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WO2025109706A1 - Dispositif d'aide à la sélection, procédé d'aide à la sélection et support d'enregistrement - Google Patents

Dispositif d'aide à la sélection, procédé d'aide à la sélection et support d'enregistrement Download PDF

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Publication number
WO2025109706A1
WO2025109706A1 PCT/JP2023/041953 JP2023041953W WO2025109706A1 WO 2025109706 A1 WO2025109706 A1 WO 2025109706A1 JP 2023041953 W JP2023041953 W JP 2023041953W WO 2025109706 A1 WO2025109706 A1 WO 2025109706A1
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Prior art keywords
selection criteria
selection
structured
clinical trial
indicating
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English (en)
Japanese (ja)
Inventor
剛範 辻川
雅洋 久保
大作 柴田
裕 宇野
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NEC Corp
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NEC Corp
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Priority to PCT/JP2023/041953 priority Critical patent/WO2025109706A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data

Definitions

  • This disclosure relates to a selection support device, etc.
  • a person in charge of a clinical trial entrusted by a pharmaceutical company sends the selection criteria for clinical trial patients to a medical institution, inquiring about information on patients who fit the clinical trial.
  • the person in charge at the medical institution who receives the inquiry extracts patients who fit the selection criteria for clinical trial patients by checking the descriptions in the medical records.
  • the person in charge at the medical institution then responds to the inquiry.
  • the person in charge of extracting patients who fit the selection criteria at the medical institution determines, for example, whether the selection criteria match the contents of the medical records.
  • the person in charge of extracting patients who fit the selection criteria then extracts patients whose selection criteria match the contents of the electronic medical records as candidate clinical trial patients.
  • a system that assists in the extraction of candidate clinical trial patients may also be used.
  • the clinical trial candidate extraction device in Patent Document 1 extracts patients who are likely to participate in clinical trials as clinical trial candidates from among patients currently being treated for the disease targeted by the investigational drug.
  • Patent Document 1 With the technology described in Patent Document 1, it can be difficult to determine where to check information for each patient.
  • the present disclosure aims to provide a selection support device, etc. that can easily identify information to refer to when selecting clinical trial patients in order to solve the above problems.
  • the selection support device disclosed herein includes an acquisition means for acquiring text indicating the selection criteria for clinical trial patients, a conversion means for converting the text indicating the selection criteria into structured selection criteria, a generation means for generating reference instructions indicating information to be referenced in selecting clinical trial patients based on the structured selection criteria, and an output means for outputting the generated reference instructions.
  • the selection support method disclosed herein acquires text indicating the selection criteria for clinical trial patients, converts the text indicating the selection criteria into structured selection criteria, generates reference instructions indicating information to be referenced in selecting clinical trial patients based on the structured selection criteria, and outputs the generated reference instructions.
  • the recording medium of the present disclosure non-temporarily records a selection support program that causes a computer to execute a process of acquiring text indicating selection criteria for clinical trial patients, a process of converting the text indicating the selection criteria into structured selection criteria, a process of generating reference instructions indicating information to be referenced in selecting clinical trial patients based on the structured selection criteria, and a process of outputting the generated reference instructions.
  • This disclosure makes it easier to identify information to refer to when selecting patients for clinical trials.
  • FIG. 1 is a diagram illustrating an example of the configuration of a clinical trial support system according to the present disclosure.
  • FIG. 1 is a diagram illustrating an example of a configuration of a selection support device according to the present disclosure.
  • FIG. 13 is a diagram showing an example of the structure of the inclusion criteria text in the present disclosure.
  • FIG. 13 is a diagram illustrating an example of a sentence configuration of an exclusion criterion in the present disclosure.
  • FIG. 2 is a diagram showing an example of the structure of the text of the structured inclusion criteria in the present disclosure.
  • FIG. 13 is a diagram showing an example of a sentence configuration of structured exclusion criteria in the present disclosure.
  • FIG. 2 is a diagram showing an example of a display screen in the present disclosure.
  • FIG. 1 is a diagram illustrating an example of the configuration of a clinical trial support system according to the present disclosure.
  • FIG. 1 is a diagram illustrating an example of a configuration of a selection support device according to the present disclosure.
  • FIG. 13 is
  • FIG. 2 is a diagram showing an example of a display screen in the present disclosure.
  • FIG. 2 is a diagram showing an example of a display screen in the present disclosure.
  • FIG. 2 is a diagram illustrating an example of an operation flow of a selection support device according to the present disclosure.
  • FIG. 2 is a diagram illustrating an example of a hardware configuration of a selection support device according to the present disclosure.
  • FIG. 1 is a diagram showing an example of the configuration of a clinical trial support system.
  • the clinical trial support system includes a selection support device 10 and a terminal device 20.
  • the selection support device 10 is connected to the terminal device 20, for example, via a network.
  • the number of terminal devices 20 may be set as appropriate.
  • the clinical trial support system is, for example, a system that outputs a reference instruction indicating information to be referred to when selecting a clinical trial patient.
  • the information to be referred to when selecting a clinical trial patient is, for example, information that needs to be confirmed when determining whether a patient meets the selection criteria for a clinical trial patient.
  • the clinical trial support system is used, for example, when a clinical trial to verify the effectiveness of a new drug is planned, to extract patients to be selected as clinical trial patients.
  • the clinical trial support system may also be used to estimate the number of clinical trial patients at the stage of creating a clinical trial protocol.
  • the uses of the clinical trial support system are not limited to the above.
  • a clinical trial is, for example, a clinical trial conducted to obtain legal approval for the manufacture and sale of pharmaceuticals or medical devices.
  • a clinical trial patient is, for example, a patient who is the subject of a clinical trial.
  • the reference instruction is an instruction regarding information that needs to be confirmed when determining whether a patient is suitable to be a clinical trial patient.
  • the reference instruction includes, for example, the location where the information to be referred to is written and the content of the information to be referred to.
  • the reference instruction is, for example, a prompt used as input for a large-scale language model. The reference instruction is not limited to a prompt.
  • Information to be referred to when selecting clinical trial patients is, for example, information that must be checked in detail to ensure that it matches the selection criteria when determining whether or not a patient is suitable as a candidate for a clinical trial patient.
  • Information to be referred to when selecting clinical trial patients is, for example, information about the patient's treatment. For example, a person who extracts patients who fit the selection criteria for clinical trial patients, such as a doctor, will check the information about the treatment based on the reference instructions and determine whether or not each patient meets the selection criteria.
  • the information regarding treatment is, for example, records regarding the patient's treatment.
  • the information regarding treatment is, for example, information recorded in a medical record and test data.
  • the information recorded in a medical record is, for example, information recorded in an electronic medical record.
  • the information regarding treatment may be, for example, either information recorded in a medical record or test data.
  • the information regarding treatment may also be information included in medical accounting data or medical receipts.
  • a medical receipt is, for example, a statement of medical fees billed to an insurer by a medical institution.
  • a medical institution may include a pharmacy that prescribes medicine.
  • the information regarding treatment may also be information included in medical literature. Medical literature may include guidelines regarding treatment. The information regarding treatment is not limited to the above.
  • the selection criteria are, for example, inclusion criteria and exclusion criteria for patients in a clinical trial.
  • the selection criteria may be either inclusion criteria or exclusion criteria for patients in a clinical trial.
  • the inclusion criteria are, for example, information indicating the conditions of patients who are eligible to be clinical trial patients.
  • the conditions of patients who are eligible to be clinical trial patients are indicated using the attributes and characteristics of patients suitable as clinical trial patients.
  • the exclusion criteria are information indicating the conditions for excluding patients from the subjects of clinical trial patients. That is, the exclusion criteria are, for example, information indicating the conditions of patients who are not selected as clinical trial patients.
  • the exclusion criteria are indicated using the attributes and characteristics of patients who are not suitable as clinical trial patients.
  • the patient attributes are, for example, the state of the patient that does not change due to treatment.
  • the patient attributes are, for example, information on one or more items of the patient's age, sex, weight, height, and race.
  • the patient attributes are not limited to the above.
  • the patient characteristics are the state of the patient that changes due to treatment.
  • the patient characteristics are, for example, information on one or more items of the treatment, surgery, examination, medication, test results, and follow-up results performed on the patient.
  • the patient characteristics are not limited to the above.
  • the medical institution's staff member is, for example, one or both of a staff member belonging to the medical institution and a staff member of an institution that has been entrusted with work by the medical institution.
  • An institution that a medical institution entrusts with handling clinical trials is, for example, an SMO (Site Management Organization).
  • a staff member of an institution that a hospital entrusts with handling clinical trials is, for example, a CRC (Clinical Research Coordinator).
  • the medical institution's staff member is not limited to the above.
  • a medical institution's staff member may receive an inquiry from a pharmaceutical company's staff member about information on patients who fit the selection criteria for patients to be included in a clinical trial.
  • a staff member at an institution that has been commissioned by a pharmaceutical company conducting a clinical trial of a new drug may send the selection criteria to the medical institution's staff member and inquire about information on patients who fit the selection criteria.
  • the person in charge at the pharmaceutical company may be, for example, a person in charge at an institution that has been commissioned by the pharmaceutical company to conduct clinical trials of new drugs.
  • the institution that has been commissioned by the pharmaceutical company to conduct clinical trials may be, for example, a CRO (Contract Research Organization).
  • the person in charge at the institution that has been commissioned by the pharmaceutical company to conduct clinical trials may be, for example, a CRA (Clinical Research Associate).
  • the CRA may be an employee of the pharmaceutical company.
  • Inquiries about information on patients who meet the selection criteria for patients to be included in clinical trials are made, for example, at the stage when the CRA prepares the clinical trial protocol. Inquiries about information on patients who meet the selection criteria for patients to be included in clinical trials may also be made at the stage when the clinical trial is carried out in accordance with the clinical trial protocol.
  • Clinical trial protocols are used, for example, to explain the contents of the clinical trial to and negotiate with medical institutions, and to submit notifications to related institutions. The uses of clinical trial protocols are not limited to the above.
  • the timing of inquiries about information on patients who meet the selection criteria is not limited to the stage when the clinical trial protocol is prepared.
  • FIG. 2 is a diagram showing an example of the configuration of the selection support device 10.
  • the selection support device 10 basically includes an acquisition unit 11, a conversion unit 12, a generation unit 14, and an output unit 15.
  • the selection support device 10 may also include, for example, an identification unit 13 and a storage unit 16.
  • the acquisition unit 11 acquires text indicating the selection criteria for clinical trial patients.
  • the acquisition unit 11 acquires text indicating the selection criteria, for example, from the terminal device 20.
  • the text indicating the selection criteria is input to the terminal device 20, for example, by the CRC.
  • the person who inputs the text indicating the selection criteria to the terminal device 20 is not limited to the CRC.
  • the text indicating the selection criteria is created, for example, by the CRA. Then, the text indicating the selection criteria is sent, for example, from the CRA to the CRC.
  • the person who creates the text indicating the selection criteria is not limited to the CRA.
  • FIG. 3 and 4 are examples of sentences showing the selection criteria for clinical trial patients.
  • the sentences showing the selection criteria are, for example, sentences that describe each of the criteria included in the selection criteria.
  • FIG. 3 and FIG. 4 are examples of sentences showing the selection criteria in a clinical trial of a drug used to treat breast cancer.
  • FIG. 3 is an example of a document showing the inclusion criteria, which is one of the selection criteria.
  • the example of the inclusion criteria in FIG. 3 includes 15 sentences showing the inclusion criteria.
  • Each sentence of the inclusion criteria shows the conditions for a patient to be selected as a clinical trial patient. For example, the sentence "Be 18 years of age or older at the time of screening" indicates that one of the conditions for being selected as a clinical trial patient is being 18 years of age or older at the time of screening.
  • the example of the exclusion criteria in FIG. 4 includes 19 sentences showing the exclusion criteria.
  • Each sentence of the exclusion criteria shows the conditions for a patient not to be selected as a clinical trial patient.
  • the sentence "Having poorly controlled or significant heart disease" indicates that one of the conditions for not being selected as a clinical trial patient is having poorly controlled or significant heart disease.
  • the example sentences showing the selection criteria in Figures 3 and 4 for example, patients who meet all of the inclusion criteria shown in the 15 sentences and do not meet any of the exclusion criteria shown in the 19 documents are selected as clinical trial patients.
  • selection of clinical trial patients may be performed using criteria in which some of the selection criteria are relaxed.
  • the acquisition unit 11 may acquire an item for which a reference indication is generated.
  • the item for which a reference indication is generated is information that specifies the content of the criterion that is the target of the reference indication, among the selection criteria.
  • the information that specifies the content of the criterion that is the target of the reference indication may be a superordinate concept of the criterion for which a reference indication is generated. For example, if the target for which a reference indication is generated is a criterion related to "lung cancer," the item is "disease name.”
  • the items are not limited to the above.
  • the identification unit 13 acquires the item for which a reference indication is generated, for example, from the terminal device 20.
  • the item for which a reference indication is generated is input, for example, to the terminal device 20 by the operation of the person to be extracted.
  • the conversion unit 12 converts the sentence indicating the selection criteria into structured selection criteria.
  • the structured selection criteria is, for example, data that associates items included in the sentence indicating the selection criteria with the conditions for each item.
  • the items indicate, for example, the content of each criterion included in the selection criteria. For example, if the criterion specifies the name of the disease that the patient is suffering from, the item is "disease name.”
  • the content of the criteria included in the items is not limited to the above.
  • the conversion unit 12 extracts the selection criteria items and the conditions for each item from the sentence indicating the selection criteria. The conversion unit 12 then converts the extracted selection criteria items into data that associates them with the conditions for each item.
  • the conversion unit 12 adds a label to a term included in a sentence indicating the selection criteria.
  • the term is, for example, a term related to the selection of clinical trial patients.
  • the term related to the selection of clinical trial patients is a term related to information that needs to be confirmed when selecting clinical trial patients. Information that needs to be confirmed when selecting clinical trial patients is, for example, information indicating the condition of a patient that may affect the implementation and results of the clinical trial.
  • the term related to the selection of clinical trial patients is, for example, a term indicating one or more of the patient's conditions, such as disease name, treatment content, drug name, examination, examination result, medical condition, and medical history.
  • the term related to the selection of clinical trial patients may include patient attributes.
  • the patient attributes are information indicating a state of the patient that does not change due to treatment, among the conditions of the patient.
  • the patient attributes are, for example, one or more of information among the patient's age, sex, weight, height, race, and family medical history.
  • the patient attributes are not limited to the above.
  • the term related to the selection of clinical trial patients is not limited to the above.
  • the conversion unit 12 for example, converts the sentence indicating the selection criteria into structured selection criteria based on the added label.
  • the conversion unit 12 uses, for example, a labeling model to add labels to terms included in the sentence indicating the selection criteria.
  • the labeling model is a machine learning model using a natural language processing method. For example, Word2vec can be used as the natural language processing method.
  • the labeling model breaks down the sentence indicating the selection criteria into terms by morphological analysis using a dictionary in the medical field. Then, the labeling model adds labels to each term using, for example, the dictionary.
  • the labeling model is generated, for example, by learning the relationship between the sentence indicating the selection criteria, the dictionary, and the labels to be added.
  • the labeling model is generated, for example, by deep learning using a neural network.
  • the labeling model is generated, for example, in a system external to the selection support device 10.
  • the dictionary includes data associating, for example, terms in the medical field with the contents of the labels of each term.
  • the contents of the labels are, for example, information indicating the classification of the terms.
  • the contents of the labels may include, for example, the part of speech of the terms.
  • the classification of the terms is, for example, information indicating the situation in which the term is used in the medical field, the disease name, the period, the subject, the guideline name, the standard, the drug name, and the item to which the term applies among conditions.
  • the situation in which the term is used is, for example, the situation in which the term is used among examinations, medication, and treatments.
  • the situation in which the term is used is not limited to the above.
  • the classification of the terms is also not limited to the above.
  • the labels may also include information regarding the inclusion relationship between the terms.
  • the information regarding the inclusion relationship between the terms is information indicating the hierarchical relationship in the meaning of the terms.
  • the labels may also include information indicating the chronological order.
  • the labeling model adds a label indicating that "drug A” and “administration” come before “drug B” and “administration” in the chronological order.
  • the conversion unit 12 generates structured selection criteria, for example, based on the selection criteria to which labels have been added.
  • Structuring refers to converting information contained in a sentence that is related to each other into a state in which the relationship can be understood.
  • structuring refers to extracting items corresponding to each criterion contained in the selection criteria and the conditions for each item, and converting the sentence indicating the selection criteria into a state in which the relationship between the items and the conditions for each item can be understood.
  • the conversion unit 12 identifies terms to which labels related to items have been added from the sentence of the selection criteria.
  • the relationship between the labels and the items is set, for example, as data in a table format.
  • the conversion unit 12 extracts terms to which labels related to items have been added from the sentence of the selection criteria and the conditions for each item. For example, when there is a term to which a drug name is added as a label, the conversion unit 12 extracts the medication history as an item. In addition, the conversion unit 12 extracts, for example, "drug A", “side effects”, and “discontinuation” as conditions corresponding to the items. After extracting the items and their respective conditions, the conversion unit 12 generates data that associates the terms labeled with the items with the respective conditions as structured selection criteria.
  • Figures 5 and 6 are diagrams showing examples of structured selection criteria.
  • Figure 5 shows an example of structured inclusion criteria among the structured selection criteria.
  • Figure 6 shows an example of structured exclusion criteria among the structured selection criteria.
  • the item "age” included in the inclusion criteria is associated with the condition corresponding to the item "18 years or older.”
  • one of the criteria included in the inclusion criteria is that the patient's age is 18 years or older.
  • the item "previous medical condition” included in the exclusion condition is associated with the condition "autoimmune or inflammatory disease" corresponding to the item.
  • one of the criteria included in the exclusion criteria is that the patient has a previous medical condition of an autoimmune or inflammatory disease.
  • the patient is excluded from being a candidate for a clinical trial patient.
  • examples of structured selection criteria are not limited to the examples in FIG. 5 and FIG. 6.
  • each item may be hierarchically organized. For example, in the item "medication history", the items "drug name”, “medication period", and “dosage” may be associated below "medication history”.
  • the hierarchical structure may have three or more layers.
  • the conversion unit 12 may use a structured model to convert the text indicating the selection criteria into structured selection criteria.
  • the structured model takes as input a text indicating the selection criteria with labels and a format of the structured selection criteria, and outputs the structured selection criteria.
  • the structured model is generated, for example, by learning the relationship between the text indicating the selection criteria with labels and the format of the structured selection criteria, and the structured data.
  • the labeled structured model may take as input a text indicating the selection criteria, and output the structured selection criteria.
  • the structured model is generated, for example, by learning the relationship between the text indicating the selection criteria with labels and the structured data.
  • the structured model is generated, for example, by deep learning using a neural network.
  • the structured model is generated, for example, in a system external to the selection support device 10.
  • the selection criteria format is information that defines how to divide the selection criteria into items when structuring the selection criteria.
  • the selection criteria format is set, for example, for each disease for which the drug being tested is administered. In clinical trials, for example, evaluation items are often determined for each disease. For this reason, for example, by using the items to be extracted as selection criteria as input together with a sentence indicating the selection criteria, it is possible to extract conditions corresponding to the items from the sentence indicating the selection criteria.
  • a large-scale language model may be used as the structured model.
  • GPT-2 Generative Pre-Training-2
  • GPT-3 Generative Pre-Training-2
  • GPT-4 may be used as the large-scale language model.
  • T5 Text-to-Text Transfer Transformer
  • BERT Bidirectional Encoder Representations from Transformers
  • RoBERTa Robottly optimized BERT approach
  • ELECTRA Efficiently Learning an Encoder that Classifies Token Replacements Accurately
  • the structured model is generated, for example, in a system external to the selection support device 10.
  • the process of generating structured selection criteria using the structured model may be performed in a system external to the selection support device 10.
  • the identification unit 13 for example, identifies a target for which a reference indication is to be generated.
  • the identification unit 13, for example, identifies a target for which a reference indication is to be generated based on the degree to which confirmation is required in the selection of clinical trial patients. For example, in the structuring process, it is desirable for the person in charge to check in detail items that may have low accuracy of conversion to the structured selection criteria and the conditions described in the items. Low accuracy of conversion to the structured selection criteria means, for example, that the compatibility between the terms included in the selection criteria and the terms in the dictionary is low, and there is a high possibility that the sentences in the selection criteria cannot be correctly analyzed.
  • the identification unit 13 identifies a target for which a reference indication is to be generated based on, for example, the certainty of the structuring process.
  • the identification unit 13 identifies an item for which a reference indication is to be generated based on, for example, the certainty of the terms assigned to the items in the structured selection criteria.
  • the identification unit 13 may also identify an item in the structured selection criteria for which the conditions are not clearly defined as a target for which a reference indication is to be generated.
  • the identification unit 13 calculates a score for each item included in the selection criteria based on, for example, the degree of match between the terms included in the selection criteria and the terms included in the dictionary.
  • the score is, for example, an index for identifying an item for which a reference indication is to be generated.
  • the score is, for example, an example of an index indicating the reliability of the structuring process. For example, when the reliability of the structuring process is low, the person in charge needs to check the information in detail. For this reason, the score can be an index indicating the need for the person in charge to check the information, for example, when selecting clinical trial patients.
  • the score is calculated so that, for example, the higher the need to generate a reference indication, the lower the value.
  • the degree of match of the term is, for example, the degree of match between the terms included in the sentence indicating the selection criteria and the terms included in the dictionary. For example, the higher the degree of match between the terms included in the dictionary and the terms included in the sentence of the selection criteria, the higher the value of the degree of match.
  • the score for each item included in the selection criteria is, for example, higher the degree of match of the term.
  • the identification unit 13 identifies an item with a score below the standard as a target for generating a reference indication.
  • the score criteria for identifying the target for which a reference instruction is to be generated are set based on, for example, the need to check the information in the electronic medical record in detail. For example, the score criteria are set so that, when the score is below the criteria, it is desirable to check the information in the electronic medical record in detail.
  • the identification unit 13 may identify an item for which a reference indication is generated based on criteria set for each disease name or condition.
  • the condition is, for example, the degree of progression of the disease.
  • the items of the selection criteria that are emphasized may change for each stage indicating the degree of progression of cancer. A slight difference in the items of the selection criteria that are emphasized may affect the clinical trial results. For this reason, for example, by establishing rules for the items for which a reference indication is generated for each disease name or condition, it is possible to generate a reference indication for an item according to the disease name or condition.
  • the identification unit 13 generates a reference indication for an item set for each disease name.
  • the identification unit 13 may generate a reference indication for an item set for each disease name and condition.
  • the identification unit 13 may identify an item for which a reference indication is generated based on the disease name extracted from the selection criteria. For example, when the selection criteria includes "breast cancer", the identification unit 13 identifies an item set for breast cancer as an item for which a reference indication is generated.
  • the identification unit 13 may generate a reference indication based on the item for generating a reference indication acquired by the acquisition unit 11.
  • the identification unit 13 acquires the item for generating a reference indication from, for example, the terminal device 20.
  • the item for generating a reference indication is input to, for example, the terminal device 20 by an operation of the person to be extracted.
  • the generating unit 14 generates reference instructions for information to be referenced when selecting clinical trial patients based on the structured selection criteria.
  • the reference instructions include, for example, the location where the information to be referenced is written and the content to be referenced.
  • the reference instructions include, for example, the location where the information to be referenced is written in the electronic medical record and the content to be referenced.
  • the generating unit 14 generates a reference indication based on the set rules.
  • the rules are set, for example, as table-format data.
  • the generating unit 14 generates a reference indication by, for example, referring to a table that associates items included in structured selection criteria with the locations of data related to treatment.
  • the locations of data related to treatment are, for example, information indicating a document in which information corresponding to the selection criteria is written and where in the document the data is written.
  • the generating unit 14 may also generate a prompt-format reference indication to be used as input for a large-scale language model that extracts information from information related to treatment.
  • the generating unit 14 may generate the reference indication using a generative model.
  • the generative model takes items included in the selection criteria as input and outputs the location of the information related to the treatment.
  • the generative model may take items included in the selection criteria and conditions associated with the items as input and output the location of the information related to the treatment.
  • the generative model may be generated, for example, by learning the relationship between the items included in the selection criteria and the location of the information related to the treatment.
  • the generative model may also be generated, for example, by learning the relationship between items included in the selection criteria and conditions associated with the items and the location of the information related to the treatment.
  • the generation unit 14 When the identification unit 13 identifies an item for which a reference indication is to be generated, the generation unit 14 generates, for example, a reference indication for the item identified by the identification unit 13.
  • the generation unit 14 may also generate a reference indication for a predetermined item.
  • the predetermined item is set using an item of the structured selection criteria or a term included in the item.
  • the generation unit 14, for example, determines an item of the structured selection criteria or a term included in the item.
  • the generation unit 14 generates a reference indication for the item determined to be a predetermined item.
  • the predetermined item is, for example, an item that requires detailed confirmation.
  • An item that requires detailed confirmation is, for example, an item for which, in selecting a clinical trial patient, it is possible that a clinical trial patient cannot be accurately selected unless the contents described in the electronic medical record are checked in detail.
  • an item in which conditions related to the administration history are described may be an item that requires detailed confirmation.
  • the output unit 15 outputs the reference indication generated by the generation unit 14.
  • the output unit 15 outputs the reference indication to, for example, the terminal device 20. Furthermore, when a reference indication in the form of a prompt is generated by the generation unit 14, the output unit 15 outputs, for example, the reference indication in the form of a prompt to be used as an input for a large-scale language model.
  • the output unit 15 may output structured selection conditions together with the reference indication.
  • the output unit 15 may output structured selection conditions instead of a reference indication.
  • the output unit 15 may output structured selection criteria when the structured selection criteria does not include an item for which a reference indication is to be generated.
  • the output unit 15 may output a score associated with each item included in the structured selection conditions. The score is, for example, an index indicating the likelihood of conversion from a sentence indicating the selection criteria to structured structure data.
  • Fig. 7 is a diagram showing an example of an output screen for information to be referenced.
  • the example display screen in Fig. 7 displays "Extraction of treatment information from doctor's findings (extraction of medication and dosage)" and "Extraction of drug efficacy information from image information (extraction of changes in cancer size and side effects)."
  • the person in charge of extracting candidate patients for clinical trials checks the entries in the electronic medical record, for example, by referring to the example display screen in Fig. 7.
  • FIGS. 8 and 9 are examples of display screens that display structured selection criteria.
  • the example display screen in FIG. 8 is a screen that displays inclusion criteria from among the structured selection criteria.
  • the criteria items included in the inclusion criteria are associated with the conditions for each item.
  • the items and the conditions for each item are further associated with a score.
  • the score is, for example, an index that indicates the likelihood of converting the sentences in the inclusion criteria into structured structure data.
  • the criteria items included in the exclusion criteria are associated with the conditions for each item. Furthermore, a score is further associated with each item and each condition for each item. The score is, for example, an index showing the likelihood of converting the sentences in the exclusion criteria into structured structure data.
  • the storage unit 16 stores, for example, data related to the process of generating reference indications.
  • the storage unit 16 also stores, for example, dictionary data.
  • the storage unit 16 stores, for example, labeling models.
  • the storage unit 16 stores, for example, structured models.
  • the storage unit 16 also stores, for example, generative models.
  • the labeling models, structured models, and generative models may be stored in a storage means external to the selection support device 10.
  • the terminal device 20 is, for example, a terminal device used by a person who extracts candidates for clinical trial patients.
  • the terminal device 20 acquires, for example, the selection criteria for clinical trial patients.
  • the selection criteria for clinical trial patients are, for example, input to the terminal device 20 by the operation of the person who extracts candidates for clinical trial patients.
  • the terminal device 20 then outputs the selection criteria for clinical trial patients to the acquisition unit 11 of the selection support device 10.
  • the terminal device 20 for example, acquires a reference instruction from the output unit 15 of the selection support device 10. Then, the terminal device 20 outputs the reference instruction to a display device (not shown). Also, the terminal device 20, for example, acquires structured selection criteria from the output unit 15 of the selection support device 10. Then, the terminal device 20 outputs the structured selection criteria to a display device (not shown).
  • Figure 10 is a diagram showing an example of the operation flow in the process of generating a reference indication.
  • the acquisition unit 11 acquires a sentence indicating the selection criteria for clinical trial patients (step S11).
  • the acquisition unit 11 acquires the sentence indicating the selection criteria, for example, from the terminal device 20.
  • the conversion unit 12 converts the sentence indicating the selection criteria into structured selection criteria (step S12).
  • the generation unit 14 If all sentences included in the selection criteria have been converted into structured selection criteria (Yes in step S13), the generation unit 14 generates reference instructions for information to be referenced when selecting clinical trial patients based on the structured selection criteria (step S14).
  • the output unit 15 When the reference indication is generated, the output unit 15 outputs the generated reference indication (step S15).
  • the output unit 15 outputs the generated reference indication to, for example, the terminal device 20.
  • the output unit 15 may further output structured selection criteria. Furthermore, when the structured selection criteria does not include an item for which a reference indication is to be generated, the output unit 15 may output the structured selection criteria.
  • step S13 If there is any sentence that has not been converted in step S13 (No in step S13), the process returns to step S12, and the conversion unit 12 converts the sentence indicating the selection criteria that has not been converted into a structured selection criteria.
  • the selection support device 10 converts the selection criteria for clinical trial patients into structured selection criteria. Based on the structured selection criteria, the selection support device 10 generates reference instructions for information to be referenced when selecting clinical trial patients. The selection support device 10 then outputs the generated reference instructions. By generating reference instructions for information to be referenced when selecting clinical trial patients, for example, a person extracting clinical trial patients can confirm the information to be referenced when selecting clinical trial patients based on the reference instructions. Therefore, by using the selection support device 10, it is possible to easily identify the information to be referenced when selecting clinical trial patients.
  • the selection support device 10 can generate appropriate reference indications according to the disease name or condition. Therefore, the selection support device 10 can further improve the accuracy of extracting patients who fit the selection criteria.
  • the selection support device 10 can support the efficient extraction of patients who meet the selection criteria.
  • the selection support device 10 can generate reference instructions optimized according to the selection criteria for clinical trial patients. Furthermore, by making it easier to understand the information that should be referenced when selecting patients, the person who selects patients who meet the selection criteria for the clinical trial can make appropriate decisions when selecting clinical trial patients.
  • the person selecting the candidate patients for the clinical trial can check the information on the treatment by referring to the accuracy of the structured selection criteria. Therefore, by outputting the structured selection criteria together with the score, the person selecting the candidate patients for the clinical trial can appropriately determine what information needs to be referred to.
  • the processes in the selection support device 10 may be distributed and executed in multiple information processing devices connected via a network. For example, the process of converting a sentence indicating the selection criteria into structured selection criteria and the process of generating reference indications may be executed in different information processing devices. It may be set as appropriate which information processing device executes each process in the selection support device 10.
  • FIG. 11 shows an example of the configuration of a computer 100 that executes a computer program that performs each process in the selection support device 10.
  • the computer 100 comprises a CPU (Central Processing Unit) 101, memory 102, a storage device 103, an input/output I/F (Interface) 104, and a communication I/F 105.
  • CPU Central Processing Unit
  • the CPU 101 reads out and executes computer programs for performing each process from the storage device 103.
  • the CPU 101 may be configured by a combination of multiple CPUs.
  • the CPU 101 may also be configured by a combination of a CPU and another type of processor.
  • the CPU 101 may be configured by a combination of a CPU and a GPU (Graphics Processing Unit).
  • the memory 102 is configured by a DRAM (Dynamic Random Access Memory) or the like, and temporarily stores the computer programs executed by the CPU 101 and data being processed.
  • the storage device 103 stores the computer programs executed by the CPU 101.
  • the storage device 103 is configured by, for example, a non-volatile semiconductor storage device. Other storage devices such as a hard disk drive may be used for the storage device 103.
  • the input/output I/F 104 is an interface that accepts input from an operator and outputs display data, etc.
  • the communication I/F 105 is an interface that transmits and receives data between the terminal device 20 and other information processing devices.
  • the terminal device 20 can also be configured in the same way as the computer 100.
  • the computer programs used to execute each process can also be distributed by storing them on a computer-readable recording medium that non-temporarily records data.
  • a computer-readable recording medium for example, a magnetic tape for recording data or a magnetic disk such as a hard disk can be used.
  • an optical disk such as a CD-ROM (Compact Disc Read Only Memory) can also be used as the recording medium.
  • a non-volatile semiconductor memory device can also be used as the recording medium.
  • the structured selection criteria are data in which items indicating the contents of each criterion included in the sentence indicating the selection criteria are associated with conditions for each of the items. 2.
  • the conversion means adds a label to a term related to the selection of the clinical trial patient included in the sentence indicating the selection criteria, and converts the sentence indicating the selection criteria into the structured selection criteria based on the added label.
  • a selection support device according to claim 1 or 2.
  • the reference designation includes a location where the information to be referenced is described and the contents to be referenced. 5.
  • a selection support device according to any one of claims 1 to 4.
  • the reference instruction includes a location in the electronic medical record where the information to be referenced is described and the content to be referenced. 6.
  • a selection support device as described in appendix 5.
  • Appendix 7 The text indicating the selection criteria includes at least one of the inclusion and exclusion conditions for the clinical trial; 7. A selection support device according to any one of appendix 1 to 6.
  • the output means further outputs the structured selection criteria. 9. A selection support device according to any one of appendix 1 to 8.
  • Appendix 10 the output means outputs a score indicating the likelihood of conversion to the structured selection criteria in association with each of the items of the structured selection criteria; 10.
  • the acquisition means acquires an item that is a target of the reference instruction from among the selected selection criteria,
  • the generating means generates the reference designation for the item that is the target of the acquired reference designation.
  • a selection support device according to claim 9 or 10.
  • the conversion means adds labels to the terms included in the selection criteria using a labeling model generated by machine learning. 4.
  • Appendix 14 A process of obtaining a document indicating the selection criteria for clinical trial patients; converting the text indicating the selection criteria into structured selection criteria; A process of generating a reference instruction indicating information to be referred to in selecting the clinical trial patient based on the structured selection criteria; and a recording medium for non-temporarily recording a selection support program for causing a computer to execute the process of outputting the generated reference indication.

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Abstract

L'invention concerne un dispositif d'aide à la sélection comprenant une unité d'acquisition, une unité de conversion, une unité de génération et une unité de sortie. L'unité d'acquisition acquiert une phrase indiquant un critère de sélection pour des patients d'essai clinique. L'unité de conversion convertit la phrase indiquant le critère de sélection en un critère de sélection structuré. L'unité de génération génère une instruction de référence indiquant des informations auxquelles se référer dans la sélection de patients d'essai clinique sur la base du critère de sélection structuré. L'unité de sortie délivre l'instruction de référence générée. L'utilisation de l'instruction de référence générée sur la base du critère de sélection structuré permet une prise de décision appropriée dans le criblage de patients d'essai clinique.
PCT/JP2023/041953 2023-11-22 2023-11-22 Dispositif d'aide à la sélection, procédé d'aide à la sélection et support d'enregistrement Pending WO2025109706A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004348271A (ja) * 2003-05-20 2004-12-09 Sanyo Electric Co Ltd 治験データ出力装置、治験データ出力方法及び治験データ出力プログラム
JP2007299064A (ja) * 2006-04-27 2007-11-15 Masanori Fukushima 医療情報管理支援装置
JP2020035036A (ja) * 2018-08-28 2020-03-05 株式会社日立製作所 試験計画策定支援装置、試験計画策定支援方法及びプログラム
JP2022180080A (ja) * 2021-05-24 2022-12-06 ＴＸＰ Ｍｅｄｉｃａｌ株式会社 情報処理システム

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004348271A (ja) * 2003-05-20 2004-12-09 Sanyo Electric Co Ltd 治験データ出力装置、治験データ出力方法及び治験データ出力プログラム
JP2007299064A (ja) * 2006-04-27 2007-11-15 Masanori Fukushima 医療情報管理支援装置
JP2020035036A (ja) * 2018-08-28 2020-03-05 株式会社日立製作所 試験計画策定支援装置、試験計画策定支援方法及びプログラム
JP2022180080A (ja) * 2021-05-24 2022-12-06 ＴＸＰ Ｍｅｄｉｃａｌ株式会社 情報処理システム

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