CN114068006A - Leukemia clinical decision, teaching and scientific research auxiliary support system and method - Google Patents

Abstract

The invention discloses an auxiliary support system and method for leukemia clinical decision, teaching and scientific research, according to the system and the method, by the aid of the matched medical and pharmaceutical data storage unit, diagnosis and treatment parameter storage unit, information input unit, comparison processor unit, diagnosis and treatment result output unit and early warning and operation recording unit, feasible clinical diagnosis and treatment scheme suggestions and corresponding clinical research and real world research evidence support can be provided for relevant medical workers according to disease specific information parameter values of patients, the whole period of diseases such as screening diagnosis, treatment, follow-up visit monitoring, out-of-hospital management and the like is covered, doctors are assisted to rapidly improve clinical diagnosis and treatment level, study and update diagnosis and treatment knowledge, the research status and development direction of clinical research and real world diagnosis and treatment are mastered, scientific research level is improved, and the contradiction that the time and energy of the medical workers are difficult to follow clinical research progress is solved.

Description

Leukemia clinical decision, teaching and scientific research auxiliary support system and method

Technical Field

The invention belongs to the field of intelligent medical treatment, and further relates to an auxiliary support system and method for leukemia clinical decision, teaching and scientific research in the technical field of digital medical treatment.

Background

According to statistics, about 392.9 ten thousands of new malignant tumor cases and about 233.8 thousands of death cases occur in 2015 in China, the burden of the cancer in China still shows a continuous rising trend, and the cancer prevention and control situation is still severe. Wherein, the new cases of leukemia are about 24.6 thousands, and the leukemia is the serious disease threatening the health of people in the sixth disease in the overall malignant tumor of China. At present, the practical conditions of capacity, time, resources and the like limit most medical and health personnel to continuously carry out systematic medical knowledge updating, so that the clinical diagnosis and treatment standardization level and the medical service level are difficult to rapidly promote, and the requirements of people on medical service are difficult to meet.

In order to alleviate the contradiction, the existing clinical decision support system can provide clinical diagnosis and treatment scheme reference and assist doctors in making diagnosis and treatment decisions based on partial basic medical knowledge, clinical guidelines and the like. For example, chinese patent application publication No. CN103455886A, published as 12/18/2013, discloses a diagnosis and treatment decision support system based on a workflow, which establishes a workflow based on a standard clinical path for a state-authorized hospital to go out of a hospital, and outputs a diagnosis and treatment suggestion through a workflow module, a rule engine module, an inference module, and the like. Although the standardization of clinical diagnosis and treatment is considered, the actual conditions of hospitals in all regions and all levels in the country are greatly different, and the actual applicable hospital range is too narrow only by carrying out standardization through the clinical path of the national authorized hospitals. And the system can not explain what clinical research data or real world research data the given diagnosis and treatment suggestion is based on, and doctors can not judge or verify the rationality of the diagnosis and treatment suggestion by combining the clinical experience of the doctors.

Meanwhile, the invention patent application with application date of 2019, 12 and 23 and application number of 201911337942.8 describes a multi-source data-assisted clinical decision support system and method, the technical scheme combines a standard clinical path and a real world clinical path to carry out clinical decision support, enhances the operability of diagnosis and treatment suggestions, provides data such as clinical research and real world research as evidence support, and better accords with evidence-based medical principles. However, the invention only aims at the western medicine system, and cannot combine Chinese and western medicine, and Chinese patent medicines and traditional Chinese medicines are widely used in China, especially in basic medical institutions, and belong to the blind point of the existing decision assistance. In addition, the diagnosis and treatment parameter library is not independently divided, and disease-specific diagnosis and treatment parameters and weight values thereof are not systematically analyzed and utilized, so that the method is deficient in the aspects of individual decision assistance, teaching and scientific research support of specific diseases.

At present, a clinical decision support system aiming at the whole disease cycle of leukemia is lacked, only a few invention patent documents and public phases relate to part of biomarker combinations related to leukemia diagnosis and treatment and part of links of a kit thereof, for example, a group of leukemia detection markers disclosed in Chinese invention patent document with publication number CN111190013A on 5-22-2020 and application thereof in preparing a leukemia screening kit, a miRNA marker disclosed in Chinese invention patent document with publication number CN103114092B and related to early relapse and prognosis after leukemia operation and application thereof, and a leukemia diagnosis and treatment marker disclosed in CN 105886627B. However, the above technical solutions can not specifically and pertinently perform clinical diagnosis and treatment assistance, teaching and training support and scientific research support for the whole disease cycle of leukemia.

Disclosure of Invention

In view of the defects of the prior art, the invention aims to provide an auxiliary support system and an auxiliary support method which are assisted by clinical research data and real world research data together and are specific to leukemia clinical decision, teaching and scientific research, the system and the method provide feasible clinical diagnosis and treatment scheme suggestions and corresponding clinical research and real world research evidence support for relevant medical workers, cover the whole period of diseases such as screening diagnosis, treatment, follow-up visit monitoring, out-of-hospital management and the like, assist doctors to quickly improve the clinical diagnosis and treatment level, learn and update diagnosis and treatment knowledge, master the current research situation and the development direction of clinical research and real world diagnosis and treatment, and solve the contradiction that the time and energy of the medical workers are difficult to follow the clinical research progress.

The technical scheme provided by the invention is as follows:

leukemia clinical decision, teaching, scientific research auxiliary support system, its characterized in that, including medical science and pharmacy data memory cell, diagnose parameter memory cell, information input unit, comparison processor unit, diagnose result output unit and early warning and operation record unit, each specific function of constituteing the unit is as follows:

a medical and pharmaceutical data storage unit for storing: and constructing a Western medicine system and medical and pharmaceutical related data of a traditional Chinese medicine system, a standard clinical path data cluster and a real world clinical path data cluster which are required by the standard clinical path and evidence library and the real world clinical path and evidence library.

The diagnosis and treatment parameter storage unit is used for storing: parameters affecting screening diagnosis, treatment and monitoring follow-up of leukemia, parameter weight values and parameter values of the parameters; the user can adjust the information in the diagnosis and treatment parameter storage unit.

The information input unit is used for acquiring patient information in a mode including but not limited to calling from systems such as hospital HIS and the like and direct doctor input, performing information structuring processing according to different input forms and information structures, generating patient information parameters and sending the patient information parameters to the comparison processor unit; and the comparison processor unit is also used for automatically calling or sending a supplementary input specific information prompt to a user according to the specific information query supplementary instruction and providing corresponding specific information to the comparison processor unit. When receiving a specific information query supplement instruction sent by the comparison processor unit, the information input unit queries the specific information from a system such as a hospital HIS or sends a supplement input specific information prompt to a user, the user can check information such as associated parameters, approximate path structures and clinical significance of missing parameters of the missing parameters, and the result is sent to the comparison processor unit after the specific information query supplement.

The comparison processor unit is used for receiving the data sent by the information input unit, calling the data of the diagnosis and treatment parameter storage unit, and comparing the two data directly or after rule processing, wherein the rule processing comprises entity relationship mapping, logical relationship reasoning and the like; when parameters of missing patient information parameter values exist in the standard clinical path or the real world clinical path, the comparison processor unit sends a specific information query supplement instruction to the information input unit, secondary comparison is carried out after a specific information query supplement result fed back by the information input unit is received, and then the standard clinical path data cluster and the real world clinical path data cluster which reach a preset or user-set matching degree threshold are sent to the diagnosis and treatment result output unit.

And the diagnosis and treatment result output unit is used for receiving the result data sent by the comparison processor unit and displaying the result data in modules.

The early warning and operation recording unit is used for carrying out reasonability early warning on treatment and medication schemes and the like specifically related to a clinical path when a doctor prescribes a prescription, recording the real diagnosis and treatment path and results of a patient, comparing whether diagnosis and treatment suggestions and medical behavior reasonability early warning suggestions displayed in the diagnosis and treatment result output unit are adopted by a user or not, feeding back the recorded real diagnosis and treatment path and result information of the patient into a medical and pharmaceutical data storage unit, expanding the data quantity of the medical and pharmaceutical data storage unit, and further continuously optimizing the information in the clinical path and the evidence storage unit.

Further, the medical and pharmaceutical data storage unit comprises:

the medical and pharmaceutical database is used for storing all medical and pharmaceutical relevant data of a western medicine system and a traditional Chinese medicine system, and comprises a series of cross-modal multi-source heterogeneous data, and the series of cross-modal multi-source heterogeneous data at least comprises: medical and pharmaceutical data of western medicine system and traditional Chinese medicine system, medical policy information, structured medical record information, patient out-of-hospital management information, disease burden information, drug instruction manual, consumable information, clinical research data, real world research data, clinical guidelines, expert consensus;

the standard clinical path and evidence base is used for storing a standard clinical path data cluster, namely a data set formed by pairing one or more of standard clinical path, continuous education data, medical behavior rationality early warning information, clinical research data and real world research data generated by analyzing the medical and pharmaceutical data storage unit;

and the real-world clinical path library is used for storing a real-world clinical path data cluster, namely a data set formed by pairing one or more of real-world clinical paths, continuous education data, medical behavior rationality early warning information and real-world research data generated by analyzing the medical and pharmaceutical data storage unit.

Further, the diagnosis and treatment parameter storage unit includes:

the screening and diagnosis parameter library is used for storing parameters influencing screening diagnosis of leukemia, parameter weight values and parameter values of the parameters;

the treatment parameter library is used for storing parameters influencing leukemia treatment, parameter weight values and parameter values of the parameters;

the monitoring follow-up parameter base is used for storing parameters influencing the leukemia monitoring follow-up, parameter weight values and parameter values of the parameters;

the tumor marker parameter library is used for storing markers of leukemia, leukemia precancerous lesion, precancerous diseases and leukemia related diseases, and the parameters stored in the tumor marker parameter library are derived from tumor tissues, tissues beside cancer, normal tissues or cells, blood or body fluid.

Further, the parameters stored in the screening and diagnosis parameter library that affect the screening diagnosis of acute lymphocytic leukemia include, but are not limited to, whether a bone marrow puncture and biopsy has been performed, whether a specific reproducible cytogenetic abnormality examination of bone marrow or peripheral blood lymphocytes has been performed, whether a medical history and physical examination has been performed, whether a cranial enhancement CT/MRI has been performed, whether a chest enhancement CT has been performed, risk stratification, whether there is a major neurological sign or symptom, whether a meningeal disease/lymphoma/CNS hemorrhage examination has been performed, whether acute lymphocytic leukemia has been diagnosed, whether risk stratification has been performed, whether there is extramedullary involvement; the parameters influencing the treatment of the acute lymphoblastic leukemia stored in the treatment parameter library include, but are not limited to, whether the acute lymphoblastic leukemia is diagnosed or not, whether risk stratification is carried out or not, risk stratification results, whether induction treatment is carried out or not, an induction treatment scheme is carried out or not, an induction treatment curative effect evaluation result, whether minimal residual disease evaluation is carried out or not, whether consolidation treatment is carried out or not, whether the treatment mode is consolidated or not, whether further treatment can be carried out or not, the age of a patient and whether important complications exist or not, whether maintenance treatment is carried out or not, whether ABL gene mutation detection is carried out or not, and whether treatment of relapsing/refractory diseases is carried out or not; the parameters which are stored in the monitoring follow-up parameter library and influence the monitoring follow-up of the acute lymphoblastic leukemia include but are not limited to the existence of abnormality of a physical examination result, the existence of abnormality of a complete blood cell count (CBC), the existence of new symptoms, the existence of a marrow puncture indication, the existence of abnormality of a marrow puncture result, and whether disease relapse is suspected clinically or not, and whether the pH is positive or negative; markers of acute lymphoblastic leukemia, and acute lymphoblastic leukemia-associated diseases, stored in the tumor marker parameter library include, but are not limited to, protein and metabolic markers (e.g., CD19, cytoplasmic CD22, CD79a, CD10, cytoplasmic CD3, CD2, CD7, anti-MPO, CD13, CD33, CDw65, CD117, BCR-ABL, MLL, etc.), nucleic acid markers, platelet tumor-mediated platlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNAs (e.g., miR-155, miR-181a, miR-210, miR-125b, miR-223, miR-10a, miR-134, miR-214, miR-484, miR-572, miR-580, etc.), mRNAs, CTCs, ctDNA/cfDNA, ctRNAs, lncRNAs (e.g., LINC00152, RUNXOR, MEG3, LLEST, IRAIN, UCA1, ANRIL, etc.), DNA methylation/histone modifications (e.g., RASSF6 methylation, RASSF10 methylation, etc.), and m6A RNA methylation.

Further, the parameters stored in the screening and diagnosis parameter library that affect the screening diagnosis of acute myeloid leukemia include, but are not limited to, whether an assessment of acute leukemia has been performed, whether cytogenetic and molecular analysis of bone marrow has been performed, whether an immunophenotyping has been performed, whether CNS is suspected to bleed, whether leukemia meningitis is suspected, whether extramedullary disease is suspected, whether symptoms are present, whether there are significant neurological symptoms or signs in the diagnosis, whether isolated extramedullary disease (myeloid sarcoma) is present, assessment and treatment of central nervous system leukemia has been performed, multidisciplinary diagnostic studies have been performed, acute myeloid leukemia has been diagnosed, neurological symptoms are present at the time of diagnosis, bleeding or tumor effects are excluded from CT/MRI, lumbar puncture is performed, lumbar puncture results, cerebrospinal fluid morphological examination results, needle aspiration or biopsy has been performed; the parameters stored in the treatment parameter library for influencing the treatment of chronic lymphocytic leukemia include, but are not limited to, whether acute myelogenous leukemia has been diagnosed or not, disease stage, patient age, risk classification, whether induction treatment has been performed or not, induction treatment mode, whether bone marrow is followed up 2-3 weeks after induction treatment, bone marrow follow-up result, whether follow-up treatment is performed or not after induction treatment, whether blood recovery and bone marrow remission state after treatment are evaluated, whether consolidation treatment is performed or not, whether donor is waited for, whether bone marrow is followed up 3-4 weeks after induction treatment, risk state, whether disease is alleviated or not, whether treatment is received after remission, whether further management has been performed or not, whether enhanced induction remission treatment is received or not, whether previous enhanced treatment mode is followed up 4-6 weeks after blood recovery, whether bone marrow state is followed up 8-12 weeks after blood recovery, whether bone marrow state is followed up after blood recovery, whether acute myelogenous leukemia is diagnosed or not, and whether bone marrow is followed up after treatment is not, and whether treatment is not, General condition of the patient, renal function condition, how the karyotype and molecular markers are, whether or not the patient is a recurrent disease; the parameters which are stored in the monitoring follow-up parameter bank and influence the monitoring follow-up of the acute myeloid leukemia include but are not limited to whether the peripheral blood smear is abnormal or not and whether a suitable relatives donor exists or not; markers of acute myeloid leukemia and acute myeloid leukemia-associated diseases stored in the tumor marker parameter library include, but are not limited to, protein and metabolic markers (e.g., CF33, CF13, RUNX1-RUNX1T1, CBFB-MYH11, PML-RARA, MLLT3-KMT2A, FEK-NUP214, GATA2, MECM, etc.), nucleic acid markers, platelet-infected platlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA (e.g., miR-150, miR-342, miR-181b-5p, miR-155, etc.), mRNA (e.g., PIM-1 mRNA, IL2RA mRNA, etc.), CTC, ctDNA/cfDNA, ctRNA, lncRNA (e.g., RUNXOR, MEG3, LLEST, IRAIN, UCA1, ANRIL, T-ALL-R-LncR1, LUNAR1, NEAT1, etc.), DNA methylation/histone modification (e.g., CEBPA methylation, GPX3 methylation, C1R methylation, etc.), and m6A RNA methylation.

Further, the parameters stored in the screening and diagnosis parameter library that affect the screening diagnosis of chronic myelogenous leukemia include, but are not limited to, whether a history/physical examination (including size of spleen palpation) has been performed, whether a leukocyte examination and classification/platelet examination has been performed, whether a biochemical examination has been performed, whether a bone marrow assessment (puncture and biopsy) has been performed, whether a cytogenetic examination has been performed, whether a molecular examination has been performed, whether a hepatitis-related examination has been performed, Ph or BCR-ABL1 status, clinical manifestations, staging of disease, whether an additional assessment has been performed, whether a risk score has been determined, whether a HLA examination has been performed, whether a flow cytometry has been determined for cell lineages, whether a mutation analysis has been performed, whether an assessment of other diseases than chronic myelogenous leukemia has been performed, the percentage of peripheral blood plasma cells, the percentage of the sum of peripheral blood plasma and promyelocytes, Peripheral blood basophil percentage, treatment-independent platelet count percentage, peripheral blood or bone marrow blast percentage, extramedullary primitive cell proliferation, whether bone marrow biopsy primitive cells are in large focal or cluster hyperplasia; the parameters stored in the treatment parameter library that affect treatment of chronic myelogenous leukemia include, but are not limited to, whether chronic myelogenous leukemia has been diagnosed, Ph or BCR-ABL1 status, clinical manifestations (chronic or advanced phase of chronic myelogenous leukemia), disease stage (accelerated or acute), type of acute change (acute or acute), risk type, whether initial treatment has been performed, patient response milestones, whether second and subsequent treatments have been performed, whether patient complications and drug interactions have been assessed, mutated genes, indications of the presence or absence of allogeneic HCT, complete cytogenetic response or non-complete cytogenetic response/recurrence, QPCR monitoring results, previous complications following TKI administration/tolerance/mutation detection/HCT; the monitoring follow-up parameter library stores parameters affecting chronic myelogenous leukemia monitoring follow-up including, but not limited to, whether response milestones have been reached, whether patient complications and drug interactions have been assessed, whether any signs of unresponsiveness have occurred, whether 1-log elevation in BCR-ABL1 transcript levels has occurred and major molecular responses have been lost; markers of chronic myeloid leukemia, and chronic myeloid leukemia-associated diseases, stored in the tumor marker parameter library include, but are not limited to, protein and metabolic markers (e.g., BCR-ABL1, hmgcl 1, Tryptase, NOTCH1, TET2, etc.), nucleic acid markers, platelet-reduced patelet, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA (e.g., miR-505-5p, miR-193b-3p, Let-7b-5p, miR-451a, etc.), mRNA, CTC, ctDNA/cfDNA, ctRNA, lncRNA (e.g., RUNXOR, MEG3, LLEST, IRAIN, UCA1, ANRIL, T-ALL-R-LncR1, LUNAR1, NEAT1, LncRNA-BGL3, etc.), DNA methylation/histone modification (e.g., SOX30 methylation, HOXA4 methylation, HOXA5 methylation, etc.), and m6A RNA methylation.

Further, the parameters influencing the screening diagnosis of chronic lymphocytic leukemia stored in the screening and diagnosis parameter library include, but are not limited to, complete physical examination, medical history inquiry, comprehensive assessment of physical condition and concomitant diseases of patients, comprehensive cytogenetic assessment, serological assessment, comprehensive virological assessment, quantitative detection of immunoglobulin, reticulocyte, haptoglobin and Coombs detection, whether a bone marrow smear and biopsy has been performed, whether whole body imaging examination has been performed, whether a blood pathology examination has been performed, whether multiple absorption gate control scan (MUGA)/echocardiogram has been performed, whether lymph node enlargement is accompanied, whether spleen enlargement is accompanied, hepatomegaly or hepatosplenomegaly, hemoglobin and platelet counts, and the number of enlarged lymph node regions; the parameters stored in the treatment parameter library that affect treatment of chronic lymphocytic leukemia include, but are not limited to, whether chronic lymphocytic leukemia has been diagnosed, disease classification, whether treatment has been received, Rai and Binet staging, whether treatment is indicated, whether further assessment has been made, whether 17p deletion/TP 53 mutation has been associated, CpG-stimulated karyotype, IGHV mutational status, whether progressive cytopenia has occurred, whether first line treatment has been performed, patient age and whether significant complications have been associated with, response to first line treatment, whether disease progression or recurrence or refractory disease has occurred, whether histological transformation or histological progression has occurred, whether follow-up treatment has been received, whether disease progression or treatment has been received after recurrence; the parameters stored in the monitoring follow-up parameter bank which influence the monitoring follow-up of the chronic lymphocytic leukemia include but are not limited to whether disease relapse is suspected, whether new symptoms exist, the age of a patient, whether tumor lysis syndrome appears or not, and whether autoimmune cytopenia appears or not; markers of chronic lymphocytic leukemia, and chronic lymphocytic leukemia-associated diseases, stored in the tumor marker parameter library include, but are not limited to, protein and metabolic markers (e.g., TP53, IGHV, NOTCH1, MYD88, SF3B1, BIRC3, CD38, CD49d, etc.), nucleic acid markers, platelet-extracted patelet, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA (e.g., miR-192, miR-15a, miR-16-1, miR-95, miR-20b-5p, etc.), mRNA (e.g., HIF1A mRNA, etc.), CTC, ctDNA/cfDNA, ctRNA, lncRNA (e.g., RUNXOR, MEG3, LLEST, IRAIN, UCA1, ANRIL, T-ALL-R-LncR1, LUNAR1, etc.), DNA methylation/histone modification, and m6A RNA methylation.

Further, the parameters stored in the screening and diagnosis parameter library that affect hairy cell leukemia screening diagnosis include, but are not limited to, whether bone marrow biopsy ± aspiration has been performed, whether characteristic hair cells are present in peripheral blood or bone marrow, whether characteristic manifestations of increased reticuloendothelial infiltration are present in bone marrow biopsy samples (dry aspiration often occurs), whether sufficient immunophenotyping has been performed, whether immunohistochemistry or flow cytometry has been performed, whether BRAF V600E mutation has been detected, whether IGHV4-34 rearrangement has been detected, liver and spleen size, whether spleen enlargement and/or liver enlargement is present, whether peripheral lymph node enlargement is present, whether peripheral blood smear has been performed, whether whole blood cell count and classification has been performed, whether hepatitis b virus detection has been performed, whether hairy cell leukemia has been confirmed (canonical); the stored parameters affecting hairy cell leukemia therapy in the therapy parameter library include, but are not limited to, whether hairy cell leukemia has been diagnosed, whether a therapeutic indication is present, whether initial therapy has been administered, patient response to initial therapy, whether supportive therapy has been administered, whether a relapsed/refractory disease has occurred, whether treatment of a relapsed/refractory disease has occurred, time to relapse, whether further progression of the disease has occurred; the parameters which are stored in the monitoring follow-up parameter library and influence the hair cell leukemia monitoring follow-up include but are not limited to whether capillary leak syndrome occurs or not, whether hemolytic uremic syndrome occurs or not, hemoglobin level, platelet count, serum creatinine level or not, whether LDH (layered double hydroxides), indirect bilirubin and schizophrenic cells in blood smears are checked or not, and other hemolytic evidences; markers of hairy cell leukemia and hairy cell leukemia related diseases stored in the tumor marker parameter library include, but are not limited to, protein and metabolic markers (e.g., CD5, CD10, CD11c, CD20, CD22, CD25, CD103, CD123, cyclin D1, etc.), nucleic acid markers, platelet-isolated plantlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA, mRNA, CTC, ctDNA/cfDNA, ctRNA, lncRNA (e.g., RUNXOR, MEG3, LLEST, IRAIN, UCA1, ANRIL, T-ALL-R-LncR1, etc.), DNA methylation/histone modification, and m6A RNA methylation

Further, the diagnosis and treatment result output unit includes:

the diagnosis and treatment scheme module is used for displaying standard clinical paths and/or real world clinical paths, wherein the standard clinical paths and/or the real world clinical paths are classified into first-choice recommendations, second-choice recommendations and other recommendations according to the sequence from high to low after the comparison by the comparison processor unit, and one or more of the information of the whole medical process such as examination, diagnosis, treatment, rehabilitation follow-up visits and the like are used as diagnosis and treatment suggestions for assisting clinical decision;

the evidence support module is used for displaying clinical research data or real world research data matched with the standard clinical path or the real world clinical path in the diagnosis and treatment scheme module, and is specifically divided into one or more categories of research information, nano-grade standard, baseline characteristic, treatment scheme, treatment result, adverse reaction, subgroup information, other prognosis and follow-up information, reasonable medication information, guideline/expert consensus opinion, medication burden and the like for visual display, and provides scientific research suggestions for assisting clinical decision and scientific research work;

and the continuous education module is used for displaying continuous education data matched with the standard clinical path or the real world clinical path in the diagnosis and treatment scheme module, including contents such as standardized diagnosis and treatment, science popularization and patient self-management directly related to the patient condition, and displaying forms including but not limited to texts, pictures, tables, audio and videos for assisting clinical decision and teaching.

Furthermore, the diagnosis information comprises western medicine diagnosis and traditional Chinese medicine syndrome differentiation and disease differentiation, the medicine scheme suggestions in the treatment information comprise western medicines, traditional Chinese medicines and Chinese patent medicines, and a user can adjust the diagnosis and treatment suggestions on the basis of the diagnosis and treatment suggestions, wherein the diagnosis and treatment suggestions comprise item increase, item reduction, item modification and the like.

Further, the early warning and operation recording unit includes:

and the medical behavior early warning module is used for receiving the medical behavior rationality early warning information sent by the diagnosis and treatment result output unit and then carrying out real-time early warning and suggestion on unreasonable treatment and medication schemes in the diagnosis and treatment process of doctors. The medical behavior reasonability early warning information is derived from professional medical and pharmaceutical data, a drug instruction book, most authoritative and latest clinical research data at home and abroad, real world research data, reasonable medication guide consensus and the like, and the presented specific data comprises but is not limited to indications, contraindications, adverse reactions, drug interaction, medication crowd early warning, drug use early warning, reasonable medication rating and price reference;

and the operation record feedback module is used for recording the diagnosis and treatment scheme finally selected by the user and feeding back and optimizing the system. The user can manually select whether to adopt the diagnosis and treatment scheme, if the user selects to adopt the diagnosis and treatment scheme recommended by the clinical decision support system, the selected data is sent back to the relevant electronic information system of the hospital, and the functions of prescription write-back, case history write-back and the like are realized; if the user chooses not to adopt the diagnosis and treatment scheme recommended by the clinical decision support system, the reason of not adopting can be filled, and the actual diagnosis and treatment path and the diagnosis and treatment ending of the case are combined for optimizing the data of the medical and pharmaceutical data storage unit and the diagnosis and treatment parameter storage unit. The system can also compare the actual diagnosis and treatment path of the patient in each electronic information system of the hospital with the diagnosis and treatment scheme recommended by the system to judge whether the user adopts the system recommendation, and feeds the actual diagnosis and treatment path and diagnosis and treatment ending of the case back to the medical and pharmaceutical data storage unit to expand the data volume of the case and further continuously optimize the data in the diagnosis and treatment parameter storage unit.

Based on the system, the corresponding leukemia clinical decision, teaching and scientific research auxiliary support method is characterized by comprising the following steps:

(1) the information of the symptoms, physical signs, medical history, examination results and the like of the patient is called from an outpatient service, an emergency management subsystem, a medical record management subsystem and the like of a hospital HIS system, or the information of the patient is input by a doctor in the modes of information selection and the like under the guidance of voice, characters, images or corresponding front ends. And generating a patient information parameter value after the patient information is identified, extracted and structured.

(2) The urinary system tumor clinical decision, teaching and scientific research auxiliary support system starts a comparison processor unit, carries out comparison judgment through the comparison processor unit, compares and judges whether a standard clinical path and/or a real world clinical path completely matched with the patient information parameter value exist or not, and judges whether the standard clinical path and/or the real world clinical path completely match with the patient information parameter value or not, wherein the judgment result comprises the following steps:

if the path data cluster exists, the completely matched standard clinical path data cluster and/or the real world clinical path data cluster are/is called and sent to a diagnosis and treatment result output unit; outputting and displaying decision support information in a diagnosis and treatment result output unit; the standard clinical pathway data cluster and/or the real world clinical pathway data cluster are data sets which are paired into groups by one or more of standard clinical pathway and/or real world clinical pathway, continuing education data, medical behavior rationality early warning information, clinical research data and real world research data; the decision support information comprises a standard clinical pathway and real-world clinical pathway based clinical recommendation for assisting clinical decision making; the diagnosis and treatment suggestion is correspondingly used for clinical evidence support and real world evidence support for assisting clinical decision and scientific research, or is correspondingly used for assisting continuous education information of medicine and teaching.

If the information does not exist, the comparison processor unit sends a specific information query supplementary instruction to the information input unit, then the information input unit queries specific information from a data source according to the specific information query supplementary instruction, for example, the specific information is searched from each electronic information system of a hospital or information missing prompt is carried out for a user, and an information supplementary recording window is presented. And when the specific information is a specific examination result, if the user confirms that the examination is not performed, pushing an examination suggestion to the diagnosis and treatment result output unit.

(3) After receiving the specific information query supplementary result fed back by the information input unit, the comparison processor unit performs secondary comparison to judge whether a standard clinical path and/or a real world clinical path completely matched with the patient information parameter value exists at the moment, and the judgment result comprises:

and if so, calling a completely matched standard clinical path data cluster and/or real world clinical path data cluster, and outputting the standard clinical path data cluster and/or real world clinical path data cluster to the diagnosis and treatment result output unit.

If the standard clinical path and the real world clinical path which are completely matched with the patient information parameter values do not exist, judging whether the matching degree of the standard clinical path and/or the real world clinical path reaches a threshold value, and judging that: if the matching degree of the standard clinical path and/or the real world clinical path reaches a threshold value, outputting and displaying the corresponding standard clinical path data cluster and/or the real world clinical path data cluster to a diagnosis and treatment result output unit, and dividing the standard clinical path data cluster and/or the real world clinical path data cluster into a first-choice recommendation, a second-choice recommendation and other recommendations according to the matching degree to present the first-choice recommendation, the second-choice recommendation and the other recommendations to a user; and if the matching degrees of the standard clinical path and the real world clinical path do not reach the threshold value, selecting the standard clinical path data cluster and the real world clinical path data cluster with the highest matching degree and sending the standard clinical path data cluster and the real world clinical path data cluster to a diagnosis and treatment result output unit, presenting diagnosis and treatment references based on the similar standard clinical path and the real world clinical path to the user, prompting missing important information to the user, providing similar real world evidence as clinical and scientific research references, and presenting continuous education information of similar medical cases as medical standardized education references. In the above process, the threshold is the optimal credit acquisition threshold of the parameter value matching degree preset according to model training, and the user can also set the threshold according to actual needs.

(4) At the moment, the user manually judges whether the diagnosis and treatment are finished or not automatically after comparing the real diagnosis and treatment path, the diagnosis and treatment ending and the diagnosis and treatment suggestion of the patient, if the diagnosis and treatment at the stage are not finished, the clinical decision support is carried out again after the information is imported through an electronic information system of the hospital or the subsequent diagnosis and treatment information of the patient is input by the user for information updating until the diagnosis and treatment at the stage are finished manually or automatically judged by the system; if the diagnosis and treatment at the stage are finished, judging whether a user adopts a diagnosis and treatment suggestion, if so, writing diagnosis and treatment options selected by the user back to a hospital HIS system, for example, writing information such as diagnosis, examination and treatment schemes back to subsystems such as medical plans and prescriptions, and when the prescriptions are prescribed, carrying out real-time early warning on the diagnosis and treatment schemes or medicines selected by a doctor through an early warning and operation recording unit (for example, when the conditions such as unreasonable diagnosis and treatment schemes or unreasonable medicine use appear), and finishing the clinical decision support after the prescription is finished; if the user chooses not to adopt the diagnosis and treatment suggestion, the user can selectively input the reason and end the clinical decision support.

When judging whether the user adopts the diagnosis and treatment suggestion, the user can manually judge; whether the user manually judges whether to adopt the diagnosis and treatment suggestion or not and whether the user selects to adopt the diagnosis and treatment suggestion or not, the user can feed back the diagnosis and treatment suggestion to the medical and pharmaceutical data storage unit, the real diagnosis and treatment path and the diagnosis and treatment outcome of the patient are recorded, the diagnosis and treatment suggestion is compared with the system diagnosis and treatment suggestion, whether the user adopts the suggestion or not is automatically judged and stored, and then corresponding diagnosis and treatment parameter storage data are continuously optimized.

By combining the composition and corresponding functions of the system, the system calls the comparison processor unit to compare the patient information parameter values with the data in the diagnosis and treatment parameter storage unit, so that corresponding diagnosis and treatment references can be obtained, and the diagnosis and treatment result output unit displays corresponding result data which can be used as corresponding diagnosis and treatment suggestions or auxiliary information for scientific research, learning and the like.

The invention has the following beneficial effects:

(1) the system and the method provided by the invention combine a large amount of medical/pharmaceutical related data of a western medicine system and a traditional Chinese medicine system, generate a standard clinical path through knowledge map deduction and analysis, and also combine real world research data to generate a real world clinical path as supplement. The real world clinical path and the standard clinical path can be compared to be used as diagnosis and treatment comprehensive reference, operability improvement on the standard clinical path is facilitated by combining reality, diagnosis and treatment nodes to be standardized in actual clinical work are conveniently found, and the requirements of clinical diagnosis and treatment normalization and actual operability are met. In addition, the real world clinical path can be used as a reference for special crowds and subdivision diagnosis and treatment stages, the real world clinical path established through real world data analysis can better reflect the actual development situation of clinical work and the actual diagnosis and treatment results, and clinical practice and clinical research can be better guided.

(2) The system and the method provided by the invention are used for carrying out decision support on the whole disease cycle of leukemia, finding parameters, parameter weight values and parameter values of the parameters which influence screening, diagnosis, treatment and monitoring follow-up of leukemia through machine learning specificity, and coding the standard clinical path and the real world clinical path through the parameter values, so that the clinical decision support on leukemia is more accurate.

(3) According to the system and the method, the output diagnosis and treatment result suggestion integrates multi-dimensional information such as clinical diagnosis and treatment paths, evidence support, early warning of rationality of medical behaviors and the like, and systematic and multi-dimensional assistance is performed in the whole medical process from diagnosis, treatment to rational medication and rehabilitation follow-up.

(4) The diagnosis and treatment assistance of the system and the method comprises a western medicine system and a traditional Chinese medicine system, the diagnosis suggestion comprises western medicine diagnosis and traditional Chinese medicine syndrome differentiation, and the treatment medicine recommendation comprises western medicines, traditional Chinese medicines and traditional Chinese medicines, so that the system and the method are more in line with the actual situation of China.

(5) The system and the method can give a doctor suggestions of specific diagnosis and treatment schemes in a standard clinical path and a real-world clinical path, can check clinical research evidence and real-world research data supporting the application of the diagnosis and treatment schemes, accord with the principle of evidence-based medicine, and the doctor can judge whether the diagnosis and treatment suggestions are reasonable or not by combining the clinical experience of the doctor and update the clinical knowledge of the doctor, so the system and the method can be simultaneously applied to diagnosis and treatment assistance and teaching assistance. The research evidence section is presented in a visual form, so that a user can quickly and comprehensively know the current research situation and the future development direction of a certain clinical direction to assist scientific research work.

(6) The system and the method can provide continuous education information, the information combines standard clinical paths and real world research data, and the level of clinical theory teaching and practice teaching can be rapidly and pertinently improved.

(7) The diagnosis and treatment information selected by the doctor in the system can be directly written back to subsystems such as a medical record and a prescription, and the like, so that the operation is simplified. When a doctor prescribes a prescription, the system can perform medical behavior rationality early warning based on the actual condition of a patient, prompt whether a treatment scheme in the prescription of the doctor accords with an indication, whether interaction exists, whether quantity adjustment is needed, clinical evidence intensity for the indication, rational medication evaluation and the like, synthesize individual differences of the patient and clinical paths to perform rational medication specification and early warning, ensure rationality and safety of the medication scheme based on the individual differences of the patient, improve the individualized medical service level, avoid the problem that an independent medication system inputs information again, and realize in-process multi-dimensional early warning.

(8) The system and the method can provide feasible clinical diagnosis and treatment proposal and corresponding clinical research and real world research evidence support for relevant medical workers, cover the whole period of diseases such as screening diagnosis, treatment, monitoring follow-up visit, out-of-hospital management and the like, assist doctors to quickly improve the clinical diagnosis and treatment level, learn and update diagnosis and treatment knowledge, master the current research situation and the development direction of clinical research and real world diagnosis and treatment, and solve the contradiction that the time and the energy of the medical workers are difficult to follow the clinical research progress.

Drawings

FIG. 1 is a block diagram of the system of the present invention.

FIG. 2 is a schematic flow chart of the method of the present invention.

Detailed Description

To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the present invention is described below with reference to the embodiments.

Example 1

In this embodiment, the leukemia clinical decision, teaching and scientific research auxiliary support system includes a medical and pharmaceutical data storage unit, a diagnosis and treatment parameter storage unit, an information input unit, a comparison processor unit, a diagnosis and treatment result output unit and an early warning and operation recording unit. Referring to fig. 1, the specific functions of the respective constituent units are as follows:

a medical and pharmaceutical data storage unit comprising: medical and pharmaceutical repositories, standard clinical pathways and evidences repositories, real world clinical pathways and evidences repositories. The information stored in the medical and pharmaceutical database comprises, but is not limited to, medical and pharmaceutical data of western medicine system and traditional Chinese medicine system, medical policy information, structured medical record information, patient out-of-hospital management information, disease burden information, drug instruction manual, consumable information, clinical research data, real world research data, clinical guidelines, expert consensus and other cross-modal multi-source heterogeneous data; the real world research data is from real world research formed by medical data subjected to data desensitization, cleaning and system analysis in various regions, medical institutions at all levels and families at home and abroad; the information stored in the medical and pharmaceutical data storage unit is extracted through technologies such as character recognition, image recognition, voice recognition and the like, is stored after being cleaned and structured, and is subjected to machine learning knowledge graph deduction to generate standard clinical path, real world clinical path, medical behavior rationality early warning information, continuous education data, and clinical research data and real world research data which serve as the content evidence support. The standard clinical pathway and the evidence base are used for storing a standard clinical pathway data cluster, and the standard clinical pathway data cluster is formed by pairing each standard clinical pathway generated by analysis in the medical and pharmaceutical data base as a data main body with one or more data information of continuous education data, medical behavior rationality early warning information, clinical research data and real world research data. The real world clinical pathway and the evidence base are used for storing a real world clinical pathway data cluster, and the real world clinical pathway data cluster is formed by pairing each real world clinical pathway generated by analysis in the medical and pharmaceutical data base as a data main body with one or more data information of continuous education data, medical behavior rationality early warning information and real world research data.

The diagnosis and treatment parameter storage unit comprises a screening diagnosis parameter library, a treatment parameter library, a monitoring follow-up parameter library and a tumor marker parameter library, and stores: and the standard clinical path and the real world clinical path are coded by the parameter values, wherein the stored parameters comprise screening diagnosis parameters, treatment parameters and monitoring follow-up parameters which influence leukemia and are discovered by machine learning. The user can adjust the information in the diagnosis and treatment parameter storage unit, including increasing parameters, decreasing parameters, adjusting parameter weights and the like.

The information input unit can be directly connected with a hospital HIS system (a hospital information system for storing and comprehensively managing various information of a hospital), and cross-modal multi-source heterogeneous information such as symptoms, physical signs, medical history, examination results and the like of a patient is called through an outpatient service management subsystem, an emergency management subsystem, a medical record management subsystem and the like, and is identified, extracted and structurally processed to generate a patient information parameter value; or the doctor inputs the patient information in modes of voice, characters, images, information selection under the guidance of the front end of the system and the like. Patient information is identified, extracted and structured to generate a patient information parameter value; sending the patient information parameter values to a comparison processor unit for parameter value comparison to match a clinical pathway. When a specific information query supplement instruction sent by the comparison processor unit is received, the specific information is queried from the HIS system, or a supplement input specific information prompt is sent to a user, and the user can check information such as associated parameters, approximate path structures, clinical significance of missing parameters and the like of the missing parameters. The information input unit sends the result to the comparison processor unit after the specific information is queried and supplemented.

And the comparison processor unit is used for calling data stored in a screening and diagnosis parameter library, a treatment parameter library, a monitoring follow-up parameter library and a tumor marker parameter library in the diagnosis and treatment parameter storage unit after receiving the patient information parameter values sent by the information input unit, and comparing the patient information parameter values with the parameter values in the four databases, including direct comparison and comparison after rule processing, wherein the used comparison technology includes but is not limited to a Spark calculation engine and other various computer algorithms. When the standard clinical path or the real world clinical path has parameters with missing patient information parameter values, the comparison processor unit sends a specific information query supplement instruction to the information input unit, and secondary comparison is performed after a specific information query supplement result fed back by the information input unit is received. And when the specific information is a specific examination result, if the user confirms that the examination is not performed, pushing an examination suggestion to the diagnosis and treatment result output unit. The system presets the optimal credit acquisition threshold value of the parameter value matching degree according to model training, and a user can also set the threshold value in the system according to actual needs. And the comparison processor unit sends the standard clinical path data cluster and the real world clinical path data cluster with the matching degree reaching the threshold value to the diagnosis and treatment result output unit according to the parameter value comparison result.

And the diagnosis and treatment result output unit comprises a diagnosis and treatment scheme module, an evidence support module and a continuing education module, and is used for dividing the standard clinical path data cluster and/or the real world clinical path data cluster into a preferred recommendation, a secondary recommendation and other recommendations according to the matching degree after receiving the data sent by the comparison processor unit, so that a user can modify the recommendation division standard. And splitting and restoring the information in the standard clinical path data cluster and the real-world clinical path data cluster into a standard clinical path, a real-world clinical path, corresponding clinical research data and real-world research data, corresponding continuing education data and corresponding medical behavior rationality early warning information. The standard clinical path and the real world clinical path are presented in the diagnosis protocol module in the form of diagnosis advice, the clinical research data and the real world research data are presented in the evidence support module, and the continuing education data are presented in the continuing education module. The standard clinical path and the real world clinical path comprise information of the whole medical process such as examination, diagnosis, treatment, rehabilitation follow-up visit and the like, and one or more of the information is output as diagnosis and treatment suggestions according to the clinical diagnosis and treatment stage positioned after the information parameters of the patient are compared with the parameters in the diagnosis and treatment parameter storage unit. The diagnosis information comprises western medicine diagnosis and traditional Chinese medicine syndrome differentiation and disease differentiation. The medicine scheme suggestion in the treatment information comprises western medicines, traditional Chinese medicines and Chinese patent medicines. The user may make adjustments based on the clinical recommendations, including item additions, item reductions, and item modifications. The clinical research data and the real world research data are presented in the evidence support module and are specifically divided into one or more categories of research information, nano-ranking standards, baseline characteristics, treatment schemes, treatment results, adverse reactions, guideline/expert consensus opinions, medication burdens and the like, so that a user can quickly and comprehensively master the current research situation and the future development direction in a certain refined clinical direction, and scientific research work is assisted. The continuing education data are presented in the continuing education module in forms of texts, pictures, tables, audios and videos according to specific data structures, and are used for standardized diagnosis and treatment teaching of the leukemia whole disease cycle. And sending the early warning information of the rationality of the medical behavior to an early warning and operation recording unit for real-time early warning in the clinical diagnosis and treatment process of doctors.

The early warning and operation recording unit comprises a medical behavior early warning module and an operation recording feedback module. And after receiving the medical behavior rationality early warning information sent by the diagnosis and treatment result output unit, the medical behavior early warning module carries out real-time early warning and suggestion on unreasonable treatment and medication schemes in the diagnosis and treatment process of doctors. The medical behavior reasonability early warning information is derived from professional medical/pharmaceutical data, a drug instruction book, most authoritative and latest clinical research data at home and abroad, real world research data, reasonable medication guide consensus and the like, and the presented specific data comprises but is not limited to indications, contraindications, adverse reactions, drug interaction, medication crowd early warning, drug use early warning, reasonable medication rating and price reference. And the operation record feedback module is used for recording the diagnosis and treatment scheme finally selected by the user and feeding back and optimizing the system. The user can manually select whether to adopt the diagnosis and treatment scheme, if the user selects to adopt the diagnosis and treatment scheme recommended by the clinical decision support system, the selected data is sent back to the relevant electronic information system of the hospital, and the functions of prescription write-back, case history write-back and the like are realized; if the user chooses not to adopt the diagnosis and treatment scheme recommended by the clinical decision support system, the reason of not adopting can be filled, and the actual diagnosis and treatment path and the diagnosis and treatment ending of the case are combined for optimizing the data of the medical and pharmaceutical data storage unit and the diagnosis and treatment parameter storage unit. The system can also compare the actual diagnosis and treatment path of the patient in each electronic information system of the hospital with the diagnosis and treatment scheme recommended by the system to judge whether the user adopts the system recommendation, and feeds the actual diagnosis and treatment path and diagnosis and treatment ending of the case back to the medical and pharmaceutical data storage unit to expand the data volume of the case and further continuously optimize the data in the diagnosis and treatment parameter storage unit.

The system combines with a corresponding auxiliary support method, as shown in fig. 2, and the specific operation steps are as follows:

(1) the information such as symptoms, physical signs, medical history and examination results of the patient is called from an outpatient service, an emergency management subsystem, a medical record management subsystem and the like of a hospital HIS system, or the information of the patient is input by a doctor in a mode of information selection under the guidance of voice, characters, images and a system front end. The patient information is identified, extracted and structured to generate patient information parameter values.

(2) The clinical decision support system starts a comparison processor unit to determine whether a standard clinical pathway and/or a real world clinical pathway completely matching the patient information parameter value exists:

if the path data cluster exists, the completely matched standard clinical path data cluster and/or the completely matched real world clinical path data cluster are/is called and sent to a diagnosis and treatment result output unit; in the diagnosis and treatment result output unit, the information presented to the user comprises diagnosis and treatment suggestions (such as diagnosis results of patients at the present stage, examinations to be performed, treatment scheme suggestions and rehabilitation follow-up visits suggestions) for auxiliary medicine based on standard clinical paths and real-world clinical paths, the diagnosis and treatment suggestions can be correspondingly used for supporting clinical evidence and real-world evidence of auxiliary medicine and scientific research (clinical research or real-world research corresponding to the diagnosis and treatment suggestions is subjected to data structural processing and then is divided into research information, nano-ranking criteria, baseline characteristics, treatment schemes, overall treatment results, adverse reactions, subgroup treatment results and adverse reactions, other prognosis and follow-up information, rational medication information, disease burden, guideline/expert consensus suggestions and the like for visual display), and the diagnosis and treatment suggestions can also be correspondingly used for supporting continuous education information of medicine and teaching (along with the patient's condition directly) And the related contents such as standardized diagnosis and treatment, science popularization, patient self management and the like are presented in the forms of texts, pictures, tables, audios, videos and the like).

If the standard clinical path and the real-world clinical path which are completely matched with the patient information parameter value do not exist, the comparison processor unit sends a specific information query supplementary instruction to the information input unit, and the information input unit queries specific information from a data source, such as searching from each electronic information system of a hospital or prompting information loss to a user and presenting an information supplementary window. And when the specific information is a specific examination result, if the user confirms that the examination is not performed, pushing an examination suggestion to the diagnosis and treatment result output unit.

And the comparison processor unit carries out secondary comparison after receiving the specific information query supplementary result fed back by the information input unit, judges whether a standard clinical path and/or a real world clinical path which are completely matched with the information parameter value of the patient exist at the moment, calls a completely matched standard clinical path data cluster and/or a real world clinical path data cluster if the standard clinical path and/or the real world clinical path data cluster exist, sends the standard clinical path data cluster and/or the real world clinical path data cluster to the diagnosis and treatment result output unit, and divides the decision support information into preferred recommendation, secondary recommendation and other recommendations according to the matching degree. And if the standard clinical path and the real-world clinical path which are completely matched with the patient information parameter value do not exist, judging whether the matching degree of the standard clinical path and/or the real-world clinical path reaches a threshold value. And if the matching degree of the standard clinical path and/or the real world clinical path reaches a threshold value, sending the corresponding standard clinical path data cluster and/or the real world clinical path data cluster to a diagnosis and treatment result output unit. If the matching degrees of the standard clinical path and the real world clinical path do not reach the threshold value, the comparison processor sends the standard clinical path data cluster and the real world clinical path data cluster with the highest matching degree to the diagnosis and treatment result output unit, diagnosis and treatment references based on the similar standard clinical path and the real world clinical path are presented to a user at the diagnosis and treatment result output unit, missing important information is prompted to the user, the diagnosis and treatment references are provided, meanwhile, similar real world evidences are provided as references, and the continuous education information of similar cases is presented to provide standardized references. In the comparison process, the threshold is the optimal confidence acquisition threshold of the system according to the parameter value matching degree preset by model training, and the user can also set the threshold in the system according to actual needs.

(3) And judging whether the diagnosis and treatment are finished by the user, if the diagnosis and treatment at the stage are not finished, calling the subsequent diagnosis and treatment information of the patient by the HIS of the hospital, importing the subsequent diagnosis and treatment information into the clinical decision support system for information updating, or manually inputting the subsequent diagnosis and treatment information of the patient by the user, and then carrying out clinical decision support again until the diagnosis and treatment at the stage are finished by the user selection. If the diagnosis and treatment at the stage are finished, whether a user adopts a diagnosis and treatment suggestion or not is judged, the diagnosis and treatment suggestion can be manually judged by the user, if the diagnosis and treatment suggestion is adopted, a diagnosis and treatment option selected by the user is written back to the HIS system, for example, information such as diagnosis, examination and treatment schemes is written back to subsystems such as a medical plan and a prescription, and when the prescription is made, the clinical decision support system can perform real-time early warning on the diagnosis and treatment scheme or medicine selected by a doctor (for example, when an unreasonable diagnosis and treatment scheme or unreasonable medicine application and other conditions occur), and the clinical decision support is finished after the completion; if the user chooses not to adopt the diagnosis and treatment suggestion, the user can selectively input the reason and end the clinical decision support. Whether the user manually judges whether to adopt the diagnosis and treatment suggestion or not and whether the user selects to adopt the diagnosis and treatment suggestion or not, the system background can record the real diagnosis and treatment path and diagnosis and treatment ending of the patient, the real diagnosis and treatment path and the diagnosis and treatment ending are compared with the diagnosis and treatment suggestion of the system, whether the user adopts the suggestion or not is automatically judged, the suggestion is fed back to the medical and pharmaceutical data storage unit, the data volume of the data storage unit is expanded, and then the data in the diagnosis and treatment parameter storage unit is continuously optimized.

Example 2

Based on the system and method implemented in embodiment 1, reference is made specifically to tables 1-2, where: table 1 lists some parameters affecting diagnosis and treatment of acute lymphoblastic leukemia, and the parameter values corresponding to the parameters are coded by different parameter values after structured processing; table 2 lists the clinical pathways of acute lymphoblastic leukemia expressed in part as parameter codes.

TABLE 1

TABLE 2

According to table 1-2, the medical record information of a certain patient acquired by the information input unit at this time is "male, 18 years old, and the patient was confirmed to be diagnosed with acute lymphoblastic leukemia (Ph +) before 1 week in our hospital, and the patient information parameter value obtained after recognition extraction, natural language processing and rule processing is" male "; 18 years old; acute lymphocytic leukemia has been diagnosed; ph + ", the parameter value matched in the parameter storage unit is a1 (confirmed acute lymphoblastic leukemia); b1 (risk stratification already performed); c1 (Ph + acute lymphoblastic leukemia). The clinical path matching degree is ranked from high to low as: A1-B1-C1-D0 (90%); A1-B1-C1-D1 (85%). The pre-set match threshold of 90% is therefore the other recommended treatment recommendation part is "attending the clinical trial or chemotherapy + TKI regimen row induction therapy or TKIs + glucocorticoid regimen induction therapy".

Example 3

Based on the system and method implemented in embodiment 1, reference is made specifically to tables 3-4, where: table 3 lists some parameters affecting diagnosis and treatment of acute myeloid leukemia, and the parameter values corresponding to the parameters are coded according to different parameter values after structured processing; table 4 lists the clinical pathway of acute myeloid leukemia expressed in part as parameter codes.

TABLE 3

TABLE 4

According to tables 3-4, the information of the medical record of a patient obtained by the information input unit at this time is "patient for first treatment, 51 years old, and the patient is admitted to the hospital because of" full-body multi-part bone pain half year, aggravated by 2 months ". Perfecting bone puncture and other examinations after hospitalization, diagnosing acute myelogenous leukemia (high risk) and multiple bone metastasis of myelogenous sarcoma by combining the results of myelogram, immune residue, immune typing and the like, and obtaining a patient information parameter value after identification, extraction, natural language processing and rule processing as 'primary treatment'; age 51; the main reasons are as follows: general bone pain in many parts-half a year, aggravation-2 months; bone puncture; bone marrow elephant; immune residues; carrying out immunological typing; diagnosing acute myeloid leukemia; high risk; myeloid sarcoma; multiple bone metastases ", the parameter value matched in the parameter storage unit is a1 (confirmed acute myeloid leukemia); b1 (age <60 years); c2 (high risk); f0 (no induction treatment). The clinical path matching degree is ranked from high to low as: A1-B1-C2-F0 (100%); A1-B1-C2-F1 (85%). The match threshold is preset to 92%, and thus, the treatment recommendation is "standard dose cytarabine + demethoxydaunorubicin (class 1) or standard dose cytarabine + daunorubicin + cladribine or high dose cytarabine + demethoxydaunorubicin (class 2B) or high dose cytarabine + daunorubicin (class 2B)".

Based on the system and method implemented in example 1, the same can be applied to chronic myelogenous leukemia, chronic lymphocytic leukemia, hairy cell leukemia. The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention.

Claims (10)

1. Leukemia clinical decision, teaching, scientific research assistance support system, its characterized in that: the diagnosis and treatment system comprises a medical and pharmaceutical data storage unit, a diagnosis and treatment parameter storage unit, an information input unit, a comparison processor unit, a diagnosis and treatment result output unit and an early warning and operation recording unit;

the medical and pharmaceutical data storage unit is used for storing all medical and pharmaceutical related data of a western medicine system and a traditional Chinese medicine system, and storing a standard clinical path data cluster and a real world clinical path data cluster;

the diagnosis and treatment parameter storage unit is used for storing: parameters influencing screening diagnosis, treatment and monitoring follow-up of leukemia, parameter weight values and parameter values of the parameters, wherein information in the diagnosis and treatment parameter storage unit can be manually adjusted;

the information input unit is used for acquiring the patient information, performing information structuring processing according to different acquisition forms and information modes, generating patient information parameters and sending the patient information parameters to the comparison processor unit; the information input unit is used for inquiring the specific information from the hospital HIS system and related systems when receiving a specific information inquiry supplement instruction sent by the comparison processor unit, or sending a prompt for supplementing and inputting the specific information, the associated parameters of the missing parameters, the approximate path structure and the clinical significance of the missing parameters to a user and presenting an information supplement window, and sending a result to the comparison processor unit after the specific information inquiry supplement; the form of acquiring the patient information comprises: the mode of calling from the hospital HIS system and the related system and the mode of direct input by the user;

the comparison processor unit is used for receiving the data sent by the information input unit, calling the data of the diagnosis and treatment parameter storage unit, comparing the two data, sending a specific information query supplement instruction to the information input unit by the comparison processor unit when a parameter with a missing patient information parameter value exists in a standard clinical path or a real world clinical path, carrying out secondary comparison after receiving a specific information query supplement result fed back by the information input unit, and then sending a standard clinical path data cluster and a real world clinical path data cluster which reach a preset or user-set matching degree threshold value to the diagnosis and treatment result output unit;

the diagnosis and treatment result output unit is used for receiving and displaying the result data sent by the comparison processor unit according to the matching degree sorting submodules so as to assist clinical decision, teaching and scientific research;

the early warning and operation recording unit is used for carrying out reasonability early warning on treatment and medication schemes and the like specifically related to a clinical path when a doctor prescribes a prescription, recording the real diagnosis and treatment path and results of a patient, comparing whether diagnosis and treatment suggestions and medical behavior reasonability early warning suggestions displayed in the diagnosis and treatment result output unit are adopted by a user or not, feeding back the recorded real diagnosis and treatment path and result information of the patient into a medical and pharmaceutical data storage unit, expanding the data quantity of the medical and pharmaceutical data storage unit, and further continuously optimizing the information in the clinical path and an evidence storage unit;

wherein, diagnosis and treatment parameter memory cell includes:

the screening and diagnosis parameter library is used for storing parameters, parameter weight values and parameter values of the parameters influencing the screening and diagnosis of the acute lymphocytic leukemia, the acute myelogenous leukemia, the chronic lymphocytic leukemia and the hairy cell leukemia; the parameters that store the screening diagnosis of acute lymphocytic leukemia including but not limited to, whether a bone marrow puncture and biopsy have been performed, whether a specific reproducible cytogenetic abnormality of bone marrow or peripheral blood lymphocytes has been performed, whether a history and physical examination has been performed, whether a cranial enhancement CT/MRI has been performed, whether a thoracic enhancement CT has been performed, risk stratification, whether there are major neurological signs or symptoms, whether a meningeal disease/chloroma/CNS bleeding examination has been performed, whether acute lymphocytic leukemia has been diagnosed, whether risk stratification has been performed, whether there is extramedullary involvement; the parameters that store the screening diagnosis of acute myeloid leukemia including but not limited to, whether the assessment of acute myeloid leukemia has been performed, whether cytogenetic and molecular analysis of bone marrow has been performed, whether immunophenotyping has been performed, whether CNS is suspected of bleeding, whether leukemic meningitis is suspected, whether extramedullary disease is suspected, whether symptoms are present, whether there are important neurological symptoms or signs in the diagnosis, whether isolated extramedullary disease is present, whether assessment and treatment of central nervous system leukemia has been performed, whether multidisciplinary diagnostic studies have been performed, whether acute myeloid leukemia has been diagnosed, whether neurological symptoms are present in the diagnosis, whether bleeding or tumor effects other than CT/MRI are present, whether lumbar puncture is performed, lumbar puncture results, cerebrospinal fluid morphological examination results, whether needle aspiration or biopsy has been performed; the parameters for storing the screening diagnosis of chronic myelogenous leukemia include, but are not limited to, history/physical examination, leucocytodiagnosis and classification/thrombocytodiagnosis, biochemical examination, myeloevaluation, cytogenetic examination, molecular examination, hepatitis-related examination, Ph or BCR-ABL1 status, clinical presentation, staging, additional evaluation, risk score, HLA examination, cell lineage determination by flow cytometry, mutation analysis, other diseases than chronic myelogenous leukemia, peripheral blood myeloblast percentage, sum of peripheral and promyelocytes, peripheral blood basophil percentage, platelet count percentage not related to treatment, peripheral blood or bone marrow primitive cell percentage, and the like, Extramedullary primary cell proliferation, whether the primary cells of the bone marrow biopsy are in large focal or cluster hyperplasia; the parameters for storing the screening diagnosis of the chronic lymphocytic leukemia include, but are not limited to, complete physical examination, disease history inquiry, patient physical status and accompanying disease, complete cytogenetic evaluation, serological index evaluation, complete virological evaluation, quantitative immunoglobulin detection, reticulocyte detection, hematogenous smear and biopsy detection, whole body imaging detection, cytopathology detection, multiple absorption gate control scans/echocardiograms, lymph node enlargement, splenomegaly, hepatomegaly or hepatosplenomegaly, hemoglobin and platelet count, and number of enlarged lymph node areas; the parameters for storing and influencing the hairy cell leukemia screening diagnosis include, but are not limited to, whether bone marrow biopsy + -aspiration is performed, whether characteristic hairy cells exist in peripheral blood or bone marrow, whether characteristic expression of reticuloendothelial increase exists in a bone marrow biopsy sample, whether sufficient immunophenotyping examination is performed, whether immunohistochemistry or flow cytometry examination is performed, whether BRAF V600E mutation examination is performed, whether IGHV4-34 rearrangement examination is performed, the size of liver and spleen, whether spleen enlargement and/or liver enlargement exist, whether peripheral lymph node enlargement exists, whether peripheral blood smear examination is performed, whether whole blood cell counting and classification is performed, whether hepatitis B virus examination is performed, whether hairy cell leukemia has been diagnosed;

the treatment parameter library is used for storing parameters, parameter weight values and parameter values of the parameters influencing the treatment of acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic leukemia and hairy cell leukemia; the parameters for storing and influencing the acute lymphoblastic leukemia therapy include, but are not limited to, whether acute lymphoblastic leukemia is diagnosed or not, whether risk stratification is carried out or not, risk stratification results, whether induction therapy is carried out or not, an induction therapy scheme is carried out or not, induction therapy curative effect evaluation results, whether minimal residual disease evaluation is carried out or not, whether consolidation therapy is carried out or not, a consolidation therapy mode is carried out or not, whether further therapy is carried out or not, the age of a patient and whether important complications exist or not, whether maintenance therapy is carried out or not, whether ABL gene mutation detection is carried out or not, and whether treatment of recurrent/refractory diseases is carried out or not; the parameters for storing and influencing the acute myeloid leukemia therapy include, but are not limited to, whether acute myeloid leukemia has been diagnosed, disease stage, patient age, risk classification, whether induction therapy has been performed, induction therapy mode, whether bone marrow is followed up 2-3 weeks after induction therapy, bone marrow follow-up results, whether follow-up therapy is performed after induction therapy, whether blood recovery and bone marrow remission state after therapy are evaluated, evaluation results, whether consolidation therapy is performed, whether an optional donor is present, whether a donor is waited for, whether bone marrow is followed up 3-4 weeks after induction therapy, risk status, whether disease is alleviated, whether treatment is received after remission, whether further management has been performed, whether intensive induction remission therapy has been received, previous intensive therapy mode, whether bone marrow state is followed up 4-6 weeks after blood recovery, whether bone marrow state is followed up 8-12 weeks after blood recovery, and the like, General condition of the patient, renal function condition, how the karyotype and molecular markers are, whether or not the patient is a recurrent disease; storing parameters affecting treatment of chronic myeloid leukemia including, but not limited to, whether chronic myeloid leukemia has been diagnosed, Ph or BCR-ABL1 status, clinical presentation, disease staging, type of acute shift, type of risk, whether initial treatment has been performed, patient response milestone, whether second and subsequent treatment has been performed, whether patient complications and drug interactions have been evaluated, mutated genes, indications of the presence or absence of allogeneic HCT, complete cytogenetic response or incomplete cytogenetic response/recurrence, QPCR monitoring outcome, complications following prior TKI drug/tolerance/mutation detection/HCT; the parameters that govern the effect of treatment of chronic lymphocytic leukemia include, but are not limited to, whether chronic lymphocytic leukemia has been diagnosed, classification of disease, whether treatment is being received, Rai and Binet staging, whether indications of treatment have been made, whether further assessments have been made, whether 17p deletion/TP 53 mutation has been associated, CpG-stimulated karyotype, IGHV mutational status, whether progressive cytopenia has occurred, whether first line treatment has been performed, patient age and whether significant complications have been associated with, response to first line treatment, whether disease progression or recurrence or refractory disease has occurred, whether histological transformation or histological progression has occurred, whether subsequent treatment is being received, whether treatment is being received after disease progression or recurrence; storing parameters that affect hairy cell leukemia therapy including, but not limited to, whether hairy cell leukemia has been diagnosed, whether there is an indication of therapy, whether initial therapy has been administered, patient response to initial therapy, whether supportive therapy has been administered, whether a relapsed/refractory disease has been administered, time of relapse, whether further progression of the disease has occurred;

the monitoring follow-up parameter base is used for storing parameters, parameter weight values and parameter values of the parameters, which influence the monitoring follow-up of the acute lymphocytic leukemia, the acute myelogenous leukemia, the chronic lymphocytic leukemia and the hairy cell leukemia; the parameters for storing and influencing the monitoring follow-up of the acute lymphocytic leukemia include but are not limited to the existence of abnormality of a physical examination result, the existence of abnormality of a complete blood cell count (CBC), the existence of new symptoms, the existence of a marrow puncture indication, the existence of abnormality of a marrow puncture result, and whether clinical suspicion of disease relapse or not, and whether the result is Ph +; the parameters for storing and influencing the follow-up visit of the acute myelogenous leukemia monitoring include but are not limited to whether a peripheral blood smear is abnormal or not and whether a suitable relatives donor exists or not; storing parameters that affect follow-up monitoring of chronic myeloid leukemia including, but not limited to, whether response milestones have been reached, whether patient complications and drug interactions have been assessed, whether any signs of unresponsiveness have occurred, whether 1-log elevation of BCR-ABL1 transcript levels has occurred and major molecular responses have been lost; the parameters that the storage affects the follow-up of monitoring chronic lymphocytic leukemia include, but are not limited to, whether disease relapse is suspected, whether new symptoms are present, patient age, whether tumor lysis syndrome is present, whether autoimmune cytopenia is present; storing parameters that affect hair cell leukemia monitoring follow-up including, but not limited to, whether capillary leak syndrome occurs, whether hemolytic uremic syndrome occurs, hemoglobin level, platelet count, serum creatinine level, whether LDH is detected, indirect bilirubin, and hemolytic evidence such as schizophrenic cells in blood smears;

the tumor marker parameter library is used for storing the markers of leukemia and leukemia related diseases, and the parameters stored in the tumor marker parameter library are derived from normal tissues or cells, blood or body fluid; the markers for storing acute lymphoblastic leukemia and acute lymphoblastic leukemia related diseases comprise but are not limited to proteins and metabolic markers, nucleic acid markers, platelet-induced platlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA, mRNA, CTC, ctDNA/cfDNA, ctRNA, lncRNA, DNA methylation/histone modification, and m6A RNA methylation; the markers for storing acute myeloid leukemia and acute myeloid leukemia related diseases include, but are not limited to, protein and metabolic markers, nucleic acid markers, platelet-induced platlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA, mRNA, CTC, ctDNA/cfDNA, ctRNA, lncRNA, DNA methylation/histone modification, and m6A RNA methylation; the markers for storing chronic myelogenous leukemia and chronic myelogenous leukemia related diseases comprise but are not limited to proteins and metabolic markers, nucleic acid markers, platelet-induced platlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA, mRNA, CTC, ctDNA/cfDNA, ctRNA, lncRNA, DNA methylation/histone modification, and m6A RNA methylation; the markers for storing chronic lymphocytic leukemia and chronic lymphocytic leukemia related diseases comprise but are not limited to protein and metabolic markers, nucleic acid markers, platelet-induced platlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA, mRNA, CTC, ctDNA/cfDNA, ctRNA, lncRNA, DNA methylation/histone modification, and m6A RNA methylation; the markers for storing hairy cell leukemia and hairy cell leukemia related diseases comprise but are not limited to protein and metabolic markers, nucleic acid markers, platelet-induced platlets, exosomes and tumor microenvironment markers; the nucleic acid markers include, but are not limited to, microRNA, mRNA, CTC, ctDNA/cfDNA, ctRNA, lncRNA, DNA methylation/histone modification, and m6A RNA methylation.

2. The leukemia clinical decision, education and scientific research assistance support system according to claim 1, wherein the medical and pharmaceutical data storage unit comprises:

the medical and pharmaceutical database is used for storing all medical and pharmaceutical relevant data of a western medicine system and a traditional Chinese medicine system, and comprises a series of cross-modal multi-source heterogeneous data, and the series of cross-modal multi-source heterogeneous data at least comprises: medical and pharmaceutical data of western medicine system and traditional Chinese medicine system, medical policy information, structured medical record information, patient out-of-hospital management information, disease burden information, drug instruction manual, consumable information, clinical research data, real world research data, clinical guidelines, expert consensus;

the standard clinical path library is used for storing standard clinical path data clusters, namely standard clinical paths, continuing education data, medical behavior rationality early warning information, clinical research data and real world research data which are generated by analyzing the medical and pharmaceutical data storage units and are paired into groups;

and the real world clinical path library is used for storing real world clinical path data clusters, namely paired and grouped real world clinical paths, continuous education data, medical behavior rationality early warning information and real world research data which are generated by analyzing the medical and pharmaceutical data storage units.

3. The leukemia clinical decision, teaching and scientific research auxiliary support system according to claim 1, wherein the diagnosis and treatment result output unit comprises:

the diagnosis and treatment scheme module is used for displaying standard clinical paths and/or real world clinical paths which are divided into first-choice recommendations, second-choice recommendations and other recommendations according to the sequence from high to low after the comparison by the comparison processor unit, and at least comprises one or more of whole medical information of examination, diagnosis, treatment and rehabilitation follow-up visits as diagnosis and treatment suggestions for assisting clinical decision;

the evidence support module is used for displaying clinical research data or real world research data matched with the standard clinical path or the real world clinical path in the diagnosis and treatment scheme module, at least comprises one or more of research information, nano-grade standard, baseline characteristic, treatment scheme, treatment result, adverse reaction, subgroup information, other prognosis and follow-up information, reasonable medication information, guideline/expert consensus opinion and medication burden, visually displays the data, provides scientific research suggestions and is used for assisting clinical decision and scientific research work;

and the continuous education module is used for displaying continuous education data matched with the standard clinical path or the real-world clinical path in the diagnosis and treatment scheme module, at least comprises contents of standardized diagnosis and treatment, science popularization and patient self management directly related to the patient illness state, and the display form comprises but is not limited to texts, pictures, tables, audio and videos for assisting clinical decision and teaching.

4. The system of claim 3, wherein the diagnosis information in the diagnosis and treatment scheme module includes western medicine diagnosis and traditional Chinese medicine syndrome differentiation and disease differentiation, the medical scheme suggestions in the treatment information include western medicine, traditional Chinese medicine and Chinese patent medicine, and the user selects adjustment modes including increase, decrease and modification based on the diagnosis and treatment suggestions.

5. The leukemia clinical decision, teaching and scientific research support system according to claim 1, wherein the early warning and operation recording unit comprises:

the medical behavior early warning module is used for receiving the medical behavior rationality early warning information sent by the diagnosis and treatment result output unit and then carrying out real-time early warning and suggestion on unreasonable treatment and medication schemes in the diagnosis and treatment process of doctors; the medical behavior rationality early warning information is derived from one or more of professional medical and pharmaceutical data, a drug instruction book, clinical research data, real world research data and a reasonable medication guide consensus, and the presented specific data comprises but is not limited to indications, contraindications, adverse reactions, drug interactions, medication crowd early warnings, drug use early warnings, reasonable medication ratings and price references;

the operation record feedback module is used for recording the diagnosis and treatment scheme finally selected by the user and feeding back and optimizing the leukemia clinical decision, teaching and scientific research auxiliary support system; the user can manually select whether to adopt the diagnosis and treatment scheme, if the user selects to adopt the diagnosis and treatment scheme recommended by the clinical decision support system, the selected data is sent back to the hospital-related electronic information system, and the functions of prescription write-back and medical record write-back are realized; if the user selects not to adopt the diagnosis and treatment scheme recommended by the clinical decision support system, the reason of not adopting can be filled, and the actual diagnosis and treatment path and the diagnosis and treatment outcome of the case are combined to optimize the data of the medical and pharmaceutical data storage unit and the diagnosis and treatment parameter storage unit; meanwhile, the actual diagnosis and treatment path of the patient in each electronic information system of the hospital can be compared with the diagnosis and treatment scheme recommended by the leukemia clinical decision, teaching and scientific research auxiliary support system to automatically judge whether the user adopts the system recommendation, the actual diagnosis and treatment path and the diagnosis and treatment outcome of the case are fed back to the medical and pharmaceutical data storage unit, the data volume is expanded, and the data in the diagnosis and treatment parameter storage unit is continuously optimized.

6. The system of claim 1, wherein the comparison processor unit compares the two data directly with each other or after performing a rule processing on the data according to actual conditions.

7. The leukemia clinical decision, teaching and scientific research auxiliary support method suitable for any one of claims 1 to 6, characterized by comprising the following steps:

(1) acquiring patient information from a hospital HIS system and a related system, or providing the patient information by a doctor, and generating a patient information parameter value after identifying, extracting and structuring the patient information;

(2) the leukemia clinical decision, teaching and scientific research auxiliary support system starts a comparison processor unit, and compares and judges whether a standard clinical path and/or a real world clinical path completely matched with the patient information parameter value exists or not through the comparison processor unit, wherein the judgment result comprises the following steps:

if the completely matched standard clinical path and/or real world clinical path exist, the completely matched standard clinical path data cluster and/or real world clinical path data cluster are/is called and sent to a diagnosis and treatment result output unit; outputting and displaying decision support information in a diagnosis and treatment result output unit; the decision support information comprises a standard clinical pathway and real-world clinical pathway based clinical recommendation for assisting clinical decision making; the diagnosis and treatment suggestion is correspondingly used for clinical evidence support and real world evidence support for assisting clinical decision and scientific research, or is correspondingly used for continuing education information for assisting clinical decision and teaching;

if the completely matched standard clinical path and/or real world clinical path does not exist, the comparison processor unit sends a specific information query supplementary instruction to the information input unit, and then the information input unit queries specific information from a data source according to the specific information query supplementary instruction; when the specific information is a specific inspection result, if the user confirms that the inspection is not carried out, pushing an inspection suggestion;

(3) after the specific information query supplementary result is obtained, secondary comparison judgment is carried out, wherein in the secondary comparison judgment result:

if the standard clinical path and the real world clinical path which are completely matched with the patient information parameter values exist, calling a completely matched standard clinical path data cluster and/or a completely matched real world clinical path data cluster, and outputting and displaying decision support information to a diagnosis and treatment result output unit;

if the standard clinical path and the real world clinical path which are completely matched with the patient information parameter values do not exist, judging whether the matching degree of the standard clinical path and/or the real world clinical path reaches a threshold value, and judging that: if the matching degree of the standard clinical path and/or the real world clinical path reaches a threshold value, outputting and displaying the corresponding standard clinical path data cluster and/or the real world clinical path data cluster to a diagnosis and treatment result output unit, and dividing the standard clinical path data cluster and/or the real world clinical path data cluster into a first-choice recommendation level, a second-choice recommendation level and other recommendation levels according to the matching degree to present the first-choice recommendation level, the second-choice recommendation level and the other recommendation levels to a user; if the matching degrees of the standard clinical path and the real world clinical path do not reach the threshold value, selecting the standard clinical path data cluster and the real world clinical path data cluster with the highest matching degree to send to a diagnosis and treatment result output unit, presenting diagnosis and treatment references based on the similar standard clinical path and the real world clinical path to a user, prompting missing important information to the user, simultaneously providing similar real world evidence as clinical and scientific research references, and presenting continuous education information of similar medical cases as medical standardized education references; the threshold is an optimal confidence acquisition threshold according to a parameter value matching degree preset by model training, or a threshold is set by a user according to actual needs;

(4) judging whether diagnosis and treatment are finished by the user, if the diagnosis and treatment are not finished at the stage, importing the diagnosis and treatment information through an electronic information system of the hospital or inputting subsequent diagnosis and treatment information of the patient by the user for information updating, and then carrying out clinical decision support again until the diagnosis and treatment at the stage are finished by the user; if diagnosis and treatment at the stage are finished at the moment, judging whether the user adopts diagnosis and treatment suggestions or not, wherein the judging mode comprises manual judgment of the user and automatic judgment after comparison of the real diagnosis and treatment path of the patient with the diagnosis and treatment outcome and the diagnosis and treatment suggestions: if the diagnosis and treatment options selected by the user are adopted, writing the diagnosis and treatment options back to the HIS system of the hospital, carrying out real-time early warning on the diagnosis and treatment scheme or the medicine selected by the doctor through an early warning and operation recording unit when the doctor prescribes a prescription, and finishing the clinical decision support after the completion; if the user chooses not to adopt the diagnosis and treatment suggestion, the clinical decision support is ended, and the user can selectively input the reason for not adopting the diagnosis and treatment suggestion before the end.

8. The leukemia clinical decision, instruction, scientific support method according to claim 7, wherein the patient information comprises at least symptoms, signs, medical history and/or examination results.

9. The leukemia clinical decision, teaching and scientific research support method according to claim 7, wherein the manner of providing patient information by the doctor at least comprises voice, text, image and information for selecting patient under corresponding front-end guidance information.

10. The leukemia clinical decision, teaching and scientific research support method according to claim 7, wherein the query of specific information from the data source in step (2) comprises searching from electronic information systems in hospitals, or prompting users for information loss and requesting users to perform information supplementary recording.

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