the preset area based on the inventory quantity of the target type of medicine in the database and the health monitoring assessment data of the target subjects in the preset area includes:

[0120] Step 151, querying the database for the inventory quantity of the target type of medicine;

[0121] Step 152: dynamically assess the health risk of the target object based on the historical health data of the target object in the preset area and the health monitoring data model to obtain health monitoring assessment data of the target object;

[0122] Step 153 : Determine the supply demand quantity of the target type of medicine in the preset area according to the inventory quantity of the target type of medicine and the health monitoring assessment data of the target subject.

[0123] In this embodiment, the health monitoring data model is specifically implemented as follows:

[0124] Acquire physiological health data and device health data. For physiological health, wearable devices can collect data such as heart rate, blood oxygen, and body temperature (sampling frequency 1Hz to 100Hz). Medical instruments can collect data such as ECG, EEG, and respiratory waveforms (high-precision time series). Device health can also collect data such as temperature, vibration, pressure, and current (for industrial equipment).

[0125] Preprocess and clean the data: remove signal loss segments.

[0126] Health status monitoring through memory network models, such as:

[0127] pass For health status monitoring, L is the lag operator, d is the difference order, ε t is white noise, φ i is physiological health data, θ i For device health data, X t is the initial noise.

[0128] In this embodiment, by monitoring health data and assessing health risks, the supply demand quantity of target type drugs in a preset area is determined, providing a decision-making basis for drug supply.

[0129] The method described in the above embodiment of the present invention can determine the supply demand quantity of target type drugs in a preset area through mining and analysis of drug data, so as to give subsequent better inventory optimization suggestions. The first type: Q1 and Q4: The demand for cardiovascular and cerebrovascular drugs is high, and stocks should be prepared in advance to cope with the winter peak. It is recommended to gradually increase the inventory of cardiovascular and cerebrovascular drugs at the end of Q3. The second type: Q2: The demand is relatively stable, and the regular purchase volume can be maintained to avoid inventory backlogs. Appropriately reduce the purchase volume of other seasonal drugs. The third type: Q3: The high temperature in summer leads to a surge in demand for gastrointestinal drugs, children's medicines and anti-allergic drugs, and it is recommended to purchase in large quantities. The demand for anti-inflammatory drugs is moderate, and the purchase volume can be flexibly adjusted according to historical sales data.

[0130] Inventory management: Use dynamic inventory models (such as the gamma coefficient) to adjust purchase quantity: Recommended purchase quantity = S B ×(1+λ×r B ); where γ is the demand fluctuation coefficient (recommended to be 1.2-1.5). Consider the sales frequency (f_C) to reduce the inventory backlog of low-frequency drugs.

[0131] like Figure 2 As shown, an embodiment of the present invention further provides a medical data processing device 20, comprising:

[0132] An acquisition module 21 is used to acquire drug procurement data in a preset area;

[0133] The processing module 22 is used to determine the drug procurement combination data and the drug purchase frequency data based on the drug procurement data in the preset area; determine the quantity of drugs that the drug agency needs to prepare for distribution based on the drug procurement combination data, the drug purchase frequency data and the disease occurrence probability data; determine the target type of drugs based on the quantity of drugs that the drug agency needs to prepare for distribution; determine the supply demand quantity of the target type of drugs in the preset area based on the inventory quantity of the target type of drugs in the database and the health monitoring data of the target objects in the preset area.

[0134] Optionally, determining the drug procurement combination data based on the drug procurement data within the preset area includes:

[0135] Determining various combinations of drugs based on the drug purchasing data and the purchasing combination data analysis model within the preset area;

[0136] Based on the multiple matching combination relationships, the optimal drug procurement combination data is determined.

[0137] Optionally, determining the drug purchase frequency data within the preset area based on the drug purchase data within the preset area includes:

[0138] The drug purchase frequency data within the preset area is determined based on the number of drug purchases within a preset time period and the quantity of drugs purchased each time in the drug procurement data within the preset area.

[0139] Optionally, the quantity of drugs that a pharmaceutical organization needs to prepare for distribution is determined based on the drug purchasing combination data, drug purchase frequency data, and disease occurrence probability data, including:

[0140] According to Q = α1(S1(1+r)P(D)+α2S2fP(D))-I+γL+δ*W, the number of drugs distributed is obtained;

[0141] Among them, Q is the number of drugs that pharmaceutical institutions need to prepare for distribution, α1 is the weight coefficient of commonly used drugs in hospitals, α2 is the weight coefficient of commonly used over-the-counter drugs, S1 is the historical sales volume of business to business, S2 is the historical sales volume of business to individuals, r is the sales growth rate on the enterprise side, P(D) is the probability data of disease occurrence, f is the purchase frequency on the individual side, I is the inventory quantity, γ is the adjustment coefficient related to the expiration date, L is the expiration date of the drug, δ is the weight coefficient of the impact of historical weather on disease incidence, and W is the disease incidence affected by historical weather.

[0142] Optionally, disease occurrence probability data is determined by the following process:

[0143] Acquire multiple infection indicator data within a preset time period in the preset area;

[0144] Disease occurrence probability data is obtained based on the multiple infection indicator data and the disease probability prediction model; wherein the disease probability prediction model is determined based on the comprehensive weather, humidity, air quality, population age distribution, and population density in a preset area.

[0145] Optionally, based on the quantity of drugs that the pharmaceutical institution needs to prepare for distribution, target types of drugs are determined, including:

[0146] Identifying the types of drugs in the drug distribution quantity that the drug agency needs to prepare, and obtaining multiple types of drugs;

[0147] At least one type of drug whose quantity is greater than a preset value is determined as a target type of drug.

[0148] Optionally, determining the supply demand quantity of the target type of medicine in the preset area based on the inventory quantity of the target type of medicine in the database and the health monitoring assessment data of the target subjects in the preset area includes:

[0149] querying the database for the inventory quantity of the target type of medicine;

[0150] Dynamically assess the health risk of the target object based on the historical health data of the target object in the preset area and the health monitoring data model to obtain health monitoring assessment data of the target object;

[0151] The supply demand quantity of the target type of medicine in the preset area is determined based on the inventory quantity of the target type of medicine and the health monitoring assessment data of the target object.

[0152] It should be noted that the device corresponds to the device of the above method, and all implementation methods in the embodiments of the above method are applicable to the embodiments of the device and can achieve the same technical effects.

[0153] An embodiment of the present invention further provides a computing device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in the above embodiment. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0154] An embodiment of the present invention further provides a computing device-readable storage medium having a program stored therein. When executed by a processor, the program implements the method described in the above embodiment. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0155] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computing device software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0156] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0158] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0159] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0160] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a storage medium readable by a computing device. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computing device software product is stored in a storage medium and includes a number of instructions for enabling a computing device (which can be a personal computing device, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, ROM, RAM, a magnetic disk, or an optical disk.

[0161] In addition, it should be noted that, in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0162] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0163] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for processing medical data, characterized in that: include: Obtain drug procurement data within a preset area; Determining drug purchasing combination data and drug purchasing frequency data based on the drug purchasing data within the preset area; Determine the quantity of drugs that the pharmaceutical agency needs to prepare for distribution based on the drug purchasing combination data, drug purchase frequency data, and disease occurrence probability data; Determine the target type of drugs based on the quantity of drugs that the pharmaceutical institution needs to prepare for distribution; Determine the supply demand quantity of the target type of medicine in the preset area based on the inventory quantity of the target type of medicine in the database and the health monitoring data of the target object in the preset area; The number of drugs that the pharmaceutical organization needs to prepare for distribution is determined based on the drug purchasing combination data, drug purchase frequency data, and disease occurrence probability data, including: according to , get the quantity of drug distribution; in, The quantity of medicines that need to be prepared for pharmaceutical institutions, is the weight coefficient of commonly used drugs in the hospital, is the weight coefficient of commonly used over-the-counter drugs, is the historical business-to-business sales volume, For business-to-individual historical sales volume, is the sales growth rate of the enterprise side, is the disease occurrence probability data, Purchase frequency for personal end, is the inventory quantity, is the adjustment coefficient related to the validity period, The expiration date of the drug. is the weight coefficient of the impact of historical weather on disease incidence, The incidence of diseases affected by historical weather; Among them, the disease occurrence probability data is determined through the following process: Acquire multiple infection indicator data within a preset time period in the preset area; Disease occurrence probability data is obtained based on the multiple infection indicator data and the disease probability prediction model; wherein the disease probability prediction model is determined based on the comprehensive weather, humidity, air quality, population age distribution, and population density in a preset area.

2. The method for processing medical data according to claim 1, characterized in that: Determining drug procurement combination data based on the drug procurement data within the preset area includes: Determining various combinations of drugs based on the drug purchasing data and the purchasing combination data analysis model within the preset area; Based on the multiple matching combination relationships, the optimal drug procurement combination data is determined.

3. The method for processing medical data according to claim 1, wherein: Determining drug purchase frequency data within the preset area based on the drug purchase data within the preset area includes: The drug purchase frequency data within the preset area is determined based on the number of drug purchases within a preset time period and the quantity of drugs purchased each time in the drug procurement data within the preset area.

4. The method for processing medical data according to claim 1, wherein: Determine the target type of drugs based on the quantity of drugs that the pharmaceutical institution needs to prepare for distribution, including: Identifying the types of drugs in the drug distribution quantity that the drug agency needs to prepare, and obtaining multiple types of drugs; At least one type of drug whose quantity is greater than a preset value is determined as a target type of drug.

5. The method for processing medical data according to claim 1, wherein: Determining the supply demand quantity of the target type of medicine in the preset area based on the inventory quantity of the target type of medicine in the database and the health monitoring assessment data of the target subjects in the preset area includes: querying the database for the inventory quantity of the target type of medicine; Dynamically assess the health risk of the target object based on the historical health data of the target object in the preset area and the health monitoring data model to obtain health monitoring assessment data of the target object; The supply demand quantity of the target type of medicine in the preset area is determined based on the inventory quantity of the target type of medicine and the health monitoring assessment data of the target object.

6. A medical data processing device, characterized in that: include: An acquisition module is used to obtain drug procurement data in a preset area; a processing module configured to determine drug purchasing combination data and drug purchasing frequency data based on the drug purchasing data within the preset area; determine the quantity of drugs that a drug agency needs to prepare for distribution based on the drug purchasing combination data, drug purchasing frequency data, and disease occurrence probability data; and determine target drug types based on the quantity of drugs that the drug agency needs to prepare for distribution; Determining the supply demand quantity of the target type of medicine in the preset area based on the inventory quantity of the target type of medicine in the database and the health monitoring data of the target subjects in the preset area; The number of drugs that the pharmaceutical organization needs to prepare for distribution is determined based on the drug purchasing combination data, drug purchase frequency data, and disease occurrence probability data, including: according to , get the quantity of drug distribution; in, The quantity of medicines that need to be prepared for pharmaceutical institutions, is the weight coefficient of commonly used drugs in the hospital, is the weight coefficient of commonly used over-the-counter drugs, is the historical business-to-business sales volume, For business-to-individual historical sales volume, is the sales growth rate of the enterprise side, is the disease occurrence probability data, Purchase frequency for personal end, is the inventory quantity, is the adjustment coefficient related to the validity period, The expiration date of the drug. is the weight coefficient of the impact of historical weather on disease incidence, The incidence of diseases affected by historical weather; Among them, the disease occurrence probability data is determined through the following process: Acquire multiple infection indicator data within a preset time period in the preset area; Disease occurrence probability data is obtained based on the multiple infection indicator data and the disease probability prediction model; wherein the disease probability prediction model is determined based on the comprehensive weather, humidity, air quality, population age distribution, and population density in a preset area.

7. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 5 is performed.

8. A computer-readable storage medium, characterized in that The device stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 5.

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