Treatment effect evaluation system, treatment effect evaluation method, and program

Abstract

To objectively and quantitatively evaluate the effectiveness of alternative medical treatments such as manual therapy and osteopathic therapy. [Solution] A treatment effect evaluation system (10) for evaluating the effect of treatment as alternative medicine by a practitioner (1) on a patient (2), comprising: a terminal device (30) including a data collection unit that connects to each device constituting a sensor group (20) and collects biometric data and lifestyle data of the patient, as well as environmental data related to the treatment environment for the patient; an analysis device (40) including an analysis unit that evaluates the effect of the treatment by analyzing the biometric data, lifestyle data and environmental data using AI; and the terminal device (30) further including an output unit that outputs the analysis results by the analysis unit.

Description

[Technical Field]

[0001] The present invention relates to a treatment effect evaluation system, a treatment effect evaluation method, and a program. [Background technology]

[0002] Conventionally, methods for evaluating the effectiveness of alternative medicine treatments such as manual therapy and osteopathic therapy have relied on the experience of the practitioner and the subjective judgment of the patient. However, such methods make it difficult to objectively and quantitatively evaluate the effectiveness of the treatment, and the effectiveness of the treatment cannot be scientifically proven.

[0003] Regarding technology for evaluating the effects of treatment, for example, Patent Document 1 describes a method for measuring the comfort provided by massage using the calming of sympathetic nerves or the stimulation of parasympathetic nerves, such as changes in skin surface temperature, as an indicator. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 9-47480 Summary of the Invention [Problem to be solved by the invention]

[0005] The condition of the person receiving treatment cannot be judged solely by the skin surface temperature, but is greatly influenced by other biological data, attribute data, and environmental data in addition to the skin surface temperature of the person receiving treatment. Therefore, it is necessary to comprehensively analyze these data to evaluate the effectiveness of the treatment.

[0006] The present invention was made in light of this situation, and aims to enable the objective and quantitative evaluation of the effects of treatment, and in particular to enable the objective evaluation of the effects of treatment using alternative medicine (manual therapy, osteopathic therapy, etc.), which has no evidence and is the polar opposite of evidence-based Western medicine (modern medicine). [Means for solving the problem]

[0007] The present application includes a number of means for solving at least part of the above problems, examples of which are as follows.

[0008] In order to solve the above-mentioned problems, one aspect of the present invention is a treatment effect evaluation system for evaluating the effect of a treatment as alternative medicine on a patient, characterized by comprising: a data collection unit that collects biometric data and lifestyle data of the patient, as well as environmental data related to the treatment environment for the patient; an analysis unit that evaluates the effect of the treatment by analyzing the biometric data, lifestyle data, and environmental data using AI including a machine learning model; and an output unit that outputs the analysis results by the analysis unit.

[0009] The analysis unit can use the AI ​​to correct for the effects of factors other than the treatment.

[0010] The analysis unit can use the AI ​​to take into account the attribute data of the person receiving treatment and perform analysis according to the individual condition of the person receiving treatment.

[0011] The analysis unit can predict the health condition of the patient after treatment by analyzing the biological data, the lifestyle data, and the environmental data using the AI.

[0012] The treatment effect evaluation system can include a memory unit that stores the collected biometric data as time-series data, and the analysis unit can analyze the biometric data as time-series data using the AI.

[0013] The analysis unit can perform at least one of integration processing and data pre-processing on the collected biometric data.

[0014] The data preprocessing may include at least one of normalizing the biometric data, interpolating missing values, and removing noise.

[0015] The biological data of the subject may include at least one of basic physical data, vital sign data, internal physiological data, genetic data, microbiome-related data, neuropsychological data, and behavioral data.

[0016] The lifestyle data of the subject may include at least one of dietary data, exercise / physical activity data, sleep data, work / study data, mental health data, luxury item usage data, digital / life rhythm data, social activity data, and daily habit data.

[0017] The environmental data may include at least one of physical data, chemical data, biological data, psychological data, social and cyclical data, and specific environmental data.

[0018] The analysis unit can calculate at least one of a blood flow improvement rate, a stress reduction rate, and an autonomic nerve balance as a numerical representation of the effect of the treatment.

[0019] The analysis unit "performs statistical and causal analyses using placebo, spontaneous healing, reproducibility, etc. as indicators to scientifically evaluate the effects of the treatment," "When evaluating the effects of the treatment, the placebo effect and the influence of spontaneous healing are statistically excluded and the pure effect of the treatment is calculated," "Comparison with past data of treated individuals to evaluate the reproducibility of the effects when the same treatment is received and determine statistical significance," "Analyzes whether changes in the treated individual's biological data are due to the treatment or other factors (diet, sleep, exercise, etc.) and calculates the contribution rate," "Evaluates the general effectiveness of the treatment while taking into account individual differences between treated individuals and calculates the confidence interval of the results," "When explaining the effects of the treatment, analyzes correlations with medical fields where medical evidence is available and reinforces the scientific explanation," "Generates statistical models related to the effectiveness of the treatment and performs comparative evaluations of each treatment method," "If the effectiveness of the treatment is confirmed, optimize the factors related to the treatment (treatment method, environmental conditions, attributes of the patient, etc.) and make suggestions to achieve higher reproducibility." "In alternative medicine, evaluate the difference between the commonly believed treatment effects and the data analysis results, and analyze the factors behind the difference." "Extract objective data that shows the effectiveness of alternative medicine treatments, and provide a scientific explanation to the practitioner and patient." "Compare with past statistical data and a database of medical papers to analyze whether the treatment effects are consistent with medical knowledge." "If a new factor that has not been seen before is discovered, or if it is presumed that it has been discovered, add, change, or delete the new factor item from the analysis items." These instructions can be given to the AI, either alone or in combination, to arrive at an answer.

[0020] The data collection unit can collect at least one of the medical data, social and economic factor data, behavioral and psychological data, life event and personal history data, digital health data, risk factor data, and lifelong learning and knowledge data of the patient, and the analysis unit can analyze at least one of the collected medical data, social and economic factor data, behavioral and psychological data, life event and personal history data, digital health data, risk factor data, and lifelong learning and knowledge data of the patient using AI.

[0021] Another aspect of the present invention is a method for evaluating the effectiveness of a treatment using a treatment effectiveness evaluation system that evaluates the effectiveness of a treatment as alternative medicine on a recipient, the method comprising: a data collection step of collecting biological data and lifestyle data of the recipient, as well as environmental data related to the treatment environment for the recipient; an analysis step of evaluating the effectiveness of the treatment by analyzing the biological data, lifestyle data, and environmental data using AI; and an output step of outputting the analysis results from the analysis step.

[0022] Yet another aspect of the present invention is a program for causing a computer to function as a treatment effect evaluation system for evaluating the effect of a treatment as alternative medicine on a patient, the program causing the computer to execute processing including: a data collection step for collecting biometric data and lifestyle data of the patient, as well as environmental data related to the treatment environment for the patient; an analysis step for evaluating the effect of the treatment by analyzing the biometric data, lifestyle data, and environmental data using AI; and an output step for outputting the analysis results obtained by the analysis step. [Effects of the Invention]

[0023] According to the present invention, it is possible to objectively and quantitatively evaluate the effects of treatment, particularly the effects of treatment by alternative medicine such as manual therapy or osteopathic therapy.

[0024] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a diagram showing an example of biometric data of a patient. [Figure 2] FIG. 2 is a diagram showing an example of biometric data of a patient. [Figure 3] FIG. 3 is a diagram showing an example of lifestyle habit data of a patient. [Figure 4] FIG. 4 is a diagram showing an example of lifestyle habit data of a patient. [Figure 5] FIG. 5 is a diagram showing an example of environment data representing a treatment environment. [Figure 6] FIG. 6 is a diagram showing an example of environment data representing a treatment environment. [Figure 7] FIG. 7 is a diagram showing an example of the configuration of a treatment effect evaluation system according to one embodiment of the present invention. [Figure 8] FIG. 8 is a diagram illustrating an example of the configuration of a terminal device. [Figure 9] FIG. 9 is a diagram illustrating an example of the configuration of an analysis device. [Figure 10] FIG. 10 is a flowchart illustrating an example of the treatment effect evaluation process performed by the treatment effect evaluation system. [Figure 11] FIG. 11 is a diagram showing an example of the state of red blood cells in blood as a treatment effect. [Figure 12] FIG. 12 is a diagram illustrating an example of medical data. [Figure 13] FIG. 13 is a diagram showing an example of social and economic factor data. [Figure 14] FIG. 14 is a diagram illustrating an example of behavioral psychology data. [Figure 15] FIG. 15 is a diagram showing an example of life event and personal history data. [Figure 16] FIG. 16 is a diagram illustrating an example of digital health data. [Figure 17]FIG. 17 is a diagram illustrating an example of risk factor data. [Figure 18] FIG. 18 is a diagram showing an example of lifelong learning and knowledge data. DETAILED DESCRIPTION OF THE INVENTION

[0026] An embodiment of the present invention will be described below with reference to the drawings. In all drawings used to describe the embodiment, identical components are generally designated by the same reference numerals, and repeated description thereof will be omitted. Furthermore, in the following embodiments, components (including element steps, etc.) are not necessarily essential unless otherwise specified, or when they are clearly considered essential in principle. Furthermore, when the terms "consisting of A," "made of A," "having A," or "including A" are used, other elements are not excluded unless otherwise specified, or when it is clearly considered that only that element is included. Similarly, in the following embodiments, when referring to the shape, positional relationship, etc. of components, etc., these terms include those that are substantially similar or similar to the shape, etc., unless otherwise specified, or when they are clearly considered otherwise in principle.

[0027] <Treatment effect evaluation system according to one embodiment of the present invention> A treatment effect evaluation system according to one embodiment of the present invention is for objectively evaluating the effect of a treatment that a practitioner has administered to a patient.

[0028] Here, the treatment in this embodiment refers to manual therapy, chiropractic therapy, and other alternative medical treatments that lack evidence and are the opposite of evidence-based Western medicine (modern medicine), and includes, for example, the various therapies listed below. · Manual Therapies: Traditional massage, acupressure, chiropractic, osteopathy, myofascial release, etc. Manual physical therapy: A method of evaluating and diagnosing each system (sensory system, connective tissue, muscular system, nervous system, joint system, circulatory system, visceral system, etc.) and then selecting the most appropriate treatment technique. Energy Therapies: Qigong, Reiki, Healing Touch, Innate Therapy, etc. Brain and nervous system adjustment therapy: cerebrospinal fluid adjustment (craniosacral therapy), autonomic nervous system adjustment therapy, neurofeedback, etc. Therapies that use physical movement: Sotai, Feldenkrais Method, Rolfing, etc. · Chiropractic therapy: skeletal adjustment, pelvic adjustment, spinal adjustment, etc. · Therapies based on traditional Chinese medicine: acupuncture, meridian therapy, Chinese herbal acupressure, etc. · Relaxation therapies: aromatherapy, reflexology, hot stone therapy, etc. Other therapies: trigger point therapy, balance therapy, lymphatic drainage, sports massage, etc.

[0029] The above-mentioned alternative medical treatments are said to be effective in managing health and improving physical condition. However, their effectiveness is often evaluated based on the practitioner's experience or the subjective sensations of the patient (pain relief, fatigue improvement, etc.), and no objective, scientific evaluation criteria have been established.

[0030] On the other hand, the biological data of the patient is an important indicator that reflects the patient's internal health condition. In addition, the patient's lifestyle data and environmental data that represent the treatment environment are important factors that affect the patient's health condition.

[0031] Therefore, in this embodiment, the effect of treatment is analyzed using objective and scientific evaluation criteria based on the biological data and lifestyle data of the patient and environmental data of the treatment environment.

[0032] 1 and 2 show examples of biological data of a patient. The biological data includes at least one of basic physical data (height, weight, body fat percentage, BMI, waist circumference, etc.), vital sign data (body temperature, blood pressure, heart rate, blood oxygen concentration, etc.), internal physiological data (blood data, urine data, hormone levels, etc.), genetic data (genomic information, epigenetic data, etc.), microbial-related data (intestinal flora, skin flora, oral flora, etc.), neuropsychological data (brain waves, autonomic nervous system data, emotional and psychological states, etc.), and behavioral data (activity level, sleep data, eating habit data, etc.).

[0033] 3 and 4 show examples of lifestyle data of a patient. The lifestyle data includes at least one of dietary data (calorie intake, nutritional balance, meal timing, meal content, etc.), exercise / physical activity data (amount of physical activity, type of exercise, frequency and time of exercise, daily activities, etc.), sleep data (sleep time, sleep rhythm, sleep quality, naps, etc.), work / study data (working hours, work content and posture, study time, etc.), mental health data (stress level, emotions, mood, relaxation, etc.), recreational substance use data (alcohol consumption, smoking amount, caffeine intake, etc.), digital / life rhythm data (screen time, time of day when digital devices are used, etc.), social activity data (social interactions, frequency of interactions, volunteer activities, etc.), and daily habit data (housework, childcare, frequency of going out, etc.).

[0034] 5 and 6 show examples of environmental data of a patient. The environmental data includes at least one of physical data (electric field, magnetic field, noise, vibration, air quality, light environment, radiation, etc.), chemical data (air quality, water quality, soil contamination, food contamination, additives, etc.), biological data (microorganisms, animals, insects, plants, etc.), psychological data (noise, congestion, color, design, landscape, social factors, etc.), social and cyclical data (day-night rhythm, seasonal changes, busy schedules, etc.), and special environmental data (climate change, natural disasters, characteristics of urban environments, etc.).

[0035] <Configuration example of treatment effect evaluation system 10 according to one embodiment of the present invention> 7 shows an example of the configuration of a medical treatment effect evaluation system 10 according to one embodiment of the present invention. The medical treatment effect evaluation system 10 includes a sensor group 20, a terminal device 30, and an analysis device 40.

[0036] The sensor group 20 includes a biometric data acquisition device 21 for acquiring biometric data as short-term time series data of the patient 2 from before treatment by the therapist 1 to after treatment, a lifestyle data acquisition device 22 for acquiring lifestyle data of the patient 2, an attribute data acquisition device 23 for acquiring attribute data of the patient 2, and an environmental data acquisition device 24 for acquiring environmental data of the treatment environment.

[0037] The biological data acquisition device 21 measures and acquires biological data of the person to be treated 2 by a non-invasive measurement method.

[0038] Specifically, the biological data acquisition device 21 comprises, for example, wearable sensors (IMU (inertial measurement unit), EMG (surface myoelectric potential) sensor, pressure sensor, etc.), physiological sensors (electrocardiogram sensor, heart rate sensor, oxygen saturation sensor, respiration sensor, body temperature sensor, blood pressure sensor, blood glucose sensor, body composition sensor, brain wave sensor, activity sensor, electromyogram sensor, galvanic skin response sensor, etc.), and facial expression recognition system (used to detect facial expressions (pain, discomfort, pleasure, stress level, mania, depression, etc.)). The therapist 1 and the patient 2 may wear and operate each sensor, or, if expertise is required, a specialist other than the therapist 1 and the patient 2 may perform the operation. Furthermore, tests such as electroencephalogram tests and genetic test information may be outsourced.

[0039] The biological data of the treatment recipient 2 can be used to understand the basic health condition and assess disease risk of the treatment recipient 2. In other words, each piece of biological data is an important health indicator individually, but comprehensive analysis can lead to the prevention and early detection of lifestyle-related diseases.

[0040] Furthermore, the biological data of the treatment recipient 2 can be utilized for personalized medicine (precision medicine). In other words, the genomic information and intestinal flora information contained in the biological data makes it possible to select treatment methods and medications that are suited to each individual treatment recipient 2.

[0041] Furthermore, the biological data of the treatment recipient 2 can be used for preventive medicine. That is, by routinely monitoring the vital sign data and behavioral data contained in the biological data of the treatment recipient 2, signs of illness can be detected early.

[0042] Furthermore, the biological data of the treatment subject 2 can be used to improve psychological health and quality of life. In other words, the neuropsychological data and behavioral data contained in the biological data of the treatment subject 2 can be used as indicators for stress management, improvement of sleep, and improvement of quality of life.

[0043] The lifestyle habit data acquisition device 22 acquires lifestyle habit data including information such as dietary content, exercise history, exercise frequency, sleep state, stress level, alcohol consumption, smoking history, and subjective changes in symptoms of the treatment subject 2 by accepting input from the treatment subject 2 in the form of a questionnaire.

[0044] The attribute data acquisition device 23 acquires attribute data such as age, sex, occupation, etc. from the person to be treated 2 by accepting the input of the data.

[0045] The lifestyle habit data of the treatment recipient 2 can be used to maintain and improve the healthy lifestyle and prevent disease of the treatment recipient 2. In other words, by monitoring the daily lifestyle habits such as exercise, sleep, and diet contained in the lifestyle habit data of the treatment recipient 2, the risk of developing lifestyle-related diseases and mental illnesses can be reduced.

[0046] Furthermore, the lifestyle habit data of the treatment recipient 2 can be utilized to provide personalized care to the treatment recipient 2. In other words, by understanding the individual behavior and preference patterns of the treatment recipient 2 from the lifestyle habit data of the treatment recipient 2, it is possible to provide advice and treatment plans that are individually suited to the treatment recipient 2.

[0047] Furthermore, the lifestyle habit data of the treatment recipient 2 can be used to support behavioral change. That is, by setting specific improvement goals based on the current lifestyle habit data of the treatment recipient 2, motivation for behavioral change can be increased.

[0048] Furthermore, the lifestyle data of the treatment recipient 2 can be used to improve the quality of life. That is, by analyzing the mental health data and social activity data contained in the lifestyle data of the treatment recipient 2, it is possible to maintain physical and mental health and increase happiness.

[0049] Furthermore, by analyzing the lifestyle habit data of the treatment recipient 2 in combination with basic physical data, genetic data, and the like contained in the biological data of the treatment recipient 2, more comprehensive health management becomes possible.

[0050] The lifestyle habit data acquisition device 22 and the attribute data acquisition device 23 are composed of terminal devices or the like into which the treatment recipient 2 inputs age, sex, occupation, dietary content, exercise history, exercise frequency, sleep state, stress level, alcohol consumption, smoking history, etc. The treatment recipient 2 may input the information using a smartphone or the like (not shown) of the treatment recipient 2.

[0051] The environmental data acquisition device 24 is composed of sensors that measure environmental data of the treatment environment. The environmental data includes at least one of the following information: temperature, air pressure, humidity, weather, noise level, amount of light, air quality (VOC concentration, etc.), fragrance, illuminance, and surrounding color (e.g., color of the room walls or curtains, color of clothing, etc.). The environmental data may also include temperature, air pressure, humidity, weather, etc. for the last few days leading up to the treatment date. The environmental data acquisition device 24 may also acquire environmental data from an external database.

[0052] Environmental data can be used to evaluate the impact of external factors on health and to improve living environments and working styles.

[0053] The collection and use of each data using the sensor group 20 will be carried out with the consent of the person being treated 2, the privacy of the person being treated 2 will be protected, the analysis results will be accurately recorded, and feedback will be provided to the practitioner 1 and the person being treated 2.

[0054] The terminal device 30 connects to each device constituting the sensor group 20 via wireless communication such as Wi-Fi (trademark) or Bluetooth (trademark), or wired communication using a USB (Universal Serial Bus) cable, for example, and collects biometric data, lifestyle habit data, attribute data, and environmental data.

[0055] In addition, the biometric data, lifestyle habit data, attribute data, and environmental data acquired by each device in the sensor group 20 may be recorded on a removable portable recording medium, and the terminal device 30 may read out the biometric data, lifestyle habit data, attribute data, and environmental data recorded on the portable recording medium.

[0056] Furthermore, the terminal device 30 is connected to the analysis device 40 via a network N, which is a two-way communication network typified by the Internet. The terminal device 30 transmits the collected biological data, lifestyle habit data, attribute data, treatment data (described later), and environmental data to the analysis device 40 and requests an analysis. Furthermore, the terminal device 30 receives an evaluation result of the treatment performed on the treatment recipient 2, obtained from the analysis device 40, and presents the evaluation result to the user (the practitioner 1) and the treatment recipient 2. Note that the terminal device 30 may be directly connected to the analysis device 40 without going through the network N. Furthermore, the terminal device 30 and the analysis device 40 may be integrated by incorporating the functions of the analysis device 40, which will be described later, into the terminal device 30.

[0057] The analysis device 40 analyzes the biometric data, lifestyle data, attribute data, treatment data, and environmental data transmitted from the terminal device 30, evaluates the treatment performed on the treatment recipient 2, and transmits the evaluation results to the terminal device 30.

[0058] 8 shows an example of the functional block configuration of the terminal device 30. The terminal device 30 has the following functional blocks: a data collection unit 31, an analysis request unit 32, a storage unit 33, a communication unit 34, and an output unit 35.

[0059] The terminal device 30 is composed of a computer such as a smartphone, tablet PC (personal computer), notebook PC, desktop PC, dedicated terminal, etc., which is equipped with a processor such as a CPU (Central Processing Unit), memory such as DRAM (Dynamic Random Access Memory), storage such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), input devices such as a keyboard, mouse, touch panel, etc., output devices such as a display, and a communication module such as a NIC (Network Interface Card) (all not shown).

[0060] The data collection unit 31 and the analysis request unit 32 are realized by the processor of the computer executing a predetermined program.

[0061] The data collection unit 31 connects to each device in the sensor group 20 via the communication unit 34, collects measured biological data, lifestyle habit data, attribute data, and environmental data, and stores it in the memory unit 33. The data collection unit 31 also collects treatment data from the therapist 1, etc. The treatment data includes the date and time of treatment, treatment time, type of treatment, treatment tools, treatment frequency, and the skill level of the therapist 1. The treatment data is input into the terminal device 30 by the therapist 1 or the person being treated 2.

[0062] The analysis request unit 32 transmits the biometric data (long-term time series data (described later)), lifestyle habit data, attribute data, environmental data, and treatment data stored in the memory unit 33 to the analysis device 40 via the communication unit 34 and the network N, and requests analysis.

[0063] The storage unit 33 is made up of the memory and storage of the computer. The storage unit 33 stores the biological data, lifestyle data, attribute data, environmental data, and treatment data collected by the data collection unit 31 from each device of the sensor group 20. The storage unit 33 associates the biological data of each treated person 2 with biological data collected during past treatments and stores the data as long-term time-series data.

[0064] The communication unit 34 is composed of a communication module of the computer. The communication unit 34 connects to each device of the sensor group 20 via wireless or wired communication to receive biological data, lifestyle data, attribute data, and environmental data. The communication unit 34 also connects to the analysis device 40 via the network N to communicate various types of data.

[0065] The output unit 35 is made up of an output device of the computer. The output unit 35 displays an operation screen for the user (practitioner 1). The operation screen may display instructions to the user, or may be configured to give instructions using voice or sound. Furthermore, the output unit 35 may be configured to receive a response from the user to the instructions.

[0066] 9 shows an example of the functional block configuration of analysis device 40. Analysis device 40 has functional blocks of a data analysis unit 41 and a communication unit 42. Analysis device 40 is made up of a computer such as a server computer that includes, for example, a processor such as a CPU, a memory such as a DRAM, a storage such as an HDD or SSD, an input device such as a keyboard, a mouse, or a touch panel, an output device such as a display, and a communication module such as a NIC (none of which are shown).

[0067] The data analysis unit 41 is realized by the processor of the computer executing a predetermined program. The data analysis unit 41 requests an AI (Artificial Intelligence) 411 to analyze the biometric data (short-term and long-term time-series data), lifestyle habit data, attribute data, environmental data, and treatment data transmitted from the terminal device 30, and obtains, as the analysis results, an evaluation of the treatment for the treatment recipient 2, a treatment content suitable for the treatment recipient 2, and a predicted health condition of the treatment recipient 2 after the treatment. By treating the biometric data as time-series data, it is possible to objectively grasp changes in the condition of the treatment recipient 2 and quantitatively evaluate physical changes in the treatment recipient 2 due to the treatment. The data analysis unit 41 can use AI including a machine learning model that analyzes the effects of the treatment and corrects for factors other than the treatment.

[0068] For example, AI411 uses LSTM (Long Short Term Memory) to analyze biological data, and a causal inference model to analyze the causal relationships between attribute data, environmental data, and treatment data to build a model to evaluate the effectiveness of treatment, and divides the data into training data and validation data to train the model and evaluate the effectiveness of treatment.In the case of alternative medicine, whose reproducibility is difficult to confirm scientifically, its effectiveness often varies greatly depending on individual differences, so by clarifying the causal relationships using a data analysis model, the effectiveness of alternative medicine can be proven and reproducibility can be increased.

[0069] AI411 also analyzes whether there are statistically significant differences in biometric data before and after treatment, eliminating the influence of placebo effects and natural healing, and determining whether alternative medicine contributes to the patient's health. This makes it possible to address the inherent problem of alternative medicine, which is the difficulty of distinguishing between placebo effects and natural healing and therapeutic effects, and the skepticism surrounding alternative medicine.

[0070] Furthermore, if the effectiveness of a treatment is confirmed, AI411 will suggest optimizing the treatment method or environmental conditions, and if the treatment is ineffective, it will specifically suggest alternative treatment methods or adjuvant therapies. Alternative medicine has been problematic due to the misconception that it is "one size fits all" and the lack of clarity on what to do if it is ineffective. By providing specific suggestions for improving treatments and alternatives, the reliability and practicality of alternative medicine can be improved.

[0071] Furthermore, AI411 periodically retrains the model using newly collected biometric data, etc., to improve accuracy. AI411 also visualizes the decision-making process using the model and provides the results to the practitioner 1 in an explainable format.

[0072] The AI ​​411 may be a so-called generative AI. For example, the generative AI is a system connected to a network N and uses machine learning and natural language processing to generate and output various documents, images, videos, audio, program codes, etc. in response to instructions (prompts). If an API (Application Programming Interface) is provided for the generative AI, the data analysis unit 41 can also access the API function to input instructions. The generative AI may be built locally and accessed by the analysis device 40 via a LAN (Local Area Network) or a WAN (Wide Area Network). For example, OpenAI's CHAT-GPT, Google's Gemini, etc. may be used as the generative AI.

[0073] Furthermore, advanced AI such as AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) may be adopted instead of AI 411. The AI ​​analysis function and utilization of time-series data of the present invention are highly applicable in growing fields such as medical technology, which is currently in the demonstration stage, the fields of psychotherapy and neurofeedback, and digital health analysis using wearable devices.

[0074] When using a generating AI, for example, a prompt including the following command will lead to an answer. For example, the data analysis unit 41 can give the generating AI the following instructions and obtain the analysis results to evaluate the effectiveness of the treatment. When giving instructions to the generating AI, all of the biometric data, lifestyle habit data, attribute data, environmental data, and treatment data may be given, or one or more selected from these may be given. Past data associated with a time series may also be given.

[0075] Example instructions: "Analyze multiple data sets of patients before and after treatment to quantitatively evaluate the effects of the treatment." "Analyzes changes due to treatment based on the patient's biological condition, attribute information, and treatment environment, and outputs the evaluation results." "When evaluating the effects of a treatment, calculate the treatment effect objectively while taking into account fluctuations due to factors other than the treatment." "Referring to the patient's past treatment data, we suggest the most appropriate treatment method." "Predict how the effects of a treatment will change over time and provide those results." "Recommendations for maximizing treatment results are provided based on the patient's characteristics and treatment history." "Based on the evaluation results of the treatment, information that can be used as evidence is generated, and data showing the effectiveness of the treatment is output." "Predicting improvement in health status after treatment and providing feedback including lifestyle suggestions." "Compare the effects of multiple treatment methods and provide criteria for determining the treatment that is best suited to the patient." "The analysis results are output in a format that is easy for users to understand, helping to explain the effects of treatment." Furthermore, taking into account the problems inherent in alternative medicines that lack evidence, the following instructions can be adopted: "To scientifically evaluate the effectiveness of the treatment, we will conduct statistical and causal analyses using indicators such as placebo, spontaneous healing, and reproducibility." "When assessing the effects of a treatment, the placebo effect and the effects of natural healing are statistically excluded to calculate the pure effect of the treatment." "Compare with past patient data to evaluate the reproducibility of the effects when the same treatment is performed and determine statistical significance." "Analyze whether changes in the patient's biological data are due to the treatment or other factors (diet, sleep, exercise, etc.), and calculate the contribution rate." "Evaluate the general effectiveness of the treatment, taking into account individual differences between patients, and calculate the confidence interval for the results." "When explaining the effects of treatment, we analyze correlations with medical fields where there is medical evidence, and reinforce scientific explanations." "Generate statistical models of treatment effectiveness, perform comparative evaluations of treatment methods, and develop a reliable list of recommendations." "If the effectiveness of the treatment is confirmed, we will optimize the factors related to the treatment (treatment method, environmental conditions, attributes of the treatment recipient, etc.) and make suggestions to achieve higher reproducibility." "In alternative medicine, students will be asked to evaluate the differences between commonly believed treatment effects and the results of data analysis, and to analyze the factors behind those differences." "We will extract objective data that demonstrates the effectiveness of alternative medicine treatments and require practitioners and patients to provide scientific explanations." "Compare the results with past statistical data and a database of medical papers to analyze whether the treatment effects are consistent with medical knowledge." "When a new factor that has not been previously known is discovered or is presumed to have been discovered, the new factor item will be added, changed, or deleted from the analysis items." These instructions may be used alone or in combination. In this way, the data analysis unit 41 instructs the generation AI to perform the processing required to objectively evaluate the treatment effect and obtains the analysis results, thereby making it possible to quantitatively evaluate the effects of the treatment and address issues such as the influence of the placebo effect and reproducibility.

[0076] When assessing the effects of a treatment, such a generative AI may utilize expertise in medicine, biomedical data analysis, statistics, and machine learning to apply various scientific methods necessary for analyzing the effects of a treatment. Specifically, it may use statistical analysis and causal inference algorithms to distinguish the effects of a treatment from placebo effects or natural healing.

[0077] In addition, the AI ​​performs time-series data analysis to predict changes in health status and trends of improvement after treatment, and evaluates the sustainability of treatment effects, making it possible to present recommended conditions for optimizing the effectiveness of treatment while taking into account individual differences between patients.

[0078] Furthermore, this generation AI can analyze not only text data but also image data and time-series data, and can visually present changes in biological conditions before and after treatment. This makes it possible to visually show the effects of treatment in an easy-to-understand manner and strengthen feedback to the practitioner and patient.

[0079] Furthermore, to evaluate the effectiveness of treatments, the generative AI uses statistical models to analyze the reproducibility of the effects of each treatment. This allows the data of multiple patients who received the same treatment to be compared and statistical significance to be verified, clarifying the scientific basis of the treatment.

[0080] The AI ​​generates visualizations of the process of estimating treatment results and outputs analysis results in a form that can be explained to both the practitioner and the patient, thereby scientifically demonstrating the basis for the effectiveness of treatment and improving the reliability of the treatment.

[0081] Furthermore, this AI can continuously learn based on accumulated treatment data, and by incorporating new treatment data and the patient's biological information, it can improve the accuracy of the evaluation of treatment effects. Furthermore, by comparing with treatment history, it can propose the optimal treatment method for each patient.

[0082] Furthermore, to ensure the protection of personal information and data security, the generative AI can employ federated learning and edge AI processing, which allows for secure analysis of patient data without centralized management on the cloud.

[0083] Furthermore, the generative AI can compare the results with past medical research and academic data to evaluate the scientific validity of the treatment effects, making it possible to provide objective evidence of the effects of alternative medicine treatments and verify their effectiveness.

[0084] The communication unit 42 is made up of a communication module of the computer. The communication unit 42 connects to the terminal device 30 via the network N and communicates various data. For example, the communication unit 42 receives an analysis request, as well as biometric data, attribute data, and environmental data of the treatment recipient 2 from the terminal device 30. The communication unit 42 also transmits to the terminal device 30 an evaluation of the effect of the treatment on the treatment recipient 2 analyzed by the AI ​​411, and treatment details suitable for the treatment recipient 2.

[0085] <Treatment effect evaluation processing by treatment effect evaluation system 10> 10 is a flowchart illustrating an example of the treatment effect evaluation process performed by the treatment effect evaluation system 10. The treatment effect evaluation process is started in response to a predetermined operation on the terminal device 30 by the user.

[0086] It is assumed that treatment has already been completed for the treatment recipient 2, and that each device constituting the sensor group 20 has acquired biometric data, lifestyle data, attribute data, and environmental data of the treatment recipient 2 from before the treatment to after the treatment.

[0087] First, the data collection unit 31 of the terminal device 30 collects biometric data, lifestyle data, attribute data, and environmental data of the patient 2 from each device constituting the sensor group 20 via the communication unit 34, and collects treatment data from the therapist 1, etc. and stores it in the memory unit 33 (step S1).

[0088] Next, the analysis request unit 32 of the terminal device 30 reads out the biometric data, lifestyle data, attribute data, environmental data, and treatment data stored in the memory unit 33, and transmits them to the analysis device 40 via the communication unit 34 and the network N to request analysis (step S2).

[0089] Next, the data analysis unit 41 of the analysis device 40 performs integration processing and data preprocessing on the biometric data, lifestyle habit data, attribute data, and environmental data (step S3). In the integration processing, the different types of data contained in the biometric data, lifestyle habit data, attribute data, and environmental data are integrated and converted into an analyzable format. In the data preprocessing, data normalization, missing value interpolation, and noise removal are performed. This minimizes the influence of abnormal data due to incorrect measurement, etc., and improves the accuracy of analysis in subsequent stages.

[0090] Next, the data analysis unit 41 of the analysis device 40 instructs the AI ​​411 to analyze the biometric data, lifestyle data, attribute data, environmental data, and treatment data. The AI ​​411 analyzes the biometric data, lifestyle data, attribute data, and environmental data after the integration process and data preprocessing, as well as the treatment data (step S4), and determines the treatment effect for the treatment recipient 2 and outputs a treatment plan personalized for the treatment recipient 2 (step S5). Specifically, for example, the AI ​​411 analyzes the correlation between the biometric data, lifestyle data, attribute data, environmental data, and treatment data to quantify the treatment effect. The AI ​​411 also learns patterns of treatment effect from past biometric data and compares them with the latest biometric data to predict the health condition and improvement effect after the treatment. The AI ​​411 then quantifies the treatment effect based on changes in the biometric data before and after the treatment, and proposes personalized treatment content and lifestyle improvement plans for the treatment recipient 2 based on the health condition of the treatment recipient 2.

[0091] The effects of treatment can be quantified by calculating, for example, the rate of improvement in blood flow, the rate of stress reduction, autonomic nervous balance, etc. The rate of improvement in blood flow can be calculated by, for example, comparing the blood condition (fluidity and aggregation state of red blood cells) before and after treatment.

[0092] Figure 11 shows an example of images taken with a phase-contrast microscope of the state of red blood cells in blood before and after the treatment, specifically, the state of aggregation and fluidity of red blood cells, quantified after data preprocessing. The left side of the figure shows the image before the treatment, and the right side shows the image after the treatment. Before the treatment, some red blood cells had lost their original elliptical shape, had spiky protrusions on their periphery, and were connected to each other. However, after the treatment, the deformation and connection between red blood cells were resolved, and fluidity was improved.

[0093] The stress reduction rate can be calculated based on changes in electroencephalogram data (alpha waves, beta waves, etc.), and the autonomic nervous system balance can be calculated by analyzing heart rate variability data, for example.

[0094] With regard to the individualized treatment content, for example, the most suitable treatment method (e.g., chiropractic, qigong, cerebrospinal fluid adjustment, etc.) can be suggested based on the condition of the treatment recipient 2. With regard to improving lifestyle habits, for example, extending sleep time, taking in specific nutrients, increasing the amount of exercise, etc. can be suggested.

[0095] Next, the communication unit 42 of the analysis device 40 transmits the determination of the treatment effect on the treatment recipient 2 and the treatment plan individualized for the treatment recipient 2 to the terminal device 30, and the output unit 35 of the terminal device 30 visualizes and displays the determination of the treatment effect on the treatment recipient 2 and the treatment plan individualized for the treatment recipient 2 on a screen, and presents them to the user (practitioner 1) and the treatment recipient 2 (step S6). Note that the data analysis unit 41 may compile a report of the determination of the treatment effect on the treatment recipient 2 and the treatment individualized for the treatment recipient 2, and the communication unit 42 may transmit the report to the terminal device 30, which may then output the report. This completes the treatment effect evaluation process by the treatment effect evaluation system 10.

[0096] According to the treatment effect evaluation process described above, the effect of the treatment given to the treatment recipient 2 can be objectively and quantitatively evaluated based on the specific sensor data digitization and AI analysis, and "utilization of time-series data" and "statistical elimination of placebo effects" can be realized. Furthermore, the practitioner 1 can modify the treatment given to the treatment recipient 2 to a treatment content that is individualized for the treatment recipient 2. Furthermore, the practitioner 1 can suggest lifestyle improvements to the treatment recipient 2.

[0097] In particular, this embodiment is targeted at treatments using alternative medicine, and can address the problems specific to alternative medicine, such as "uncertainty of effects," "confusion with placebos or natural healing," and "lack of reproducibility." However, the present invention can also be applied to fields other than alternative medicine, such as rehabilitation, sports training, and psychotherapy.

[0098] <Modification> The data analysis unit 41 of the analysis device 40 may be moved to the terminal device 30, and the treatment effect evaluation system 10 may be realized by the terminal device 30 alone. Alternatively, if the terminal device 30 is a smartphone or tablet computer, the above-mentioned functional units may be realized by installing an application program on the smartphone or the like serving as the terminal device 30. Alternatively, all functional units may be held by the analysis device 40 (server), and information may be presented to the user and instruction input may be accepted by a general-purpose browser executed on the terminal device 30.

[0099] Furthermore, the treatment effect evaluation system 10 may communicate with and cooperate with an external AI system (such as an AI server or a robot system with built-in AI). It may have an interface or protocol for cooperation (such as an API, messaging protocol, gRPC, WebSocket, or file sharing). This allows the AI ​​411 (own AI) of the treatment effect evaluation system 10 to have an expert AI that is knowledgeable in the required field (such as an AI that is skilled in medicine, law, or the latest information) provide an answer. In this case, the expert AI is the master and the own AI is the slave. Conversely, for example, the own AI may be the master and give instructions to a working AI robot. Then, the own AI may output an answer in response to a request (question) from the working AI robot.

[0100] In this embodiment, biometric data, lifestyle habits, and environmental data were used in the analysis to evaluate treatment content and as important factors that affect a person's health. However, medical data, social and economic factor data, behavioral and psychological data, life event and personal history data, digital health data, risk factor data, and lifelong learning and knowledge data that may affect a person's health may also be included in the analysis.

[0101] Figure 12 shows an example of medical data. The medical data includes at least one of the following: medical history data (disease history, surgical history, family medical history, vaccination status, etc.), diagnostic data (doctor's diagnosis, medical certificate, test results, etc.), prescription data (past and current medication history, allergy medication, dosage, etc.), and vaccination record data (vaccination record, vaccination timing, type of vaccination, etc.). By including medical data in the analysis, it is possible to develop appropriate treatment and prevention plans using past medical history.

[0102] Figure 13 shows an example of social and economic factor data. The social and economic factor data includes at least one of the following: economic status data (income, savings, income disparity, etc.), education level data (educational background, knowledge literacy, etc.), employment status data (job stability, employment type, work environment, etc.), housing status data (owner-occupied, rented, space size, living environment, etc.), and data on connections with the local community (frequency of interaction with neighbors, participation in community activities, etc.). By including social and economic factor data in the analysis, it is possible to analyze health disparities and mental health risks from a social background perspective.

[0103] Figure 14 shows an example of behavioral psychology data. The behavioral psychology data includes at least one of personality tendency data (personality traits such as introversion or extroversion), stress response data (behavior in stressful situations, coping methods, etc.), motivation data (whether or not one is motivated to continue healthy behavior, etc.), and hobby and entertainment data (frequency of hobby activities, relaxation, etc.). By including behavioral psychology data in the analysis, it is possible to design mental care and behavior improvement programs.

[0104] Figure 15 shows an example of life event and personal history data. The life event and personal history data includes at least one of the following: life event data (marriage, childbirth, relocation, bereavement, divorce, retirement, etc.), cultural and religious background data (religious taboos, cultural values, etc.), and taste and value data (values ​​regarding health and beauty, social influences). By including the life event and personal history data in the analysis, it is possible to analyze the impact of life events on health and adaptive status.

[0105] Figure 16 shows an example of digital health data. Digital health data includes at least one of wearable device data (data obtained from smartwatches, fitness trackers, etc.), health app data (data recorded in diet apps, apps to prevent giving up after three days, etc.), and health data from social media (health-related posts, content reflecting lifestyles, etc.). By including digital health data in the analysis, it can be applied to health status monitoring and behavior management.

[0106] 17 shows an example of risk factor data. The risk factor data includes at least one of family history data (e.g., medical history of family members or relatives), obesity index data (e.g., obesity level, fat distribution), smoking status data (e.g., number of cigarettes smoked, smoking history), and drinking habit data (e.g., drinking frequency, amount of alcohol consumed). Including risk factor data in the analysis can be useful for identifying disease risks and for early intervention.

[0107] Figure 18 shows an example of lifelong learning and knowledge data. The lifelong learning and knowledge data includes at least one of health literacy data (health-related information gathering ability, knowledge level, etc.) and learning activity data (hobby learning, reading, skill acquisition, etc.). Including lifelong learning and knowledge data in the analysis can improve the ability to understand and utilize health information and contribute to promoting healthy behavior.

[0108] The present invention is not limited to the above-described embodiments and modifications, and various other modifications are possible. For example, the above-described embodiments and modifications have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all of the described configurations. Furthermore, it is possible to replace a part of a modification with another modification, or to combine modifications.

[0109] Furthermore, some or all of the above-described configurations, functions, processing units, processing means, etc. may be implemented in hardware, for example, by designing them as integrated circuits. Furthermore, the above-described configurations, functions, etc. may be implemented in software by a processor interpreting and executing a program that implements each function. Information such as programs, tables, and files that implement each function may be stored in memory, a storage device such as a hard disk or SSD, or a storage medium such as an IC card, SD card, or DVD. Furthermore, the control lines and information lines shown are those considered necessary for explanation, and do not necessarily represent all control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected.

[0110] The present invention is not limited to a system or an apparatus, but can be provided in various forms such as a method, a computer-readable program, and the like. [Explanation of symbols]

[0111] 1 Practitioner, 2 Patient, 10 Treatment effect evaluation system, 20 Sensor group, 21 Biometric data acquisition device, 22 Lifestyle habit data acquisition device, 23 Attribute data acquisition device, 24 Environmental data acquisition device, 30 Terminal device, 31 Data collection unit, 32 Analysis request unit, 33 Memory unit, 34 Communication unit, 35 Output unit, 40 Analysis device, 41 Data analysis unit, 411 AI, 42 Communication unit

Claims

1. A treatment effect evaluation system for evaluating the effect of a treatment as alternative medicine on a patient, a data collection unit that collects biological data and lifestyle data of the person receiving treatment, and environmental data related to the treatment environment for the person receiving treatment; an analysis unit that evaluates the effect of the treatment by analyzing the biological data, the lifestyle habit data, and the environmental data using AI including a machine learning model; an output unit that outputs the analysis result by the analysis unit; A treatment effect evaluation system comprising:

2. The treatment effect evaluation system according to claim 1, The analysis unit uses the AI ​​to consider the attribute data of the patient and performs an analysis according to the individual condition of the patient. A treatment effect evaluation system characterized by:

3. The treatment effect evaluation system according to claim 1, The analysis unit predicts the health condition of the patient after the treatment by analyzing the biological data, the lifestyle habit data, and the environmental data using the AI. A treatment effect evaluation system characterized by:

4. The treatment effect evaluation system according to claim 1, a storage unit that stores the collected biological data as time-series data, The analysis unit analyzes the biological data as the time-series data using the AI. A treatment effect evaluation system characterized by:

5. The treatment effect evaluation system according to claim 1, The analysis unit performs at least one of integration processing and data preprocessing on the collected biometric data. A treatment effect evaluation system characterized by:

6. The treatment effect evaluation system according to claim 5, The data preprocessing includes at least one of normalizing the biological data, interpolating missing values, and removing noise. A treatment effect evaluation system characterized by:

7. The treatment effect evaluation system according to claim 1, The biometric data of the subject includes at least one of basic physical data, vital sign data, internal physiological data, genetic data, microbiome-related data, neuropsychological data, and behavioral data. A treatment effect evaluation system characterized by:

8. The treatment effect evaluation system according to claim 1, The lifestyle data of the patient includes at least one of dietary data, exercise / physical activity data, sleep data, work / learning data, mental health data, luxury item usage data, digital / life rhythm data, social activity data, and daily habit data. A treatment effect evaluation system characterized by:

9. The treatment effect evaluation system according to claim 1, The environmental data includes at least one of physical data, chemical data, biological data, psychological data, social and cyclical data, and specific environmental data. A treatment effect evaluation system characterized by:

10. The treatment effect evaluation system according to claim 1, The analysis unit calculates at least one of a blood flow improvement rate, a stress reduction rate, and an autonomic nerve balance as a quantification of the effect of the treatment. A treatment effect evaluation system characterized by:

11. The treatment effect evaluation system according to claim 1, The analysis unit "To scientifically evaluate the effectiveness of the treatment, statistical and causal analyses will be conducted using indicators such as placebo, spontaneous healing, and reproducibility." "When evaluating the effects of a treatment, the placebo effect and the influence of natural healing are statistically excluded to calculate the pure effect of the treatment." "Compare with past patient data to evaluate the reproducibility of the effects when receiving the same treatment and determine statistical significance." "Analyze whether changes in the patient's biological data are due to the treatment or other factors (diet, sleep, exercise, etc.) and calculate the contribution rate." "Evaluate the general effectiveness of the treatment, taking into account individual differences between patients, and calculate the confidence interval of the results." "When explaining the effects of treatment, we analyze correlations with medical fields where there is medical evidence, and reinforce scientific explanations." "Generate statistical models of treatment effectiveness, perform comparative evaluations of treatment methods, and create a reliable list of recommendations." "If the effectiveness of the treatment is confirmed, we will optimize the factors related to the treatment (treatment method, environmental conditions, attributes of the treatment recipient, etc.) and make suggestions to achieve higher reproducibility." "In alternative medicine, evaluate the difference between the commonly believed treatment effects and the results of data analysis, and analyze the factors behind those differences." "To extract objective data that shows the effectiveness of alternative medicine treatments and to provide scientific explanations for practitioners and patients." "Compare with past statistical data and a database of medical papers to analyze whether the treatment effects are consistent with medical knowledge." "When a new factor that has not been previously known is discovered or is presumed to have been discovered, the new factor item will be added, changed, or deleted from the analysis items." The AI ​​is given one of the following instructions, either alone or in combination, to guide it to an answer. A treatment effect evaluation system characterized by:

12. The treatment effect evaluation system according to claim 1, The data collection unit collects at least one of medical data, social and economic factor data, behavioral and psychological data, life event and personal history data, digital health data, risk factor data, and lifelong learning and knowledge data of the patient; The analysis unit analyzes at least one of the collected medical data, social and economic factor data, behavioral psychology data, life event and personal history data, digital health data, risk factor data, and lifelong learning and knowledge data of the patient using AI including a machine learning model. A treatment effect evaluation system characterized by:

13. A treatment effect evaluation method using a treatment effect evaluation system that evaluates the effect of a treatment as alternative medicine on a patient, a data collection step of collecting biological data and lifestyle data of the person to be treated, and environmental data regarding the treatment environment for the person to be treated; an analysis step of evaluating the effect of the treatment by analyzing the biological data, the lifestyle habit data, and the environmental data using AI including a machine learning model; an output step of outputting an analysis result obtained by the analysis step; A method for evaluating the effect of a treatment, comprising:

14. A program for causing a computer to function as a treatment effect evaluation system for evaluating the effect of a treatment as an alternative medicine on a patient, The computer, a data collection step of collecting biological data and lifestyle data of the person to be treated, and environmental data regarding the treatment environment for the person to be treated; an analysis step of evaluating the effect of the treatment by analyzing the biological data, the lifestyle habit data, and the environmental data using AI including a machine learning model; an output step of outputting an analysis result obtained by the analysis step; A program characterized by executing a process including:

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