A Big Data-Based Assessment System and Method for Geriatric Cancer Nursing

Abstract

This application relates to the field of medical and nursing assessment technology, and in particular to a system and method for assessing geriatric oncology nursing care based on big data analysis. The method includes the following steps: acquiring patient medication data and comorbidity data; assessing the degree of medication matching based on the patient medication data and comorbidity data; assessing the risk of polypharmacy by combining the degree of medication matching with the patient medication data; assessing the risk of comorbidities by combining a cumulative disease score table with the patient's comorbidity data; assigning drug weights based on the patient's comorbidity risk list; and adjusting the patient's medication regimen and providing early warning of medication risk by comprehensively considering the results of the polypharmacy risk assessment and the drug weight allocation. This application effectively improves the accuracy and timeliness of nursing interventions by quantifying the risks of polypharmacy and comorbidities and then adjusting the patient's medication regimen accordingly.

Description

Technical Field

[0001] This application relates to the field of medical care assessment technology, and in particular to a system and method for assessing elderly oncology care based on big data analysis. Background Technology

[0002] Elderly cancer patients often experience physiological decline and, in addition to cancer treatment, are usually accompanied by various comorbidities. These comorbidities can affect cancer treatment and care in multiple ways, such as: (1) acting as confounding factors that complicate cancer diagnosis and treatment; and (2) indirectly affecting cancer care and influencing cancer incidence. Therefore, incorporating comorbidities into the decision-making process for elderly cancer care has become a key element in assessing the risks and evaluating the benefits of treatment for elderly cancer patients.

[0003] Comorbidities in elderly cancer patients often require multiple drug treatments, which can easily lead to polypharmacy. Polypharmacy refers to inappropriate use of multiple drugs, that is, the use of drugs that exceed the patient's clinical indications, such as drugs that are not necessary for treatment, lack evidence-based medical support, or repeated use. This can cause potential adverse clinical consequences for the patient due to excessive or inappropriate prescriptions.

[0004] Existing assessment methods for geriatric oncology care often rely on nurses' subjective judgment of the risk of comorbidities and multiple medications when dealing with patients with multiple medications due to comorbidities. However, subjective judgment is easily influenced by nurses' experience, knowledge, and subjective biases, lacking scientific and quantitative assessment criteria. This results in certain limitations in assessing the risk of comorbidities and multiple medications, affecting the accuracy of risk assessment and potentially delaying the discovery of potential medication risks, thus reducing the timeliness and effectiveness of nursing interventions. Summary of the Invention

[0005] To overcome the shortcomings and deficiencies of existing technologies, this application provides a big data analysis-based assessment system and method for elderly oncology nursing. By quantifying the risks of multiple medications and comorbidities in patients, the system can adjust patients' medication regimens in a targeted manner, effectively improving the accuracy and timeliness of nursing interventions.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides a method for assessing elderly oncology care based on big data analysis, including the following steps:

[0008] Obtain patient medication data and patient comorbidity data;

[0009] Assess the degree of patient medication fit using patient medication data and patient comorbidity data;

[0010] The risk of multiple medication use in patients is assessed by combining patient medication matching with patient medication data.

[0011] The risk of comorbidities in patients is assessed by combining a cumulative disease score sheet with data on comorbidities.

[0012] The risk of patients' comorbidities is assessed, and drug weights are assigned based on the list of patients' comorbidity risks.

[0013] The patient's medication regimen was adjusted based on the results of the comprehensive risk assessment of multiple medication use and the drug weighting allocation, and the patient's medication risk warning was issued.

[0014] Preferably, the assessment of patient medication matching specifically includes:

[0015] Obtain patient medication data and patient comorbidity data;

[0016] We construct drug indication and contraindication sets using patient medication data, and construct patient comorbidity sets using patient comorbidity data.

[0017] If the patient's set of comorbidities and the drug's set of contraindications do not overlap, but the patient's set of comorbidities and the drug's set of indications overlap, then the drug's drug use matching degree is 1; otherwise, the drug's drug use matching degree is 0.

[0018] The ratio of the sum of the drug matching scores of all drugs in the patient's medication data to the number of drugs is used as the patient's drug matching score. The patient's drug matching score is used to quantitatively assess the degree of matching between patients' medication use and medication.

[0019] Preferably, the assessment of the risk of multiple medication use by combining patient medication matching degree with patient medication data specifically includes:

[0020] Obtain the patient medication matching degree. When the patient medication matching degree is greater than or equal to the preset patient medication matching threshold, obtain the drug's indication set and calculate the number of intersection elements of the indication sets of any two drugs in the patient medication data through the indication set.

[0021] The ratio of the sum of the number of elements in the intersection of the drug's indications set to the number of elements in the drug's indications set is used as the drug's multiple drug use risk index.

[0022] The sum of the multiple drug risk indices of all drugs in the patient's medication data is used as the patient's multiple drug risk index, which is used to quantitatively assess the risk of multiple drug use in patients.

[0023] Preferably, the assessment of the patient's comorbidity risk specifically includes:

[0024] Acquire patient comorbidity data and assess the severity of disease in each organ system based on the MCIRS-G cumulative disease score table and patient comorbidity data to obtain organ system disease scores;

[0025] The patient comorbidity risk index is the ratio of the sum of the organ system disease scores corresponding to all organ systems involved in the comorbidity data to the sum of the maximum organ system disease scores corresponding to all organ systems. The patient comorbidity risk index is used to quantitatively assess the risk level corresponding to different comorbidities.

[0026] Preferably, the allocation of drug weights based on the patient's comorbidity risk list specifically includes:

[0027] Obtain the patient comorbidity risk index corresponding to each comorbidity in the patient comorbidity data;

[0028] The patients' comorbidity risk list is obtained by sorting them in ascending order according to their comorbidity risk index.

[0029] The ratio of the number of times a comorbidity appears in the patient's comorbidity risk list to the number of comorbidities in the patient's comorbidity risk list is used to obtain the patient's comorbidity risk weight corresponding to the comorbidity.

[0030] The sum of the risk weights of the patient's comorbidities corresponding to the intersection elements of the drug's indication set and the patient's comorbidity set is used as the drug weight.

[0031] Preferably, the adjustment of the patient's medication regimen and the provision of patient medication risk warning specifically include:

[0032] Obtain patient medication matching degree, patient polypharmacy risk index, and polypharmacy risk index and drug weight for each drug in patient medication data;

[0033] The drug replacement index is calculated based on the multiple medication risk index and drug weights;

[0034] When the patient medication matching degree is less than the preset patient medication matching threshold, a patient medication matching risk warning is issued and drugs with a matching degree of 0 in the patient's medication plan are deleted;

[0035] When the patient's medication matching degree is greater than or equal to the preset patient medication matching threshold and the patient's multiple medication risk index is greater than the preset patient multiple medication risk threshold, a patient multiple medication risk warning is issued and drugs in the patient's medication plan whose drug replacement index is greater than the preset drug replacement threshold are deleted.

[0036] Preferably, the calculation of the drug replacement index based on the multiple medication risk index and drug weights specifically includes:

[0037] The list of risks associated with multiple medications is obtained by sorting them in ascending order according to the risk index of multiple medications.

[0038] The ratio of the number of times a drug appears in the multidrug use risk list to the number of drugs in the multidrug use risk list is used to obtain the multidrug use risk weight corresponding to the drug.

[0039] The risk weight of multiple medication is added to the drug weight to obtain the sum of drug weights, and the ratio of the risk weight of multiple medication to the sum of drug weights is used as the drug replacement index.

[0040] Secondly, this application provides a geriatric oncology care assessment system based on big data analysis, including:

[0041] The data acquisition module is used to acquire patient medication data and patient comorbidity data;

[0042] The first assessment module is used to assess the degree of matching between patient medication and patient comorbidity data.

[0043] The second assessment module is used to assess the risk of multiple medication use in patients by combining the degree of patient medication matching with patient medication data.

[0044] The third assessment module is used to assess the risk of comorbidities in patients by combining the cumulative disease score table with the patient's comorbidity data.

[0045] The drug weighting module is used to assess the risk of patient comorbidities and assign drug weights based on the patient comorbidity risk list.

[0046] The risk warning module is used to adjust the patient's medication regimen and provide risk warnings based on the comprehensive risk assessment results of multiple medication use and the drug weighting allocation results.

[0047] Thirdly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a big data analysis-based assessment method for geriatric cancer care by calling the computer program stored in the memory.

[0048] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for assessing geriatric cancer care based on big data analysis.

[0049] Compared with the prior art, this application has the following advantages and beneficial effects:

[0050] This application assesses the degree of patient medication matching by analyzing patient medication data and comorbidity data. It quantifies the risk of multiple medication use and comorbidity by combining the assessment results and assigns drug weights based on the risk of comorbidity. Then, it adjusts the patient's medication plan in a targeted manner by combining the assessment results of the risk of multiple medication use and the drug weight allocation results, which effectively improves the accuracy and timeliness of nursing intervention. Attached Figure Description

[0051] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0052] Figure 1 This is a flowchart illustrating the elderly oncology nursing assessment method based on big data analysis provided in the embodiments of this application;

[0053] Figure 2 This is a schematic diagram of the structure of the elderly oncology nursing assessment system based on big data analysis provided in the embodiments of this application;

[0054] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0055] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0056] Please see Figure 1 , Figure 1 This is a flowchart illustrating the elderly oncology nursing assessment method based on big data analysis provided in this application embodiment, which specifically includes the following steps:

[0057] S110: Obtain patient medication data and patient comorbidity data.

[0058] S120: Assess the degree of patient medication matching through patient medication data and patient comorbidity data;

[0059] By analyzing the matching between patient comorbidity data and the indications and contraindications of the medications used by the patient, potential risks in patient medication can be accurately identified, avoiding situations where medications are not suitable for the patient's actual condition or where drug contraindications are overlooked. The degree of medication matching can be assessed, specifically including:

[0060] Obtain patient medication data and patient comorbidity data;

[0061] We construct drug indication and contraindication sets using patient medication data, and construct patient comorbidity sets using patient comorbidity data.

[0062] If the patient's set of comorbidities and the drug's set of contraindications have no overlap, but the patient's set of comorbidities and the drug's set of indications overlap, then the drug's drug suitability is 1; otherwise, the drug suitability is 0. The formula for calculating the drug suitability is as follows:

[0063]

[0064] Among them, S c For the patient's set of comorbidities, S′ d,i Let S be the set of contraindications for the i-th drug. d,i Let i be the set of indications for the i-th drug. M is an empty set. i Let be the drug matching degree for the i-th drug;

[0065] The patient medication matching score is defined as the ratio of the sum of the medication matching scores of all drugs in the patient medication data to the number of drugs. This score is used to quantitatively assess the degree of patient medication matching. The formula for calculating the patient medication matching score is as follows:

[0066]

[0067] Among them, M i Let M be the drug matching degree for the i-th drug, N be the number of drugs in the patient's medication data, and M be the patient's drug matching degree.

[0068] S130: Assess the risk of multiple medication use in patients by combining patient medication matching with patient medication data;

[0069] In cases of polypharmacy, overlapping indications between drugs can lead to duplicated effects, excessive dosage, or potential adverse reactions. Calculating a polypharmacy risk index quantifies the degree of overlap in drug indications, accurately identifying the risks associated with polypharmacy, and helping nurses quickly identify potential drug conflicts or redundancies in medication regimens, optimize drug combinations, and reduce overuse or inappropriate use of drugs. The risk of polypharmacy is assessed by combining patient medication matching with patient medication data, specifically including:

[0070] Obtain the patient medication matching degree. When the patient medication matching degree is greater than or equal to the preset patient medication matching threshold, obtain the drug's indication set and calculate the number of intersection elements of the indication sets of any two drugs in the patient medication data through the indication set.

[0071] The ratio of the sum of the number of elements in the intersection of the drug's indications to the total number of elements in the drug's indications set is used as the drug's multiple drug use risk index. The formula for calculating the drug's multiple drug use risk index is as follows:

[0072]

[0073] Among them, S d,i Let S be the set of indications for the i-th drug. d,j Let C(S) be the set of indications for the j-th drug. d,i ∩S d,j ) to calculate the set of indications S d,i With indications set S d,j Number of elements in the intersection, C(S) d,i R is used to calculate the number of elements in the indication set for the i-th drug. i Let be the risk index for multiple drug use of the i-th drug;

[0074] The sum of the multidrug use risk indices for all drugs in the patient's medication data is used as the patient's multidrug use risk index. This index is used to quantitatively assess the risk of multidrug use in patients. The formula for calculating the patient's multidrug use risk index is as follows:

[0075]

[0076] Among them, R i Let R be the risk index for multiple drug use of the i-th drug, N be the number of drugs in the patient's medication data, and R be the risk index for multiple drug use of the patient.

[0077] S140: Assess the risk of comorbidities in patients by combining the cumulative disease score sheet with patient comorbidity data;

[0078] Quantitatively assessing the severity of comorbidities using a cumulative disease scoring scale allows for a comprehensive understanding of the impact of diseases in different organ systems on a patient's health. Furthermore, by linking drug indications to a patient's comorbidities and assigning weights to drugs based on comorbidity risk, key medications with significant impacts on patient health can be effectively identified. This provides a scientific basis for optimizing drug selection and assessing patient comorbidity risk, specifically including:

[0079] Patient comorbidity data were acquired and the severity of diseases in each organ system was assessed based on the MCIRS-G (Modified Cumulative Illness Rating Scale for Geriatrics) disease cumulative scoring scale, resulting in organ system disease scores. Each organ system disease was indicated by a scoring system of grades 1, 2, 3, 4, and 5, with the following correspondences: Grade 1: No damage (0 points); Grade 2: Mild damage, but does not interfere with normal activities, requires no treatment, and has a good prognosis (1 point); Grade 3: Moderate damage, interferes with normal activities, requires treatment, and has a relatively good prognosis (2 points); Grade 4: Severe damage, requires immediate treatment, and has a poor prognosis (3 points); Grade 5: Fatal damage, requires emergency treatment, has an extremely poor prognosis, and may lead to organ failure (4 points). When multiple diseases occur in the same organ system, the score of the most severe disease is used as the organ system disease score.

[0080] The patient comorbidity risk index is the ratio of the sum of the organ system disease scores corresponding to all organ systems involved in the comorbidity data to the sum of the maximum organ system disease scores corresponding to all organ systems. The patient comorbidity risk index is used to quantitatively assess the risk level corresponding to different comorbidities.

[0081] S150: Assess the patient's comorbidity risk and assign drug weights based on the patient's comorbidity risk list;

[0082] Drug weights are assigned based on a patient's comorbidity risk list, specifically including:

[0083] Obtain the patient comorbidity risk index corresponding to each comorbidity in the patient comorbidity data;

[0084] The patient comorbidity risk list is obtained by sorting patients in ascending order according to their comorbidity risk index. When patients with different comorbidities have the same comorbidity risk index, they will have the same rank in the patient comorbidity risk list.

[0085] The ratio of the number of times a comorbidity appears in the patient's comorbidity risk list to the number of comorbidities in the patient's comorbidity risk list is used to obtain the patient's comorbidity risk weight corresponding to the comorbidity.

[0086] The sum of the risk weights of the patient's comorbidities corresponding to the intersection elements of the drug's indication set and the patient's comorbidity set is used as the drug weight.

[0087] S160: Adjust patient medication regimens and provide early warnings of medication risks by comprehensively considering the results of patient polypharmacy risk assessment and drug weighting allocation;

[0088] Multidrug use risk assessment results can identify potential indication overlap or adverse interactions between drugs, while drug weighting prioritizes the rational use of drugs related to high-risk comorbidities. Through comprehensive quantitative analysis of multidrug use risks and drug weights, unnecessary drug overlap or redundancy can be effectively avoided when optimizing medication regimens, while ensuring the effectiveness of key drugs, reducing the risk of adverse events caused by multidrug use, adjusting patient medication regimens, and providing early warnings of medication risks. Specifically, this includes:

[0089] Obtain patient medication matching degree, patient polypharmacy risk index, and polypharmacy risk index and drug weight for each drug in patient medication data;

[0090] The drug replacement index is calculated based on the multiple medication risk index and drug weights;

[0091] When the patient medication matching degree is less than the preset patient medication matching threshold, a patient medication matching risk warning is issued and drugs with a matching degree of 0 in the patient's medication plan are deleted;

[0092] When the patient's medication matching degree is greater than or equal to the preset patient medication matching threshold and the patient's multiple medication risk index is greater than the preset patient multiple medication risk threshold, a patient multiple medication risk warning is issued and drugs in the patient's medication plan whose drug replacement index is greater than the preset drug replacement threshold are deleted.

[0093] The drug replacement index is calculated based on the multiple medication risk index and drug weights, specifically including:

[0094] The list of risks associated with multiple medications is obtained by sorting them in ascending order according to the risk index of multiple medications.

[0095] The ratio of the number of times a drug appears in the multidrug use risk list to the number of drugs in the multidrug use risk list is used to obtain the multidrug use risk weight corresponding to the drug.

[0096] The drug replacement index is calculated by adding the risk weight of multiple medication to the drug weight, and then using the ratio of the risk weight of multiple medication to the sum of the drug weights. The formula for calculating the drug replacement index is as follows:

[0097]

[0098] Among them, R i W represents the risk index of multiple drug use for the i-th drug. i Let I be the drug weight corresponding to the i-th drug. i Let be the drug replacement index for the i-th drug.

[0099] It should be noted that the setting parameters in this application embodiment, such as the preset patient medication matching threshold, the preset patient multiple medication risk threshold, and the preset drug replacement threshold, are determined as follows: a dataset is constructed by acquiring patient medication data and patient comorbidity data, and the patient medication matching degree, the patient multiple medication risk index, and the drug replacement index are substituted into the dataset to calculate the patient medication matching degree, the patient multiple medication risk index, and the drug replacement index. The expert's judgment results on the patient medication matching degree, the patient multiple medication risk, and whether the drug needs to be replaced are obtained. The patient medication matching degree, the patient multiple medication risk index, the drug replacement index, and the judgment results are imported into the fitting software, and the preset patient medication matching threshold, the preset patient multiple medication risk threshold, and the preset drug replacement threshold that meet the maximum judgment accuracy are output.

[0100] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of an assessment system for geriatric oncology care based on big data analysis provided in an embodiment of this application. This embodiment provides an assessment system for geriatric oncology care based on big data analysis, including:

[0101] Data acquisition module 210 is used to acquire patient medication data and patient comorbidity data;

[0102] The first assessment module 220 is used to assess the degree of patient medication matching through patient medication data and patient comorbidity data;

[0103] The second assessment module 230 is used to assess the risk of multiple medication use in patients by combining the degree of patient medication matching with patient medication data.

[0104] The third assessment module 240 is used to assess the patient's risk of comorbidities by combining the disease cumulative score table with the patient's comorbidity data.

[0105] The drug weighting module 250 is used to calculate and assign drug weights based on the patient's comorbidity risk list.

[0106] The risk warning module 260 is used to adjust the patient's medication regimen and provide risk warnings based on the comprehensive risk assessment results of multiple medication use and the drug weight allocation results.

[0107] In this embodiment of the application, the first assessment module 220 is used to assess the degree of patient medication matching through patient medication data and patient comorbidity data. The assessment of the degree of patient medication matching specifically includes:

[0108] Obtain patient medication data and patient comorbidity data;

[0109] We construct drug indication and contraindication sets using patient medication data, and construct patient comorbidity sets using patient comorbidity data.

[0110] If the patient's set of comorbidities and the drug's set of contraindications do not overlap, but the patient's set of comorbidities and the drug's set of indications overlap, then the drug's drug use matching degree is 1; otherwise, the drug's drug use matching degree is 0.

[0111] The ratio of the sum of the drug matching scores of all drugs in the patient's medication data to the number of drugs is used as the patient's drug matching score. The patient's drug matching score is used to quantitatively assess the degree of matching between patients' medication use and medication.

[0112] In this embodiment, the second assessment module 230 is used to assess the risk of multiple medication use in patients by combining patient medication matching degree with patient medication data. Specifically, this assessment includes:

[0113] Obtain the patient medication matching degree. When the patient medication matching degree is greater than or equal to the preset patient medication matching threshold, obtain the drug's indication set and calculate the number of intersection elements of the indication sets of any two drugs in the patient medication data through the indication set.

[0114] The ratio of the sum of the number of elements in the intersection of the drug's indications set to the number of elements in the drug's indications set is used as the drug's multiple drug use risk index.

[0115] The sum of the multiple drug risk indices of all drugs in the patient's medication data is used as the patient's multiple drug risk index, which is used to quantitatively assess the risk of multiple drug use in patients.

[0116] In this embodiment, the third assessment module 240 is used to assess the patient's comorbidity risk by combining a cumulative disease score table with the patient's comorbidity data. The assessment of the patient's comorbidity risk specifically includes:

[0117] Acquire patient comorbidity data and assess the severity of disease in each organ system based on the MCIRS-G cumulative disease score table and patient comorbidity data to obtain organ system disease scores;

[0118] The patient comorbidity risk index is the ratio of the sum of the organ system disease scores corresponding to all organ systems involved in the comorbidity data to the sum of the maximum organ system disease scores corresponding to all organ systems. The patient comorbidity risk index is used to quantitatively assess the risk level corresponding to different comorbidities.

[0119] In this embodiment, the drug weight allocation module 250 is used to calculate and allocate drug weights based on the patient's comorbidity risk list. Specifically, the allocation of drug weights based on the patient's comorbidity risk list includes:

[0120] Obtain the patient comorbidity risk index corresponding to each comorbidity in the patient comorbidity data;

[0121] The patients' comorbidity risk list is obtained by sorting them in ascending order according to their comorbidity risk index.

[0122] The ratio of the number of times a comorbidity appears in the patient's comorbidity risk list to the number of comorbidities in the patient's comorbidity risk list is used to obtain the patient's comorbidity risk weight corresponding to the comorbidity.

[0123] The sum of the risk weights of the patient's comorbidities corresponding to the intersection elements of the drug's indication set and the patient's comorbidity set is used as the drug weight.

[0124] In this embodiment, the risk warning module 260 is used to adjust the patient's medication regimen and provide a risk warning based on a comprehensive assessment of the patient's multiple medication risks and the drug weighting allocation results.

[0125] Adjusting patient medication regimens and providing medication risk warnings, specifically including:

[0126] Obtain patient medication matching degree, patient polypharmacy risk index, and polypharmacy risk index and drug weight for each drug in patient medication data;

[0127] The drug replacement index is calculated based on the multiple medication risk index and drug weights;

[0128] When the patient medication matching degree is less than the preset patient medication matching threshold, a patient medication matching risk warning is issued and drugs with a matching degree of 0 in the patient's medication plan are deleted;

[0129] When the patient's medication matching degree is greater than or equal to the preset patient medication matching threshold and the patient's multiple medication risk index is greater than the preset patient multiple medication risk threshold, a patient multiple medication risk warning is issued and drugs in the patient's medication plan whose drug replacement index is greater than the preset drug replacement threshold are deleted.

[0130] The drug replacement index is calculated based on the multiple medication risk index and drug weights, specifically including:

[0131] The list of risks associated with multiple medications is obtained by sorting them in ascending order according to the risk index of multiple medications.

[0132] The ratio of the number of times a drug appears in the multidrug use risk list to the number of drugs in the multidrug use risk list is used to obtain the multidrug use risk weight corresponding to the drug.

[0133] The risk weight of multiple medication is added to the drug weight to obtain the sum of drug weights, and the ratio of the risk weight of multiple medication to the sum of drug weights is used as the drug replacement index.

[0134] The steps for implementing the corresponding functions of each parameter and unit module in the big data analysis-based geriatric oncology nursing assessment system of this application can be referred to the parameters and steps in the embodiments of the big data analysis-based geriatric oncology nursing assessment method above, and will not be repeated here.

[0135] Please refer to Figure 3 The present invention also provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores an elderly tumor care assessment method based on big data analysis, which can be loaded and executed by the processor 320 as provided in the above embodiments.

[0136] The memory 310 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the geriatric cancer care assessment method based on big data analysis provided in the above embodiments, etc. The data storage area may store data involved in the geriatric cancer care assessment method based on big data analysis provided in the above embodiments, etc.

[0137] Processor 320 may include one or more processing cores. Processor 320 executes instructions, programs, code sets, or instruction sets stored in memory 310, and calls data stored in memory 310 to perform various functions and process data as described in this application. Processor 320 may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 320 may also be other types, and this application embodiment does not specifically limit the specific devices used.

[0138] The communication bus 330 may include a path for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0139] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a big data analysis-based geriatric tumor care assessment method.

[0140] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0141] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0142] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for assessing elderly cancer care based on big data analysis, characterized in that, Includes the following steps: Obtain patient medication data and patient comorbidity data; Assess the degree of patient medication fit using patient medication data and patient comorbidity data; The risk of multiple medication use in patients is assessed by combining patient medication matching with patient medication data. The risk of comorbidities in patients is assessed by combining a cumulative disease score sheet with data on comorbidities. Patients are ranked according to their comorbidity risk and drug weights are assigned based on the list of patients with comorbidity risk. Adjust patient medication regimens and provide early warnings of medication risks by comprehensively considering the results of patient polypharmacy risk assessment and drug weighting allocation; The assessment of patient medication fit specifically includes: Obtain patient medication data and patient comorbidity data; We construct drug indication and contraindication sets using patient medication data, and construct patient comorbidity sets using patient comorbidity data. If the patient's set of comorbidities and the drug's set of contraindications do not overlap, but the patient's set of comorbidities and the drug's set of indications overlap, then the drug's drug use matching degree is 1; otherwise, the drug's drug use matching degree is 0. The ratio of the sum of the drug matching scores of all drugs in the patient's medication data to the number of drugs is used as the patient's drug matching score. The patient's drug matching score is used to quantitatively assess the degree of matching between patients' medications. The assessment of the risk of polypharmacy by combining patient medication matching with patient medication data specifically includes: Obtain the patient medication matching degree. When the patient medication matching degree is greater than or equal to the preset patient medication matching threshold, obtain the drug's indication set and calculate the number of intersection elements of the indication sets of any two drugs in the patient medication data through the indication set. The ratio of the sum of the number of elements in the intersection of the drug's indications set to the number of elements in the drug's indications set is used as the drug's multiple drug use risk index. The sum of the multiple drug risk indices of all drugs in the patient's medication data is used as the patient's multiple drug risk index, which is used to quantitatively assess the risk of multiple drug use in patients.

2. The method for assessing elderly cancer care based on big data analysis according to claim 1, characterized in that, The assessment of a patient's risk of comorbidities specifically includes: Acquire patient comorbidity data and assess the severity of disease in each organ system based on the MCIRS-G cumulative disease score table and patient comorbidity data to obtain organ system disease scores; The patient comorbidity risk index is the ratio of the sum of the organ system disease scores corresponding to all organ systems involved in the comorbidity data to the sum of the maximum organ system disease scores corresponding to all organ systems. The patient comorbidity risk index is used to quantitatively assess the risk level corresponding to different comorbidities.

3. The method for assessing elderly cancer care based on big data analysis according to claim 1, characterized in that, The allocation of drug weights based on the patient's comorbidity risk list specifically includes: Obtain the patient comorbidity risk index corresponding to each comorbidity in the patient comorbidity data; The patients' comorbidity risk list is obtained by sorting them in ascending order according to their comorbidity risk index. The ratio of the number of times a comorbidity appears in the patient's comorbidity risk list to the number of comorbidities in the patient's comorbidity risk list is used to obtain the patient's comorbidity risk weight corresponding to the comorbidity. The sum of the risk weights of the patient's comorbidities corresponding to the intersection elements of the drug's indication set and the patient's comorbidity set is used as the drug weight.

4. The method for assessing elderly cancer care based on big data analysis according to claim 1, characterized in that, The adjustment of patient medication regimens and the provision of patient medication risk warnings specifically include: Obtain patient medication matching degree, patient polypharmacy risk index, and polypharmacy risk index and drug weight for each drug in patient medication data; The drug replacement index is calculated based on the multiple medication risk index and drug weights; When the patient medication matching degree is less than the preset patient medication matching threshold, a patient medication matching risk warning is issued and drugs with a matching degree of 0 in the patient's medication plan are deleted; When the patient's medication matching degree is greater than or equal to the preset patient medication matching threshold and the patient's multiple medication risk index is greater than the preset patient multiple medication risk threshold, a patient multiple medication risk warning is issued and drugs in the patient's medication plan whose drug replacement index is greater than the preset drug replacement threshold are deleted.

5. The method for assessing elderly cancer care based on big data analysis according to claim 4, characterized in that, The calculation of the drug replacement index based on the multiple medication risk index and drug weights specifically includes: The list of risks associated with multiple medications is obtained by sorting them in ascending order according to the risk index of multiple medications. The ratio of the number of times a drug appears in the multidrug use risk list to the number of drugs in the multidrug use risk list is used to obtain the multidrug use risk weight corresponding to the drug. The risk weight of multiple medication is added to the drug weight to obtain the sum of drug weights, and the ratio of the risk weight of multiple medication to the sum of drug weights is used as the drug replacement index.

6. A big data-based assessment system for geriatric cancer care, applied to any one of the big data-based assessment methods for geriatric cancer care according to claims 1-5, characterized in that, The system includes: The data acquisition module is used to acquire patient medication data and patient comorbidity data; The first assessment module is used to assess the degree of matching between patient medication and patient comorbidity data. The second assessment module is used to assess the risk of multiple medication use in patients by combining the degree of patient medication matching with patient medication data. The third assessment module is used to assess the risk of comorbidities in patients by combining the cumulative disease score table with the patient's comorbidity data. The drug weighting module is used to assess the risk of patient comorbidities and assign drug weights based on the patient comorbidity risk list. The risk warning module is used to adjust the patient's medication regimen and provide risk warnings based on the combined risk assessment results of multiple medication use and the drug weighting allocation results.

7. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the geriatric tumor care assessment method based on big data analysis as described in any one of claims 1-5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the geriatric oncology care assessment method based on big data analysis as described in any one of claims 1-5.

Images