Program, device, and system for assisting in formulation of treatment plan for patient

Abstract

The purpose of the present disclosure is to provide a program, a device, and a system for assisting in formulation of a treatment plan for a patient. More specifically, the present disclosure provides a program for assisting in formulation of a treatment plan for a patient, the program causing one or more computers to execute: a step for acquiring patient information; a step for generating a treatment draft plan on the basis of the patient information; and a step for outputting the treatment draft plan. In addition, a device and a system having the same functions are also provided. Furthermore, a program, a device, and a system that assist in outcome prediction for assisting in formulation of a treatment plan for a patient are also provided.

Description

Program, device and system for supporting the formulation of patient treatment plans

[0001] The present disclosure relates to a program, device, and system for supporting the formulation of a patient's treatment plan. More specifically, the present disclosure relates to a program, device, and system that generates a proposed treatment plan using a correspondence table (matrix) or the like based on new algorithms, such as treatment selection criteria, including treatment inapplicability criteria and / or inapplicability criteria, in addition to existing treatment decision-making algorithms. Even more specifically, the present disclosure relates to a program, device, and system that generates a proposed treatment plan based on patient information using a large-scale language model (LLM) and retrieval-augmented generation (RAG). The present disclosure also relates to a program, device, and system that supports outcome prediction to support the formulation of a patient's treatment plan.

[0002] Treatment for cancer (synonymous with malignant disease) is determined by the condition of the disease (mainly the type of cancer and its stage) (existing treatment decision algorithm). Stage of cancer is classified according to the TNM classification, which is based on the extent of the primary tumor, the degree of lymph node metastasis, and the presence or absence of distant metastasis. Treatment is based on the feasibility of surgery, and a rough algorithm has been created to choose from surgery, radiation therapy, and chemotherapy.

[0003] In actual clinical practice, the attending physician performs the TNM classification in their head and selects treatment based on an algorithm stored in their memory. The treatment selected based on the limited knowledge and experience of each attending physician is heavily influenced by the physician's specialty and may not be optimal for the patient. For example, if the attending physician is an oncologist who specializes in pharmacological treatment, even one metastasis will often be classified as distant metastasis. Even if a single metastasis actually allows for curative resection, the patient will miss the opportunity for cure if the attending physician does not have this knowledge.

[0004] Furthermore, in current clinical practice, patients must choose the treatment selected and presented by their doctor, or decide whether or not to accept the treatment, without any knowledge of the treatment in question.

[0005] Meanwhile, advances in medical care have led to an increase in treatment options, making treatment selection more complex and exceeding the knowledge and experience of attending physicians. Diseases can now be further subdivided, and not only have the number of treatments (major categories) increased, but the treatments themselves have also been subdivided (subcategorization). For example, the major categories of cancer treatment were surgery, radiation therapy, and systemic chemotherapy. However, with the addition of new medical treatments such as immunotherapy, interventional radiology, photoimmunotherapy, and viral therapy, the number of treatments has increased from three to seven. Regarding subcategorization, for example, radiation therapy includes linac, more advanced high-precision X-rays, gamma knife, brachytherapy, particle therapy (heavy ion therapy, proton therapy), BNCT, and theranostics, and the number of treatments is expected to continue to increase.

[0006] Because the therapeutic effects and adverse events vary for each specialized treatment, subdividing the condition and selecting the appropriate treatment from among the many specialized treatments can improve survival rates, including a cure, and reduce adverse events. Furthermore, a cure would also contribute to solving social issues and benefiting the medical economy. Conversely, the current rough treatment selection based on the TNM classification by attending physicians misses opportunities for a cure, resulting in social losses. This applies not only to cancer (malignant diseases) but also to benign diseases. For example, diabetes, which is a common disease, is often treated by doctors other than specialists, but because attending physicians are unaware of the details of the treatment plan, progression cannot be halted and patients are forced to undergo dialysis.

[0007] Japanese Patent Application No. 2018-147274 discloses a treatment policy decision support device that supports the determination of a treatment policy for a patient based on medical procedure information indicating the details of the medical procedure performed on each of a plurality of cancer patients, the treatment policy decision support device comprising: a construction means for constructing a master database based on the medical procedure information, in which a plurality of treatment results corresponding to each of the plurality of patients and each including a cancer type and a TNM classification value are registered; and when a treatment result request including a desired cancer type and TNM classification value is received, the treatment policy decision support device detects from the master database constructed by the construction means one or more treatment results each including a cancer type and TNM classification value corresponding to the desired cancer type and TNM classification value, and returns the detected one or more treatment results to the issuer of the treatment result request.

[0008] However, understanding the many different medical conditions, memorizing the pros and cons of each treatment, and their effects and adverse events, and making accurate treatment choices is difficult and cannot be expected of all doctors. This is because, for doctors who are extremely busy with their daily medical practice, the vertical division of medical care by department makes it difficult to catch up on knowledge outside of their own department, and it is also difficult to gain treatment experience. For example, it is difficult for internists to correctly determine whether surgery is possible or to select the radiation therapy with the highest probability of cure from the many available radiation treatments.

[0009] To avoid this situation, clinical practice guidelines have been created for each disease and condition, providing guidance on optimal treatment for each more specific condition. However, the amount of information that attending physicians must refer to in these guidelines is enormous, and there are limits to how much they can read through them all, memorize the optimal treatment for each disease and condition, and apply it to treatment. For example, the clinical practice guidelines that gastroenterologists must read, for cancer alone, include the Esophageal Cancer Clinical Practice Guidelines, Gastric Cancer Clinical Practice Guidelines, Liver Cancer Clinical Practice Guidelines, Pancreatic Cancer Clinical Practice Guidelines, Biliary Tract Cancer Clinical Practice Guidelines, Colorectal Cancer Clinical Practice Guidelines, Small Intestine Cancer Clinical Practice Guidelines, Peritoneal Dissemination Clinical Practice Guidelines, and Metastatic Liver Tumor Clinical Practice Guidelines. Furthermore, clinical practice guidelines are updated almost annually, limiting their ability to keep up with the latest treatment information.

[0010] This situation is not limited to cancer, but applies to all chronic diseases, including rheumatoid arthritis, collagen diseases, diabetes, and dyslipidemia. Rheumatoid arthritis and collagen diseases are also subdivided into specific conditions, and guidelines for optimal treatment according to the condition are presented in clinical guidelines. And, just like cancer treatment, the guidelines themselves are updated daily.

[0011] Patent application 2018-147274

[0012] The ideal treatment selection is based on the premise that medical care places a considerable burden on patients both physically and mentally, and therefore the principle is to provide neither too much nor too little treatment, and from a comprehensive list of treatment candidates updated daily in real time for each disease and pathological condition, effective and feasible treatment candidates are presented for each subdivided medical condition in accordance with the priority of each disease, and the content, effectiveness (treatment results, etc.) and safety (adverse events, etc.) of each treatment are presented, allowing the patient to understand the treatment and make a choice together with their doctor that is in line with their values. However, the current situation is as described in the background, and there is a large gap (challenge) between this and the ideal.

[0013] In light of the above situation, the inventor has invented a new program, device, and system to assist in formulating treatment plans for patients by utilizing large-scale language models (LLMs), also known as generative AI, and up-to-date and comprehensive treatment information databases that are updated in real time, such as PubMed and clinical trial databases, as well as an AI / IT technology called RAG (Retrieval-Augmented Generation) that connects them.

[0014] The present disclosure provides a program, device, and system for supporting the formulation of a patient's treatment plan. The present disclosure provides the following as more specific examples. [Embodiment 1] A program for supporting the formulation of a patient's treatment plan, the program causing one or more computers to execute the steps of: acquiring patient information; generating a proposed treatment plan based on the patient information using a large-scale language model (LLM), wherein the proposed treatment plan includes a list of one or more available treatment methods selected based on the patient information and treatment selection criteria (such as non-indication criteria and / or indication criteria) input as prompts to the LLM by Retrieval-Augmented Generation (RAG); and outputting the proposed treatment plan, wherein the proposed treatment plan further includes information including advantages and disadvantages of the selected one or more available treatment methods, and / or information including predicted information on the cure probability and / or outcome of the selected one or more available treatment methods. [Embodiment 2] A program for supporting the formulation of a patient's treatment plan, the program causing one or more computers to execute the steps of: acquiring patient information; generating a proposed treatment plan based on the patient information; and outputting the proposed treatment plan. [Embodiment 3] The program according to embodiment 2, wherein the step of generating a proposed treatment plan based on patient information includes a step of selecting one or more available treatment methods based on the patient information and predefined treatment selection criteria. [Embodiment 4] The program according to any one of embodiments 2 and 3, wherein the step of selecting one or more available treatment methods based on the patient information and predefined treatment selection criteria is performed using a large-scale language model (LLM). [Embodiment 5] The program according to embodiment 4, wherein the treatment selection criteria are input to the large-scale language model (LLM) as prompts. [Embodiment 6] The program according to embodiment 5, wherein the treatment selection criteria are input to the large-scale language model (LLM) by Retrieval-Augmented Generation (RAG). [Embodiment 7] The program according to embodiment 6, wherein the treatment selection criteria are stored in a database.[Embodiment 8] The program according to embodiment 7, wherein the treatment selection criteria are stored in a database in the form of a correspondence table that associates patient information with available treatment methods. [Embodiment 9] The program according to any one of embodiments 2 to 8, wherein the proposed treatment plan includes information including the advantages and disadvantages of one or more selected available treatment methods. [Embodiment 10] The program according to any one of embodiments 2 to 9, wherein the proposed treatment plan includes information including prediction information on the cure probability and / or outcome of one or more selected available treatment methods. [Embodiment 11] 11. The program according to any one of embodiments 1 to 10, wherein the patient information includes at least one piece of information selected from the group consisting of age, sex, disease name (disease name, primary name, condition name such as metastasis or recurrence, complication name), name of left hepatic vein invasion, middle hepatic vein invasion, left hepatic vein invasion, vascular invasion, bile duct invasion, gastrointestinal tract invasion, Glisson's sheath invasion, metastasis to other organs, low liver function, S1, FLR (normal liver), FLR (damaged liver), major hepatectomy (tri-segmentectomy, etc.), major hepatectomy (right hepatectomy), 3-segment branch, left hepatic vein invasion, middle hepatic vein invasion, right hepatic vein invasion, portal vein invasion, distance from the gastrointestinal tract (mm), anatomical name and positional relationship such as vascular invasion, bile duct invasion, gastrointestinal tract invasion, proximity to blood vessels, proximity to the bile duct, proximity to the gastrointestinal tract, and proximity to Glisson's sheath, progression level, histological and pathological findings, genetic information such as genetic abnormalities and their degree (including histological findings, gene expression levels, etc.), immunological findings such as cancer microenvironment, and information on the degree of function. [Embodiment 12] The program according to any one of embodiments 1 to 11, wherein at least a part of the patient information is generated by automatic interpretation of images.[Embodiment 13] The available treatment methods include conventional surgery, advanced surgery, TSH stage 2 surgery, ALPPS, ALPTPS, venous surgery, robotic surgery, arthroscopic surgery, radiation therapy, external radiation therapy, particle therapy, heavy ion therapy, proton therapy, high-precision X-ray therapy, IMRT, stereotactic irradiation, CyberKnife, internal radiation therapy, brachytherapy, brachytherapy, theranostics, BNCT boron neutron capture therapy, IVR, ablation, radiofrequency ablation, microwave ablation, cryotherapy, intra-arterial embolization therapy, ethanol injection, HYF high-intensity focused ultrasound, irreversible electroporation therapy (IRE), photoimmunotherapy, viral therapy, chemotherapy, hormone therapy, molecular targeted drug, antibody-drug conjugate (ADC), immunotherapy, immune checkpoint inhibitor, fecal transplant therapy, cancer vaccine, CAR-T therapy, combined immunotherapy, gene therapy, stem cell transplant, mRNA vaccine, CRISPR / Cas9, hyperthermia, TS The program according to any one of embodiments 1 to 12, comprising at least one treatment method selected from the group consisting of H2 stage surgery, ALPPS, venous combined surgery, conventional RFA, high-precision RFA, TACE, drug therapy, anticancer drugs, immunosuppressants, investigational drugs, herbal medicines, transplants (organ transplants, bone marrow transplants, etc.), nerve blocks, CART, dialysis, shunts (Denver shunts, etc.), nerve decompression, nerve transplants, nerve repair, stem cell therapy, stem cell transplants, inhalants, infusions, transdermal drugs, transdermal formulations, transdermal patches, transdermal formulations (TTS), eye drops, intracavitary administration, continuous administration, and new surgical procedures, psychotherapy (cognitive behavioral therapy, CBT, etc.), physical therapy (electroconvulsive therapy: ECT, transcranial magnetic stimulation: TMS, transcranial direct current stimulation: tDCS, deep brain stimulation (DBS), spinal cord stimulation, etc.), diet therapy, exercise therapy, weight loss surgery, oriental medical treatments such as acupuncture and moxibustion, and antioxidants. [Embodiment 14] The program according to embodiment 2, wherein the step of generating a proposed treatment plan based on patient information is performed using a machine learning model that takes patient information as input and outputs available treatment methods.

[0015] FIG. 1 shows a schematic configuration of an exemplary computer that can be used to implement the present disclosure. The exemplary computer (100) includes a control unit (101), a memory unit (102), a peripheral device I / F unit (103), an input unit (104), a display unit (105), a communication unit (106), and a bus (110). The computer (100) can be connected to an external server (130) and a database (140) via a network (120). FIG. 2 shows an example of formulating a patient treatment plan using RAG in the program of the present disclosure. FIG. 3 shows an overview of treatment selection and outcome prediction using the treatment selection support program of the present disclosure.

[0016] In one aspect, the present disclosure relates to a program for supporting the formulation of a patient's treatment plan. The patient's disease may be, but is not limited to, a malignant disease such as cancer (synonymous with malignant disease; classified as carcinoma, sarcoma, or blood cancer), a benign disease such as rheumatism, collagen disease, diabetes, or dyslipidemia, or a general disease, primarily a chronic disease. Furthermore, the program is not limited to patients already suffering from a disease, but also includes patients at high risk of developing a disease and patients who have undergone treatment. The former includes, for example, intraductal papillary mucinous neoplasm (IPMN) in pancreatic cancer, hepatitis in liver cancer, and hyperglycemic states in diabetes. The latter includes, for example, post-surgical treatment for cancer. The patient can be, for example, a human cancer patient, such as a patient with any type of cancer, including breast cancer, lung cancer, prostate cancer, colon cancer, pancreatic cancer, gastric cancer, hepatocellular carcinoma, leukemia, skin cancer, uterine cancer, sarcoma, soft tissue sarcoma, lymphoma, head and neck cancer, biliary tract cancer, brain tumor, esophageal cancer, cancer of unknown primary, prostate cancer, oral cancer, or ovarian cancer. Cancer patients can have metastases and can have primary as well as recurrent cancer.

[0017] In some embodiments, the present disclosure provides a program for assisting in the formulation of a patient treatment plan, the program causing one or more computers to execute the steps of acquiring patient information, generating a treatment plan proposal based on the patient information, and outputting the treatment plan proposal. The one or more computers do not need to be physically located in the same place; multiple computers with different roles (e.g., an input / output terminal, an LLM server, a database server, etc.) may be connected via a network and function cooperatively.

[0018] As used herein, "treatment policy" includes the selection of a treatment method to be applied to a patient. For example, in the case of a cancer patient, available treatment methods include conventional surgery, advanced surgery, TSH stage 2 surgery, ALPPS, ALPTPS, venous surgery, robotic surgery, arthroscopic surgery, radiation therapy, external radiation therapy, particle therapy, heavy ion therapy, proton therapy, high-precision X-ray therapy, IMRT, stereotactic radiotherapy, CyberKnife, internal radiation therapy, brachytherapy, brachytherapy, theranostics, BNCT (boron neutron capture therapy), IVR, ablation, radiofrequency ablation, microwave ablation, cryotherapy, intra-arterial embolization therapy, ethanol injection, hyphenated high-intensity focused ultrasound, irreversible electroporation (IRE), photoimmunotherapy, viral therapy, chemotherapy, hormone therapy, molecular targeted drugs, antibody-drug conjugates (ADC), immunotherapy, immune checkpoint inhibitors, fecal transplantation, cancer vaccine, CAR-T therapy, combined immunotherapy, gene therapy, stem cell transplantation, mRNA These include, but are not limited to, vaccines, CRISPR / Cas9, hyperthermia, TSH2 surgery, ALPPS, combined venous surgery, conventional RFA, high-precision RFA, TACE, drug therapy, anticancer drugs, immunosuppressants, investigational drugs, herbal medicines, transplants (organ transplants, bone marrow transplants, etc.), nerve blocks, CART, dialysis, shunts (Denver shunts, etc.), nerve decompression, nerve transplants, nerve repair, stem cell therapy, stem cell transplants, inhaled medications, infusions, transdermal medications, transdermal formulations, transdermal patches, transdermal therapies (TTS), eye drops, intracavitary administration, continuous infusion, and new surgical procedures, psychological therapies (cognitive behavioral therapy, etc.), physical therapies (electroconvulsive therapy (ECT), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), spinal cord stimulation, etc.), dietary therapy, exercise therapy, weight loss surgery, traditional Chinese medicine treatments such as acupuncture and moxibustion, and antioxidants. When multiple treatment modalities are available, they may be combined and applied simultaneously or sequentially. As used herein, "treatment policy" may also include a plan for how to apply available treatment modalities.

[0019] As used herein, "patient information" includes multiple items related to the patient's physical characteristics and medical condition. For example, in the case of a cancer patient, patient information includes, but is not limited to, age, sex, disease name (disease name, primary name, condition name such as metastasis or recurrence, complication name), name of left hepatic vein invasion, middle hepatic vein invasion, left hepatic vein invasion, vascular invasion, bile duct invasion, gastrointestinal tract invasion, Glisson's sheath invasion, metastasis to other organs, low liver function, S1, FLR (normal liver), FLR (damaged liver), major hepatectomy (trisegmentectomy etc.), major hepatectomy (right hepatectomy), 3-segment branch, left hepatic vein invasion, middle hepatic vein invasion, right hepatic vein invasion, portal vein invasion, distance to the gastrointestinal tract (mm), anatomical names and locational relationships such as vascular invasion, bile duct invasion, gastrointestinal tract invasion, proximity to blood vessels, proximity to the bile duct, proximity to the gastrointestinal tract, and proximity to Glisson's sheath, degree of progression, histological and pathological findings, genetic information such as genetic abnormalities and their degree (including histological findings, gene expression levels, etc.), immunological findings such as the cancer microenvironment, and information on the degree of function.

[0020] Patient information may be acquired, for example, by input from a keyboard, input via a communication line such as the Internet, or input from storage such as a hard disk. Patient information may be stored in a patient information database and acquired as needed by accessing the database. At least a portion of the patient information may be, for example, information in an electronic medical record. At least a portion of the patient information may also be acquired by character recognition of a handwritten medical record. Furthermore, at least a portion of the patient information may be generated by automatic interpretation on a computer from test images such as PET, CT, MRI, and X-ray images. AI-based image recognition technology may be used for the automatic interpretation.

[0021] In some embodiments, generating a proposed treatment plan based on patient information includes, for example, selecting one or more available treatments based on patient information and predefined treatment selection criteria (inapplicable criteria and / or eligible criteria and / or semi-inapplicable criteria) for a malignant disease (cancer). The selected available treatments may be one or more, and a list of available treatments may be created and included in the proposed treatment plan. The treatments displayed in the list may be presented in order of the outcome prioritized for each disease. For example, for malignant diseases (cancer), the outcome of cure is important, and treatments may be presented in order of the probability of cure. The probability of cure may be calculated from the patient's condition information. A correspondence table between progression and treatment established for each disease may also be used. For example, for a malignant disease, a correspondence table between stage (progression) and treatment is used. For example, for a benign disease such as rheumatoid arthritis, a phase (progression) is determined based on the response (effect) to treatment obtained from the course of treatment, and treatments are presented according to that phase. In the case of asthma, for example, a correspondence table is used that uses symptoms and treatment progress as criteria for treatment selection. For benign diseases, original correspondence tables that match subdivided symptoms with treatments can also be created and used.

[0022] As used herein, the term "indication criteria" refers to criteria for excluding a particular treatment method from treatment options based on one or more items of patient information. For example, the indices for pancreatic cancer surgery include tumor contact or infiltration of the SMA, CA, or CHA, or contact or infiltration of the SMV / PV at less than 180 degrees and no obstruction, and tumor contact or infiltration of the SMA or CA at 180 degrees or more, or tumor contact or infiltration of the proper hepatic artery or CA. Therefore, if patient information indicates contact of the SMA / PHA at 180 degrees or more, pancreatic cancer surgery will not be selected as an available treatment method.

[0023] In addition, for colorectal cancer patients with liver metastasis, if the patient information indicates left hepatic vein invasion, standard surgery is not indicated, but advanced surgery (S1, etc.), TSH stage 2 surgery, and ALPPS surgery are available.

[0024] As used herein, "eligibility criteria" refers to criteria for including a particular treatment method in treatment options based on one or more items of patient information.

[0025] As used herein, the term "quasi-indication criteria" refers to criteria that, based on multiple items of patient information (e.g., two or more, three or more, four or more, or five or more items), can exclude a particular treatment method from treatment options or defer that decision until certain conditions are met.

[0026] As used herein, the term "treatment selection criteria" refers to a patient's condition or condition for implementing treatment. As used herein, "treatment selection criteria" is intended to encompass "non-label criteria," "label criteria," and "quasi-non-label criteria."

[0027] In some embodiments, the step of selecting one or more available treatment methods based on patient information and predefined treatment selection criteria (non-labeled criteria and / or labeled criteria and / or semi-non-labeled criteria) may be performed using a large-scale language model (LLM).

[0028] In the context of this disclosure, examples of large-scale language models (LLMs) include GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), and Llama (Large Language Model Meta AI). As used herein, "large-scale language models (LLMs)" refers to advanced AI models that can learn from massive amounts of text data and understand and generate human language. Such models, also known as generative AI, are widely used in the field of natural language processing (NLP) and have demonstrated high performance in a wide range of tasks, including text summarization, translation, question answering, text generation, and sentiment analysis. LLMs may be run, for example, on cloud servers.

[0029] LLMs are typically trained using large datasets containing billions of words collected from books, articles, websites, and other sources available on the internet. The training process uses a multi-layered neural network architecture called a Transformer. Transformers are particularly adept at effectively processing context-rich information and understanding interlinguistic dependencies. A distinctive feature of this architecture is its self-attention mechanism, which weights each word based on its context, taking into account its relevance to other words.

[0030] Furthermore, these models can be fine-tuned for specific tasks and adapted to advanced language-related tasks suited to specific applications or industries. For example, in the medical field, LLMs fine-tuned using specialized text data can be effectively used for complex tasks requiring specialized knowledge. Large-scale language models can serve as the foundational technology for providing innovative solutions in a wide range of fields, from general-purpose natural language processing tasks to specialized applications in specific fields.

[0031] In some embodiments, the large-scale language model (LLM) used in the program of the present disclosure may be fine-tuned specifically for formulating a patient's treatment plan. Fine-tuning may involve using treatment selection criteria and algorithms, such as inappropriate criteria, inappropriate criteria, and / or semi-inappropriate criteria, extracted from the latest treatment guidelines related to individual diseases, databases of past clinical cases, and literature databases such as PubMed, or organized and restructured information obtained from these sources. Existing, widely used disease state / progression correspondence tables can be used, and these correspondence tables can be used as is. However, all of these correspondence tables can also be restructured to use treatment selection criteria, such as inappropriate criteria, inappropriate criteria, or semi-inappropriate criteria. By utilizing such information (datasets) to retrain the model, performance on the desired task of selecting available treatment methods can be significantly improved.

[0032] In another embodiment, the LLM used in the program of the present disclosure may have undergone transfer learning for the task of formulating a patient's treatment plan. Transfer learning involves using the weights of a model previously trained on a broad dataset as initial values ​​and then retraining the model to adapt to a new task. Specifically, a model already trained in general natural language processing or medical fields can be fine-tuned using data related to specific treatment plan formulation, enabling rapid and efficient adaptation to new tasks. Transfer learning is often considered part of fine-tuning, which optimizes a model for a specific task. This technique allows a model to quickly adapt to specialized and advanced tasks while making the most of previously accumulated knowledge. As a result, the LLM used in the program of the present disclosure can serve as a powerful support tool for formulating optimal treatment plans tailored to individual patient conditions.

[0033] As used herein, a "prompt" for a large-scale language model (LLM) refers to a textual input provided to the model. This prompt serves as a starting point or instruction for the model to generate a response and is an important factor in determining the content and format of the generated text. Prompts can serve a variety of purposes, such as directing the language model to focus on a specific topic or task and providing appropriate direction so that the model can generate appropriate and relevant information within the given context.

[0034] The input prompt determines the context of the generated text and specifies the scope of information the model will reference. Prompts can also instruct the model to generate text in a specific format or style. For example, prompts can be used to instruct the model to generate answers in the form of questions or to create summaries of the given text. Furthermore, prompt design is crucial for the model to generate more accurate responses. The content and structure of the prompt significantly affect what information the model prioritizes and the format in which it generates a response. Therefore, appropriately designed prompts can make the LLM output more specific and precise, thereby improving the quality of the model's responses.

[0035] The program disclosed herein can generate prompts based on patient information to be input to a large-scale language model (LLM). The prompts may directly use the patient information entered by the user, or they may modify the input information appropriately. The technique of designing prompts to be input to an LLM so that the model generates the desired output is known as "prompt engineering," and this is an important technique for improving the accuracy of the model's responses.

[0036] In some embodiments, the prompts can include multiple task-relevant examples on which the model can make inferences. This technique, known as "few-shot learning," can effectively improve a model's performance by presenting a small number of examples. Few-shot learning is particularly useful in data-limited situations, improving the model's ability to quickly adapt to new tasks.

[0037] Furthermore, in some embodiments, prompts entered into the LLM can be generated using Retrieval-Augmented Generation (RAG). RAG is a method for generating more accurate and detailed responses by searching and extracting external data (e.g., treatment selection criteria related to patient information (e.g., non-indication criteria, indication criteria, and semi-indication criteria)) from a RAG database in real time and combining the search results with the original question (prompt). This process enables the LLM to incorporate the latest relevant information and propose a highly accurate treatment plan. When using RAG to obtain necessary information, for example, information highly relevant to the query, such as patient information, can be extracted from the RAG database based on cosine similarity. Using RAG enables real-time information updates to the LLM, reducing the LLM's training cost while generating responses that reflect the latest medical information. Furthermore, the above-mentioned fine-tuning can also be achieved using the RAG function. Further accuracy can be improved by storing the information obtained during fine-tuning in the RAG database. Figure 2 shows an example of patient treatment plan formulation using RAG in the program disclosed herein. By utilizing RAG, it is possible to obtain the latest external information in a timely manner and ask questions to the LLM based on that information, thereby realizing more advanced medical support. The RAG database may be located in a location physically separate from the computer used as the user terminal, for example, on a cloud server.

[0038] In the context of this disclosure, examples of prompts entered into the LLM include the following: (Prompt 1) "In the case of pancreatic cancer surgery, the criteria for exclusion are tumor contact with or invasion of the SMA, CA, or CHA, or contact with or invasion of the SMV / PV at less than 180 degrees, without obstruction, and contact with or invasion of the SMA or CA at more than 180 degrees, or contact with or invasion of the proper hepatic artery or CA." (Prompt 2) "The following is information about the patient's condition: - Invasion of the duodenum - Invasion of the lower part of the common bile duct - Almost complete occlusion of the superior mesenteric vein (SMV) - Contact with the superior mesenteric artery (SMA) / hepatic artery (PHA) at more than 180 degrees - Invasion of the horizontal limb of the duodenum. Can surgery be performed on this patient?"

[0039] An example of an LLM's response to the above input is as follows: "The patient is considered a non-surgical candidate due to the near-occlusion of the superior mesenteric vein (SMV), the presence of tumor contact with the superior mesenteric artery (SMA) and hepatic artery (PHA) at a 180-degree angle or more, and the presence of invasion into the duodenum and lower common bile duct. Due to these conditions, surgery is extremely risky and not recommended. Non-surgical treatments such as chemotherapy and radiation therapy should be considered as alternatives."

[0040] In the above example, the user provides the non-indication criteria as a prompt. However, the prompt may be generated using RAG based on patient information, and the LLM may learn treatment selection criteria, such as non-indication criteria. That is, in some embodiments, the treatment selection criteria (non-indication criteria and / or indication criteria) may be input to the large-scale language model (LLM) as a prompt. Also, in some embodiments, the treatment selection criteria (non-indication criteria and / or indication criteria and / or semi-indication criteria) may be input to the large-scale language model (LLM) using Retrieval-Augmented Generation (RAG). In some embodiments, the treatment selection criteria (non-indication criteria, non-indication criteria, semi-indication criteria) are stored in a database. The treatment selection criteria (non-indication criteria, non-indication criteria, or semi-indication criteria) stored in the database may be used in the RAG. This is also true when using an existing treatment decision algorithm stored in the database.

[0041] In some embodiments, the treatment selection criteria (non-indicated criteria and / or indicated criteria and / or semi-indicated criteria) are stored in a database in the form of a correspondence table (also referred to as a correspondence table or matrix) that associates patient information with available treatments. The correspondence table may be automatically updated and / or expanded by the LLM, if necessary. In some embodiments, when the treatment selection criteria (non-indicated criteria and / or indicated criteria and / or semi-indicated criteria) are stored in a database in the form of a correspondence table that associates patient information with available treatments, available treatments may be selected using a standard matching algorithm (searching for correspondence between patient information and available treatments) without using the LLM. For example, a treatment can be selected by matching patient information, such as test results, with indicated or indicated treatments in the correspondence table to create a list of applicable treatments. For example, treatments can be selected from a list of all currently available treatments, excluding treatments that are unavailable depending on the patient's condition, etc., and finally selecting the treatments remaining on the list. Thus, in some embodiments, a program according to the present disclosure may be a program that executes steps of acquiring patient (clinical) information, generating a proposed treatment plan based on the patient information, and outputting the proposed treatment plan, wherein the step of generating a proposed treatment plan based on the patient information includes selecting one or more available treatment methods by referring to a correspondence table that associates the patient information with available treatment methods, even when using an existing treatment decision algorithm stored in a database.

[0042] Outputting the Proposed Treatment Course In some embodiments, outputting the proposed treatment course may include, for example, outputting to a display directly or indirectly connected to the computer, audio output to a speaker, or outputting to a printer or storage connected to the computer.

[0043] In some embodiments, the output treatment plan proposal may include information on the advantages (e.g., effectiveness) and disadvantages (e.g., adverse events) of one or more selected available treatment methods. Information on the advantages and disadvantages of each treatment method may be stored in a database used by the RAG or in a separate, independent database. Information on the advantages and disadvantages of each treatment method may be obtained by the LLM or by a standard database query. When multiple available treatment methods are selected, they may be prioritized based on their advantages and disadvantages.

[0044] In some embodiments, the output treatment plan proposal may include information including the cure probability of one or more selected available treatment methods. Information including the outcome priority for each treatment method by disease (e.g., cure probability for cancer) may be stored in a database used by the RAG or in a separate, independent database. Information including the cure probability for each treatment method may be obtained by the LLM or by a regular database query. When multiple available treatment methods are selected, they may be prioritized (by disease) based on the cure probability.

[0045] In some embodiments, the output treatment plan proposal may include predicted outcome information such as recurrence. The predicted outcome information may be automatically calculated by the LLM by retrieving paper information from a paper database such as PubMed and comparing it with patient information according to an algorithm in the paper information.

[0046] In some embodiments, a program for assisting in the formulation of a patient treatment strategy according to the present disclosure may be a program that causes one or more computers to execute the steps of: acquiring patient information; generating a proposed treatment strategy based on the patient information using a large-scale language model (LLM), where the proposed treatment strategy includes a list of one or more available treatments selected based on treatment selection criteria (e.g., non-indication criteria and / or indication criteria) input as prompts into the LLM by a Retrieval-Augmented Generation (RAG) and the patient information; and outputting the proposed treatment strategy, where the proposed treatment strategy further includes information including advantages and disadvantages of the selected one or more available treatments and / or information including predicted information on the cure probability and / or outcome of the selected one or more available treatments. In some embodiments, the program according to the present disclosure may further define a treatment name and treatment selection criteria for each treatment and store them in a database. In some embodiments, the program according to the present disclosure may define a step of collecting the predefined treatment name and treatment selection criteria for each treatment from a pre-created database or a database that is continuously updated by the RAG.

[0047] In some embodiments, the step of generating a proposed treatment plan based on patient information may be performed using a large-scale language model (LLM) or any other suitable machine learning model (e.g., neural network, random forest, etc.) that takes patient information as input and outputs possible treatments.

[0048] Figure 3 shows an overview of treatment selection and outcome prediction using the treatment selection support program disclosed herein. Patient information correspondence tables can be used in the algorithms for treatment selection and outcome / result prediction (such as recurrence prediction). In other words, patient stratification can be achieved for each treatment using the correspondence tables. Outcome prediction enables precise treatment selection. For example, the risk of recurrence (recurrence score) after breast cancer surgery can be calculated using outcome prediction, and the degree of need for adjuvant therapy can be presented based on treatment information on the additional effect of adjuvant chemotherapy obtained from a paper and the recurrence score.

[0049] There may be two types of treatment selection tables. One is the traditional table that maps progression / phase to treatment, which can be an approach based on the patient's "condition" (patient information). The other lists all treatments available for each disease and matches the selection criteria (inapplicability criteria, inapplicability criteria) with the patient's condition (patient information). Treatment options can be selected based on treatments that do not violate the inapplicability criteria or are applicable. Diseases (illnesses) can be broadly classified as malignant (cancer) and benign, but the applicability of local treatments for malignant diseases can be primarily determined by inapplicability criteria. Outcome predictions can be calculated using a table that maps patient information and factors / items related to outcomes / results proven in papers, etc., to outcomes / results, or using a formula.

[0050] 1) Creation of the original database (DB) (1) The original DB (1) contains, for example, correspondence tables for treatment selection, or correspondence tables and calculation formulas for predicting treatment outcomes and results. This treatment selection or outcome / result prediction algorithm (correspondence tables, calculation formulas) can be created using an external medical database using RAG to obtain the data necessary to create the algorithm, and then stored in the original DB (1) using LLM or similar.

[0051] 2) Creation of an original database (DB) (2) External medical DBs can include DBs such as PubMed for obtaining the latest paper information, DBs containing treatment information that incorporates doctor consensus such as guidelines, clinical trial information DBs, and other DBs. The original DB (2), for example, stores treatment information for each treatment (treatment content / explanation, treatment effects, adverse events, etc.), and the treatment information can be obtained from the external DB using RAG, organized using LLM, and stored in DB (2).

[0052] 3) Organizing patient information Patient information such as disease names (e.g., colon cancer, liver metastasis, lung metastasis, etc.) obtained from medical information forms, image information (e.g., CT and MRI image interpretation findings), treatment progress information (date, treatment details, findings), test information (blood tests, etc.), pathology information, genetic information, symptoms, findings, etc. can be organized using LLM, and the patient information can then be organized in accordance with (adjusted to) a correspondence table and converted into "condition data."

[0053] 4) Presenting treatment options using a comparison table and calculating outcomes and results. By comparing the "condition data" (patient information organized in 3 above) with the comparison table obtained from DB (1) using RAG using a checklist or LLM, treatment can be selected or outcomes can be predicted.

[0054] 4a) In the case of cancer treatment selection An example of an LLM prompt is, "Access DB (1), obtain the correspondence table from the disease name, compare the disease condition with the comparison table, and if any of the exclusion criteria are met, remove the treatment from the treatment options. If the inclusion criteria are not met, remove it. Display the remaining treatments under these conditions in the same order as in the correspondence table."

[0055] 4b) For outcome prediction (risk of recurrence) When using a correspondence table, the LLM prompt may be, for example, "Access DB (1), obtain the comparison table for the desired prediction, and calculate the predicted value based on the disease state and the comparison table" (for example, stage 2 surgery for colorectal cancer liver metastasis, risk of recurrence between stage 1 and stage 2). When using a formula, the prompt may be, "Extract the items and their values ​​necessary for prediction from the disease state, substitute them into the formula, and calculate the predicted value" (for example, a breast cancer recurrence rate calculation published as a paper in NEJM and used in OncotypeDX (registered trademark), using an equation weighting the expression levels of cancer-related genes).

[0056] 5) Acquisition and display of treatment information Information about treatment options can be acquired from the original DB (2) using RAG and LLM. An example of an LLM prompt for acquisition is, "Please access DB (2) and acquire and display treatment information for each presented treatment."

[0057] 6) Original DB (3): Patient database. Patient information and output results for each patient may be saved in one file, and the file may be stored in the original DB (3).

[0058] 7) Original DB (3): Utilization of patient database. This can be completely anonymized and used as real-world data to provide feedback and improve the accuracy of the software disclosed herein.

[0059] Those skilled in the art will understand that the features described in this specification as characteristics of the program according to the present disclosure can also be applied as appropriate to the devices and systems described below.

[0060] Apparatus for assisting in the formulation of a patient's treatment plan In one aspect, the present disclosure relates to an apparatus for assisting in the formulation of a patient's treatment plan.

[0061] In some embodiments, the present disclosure relates to an apparatus for assisting in formulating a treatment plan for a patient, the apparatus including a patient information acquisition unit that acquires patient information, a treatment plan proposal generation unit that generates a treatment plan proposal based on the patient information, and a treatment plan proposal output unit that outputs the treatment plan proposal.

[0062] In some embodiments, an apparatus according to the present disclosure may include a processor for performing various processes and a memory coupled to the processor. In some embodiments, an apparatus according to the present disclosure may be implemented by executing a program according to the present disclosure on a general-purpose computer. In some embodiments, an apparatus according to the present disclosure includes a processor coupled to a memory device that stores a computer program according to the present disclosure and that can execute instructions of the program.

[0063] In some embodiments, the device according to the present disclosure includes a means for inputting data, such as a keyboard, a mouse, or the like.

[0064] In some embodiments, the device according to the present disclosure includes a central processing unit (CPU) connected to a keyboard, mouse, etc. for inputting data, connected to a hard disk, flash memory, etc. as a storage unit, and connected to memory (storage means) such as ROM, RAM, etc.

[0065] Examples of means for outputting data including prediction results include a monitor, a printer, etc. Other examples of output means include means for storing data in storage means such as a hard disk, flash memory, ROM, RAM, etc.

[0066] The apparatus according to the present disclosure may include a means for storing a program for assisting in the formulation of a patient treatment plan according to the present disclosure. Examples of the means for storing the program include a hard disk, a flash memory, and the like. Such a storage means may be connected via a communication line. That is, the apparatus according to the present disclosure may be part of a system obtained by connecting via a communication line to a device including the means for storing the program.

[0067] Fig. 1 is a schematic diagram showing an exemplary embodiment of an apparatus according to the present disclosure. In Fig. 1, reference numeral 100 denotes a computer, which includes a control unit 101, a storage unit 102, a peripheral device I / F unit 103, an input unit 104, a display unit 105, and a communication unit 106, all of which are connected by a bus 110. Note that this configuration is merely an example, and various other configurations may be adopted as appropriate.

[0068] The control unit 101 is composed of a central processing unit (CPM), read-only memory (ROM), random access memory (RAM), etc. The CPU loads programs stored in the memory unit 102, ROM, recording medium, etc. into a work memory area on the RAM and executes them, driving and controlling each device connected via the bus 110 and realizing the processing performed by the computer. The ROM is a non-volatile memory that stores programs, data, etc., such as the boot program and BIOS of the computer 100. The RAM is a volatile memory that temporarily stores programs, data, etc. loaded from the memory unit 102, ROM, recording medium, etc., and also provides a work area used by the control unit 101 when performing various processing. The memory unit 102 is, for example, a hard disk drive (HDD) and stores the programs executed by the control unit 101 and various other data.

[0069] The peripheral device I / F (interface) unit 103 is a port for connecting the computer 100 to peripheral devices. The peripheral device I / F unit 103 is configured with USB, Bluetooth, IEEE1394, RS-232C, etc. The connection with the peripheral devices may be wired or wireless. The input unit 104 has input devices such as a keyboard, a pointing device such as a mouse, and a numeric keypad, and issues operation instructions, operational instructions, data input, etc. to the computer 100. The display unit 105 is a logic circuit or device driver for displaying videos, images, etc. on a display device such as a liquid crystal panel. The input unit 104 and the display unit 105 can also be configured integrally as a touch display.

[0070] The communication unit 106 has a communication control device, a communication port, etc., and is a wired or wireless communication interface that mediates communication with the network 120. The bus 110 is a communication path that mediates the exchange of control signals, data signals, etc. between each device. The network 120 can be further connected to an external server 130 and a database (or net storage) 140.

[0071] In some embodiments, the device of the present disclosure may further include a prompt generation unit that generates prompts using RAG (Retrieval-Augmented Generation), an LLM input / output unit that inputs prompts into the LLM to obtain output, an LLM operation unit that operates the LLM, a treatment method information acquisition unit that acquires information about available treatment methods, and a treatment method information storage unit that stores information about available treatment methods.

[0072] SYSTEM FOR SUPPORTING DEVELOPMENT OF PATIENT TREATMENT POLICY The present disclosure, in one aspect, relates to a system for supporting the formulation of a patient treatment policy. The system according to the present disclosure may be comprised of one or more computers. The multiple computers may be connected via a communication network such as the Internet, and some of the computers may be configured on a cloud server. An exemplary configuration of the system according to the present disclosure includes a system comprising a client terminal that mainly handles input / output and a server that mainly handles calculation processing.

[0073] In some embodiments, the system of the present disclosure relates to a system for supporting the formulation of a treatment plan for a patient, the system including one or more computers, the one or more computers including a memory and a processor connected to the memory, the processor performing steps of acquiring patient information input by a user, generating a treatment plan proposal based on the patient information, and outputting the treatment plan proposal. In some embodiments, the memory or storage device of the computer included in the system of the present disclosure stores the program of the present disclosure, and when the program is executed, each of the above steps is performed.

[0074] In some embodiments, the system of the present disclosure may further perform the steps of generating prompts using Retrieval-Augmented Generation (RAG), inputting the prompts into the LLM to obtain output, running the LLM, obtaining information about available treatments, and storing information about available treatments.

[0075] In one aspect, the present disclosure relates to a non-transitory computer-readable recording medium storing the program of the present disclosure. Examples of computer-readable recording media include, but are not limited to, hard disk drives (HDDs), solid-state drives (SSDs), flash memories (such as USB memories and SD cards), optical disks (such as CDs, DVDs, and Blu-ray (registered trademark) disks), magnetic tapes, floppy disks, and cloud storage.

[0076] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potentially preferred methods and materials are now described. All publications mentioned herein are incorporated by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. It is understood that the present disclosure supersedes the disclosure of the incorporated publication in the case of a conflict.

[0077] Where a range of values ​​is described, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated or intervening value in a stated range and any other stated or intervening value within that stated range is also encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included or excluded, and each range including either, either, or both limits in the smaller ranges is also encompassed within the invention, but the specifically excluded limit in the stated range is reserved. When a stated range includes one or both of the limits, ranges excluding either or both of the included limits are also included in the invention. The term "about" with respect to a numerical value means within 5%.

[0078] The embodiments described herein are intended to be merely exemplary, and those skilled in the art will be able to make numerous variations and modifications without departing from the spirit of the present invention. Furthermore, certain variations and modifications may produce less than optimal results, but still provide satisfactory results. All such variations and modifications are intended to be within the scope of the present invention as defined by the appended claims. Furthermore, any combination of the components disclosed herein, or any transformation of the disclosed expression into a method, apparatus, system, computer program, data structure, recording medium, or the like, is also valid as an aspect of the present disclosure. Therefore, details described regarding the method of the present disclosure may also be applied to the system, computer program, data structure, recording medium, or the like.

[0079] The present disclosure will be further understood by reference to the following examples. These examples are provided solely to illustrate the claimed disclosure; the disclosure is not limited in scope by the exemplified embodiments, which are intended only as illustrations of single aspects of the disclosure. Any functionally equivalent methods are within the scope of the present disclosure. Various modifications of the present disclosure, in addition to those described herein, will be apparent to those skilled in the art from the foregoing description. Such modifications are intended to fall within the scope of the appended claims.

[0080] REFERENCE SIGNS LIST 100 Computer 101 Control unit 102 Storage unit 103 Peripheral device I / F unit 104 Input unit 105 Display unit 106 Communication unit 110 Bus 120 Network 130 External server 140 Database

Claims

1. A program for assisting in the formulation of a patient treatment plan, the program causing one or more computers to execute the steps of: acquiring patient information; generating a proposed treatment plan based on the patient information using a large-scale language model (LLM), wherein the proposed treatment plan includes a list of one or more available treatment methods selected based on the patient information and treatment selection criteria input as prompts into the LLM by a Retrieval-Augmented Generation (RAG); and outputting the proposed treatment plan, wherein the proposed treatment plan further includes information including the advantages and disadvantages of the selected one or more available treatment methods, and / or information including predicted information on the probability of cure and / or outcome of the selected one or more available treatment methods.

2. A program for supporting the formulation of a patient treatment plan, which causes one or more computers to execute the steps of acquiring patient information, generating a treatment plan proposal based on the patient information, and outputting the treatment plan proposal.

3. The program of claim 2, wherein the step of generating a proposed treatment plan based on the patient information includes the step of selecting one or more available treatment methods based on the patient information and predefined treatment selection criteria.

4. The program according to any one of claims 2 to 3, wherein the step of selecting one or more available treatment methods based on patient information and predefined treatment selection criteria is performed using a large-scale language model (LLM).

5. The program of claim 4, wherein the treatment selection criteria are input as prompts into the large-scale language model (LLM).

6. The program of claim 5, wherein the treatment selection criteria are input into a large-scale language model (LLM) by Retrieval-Augmented Generation (RAG).

7. The program of claim 6, wherein the treatment selection criteria are stored in a database.

8. The program according to claim 7, wherein the treatment selection criteria are stored in the database in the form of a correspondence table that associates patient information with available treatment methods.

9. The program according to any one of claims 2 to 8, wherein the proposed treatment plan includes information including the advantages and disadvantages of one or more selected available treatment methods.

10. A program according to any one of claims 2 to 9, wherein the proposed treatment plan includes information including prediction information on the cure probability and / or outcome of one or more selected available treatment methods.

11.

11. The program according to any one of claims 1 to 10, wherein the patient information includes at least one piece of information selected from the group consisting of age, sex, disease name (disease name, primary name, condition name such as metastasis or recurrence, complication name), name of left hepatic vein invasion, middle hepatic vein invasion, left hepatic vein invasion, vascular invasion, bile duct invasion, gastrointestinal tract invasion, Glisson's sheath invasion, metastasis to other organs, low liver function, S1, FLR (normal liver), FLR (damaged liver), major hepatectomy (tri-segmentectomy etc.), major hepatectomy (right hepatectomy), 3-segment branch, left hepatic vein invasion, middle hepatic vein invasion, right hepatic vein invasion, portal vein invasion, distance to the gastrointestinal tract (mm), anatomical name and positional relationship such as vascular invasion, bile duct invasion, gastrointestinal tract invasion, proximity to blood vessels, proximity to the bile duct, proximity to the gastrointestinal tract, and proximity to Glisson's sheath, degree of progression, histological and pathological findings, genetic information such as genetic abnormalities and their degree (including histological findings, gene expression levels, etc.), immunological findings such as the cancer microenvironment, and information on the degree of function.

12. The program according to any one of claims 1 to 11, wherein at least a portion of the patient information is generated by automatic interpretation of images.

13. Available treatment methods include conventional surgery, advanced surgery, TSH stage 2 surgery, ALPPS, ALPTPS, venous surgery, robotic surgery, endoscopic surgery, radiation therapy, external beam radiation therapy, particle therapy, heavy ion therapy, proton therapy, high-precision X-ray therapy, IMRT, stereotactic irradiation, CyberKnife, internal radiation therapy, brachytherapy, brachytherapy, theranostics, BNCT (boron neutron capture therapy), IVR, ablation, radiofrequency ablation, microwave ablation, cryotherapy, intra-arterial embolization, ethanol injection, HYF high-intensity focused ultrasound, irreversible electroporation (IRE), photoimmunotherapy, viral therapy, chemotherapy, hormone therapy, molecular targeted drugs, antibody-drug conjugates (ADC), immunotherapy, immune checkpoint inhibitors, fecal transplant therapy, cancer vaccines, CAR-T therapy, combined immunotherapy, gene therapy, stem cell transplant, mRNA vaccine, CRISPR / Cas9, hyperthermia, T The program according to any one of claims 1 to 12, comprising at least one treatment method selected from the group consisting of SH2 stage surgery, ALPPS, venous combined surgery, conventional RFA, high-precision RFA, TACE, drug therapy, anticancer drugs, immunosuppressants, investigational drugs, herbal medicines, transplants (organ transplants, bone marrow transplants, etc.), nerve blocks, CART, dialysis, shunts (Denver shunts, etc.), nerve decompression, nerve transplants, nerve repair, stem cell therapy, stem cell transplants, inhalants, infusions, transdermal drugs, transdermal formulations, transdermal patches, transdermal formulations (TTS), eye drops, intracavitary administration, continuous administration, and new surgical procedures, psychotherapy (cognitive behavioral therapy, CBT, etc.), physical therapy (electroconvulsive therapy: ECT, transcranial magnetic stimulation: TMS, transcranial direct current stimulation: tDCS, deep brain stimulation (DBS), spinal cord stimulation, etc.), diet therapy, exercise therapy, weight loss surgery, oriental medical treatments such as acupuncture and moxibustion, and antioxidants.

14. The program according to claim 2, wherein the step of generating a proposed treatment plan based on patient information is performed using a machine learning model that takes patient information as input and outputs available treatment methods.

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