WO2021151295A1 - Procédé, appareil, dispositif informatique et support de stockage pour déterminer un plan de traitement de patient - Google Patents

Procédé, appareil, dispositif informatique et support de stockage pour déterminer un plan de traitement de patient Download PDF

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WO2021151295A1
WO2021151295A1 PCT/CN2020/118873 CN2020118873W WO2021151295A1 WO 2021151295 A1 WO2021151295 A1 WO 2021151295A1 CN 2020118873 W CN2020118873 W CN 2020118873W WO 2021151295 A1 WO2021151295 A1 WO 2021151295A1
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patient
treatment plan
target
preset
data
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PCT/CN2020/118873
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English (en)
Chinese (zh)
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徐卓扬
赵惟
左磊
孙行智
胡岗
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • This application relates to the field of digital medicine, and in particular to a method, device, computer equipment, and medium for determining a patient's treatment plan.
  • Deep reinforcement learning is one of the machine learning methods. It completes the learning from the environment state to the action mapping, selects the optimal strategy according to the maximum feedback value, selects the optimal action for the search strategy, and causes the change of the state to obtain the delayed feedback value and evaluate Function, iterative loop, until the learning condition is met, the learning is terminated.
  • a method for determining a patient's treatment plan including:
  • the target treatment plan of the target patient is obtained by analysis.
  • a device for determining a patient's treatment plan comprising:
  • the creation module is used to create a patient grouping model for processing time series data based on deep reinforcement learning DQN;
  • a training module configured to train the patient clustering model by using sample data marked with clustering results, so that the patient clustering model meets a preset training standard
  • the input module is used to input target patient data in a preset time period into a patient grouping model that meets the preset training standard, and obtain the target group to which the target patient belongs;
  • a determining module configured to determine the first treatment plan of the target patient based on the characteristics of the population in the target group
  • An extraction module for extracting contraindicated drugs of the target patient according to the target patient data, and selecting a second treatment plan containing the contraindicated drugs from the first treatment plan;
  • the analysis module is used to analyze and obtain the target treatment plan of the target patient according to the first treatment plan and the second treatment plan.
  • a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the following steps:
  • the target treatment plan of the target patient is obtained by analysis.
  • a computer device including a storage medium, a processor, and a computer program stored on the storage medium and running on the processor.
  • the processor executes the following steps when the program is executed :
  • the target treatment plan of the target patient is obtained by analysis.
  • FIG. 1 shows a schematic flowchart of a method for determining a patient's treatment plan provided by an embodiment of the present application
  • FIG. 2 shows a schematic flowchart of another method for determining a patient's treatment plan provided by an embodiment of the present application
  • FIG. 3 shows a network structure diagram of a patient grouping model provided by an embodiment of the present application
  • FIG. 4 shows a schematic structural diagram of a device for determining a patient's treatment plan provided by an embodiment of the present application
  • Fig. 5 shows a schematic structural diagram of another device for determining a patient treatment plan provided by an embodiment of the present application.
  • an embodiment of the present application provides a method for determining a patient's treatment plan, as shown in FIG. 1 , The method includes:
  • the purpose is to improve the traditional deep reinforcement learning DQN model, extend the model to a time series model, and add an Attention mechanism, and use the improved DQN model to process patients into groups so that they can be used to process time series Data, and can realize the interpretability of patient characteristics.
  • the grouping decision rules can be set in advance, and the group to which the sample data belongs can be determined based on the grouping decision rules, and then the grouping results can be marked in the corresponding sample data in a similarly labeled form for use
  • the patient clustering model is verified against the sample data output results, and then the training status of the patient clustering model is determined. If the output result of the patient clustering model is determined to have a small error with the labeling result, it can be determined that the patient clustering model conforms to Preset training standards.
  • the preset time period can be set according to actual application requirements.
  • the preset time period can be set to include the current time in the previous month, and the corresponding historical target patient data is a record recorded in the preset time period. Or multiple follow-up data about the target patient.
  • the single follow-up information cannot fully represent the patient's long-term follow-up status, which may easily lead to inaccurate analysis results. Therefore, in this embodiment, in addition to the patient follow-up data at the current moment as input, all historical patient follow-up data existing in a preset time period can also be used as input, and the output results of the follow-up data of each patient are integrated to determine The final relatively accurate target grouping result.
  • the Attention mechanism can also be used to explain the contribution, attention coefficient, contribution ratio, etc. of each feature at each time point to the clustering result.
  • a patient with a high similarity to the target patient population can be determined, so as to be based on the patient’s generated data.
  • the first treatment plan that can be selected by the target patient is screened out.
  • the contraindicated drugs of the target patient should be extracted first, so that the first treatment plan containing the corresponding contraindicated drugs should be screened out.
  • the second treatment plan so that the second treatment plan is not considered when the treatment plan recommendation is finally generated.
  • the first treatment plan and the second treatment plan analyze and obtain the target treatment plan of the target patient.
  • the second treatment plan will be excluded from the first treatment plan, and the eliminated first treatment plan will be determined as
  • the target treatment plan of the target patient in this embodiment, takes into account the drug contraindication factors, so as to ensure the safety of the patient's treatment.
  • the target patient data in the preset time period into the patient grouping model that meets the preset training standards, and then the target grouping result can be obtained, and then the first treatment plan of the target patient can be determined by using the characteristics of the population in the target group ;
  • the target patient’s contraindicated drugs can also be determined based on the target patient’s data, so that the second treatment plan containing the contraindicated drugs can be screened from the first treatment plan; finally, the first treatment plan and the second treatment plan can be used The treatment plan is analyzed to obtain the target treatment plan suitable for the target patient.
  • the digital processing of the patient's treatment plan can be realized, and the calculation process of the expected reward value Q can be extended to a time series structure, which can consider more information, and by integrating artificial intelligence and deep learning algorithms, The analysis result is more accurate.
  • the method includes:
  • step 201 of the embodiment may specifically include: splitting the deep reinforcement learning DQN corresponding to the last fully connected layer in the network structure into a first fully connected layer and a second recurrent neural network Layer, the third cyclic neural network layer; use the deep reinforcement learning DQN after changing the network structure to construct a patient grouping model, so that when the patient data containing multiple time points is input to the patient grouping model, the first fully connected layer outputs each time The point corresponds to the embedded value of the patient's state, the second recurrent neural network layer outputs the first degree of attention corresponding to the patient state at each time point, and the third recurrent neural network layer outputs the second degree of attention corresponding to the grouping result at each time point, and is based on The embedded value, the first degree of attention, and the second degree of attention are used to calculate the expected reward value of the patient data corresponding to each preset group.
  • the abstract features extracted by the convolutional layer are divided into three branches, that is, the last fully connected layer in the corresponding network structure of the deep reinforcement learning DQN is split into :
  • the first fully connected layer 1 is used to output the embedded value corresponding to the patient state at each time point
  • the second cyclic neural network layer 2 Is the state value function (value function), used to output the first degree of attention corresponding to the patient state at each time point
  • the third recurrent neural network layer 3 is the action advantage function (advantage function), used to output the clustering results corresponding to each time point
  • the second degree of attention is the abstract features extracted by the convolutional layer.
  • the sample data in order to monitor the training status of the patient clustering model when using the sample data to train the patient clustering model, it is necessary to mark the sample data to belong to the group in advance, which specifically includes: The sample data is grouped into groups, and the grouping result corresponding to each sample data is obtained; the sample data is marked based on the grouping result.
  • the preset grouping decision rules can be set according to actual needs.
  • the grouping decision rules can be set according to the patient's personal characteristic information and combined with the inspection index information for classification.
  • group division patients with high similarity in personal characteristic information and containing the same examination indicators and the same examination results can be divided into a group.
  • the sample data is time series data containing the current time point and a preset number of historical time points, and can include patient data information at the current time and historical time.
  • the patient data information can be personal identification information (such as name, gender, age, etc.) ), treatment plan information (drug combination, medication cycle, dosage, etc.), inspection index information (such as blood sugar, blood pressure, electrocardiogram and other inspection indicators and corresponding inspection results, etc.), etc.;
  • the expected reward value is calculated at the same time point After the first sum of the first degree of attention and the second degree of attention, and the product of the first sum and the embedded value, it is obtained by accumulating the product of the current time point and the historical time point.
  • the network structure diagram of the patient clustering model shown in Figure 3 if the current patient status (s 3 ) corresponding to the sample data is input to the patient clustering model plus the patient status at two historical time points (s 1 , s 2 ) .
  • the e(e 1 , e 2 , e 3 ) output at each time point of the first fully connected layer can be obtained, and the second recurrent neural network layer Output V (V 1 , V 2 , V 3 ) at each time point, A (A 1 , A 2 , A 3 ) at each time point output by the third loop neural network layer, and then use V in the same time step Add to A, then multiply it element-wise with e, and then accumulate the Q value (Q 3 ) of the current state.
  • V represents the degree of attention corresponding to the patient state at each time point
  • A represents the degree of attention corresponding to the patient state at each time point
  • e represents the embedded
  • h V1 ,h V2 ,h V3 LSTM-V(s 1 ,s 2 ,s 3 )
  • h A1 ,h A2 ,h A3 LSTM-A(s 1 ,s 2 ,s 3 )
  • a 1 ,A 2 ,A 3 (W A h A1 ,W A h A2 ,W A h A3 )
  • v 1 ,v 2 ,v 3 (W I s 1 ,W I s 2 ,W I s 3 )
  • s i, h vi, w v, h Ai, A i, v i, e i, Q 3 is a vector
  • V i is a scalar
  • W A, W I, W II matrix, O for corresponding elements are multiplied .
  • the interpretation method of the model decision can be: the contribution of each patient characteristic at each time point to the final Q value can be positively derived from all the input s i.
  • each sample data corresponds to a unique label group.
  • the expected reward value corresponding to each preset group will be obtained.
  • the first expected reward value is the expected reward value in the current patient state corresponding to the output of the marked group
  • the real expected reward value is the largest expected reward value in the next patient state + the actual reward (reward), which is further calculated , which is the actual expected reward value of the corresponding marked group.
  • the mean square error loss based on the first expected reward value and the real expected reward value to further determine whether the loss function has reached the convergence state.
  • the loss When the function reaches the convergence state, it can be determined that the patient grouping model meets the preset training standard.
  • the sample data is used to repeatedly train the patient grouping model, so that the patient grouping model meets the preset training standard.
  • the target patient information is time series data
  • all target patient information at the current time and historical time needs to be input into the patient grouping model to obtain the grouping results
  • the target patient information is not time series data
  • only the target patient information at the current time needs to be
  • the patient information is input into the patient clustering model, and the parameter value corresponding to the historical time point in the patient clustering model is set to 0 to obtain the clustering result.
  • step 206 of the embodiment may specifically include: extracting historical patient follow-up data and current patient follow-up data of the target patient within a preset time period; Patient follow-up data and current patient follow-up data are input into the patient grouping model that meets the preset training standards to obtain the expected reward value corresponding to each preset group; the preset group with the largest expected reward value is determined as the target patient corresponding The target group for.
  • step 207 of the embodiment may specifically include: screening the target group in the target group according to the target patient data and the similarity of the characteristics of the population corresponding to the target patient is greater than
  • the population characteristics include at least condition information and personal information
  • the plan is determined as the first treatment plan; or a preset treatment plan created according to the characteristics of the target group is obtained, and the preset treatment plan is determined as the first treatment plan.
  • the target group contains the data information of multiple sample patients.
  • the data information can also include information about the treatment effect.
  • Score information and treatment plan information such as medication combination, medication cycle, dosage, etc.; the first preset threshold and the second preset threshold are both data greater than 0 and less than or equal to 1, and the specific values can be set according to specific application scenarios , It should be noted that when the value set by the first preset threshold is closer to 1, it can indicate that the feature similarity between the first patient and the target patient selected is higher; when the value set by the second preset threshold is higher Close to 1, it can indicate that the first treatment plan selected, the better the treatment effect after patient feedback.
  • the target patient’s personal identity information, inspection index information, diagnosis result information and other multi-dimensional feature information can be extracted from the target patient’s information in advance.
  • the first patient whose matching degree with the feature information of the target patient is greater than the first preset threshold is selected from the group, and then the treatment plan whose score value corresponding to the treatment effect of the first patient is greater than the second preset threshold is extracted, and the treatment plan Determined as the first treatment plan.
  • the first patient whose feature similarity with the target patient's corresponding population is greater than the first preset threshold is selected in the target group according to the target patient data, including four first patients A, B, C, and D, among which the first patient A
  • the corresponding medication combination is a+c+d
  • the medication combination corresponding to the first patient B is a+c+e
  • the medication combination corresponding to the first patient C is a+b+c
  • the medication combination corresponding to the first patient D is a+c+d
  • the score values of the three plans regarding the treatment effect can be obtained, for example
  • the score value corresponding to the treatment plan a+c+d is 0.75
  • the score value corresponding to the treatment plan a+b+e is 0.91
  • the score value corresponding to the treatment plan a+b+c is 0.88.
  • the preset treatment plan corresponding to each target group may be determined in advance according to the characteristics of the population in the target group and the diagnosis result of the physician, for example, for the target group
  • the diagnosis result of the physician for example, for the target group
  • the commonly used treatment options include A and B
  • treatment options A and B can be directly determined as the preset treatment options corresponding to the target group.
  • treatment plans A and B can be determined as the first treatment plan corresponding to the target patient.
  • step 208 of the embodiment may specifically include: determining, according to the drug contraindication data, the target patient corresponding to the population type that is not suitable for taking the second treatment plan.
  • a contraindicated drug based on the drug allergy history in the target patient's data, determine the second contraindicated drug in which the target patient has an allergic reaction; determine the first treatment plan containing the first contraindicated drug and/or the second contraindicated drug as the second treatment plan .
  • the first contraindication drug of the target patient may correspond to the drug forbidden by the pregnant woman; when the target patient is a penicillin allergic population, penicillin drugs can be determined as the second contraindication drug of the target patient.
  • step 209 of the embodiment may specifically include: excluding the second treatment plan from the first treatment plan to obtain the target treatment plan.
  • an interpretable deep reinforcement learning model DQN network structure is proposed to create a patient clustering model for processing time series data, and then use sample data to train the patient clustering model to achieve the expected Set training standards. Then input the target patient data in the preset time period into the patient grouping model that meets the preset training standards, and then the target grouping result can be obtained, and then the first treatment plan of the target patient can be determined by using the characteristics of the population in the target group ; To enhance the safety of diagnosis, the target patient’s contraindicated drugs can also be determined based on the target patient’s data, so that the second treatment plan containing the contraindicated drugs can be screened from the first treatment plan; finally, the first treatment plan and the second treatment plan can be used The treatment plan is analyzed to obtain the target treatment plan suitable for the target patient.
  • the digital processing of the patient's treatment plan can be realized, and the calculation process of the expected reward value Q can be extended to a time series structure, which can consider more information, and by integrating artificial intelligence and deep learning algorithms, The analysis result is more accurate.
  • the Attention mechanism is added in the process of calculating the expected reward value, which can achieve a certain degree of interpretability.
  • an embodiment of the present application provides a device for determining a patient's treatment plan.
  • the device includes: a creation module 31, a training module 32, and an input Module 33, determination module 34, extraction module 35, analysis module 36.
  • the creation module 31 can be used to create a patient grouping model for processing time series data based on deep reinforcement learning DQN;
  • the training module 32 can be used to train the patient clustering model by using the sample data with marked clustering results, so that the patient clustering model meets the preset training standard;
  • the input module 33 can be used to input target patient data within a preset time period into a patient grouping model that meets the preset training standard, and obtain the target grouping result;
  • the determining module 34 can be used to input target patient data in a preset time period into a patient grouping model that meets the preset training standard, and obtain the target group to which the target patient belongs;
  • the extraction module 35 can be used to extract the contraindicated drugs of the target patient based on the target patient's data, and screen out the second treatment plan containing the contraindicated drugs from the first treatment plan;
  • the analysis module 36 can be used to analyze and obtain the target treatment plan of the target patient according to the first treatment plan and the second treatment plan.
  • the creation module 31 may specifically include: a splitting unit 311 and a construction unit 312;
  • the splitting unit 311 can be used to split the deep reinforcement learning DQN corresponding to the last fully connected layer in the network structure into a first fully connected layer, a second cyclic neural network layer, and a third cyclic neural network layer;
  • the construction unit 312 can be used to construct a patient grouping model by using the deep reinforcement learning DQN after changing the network structure, so that when the patient data containing multiple time points is input to the patient grouping model, the first fully connected layer outputs the corresponding patients at each time point
  • the embedded value of the state the second recurrent neural network layer outputs the first degree of attention corresponding to the patient state at each time point
  • the third recurrent neural network layer outputs the second degree of attention corresponding to the grouping result at each time point, and based on the embedded value,
  • the first degree of attention and the second degree of attention calculate the expected reward value of the patient data corresponding to each preset group.
  • the training module 32 may specifically include: a first input unit 321, a first extraction unit 322, a calculation unit 323, and a training unit 324;
  • the first input unit 321 can be used to input the sample data at the current time point and the historical time point into the patient grouping model to obtain a preset number of groups, and each sample data corresponds to the expected reward value of each group, the expected reward The value is obtained by accumulating the product of the current time point and the historical time point after calculating the first sum of the first degree of attention and the second degree of attention at the same time point, and the product of the first sum and the embedded value;
  • the first extraction unit 322 may be used to extract the label group corresponding to the sample data, and determine the first expected reward value corresponding to the output of the label group as the training output result of the patient grouping model;
  • the calculation unit 323 can be used to calculate the mean square error loss between the first expected reward value and the real expected reward value. If it is determined that the loss function reaches the convergence state based on the mean square error loss, it is determined that the patient grouping model meets the preset training standard;
  • the training unit 324 can be used to repeatedly train the patient clustering model by using the sample data if it is determined that the loss function has not reached the convergence state, so that the patient clustering model meets the preset training standard.
  • the input module 33 may specifically include: a second extraction unit 331, a second input unit 332, and a first determination unit 333;
  • the second extraction unit 331 can be used to extract historical patient follow-up data and current patient follow-up data of the target patient within a preset time period;
  • the second input unit 332 can be used to input historical patient follow-up data and current patient follow-up data into a patient grouping model that meets the preset training standards to obtain the expected reward value corresponding to each preset group;
  • the first determining unit 333 may be used to determine the preset group with the largest expected reward value as the target group corresponding to the target patient.
  • the determining module 34 may specifically include: a screening unit 341 and a second determining unit 342;
  • the screening unit 341 can be used to screen the first patients whose population characteristics similarity to the target patient is greater than a first preset threshold in the target group based on the target patient data, and the population characteristics include at least medical condition information and personal information;
  • the second determining unit 342 may be used to extract the treatment plan corresponding to the first patient and the score value of the treatment plan with respect to the treatment effect, and determine the treatment plan with the score value greater than the second preset threshold as the first treatment plan; or
  • the second determining unit 342 may also be used to obtain a preset treatment plan created according to the characteristics of the target group, and determine the preset treatment plan as the first treatment plan.
  • the extraction module 35 may specifically include: a third determination unit 351;
  • the third determining unit 351 can be used to determine the first contraindicated drug that the target patient corresponds to the population type that is not suitable for taking according to the drug contraindicated data;
  • the third determining unit 351 can also be used to determine the second contraindicated drug for which the target patient has an allergic reaction based on the drug allergy history in the target patient's data;
  • the third determining unit 351 may also be used to determine the first treatment plan including the first contraindication drug and/or the second contraindication drug as the second treatment plan.
  • the analysis module 36 may specifically include: a rejection unit 361;
  • the rejection unit 361 can be used to remove the second treatment plan from the first treatment plan to obtain the target treatment plan.
  • an embodiment of the present application also provides a computer-readable storage medium.
  • the computer-readable storage medium may include non-volatile and/or volatile memory.
  • a computer program is stored thereon, and when the program is executed by the processor, the method for determining the patient's treatment plan as shown in FIG. 1 and FIG. 2 is realized.
  • the technical solution of the present application can be embodied in the form of a software product.
  • the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk, etc.), including several
  • the instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute the methods in each implementation scenario of the present application.
  • an embodiment of the present application also provides a computer device, which may be a personal computer, Servers, network devices, etc., the physical device includes a storage medium and a processor; the storage medium is used to store a computer program, and may include non-volatile and/or volatile memory; the processor is used to execute the computer program to achieve the above
  • a computer device which may be a personal computer, Servers, network devices, etc.
  • the physical device includes a storage medium and a processor; the storage medium is used to store a computer program, and may include non-volatile and/or volatile memory; the processor is used to execute the computer program to achieve the above
  • the method for determining the patient's treatment plan is shown in Figure 1 and Figure 2.
  • the computer device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, and so on.
  • the user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, and the like.
  • the optional network interface can include standard wired interface, wireless interface (such as Bluetooth interface, WI-FI interface) and so on.
  • the computer device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or arrange different components.
  • the non-volatile readable storage medium may also include an operating system and a network communication module.
  • the operating system is a program that analyzes the hardware and software resources of the physical device for the semantic similarity of text, and supports the operation of information processing programs and other software and/or programs.
  • the network communication module is used to implement communication between various components in the non-volatile readable storage medium, and communication with other hardware and software in the physical device.
  • the target patient data in the preset time period into the patient grouping model that meets the preset training standards, and then the target grouping result can be obtained, and then the first treatment plan of the target patient can be determined by using the characteristics of the population in the target group ;
  • the target patient’s contraindicated drugs can also be determined based on the target patient’s data, so that the second treatment plan containing the contraindicated drugs can be screened from the first treatment plan; finally, the first treatment plan and the second treatment plan can be used The treatment plan is analyzed to obtain the target treatment plan suitable for the target patient.
  • the digital processing of the patient's treatment plan can be realized, and the calculation process of the expected reward value Q can be extended to a time series structure, which can consider more information, and by integrating artificial intelligence and deep learning algorithms, The analysis result is more accurate.
  • the Attention mechanism is added in the process of calculating the expected reward value, which can achieve a certain degree of interpretability.

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Abstract

L'invention concerne un procédé, un appareil et un dispositif informatique pour déterminer un plan de traitement de patient, qui peuvent résoudre le problème d'un résultat généré insuffisamment précis lors de la génération d'un plan de traitement de patient en ligne. Le procédé comprend les étapes consistant à créer un modèle de regroupement de patients utilisé pour le traitement de données de séries chronologiques (101), sur la base d'un apprentissage Q profond (DQN) ; à utiliser des données d'échantillon marquées avec le résultat de regroupement pour entraîner un modèle de regroupement de patients de manière à amener le modèle de regroupement de patients à satisfaire une norme d'apprentissage prédéfinie (102) ; à entrer des données de patient cible dans une période de temps prédéfinie dans le modèle de regroupement de patients qui satisfait la norme d'apprentissage prédéfinie, afin d'obtenir un groupe cible auquel appartient le patient cible (103) ; à déterminer un premier plan de traitement du patient cible sur la base des caractéristiques de la population dans le groupe cible (104) ; à extraire des médicaments contre-indiqués du patient cible selon les données de patient cible, et, à partir du premier plan de traitement, à filtrer un second plan de traitement contenant les médicaments contre-indiqués (105) ; selon le premier plan de traitement et le second plan de traitement, à analyser et à obtenir un plan de traitement cible pour le patient cible (106).
PCT/CN2020/118873 2020-06-29 2020-09-29 Procédé, appareil, dispositif informatique et support de stockage pour déterminer un plan de traitement de patient WO2021151295A1 (fr)

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