CN116259396A - Treatment expense prediction method, system, equipment and storage medium based on machine learning - Google Patents
Treatment expense prediction method, system, equipment and storage medium based on machine learning Download PDFInfo
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- CN116259396A CN116259396A CN202210731054.XA CN202210731054A CN116259396A CN 116259396 A CN116259396 A CN 116259396A CN 202210731054 A CN202210731054 A CN 202210731054A CN 116259396 A CN116259396 A CN 116259396A
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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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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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
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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
- G16H70/00—ICT specially adapted for the handling or processing of medical references
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- 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
Abstract
The invention relates to a treatment expense prediction method, a treatment expense prediction system, a treatment expense prediction device and a treatment expense prediction storage medium based on machine learning. Comprising the following steps: acquiring basic information data and diagnostic information data of a patient; inputting the basic information data and the diagnosis information data into a disease diagnosis model trained in advance to obtain disease types of patients; according to the disease type, invoking a data subset of different treatment schemes of a hospital medical record database under the disease type; comparing the basic information data and the diagnosis data information of the patient with the basic information data and the diagnosis information data of medical records in the data subsets, and matching to obtain the medical records closest to the patient in the data subsets of different treatment schemes, so as to obtain the treatment scheme and the treatment cost of the medical records; outputting different treatment schemes and treatment cost of each treatment scheme of the patient according to the treatment scheme and treatment cost of the case. The method has important application value in improving doctor-patient relationship.
Description
Technical Field
The present invention relates to the field of intelligent medical technology, and more particularly, to a treatment cost prediction method, system, device and storage medium based on machine learning.
Background
At present, when patients and families face treatment scheme decisions in clinical scenes, due to lack of relevant expertise, doctors can only passively rely on suggestions, and the advantages and disadvantages of each clinical treatment path are difficult to fully know in a short time, so that decisions which are more beneficial to themselves are made. In addition, existing clinical treatment protocols have high demands on the clinical experience of the physician, present significant challenges to the physician with inadequate clinical experience, and the clinician may have difficulty in taking care of the patient's own needs.
Disclosure of Invention
In order to solve the problems, the application establishes a treatment expense prediction method, a treatment expense prediction system, a treatment expense prediction device and a treatment expense prediction storage medium based on machine learning.
The application aims to provide a treatment expense prediction method based on machine learning, which comprises the following specific steps:
acquiring basic information data and diagnostic information data of a patient;
inputting the basic information data and the diagnosis information data into a disease diagnosis model trained in advance to obtain disease types of patients;
according to the disease types, the data subsets of different treatment schemes of the hospital medical record database under the disease types are called, and the data subsets of the different treatment schemes of the hospital medical record database under the disease types are obtained by carrying out cluster analysis on the hospital medical record database;
comparing the basic information data and the diagnosis data information of the patient with the basic information data and the diagnosis information data of medical records in the data subsets, and matching to obtain the medical records closest to the patient in the data subsets of different treatment schemes, so as to obtain the treatment scheme and the treatment cost of the medical records;
outputting different treatment schemes and treatment cost of each treatment scheme of the patient according to the treatment scheme and treatment cost of the case.
Further, the training process of the pre-trained disease diagnosis model is as follows: basic information data and diagnosis information data of a patient are obtained as a training set, a machine learning model is trained, the predicted disease type is compared with the actual disease type, a loss value is generated, the back propagation is carried out, and the disease diagnosis model is optimized, so that the pre-trained disease diagnosis model is obtained;
optionally, the basic information data includes information data such as age, sex, time of admission, time of discharge, and the diagnostic information data includes diagnostic information data such as admission diagnosis, inspection data, nursing diagnosis, legacy data, discharge diagnosis, and the like.
Further, the algorithm adopted by the pre-trained disease diagnosis model is selected from a plurality of machine learning algorithms, and the machine learning algorithm is selected from one or more of the following machine learning algorithms: support vector machine, random forest, XGBoost, lightGBM.
Further, the constructing process of the data subset of the different treatment schemes of the hospital medical record database under the disease category comprises the following steps: obtaining all medical record sets of a hospital medical record database under the disease types, and performing cluster analysis based on the differences of treatment schemes to obtain data subsets of different treatment schemes under the disease types;
preferably, the clustering analysis can adopt FCM fuzzy clustering and K-Means clustering; optionally, the medical record database of the hospital is a dynamically updated database.
Further, the method further comprises the steps of correcting the treatment scheme and the treatment cost of the obtained medical record to obtain the treatment scheme and the treatment cost of the corrected medical record, outputting different treatment schemes and the treatment cost of each treatment scheme of the patient according to the treatment scheme and the treatment cost of the corrected medical record, wherein the correction of the treatment scheme and the treatment cost of the medical record comprises the correction of the treatment scheme and/or the correction of the treatment cost of the medical record, and the correction of the treatment cost is to restore the medical insurance reduced cost in the medical record to the full self-cost to obtain the treatment cost of the corrected medical record; the correction of the treatment scheme is to match and correct the medicine and the price of the medicine in the medical record and the medicine and the price of the medicine in the hospital medicine library, so as to obtain the treatment scheme of the corrected medical record.
Further, the correction of the treatment scheme is to match and correct the medicine and the price of the medicine in the medical record and the medicine and the price of the medicine in the hospital to obtain the corrected medical record treatment scheme, and when the medicine in the treatment scheme is matched with the medicine in the hospital, the price of the medicine is updated to obtain the treatment scheme and the treatment cost of the medical record after the price correction; when the medicines in the treatment scheme are inconsistent with the medicines in the hospital medicine library, the medicines are replaced by the similar medicines, so that the treatment scheme and the treatment cost of medical records after the medicines and the price are corrected are obtained;
alternatively, the replacement of the same type of drug takes into account firstly the same generic name of drug and secondly the same function of drug.
Further, the method steps further include selecting a patient settlement type before outputting the different treatment plan and the treatment cost of each treatment plan of the patient according to the treatment plan and the treatment cost of the case, and outputting the treatment cost of the different treatment plans and each treatment plan under the patient settlement type according to the patient settlement type, the treatment plan and the treatment cost of the case;
optionally, the settlement type includes a general patient, a medical insurance patient, or a public fee medical patient.
It is an object of the present application to provide a machine learning based treatment cost prediction system, the prediction system comprising:
an acquisition unit for acquiring basic information data and diagnostic information data of a patient;
the diagnosis unit is used for inputting the basic information data and the diagnosis information data into a disease diagnosis model trained in advance to obtain the disease type of a patient;
the calling unit is used for calling the data subsets of different treatment schemes of the hospital medical record database under the disease type according to the disease type, wherein the data subsets of the different treatment schemes of the hospital medical record database under the disease type are obtained by carrying out cluster analysis on the hospital medical record database;
the matching unit is used for comparing the basic information data and the diagnosis data information of the patient with the basic information data and the diagnosis information data of medical records in the data subsets, matching to obtain the medical records closest to the patient in the data subsets of different treatment schemes, and obtaining the treatment scheme and the treatment cost of the medical records;
and the output unit is used for outputting different treatment schemes and treatment cost of each treatment scheme of the patient according to the treatment scheme and the treatment cost of the case.
It is an object of the present application to provide a treatment cost prediction apparatus based on machine learning, the prediction apparatus comprising: a memory and a processor;
the memory is used for storing program instructions;
the processor is used for calling program instructions, and when the program instructions are executed, the treatment expense prediction method based on machine learning is realized.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the machine learning based treatment cost prediction method described above.
The application has the advantages that:
1. according to the method and the device, the diseases of the patient can be rapidly diagnosed according to the basic information and the diagnosis information of the patient, and the treatment scheme and the treatment cost of the related medical record can be rapidly matched in a hospital database based on the diagnosis result, so that more clinical decisions can be provided for the patient and doctors in early stage;
2. the method predicts the treatment scheme and the treatment cost of the patient in early stage, so that the patient can participate in clinical decision by combining with the self condition, and the diagnosis and treatment transparency can greatly avoid possible doctor-patient contradiction;
3. the method considers various conditions of hospital treatment cost prediction, including related deviation factors of the existing medical record cost, actual conditions of a medicine warehouse, various actual conditions of price fluctuation and the like caused by medicine updating, belt quantity purchase and the like, and provides more accurate treatment cost prediction by combining patient settlement types and the like;
4. the disease diagnosis model provided by the application assists doctors in judging the disease types of patients more quickly and accurately before the year; the valuable intangible asset of the medical record database of the hospital is fully utilized, different treatment schemes of the same type of diseases in the past are provided for doctors, and the dependence on personal experiences of the doctors is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a treatment cost prediction method based on machine learning according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a treatment cost prediction system based on machine learning according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a treatment fee prediction apparatus based on machine learning according to an embodiment of the present invention.
Detailed Description
In order to enable those skilled in the art to better understand the present invention, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present invention with reference to the accompanying drawings.
In some of the flows described in the specification and claims of the present invention and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a schematic flowchart of a treatment expense prediction method based on machine learning according to an embodiment of the present invention, specifically, the method includes the following steps:
101: acquiring basic information data and diagnostic information data of a patient;
in one embodiment, the basic information data includes information data of age, sex, time of admission, time of discharge, etc., and the diagnosis information data includes diagnosis information data of admission diagnosis, examination data, nursing diagnosis, legacy data, discharge diagnosis, etc.
102: inputting the basic information data and the diagnosis information data into a disease diagnosis model trained in advance to obtain disease types of patients;
in one embodiment, the training process of the pre-trained disease diagnosis model is: basic information data and diagnosis information data of a patient are obtained as training sets, a machine learning model is trained, the predicted disease type is compared with the actual disease type, loss values are generated, the back propagation is carried out, and the disease diagnosis model is optimized, so that the pre-trained disease diagnosis model is obtained.
In one embodiment, the pre-trained disease diagnostic model employs an algorithm selected from a plurality of machine learning algorithms selected from one or more of the following machine learning algorithms: support vector machine, random forest, XGBoost, lightGBM.
103: according to the disease types, the data subsets of different treatment schemes of the hospital medical record database under the disease types are called, and the data subsets of the different treatment schemes of the hospital medical record database under the disease types are obtained by carrying out cluster analysis on the hospital medical record database;
in one embodiment, the constructing of the subset of data for different treatment regimens for the disease category of the hospital medical record database comprises: obtaining all medical record sets of a medical record database of a hospital under the disease category, and carrying out cluster analysis based on the difference of treatment schemes to obtain data subsets of different treatment schemes under the disease category, wherein the cluster analysis can adopt FCM fuzzy clustering and K-Means clustering; optionally, the medical record database of the hospital is a dynamically updated database.
104: comparing the basic information data and the diagnosis data information of the patient with the basic information data and the diagnosis information data of medical records in the data subsets, and matching to obtain the medical records closest to the patient in the data subsets of different treatment schemes, so as to obtain the treatment scheme and the treatment cost of the medical records;
in one embodiment, the method further includes the steps of obtaining a corrected medical record treatment plan and treatment cost for the obtained medical record, outputting different treatment plans of the patient and treatment cost for each treatment plan according to the corrected medical record treatment plan and treatment cost, wherein the correction of the medical record treatment plan and treatment cost includes correction of the medical record treatment plan and/or correction of the treatment cost, and the correction of the treatment cost is to reduce the medical insurance reduced cost in the medical record to a full self-cost, and obtain the corrected medical record treatment cost; the correction of the treatment scheme is to match and correct the medicine and the price of the medicine in the medical record and the medicine and the price of the medicine in the hospital medicine library, so as to obtain the treatment scheme of the corrected medical record.
In one embodiment, the correction of the treatment scheme is to match and correct the medicine and the price of the medicine in the medical record with the medicine and the price of the medicine in the hospital medicine library to obtain the corrected medical record treatment scheme, and when the medicine in the treatment scheme is matched with the medicine in the hospital medicine library, the medicine price is updated to obtain the treatment scheme and the treatment cost of the medical record with the price corrected; when the medicines in the treatment scheme are inconsistent with the medicines in the hospital medicine library, the medicines are replaced by the similar medicines, so that the treatment scheme and the treatment cost of medical records after the medicines and the price are corrected are obtained; alternatively, the replacement of the same type of drug takes into account firstly the same generic name of drug and secondly the same function of drug.
105: outputting different treatment schemes and treatment cost of each treatment scheme of the patient according to the treatment scheme and treatment cost of the case.
In a specific embodiment, the method further comprises selecting a patient settlement type before outputting the different treatment plan and the treatment cost of each treatment plan of the patient according to the treatment plan and the treatment cost of the case, and outputting the treatment cost of the different treatment plan and each treatment plan under the patient settlement type according to the patient settlement type, the treatment plan and the treatment cost of the case; optionally, the settlement type includes a general patient, a medical insurance patient, or a public fee medical patient.
Fig. 2 is a schematic diagram of a treatment fee prediction system based on machine learning according to an embodiment of the present invention, including:
an acquisition unit 201 for acquiring basic information data and diagnostic information data of a patient;
a diagnosis unit 202, configured to input the basic information data and the diagnosis information data into a disease diagnosis model trained in advance, so as to obtain a disease type of the patient;
a retrieving unit 203, configured to retrieve a subset of data of different treatment schemes of the hospital medical record database under the disease type according to the disease type, where the subset of data of different treatment schemes of the hospital medical record database under the disease type is obtained by performing cluster analysis on the hospital medical record database;
a matching unit 204, configured to compare the basic information data and the diagnostic data information of the patient with the basic information data and the diagnostic information data of medical records in the data subsets, and match the basic information data and the diagnostic information data to obtain medical records of the patient closest to the data subsets of different treatment schemes, so as to obtain a treatment scheme and a treatment cost of the medical records;
and an output unit 205 for outputting different treatment plans of the patient and treatment costs of each treatment plan according to the treatment plans and treatment costs of the cases.
Fig. 3 is a machine learning-based treatment fee prediction apparatus according to an embodiment of the present invention, including: a memory and a processor;
the memory is used for storing program instructions;
the processor is configured to invoke the program instructions, which when executed implement the machine learning-based treatment cost prediction method described above.
It is an object of the present invention to provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described machine learning based treatment cost prediction method.
The results of the verification of the present verification embodiment show that assigning an inherent weight to an indication may moderately improve the performance of the present method relative to the default settings.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.
While the foregoing describes a computer device provided by the present invention in detail, those skilled in the art will appreciate that the foregoing description is not meant to limit the invention thereto, as long as the scope of the invention is defined by the claims appended hereto.
Claims (10)
1. A treatment expense prediction method based on machine learning is characterized by comprising the following specific steps: acquiring basic information data and diagnostic information data of a patient;
inputting the basic information data and the diagnosis information data into a disease diagnosis model trained in advance to obtain disease types of patients;
according to the disease types, the data subsets of different treatment schemes of the hospital medical record database under the disease types are called, and the data subsets of the different treatment schemes of the hospital medical record database under the disease types are obtained by carrying out cluster analysis on the hospital medical record database;
comparing the basic information data and the diagnosis data information of the patient with the basic information data and the diagnosis information data of medical records in the data subsets, and matching to obtain the medical records closest to the patient in the data subsets of different treatment schemes, so as to obtain the treatment scheme and the treatment cost of the medical records;
outputting different treatment schemes and treatment cost of each treatment scheme of the patient according to the treatment scheme and treatment cost of the case.
2. The machine learning based treatment cost prediction method according to claim 1, wherein the training process of the pre-trained disease diagnosis model is: basic information data and diagnosis information data of a patient are obtained as a training set, a machine learning model is trained, the predicted disease type is compared with the actual disease type, a loss value is generated, the back propagation is carried out, and the disease diagnosis model is optimized, so that the pre-trained disease diagnosis model is obtained; optionally, the basic information data includes information data such as age, sex, time of admission, time of discharge, and the diagnostic information data includes diagnostic information data such as admission diagnosis, inspection data, nursing diagnosis, legacy data, discharge diagnosis, and the like.
3. The machine learning based treatment cost prediction method according to claim 2, wherein the pre-trained disease diagnosis model employs an algorithm selected from a plurality of machine learning algorithms selected from one or more of the following machine learning algorithms: support vector machine, random forest, XGBoost, lightGBM.
4. The machine learning based treatment expense prediction method according to claim 1, wherein the construction process of the data subset of the different treatment protocols of the hospital medical record database under the disease category comprises: obtaining all medical record sets of a medical record database of a hospital under the disease category, and carrying out cluster analysis based on the difference of treatment schemes to obtain data subsets of different treatment schemes under the disease category, wherein the cluster analysis can adopt FCM fuzzy clustering and K-Means clustering; optionally, the medical record database of the hospital is a dynamically updated database.
5. The method according to claim 1, wherein the method further comprises the steps of obtaining a corrected medical record treatment plan and treatment cost, outputting different treatment plans of the patient and treatment cost of each treatment plan according to the corrected medical record treatment plan and treatment cost, wherein the medical record treatment plan and treatment cost correction comprises a medical record treatment plan correction and/or a medical cost correction, and the medical insurance reduced cost in the medical record is reduced to a full self-cost, and the corrected medical record treatment cost is obtained; the correction of the treatment scheme is to match and correct the medicine and the price of the medicine in the medical record and the medicine and the price of the medicine in the hospital medicine library, so as to obtain the treatment scheme of the corrected medical record.
6. The machine learning-based treatment expense prediction method according to claim 5, wherein the correction of the treatment scheme is to match and correct the medicine and the medicine price in the medical record with the medicine and the medicine price in the hospital medicine library to obtain the corrected treatment scheme of the medical record, and when the medicine in the treatment scheme is matched with the medicine in the hospital medicine library, the medicine price is updated to obtain the treatment scheme and the treatment expense of the medical record after the price correction; when the medicines in the treatment scheme are inconsistent with the medicines in the hospital medicine library, the medicines are replaced by the similar medicines, so that the treatment scheme and the treatment cost of medical records after the medicines and the price are corrected are obtained; alternatively, the replacement of the same type of drug takes into account firstly the same generic name of drug and secondly the same function of drug.
7. The machine learning based treatment expense prediction method according to claim 1, wherein the method step further comprises selecting a settlement type of the patient before outputting the treatment expense of the different treatment regimen and each treatment regimen of the patient according to the treatment regimen and the treatment expense of the case, and outputting the treatment expense of the different treatment regimen and each treatment regimen under the settlement type of the patient according to the settlement type of the patient, the treatment regimen and the treatment expense of the case; optionally, the settlement type includes a general patient, a medical insurance patient, or a public fee medical patient.
8. A machine learning based treatment expense prediction system, comprising:
an acquisition unit for acquiring basic information data and diagnostic information data of a patient;
the diagnosis unit is used for inputting the basic information data and the diagnosis information data into a disease diagnosis model trained in advance to obtain the disease type of a patient;
the calling unit is used for calling the data subsets of different treatment schemes of the hospital medical record database under the disease type according to the disease type, wherein the data subsets of the different treatment schemes of the hospital medical record database under the disease type are obtained by carrying out cluster analysis on the hospital medical record database;
the matching unit is used for comparing the basic information data and the diagnosis data information of the patient with the basic information data and the diagnosis information data of medical records in the data subsets, matching to obtain the medical records closest to the patient in the data subsets of different treatment schemes, and obtaining the treatment scheme and the treatment cost of the medical records;
and the output unit is used for outputting different treatment schemes and treatment cost of each treatment scheme of the patient according to the treatment scheme and the treatment cost of the case.
9. A machine learning-based treatment fee prediction apparatus, comprising: a memory and a processor;
the memory is used for storing program instructions;
the processor is configured to invoke program instructions that when executed implement the machine learning based treatment cost prediction method of any of claims 1-7.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the machine learning based treatment cost prediction method of any of claims 1-7.
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