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

Abstract

We provide technology that can appropriately evaluate the effectiveness of treatments and present the evaluation results in an easy-to-understand manner to both the patient and the practitioner. [Solution] A treatment effect evaluation system for evaluating the effects of treatment on a person receiving treatment, comprising: a data collection unit that collects the person receiving treatment's biological data and lifestyle data, as well as environmental data related to the treatment environment for the person receiving treatment; an analysis unit that obtains analysis results including the effects of treatment by analyzing this data using AI including a machine learning model; and an output unit that outputs the analysis results from the analysis unit. The analysis unit extracts improved biological data before and after treatment, or in the case of continuous treatment, over a series of treatments, and instructs the AI ​​to obtain analysis results that include positive words about continuing the treatment.

Description

Technical Field

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

Background Art

[0002] Conventionally, methods for evaluating the effects of treatments such as manual therapy and osteopathy have relied on the experience of the therapist and the subjective judgment of the patient. However, with such methods, it is difficult to objectively and quantitatively evaluate the effects of the treatment, and it is impossible to scientifically prove the effectiveness of the treatment.

[0003] Regarding the technology for evaluating the effects of treatments, for example, Patent Document 1 describes a method for measuring comfort obtained by massage using, as an index, the sedation of the sympathetic nerves or the promotion of the parasympathetic nerves, such as changes in skin surface temperature.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Conventionally, it has been difficult to appropriately grasp the effects of treatments on patients, and there has been a problem that the evaluation of treatments depends on the experience of the therapist and the subjectivity of the patient. Also, when the effects of the treatment cannot be sufficiently felt, there has been a problem that it becomes difficult to continue the treatment.

[0006] In view of such problems, an object of the present invention is to provide a technology capable of appropriately evaluating the effects of treatments and providing the evaluation results in an easy-to-understand manner to patients and therapists.

Means for Solving the Problems

[0007] This application includes several means to solve at least some of the above problems, and some examples are as follows.

[0008] To solve the above problems, one aspect of the present invention is a treatment effect evaluation system for evaluating the effect of treatment on a person receiving treatment, comprising: a data collection unit that collects the person receiving treatment's biological data and lifestyle data, as well as environmental data relating to the treatment environment for the person receiving treatment; an analysis unit that obtains analysis results including the effect of the treatment by analyzing the biological data, lifestyle data, and environmental data using AI including a machine learning model; and an output unit that outputs the analysis results from the analysis unit. The analysis unit is characterized in that it instructs the AI ​​to extract improved biological data before and after treatment, or in the case of continuous treatment, over a series of treatments, and to obtain analysis results that include positive words regarding the continuation of treatment.

[0009] The analysis unit may also instruct the AI ​​to obtain analysis results that explain that improvement in symptoms can be expected, even if it cannot be determined that there has been a clear improvement before and after the treatment, or in the case of continuous treatment, after a series of treatments, by linking the changes in biological data before and after the treatment.

[0010] The analysis unit may instruct the AI ​​to obtain analysis results that visually or verbally present the risk of deterioration in the patient's health condition (including cascading deterioration) that may occur if the treatment is neglected, or if the treatment is delayed or reduced in frequency, when the patient's biometric data indicates a potential deterioration before and after the treatment, or in the case of continuous treatment, during a series of treatments.

[0011] The analysis unit may also instruct the AI ​​to obtain analysis results that include words that have the effect of drawing out the body's natural healing ability through the placebo effect, even if it cannot be determined that there has been a clear improvement before and after the treatment, or in the case of continuous treatment, after a series of treatments.

[0012] The analysis unit may also instruct the AI ​​to obtain analysis results that include the effect of performing preventive treatments for symptoms that have not yet appeared in the patient.

[0013] The analysis unit may instruct the AI ​​to obtain analysis results that include words that will encourage the patient to continue the treatment.

[0014] The analysis unit may also instruct the AI ​​to obtain analysis results that include words that will encourage the practitioner to continue the treatment.

[0015] The biological data of the person receiving treatment may include at least one of the following: basic physical data, vital sign data, internal physiological data, genetic data, microbiological data, neuropsychological data, and behavioral data.

[0016] The lifestyle data of the person receiving treatment may include at least one of the following: dietary data, exercise / physical activity data, sleep data, work / study data, mental health data, usage data of luxury goods, digital / lifestyle rhythm data, social activity data, and daily habit data.

[0017] The aforementioned environmental data may include at least one of the following: physical data, chemical data, biological data, psychological data, social / periodic data, and special environmental data.

[0018] The data collection unit collects at least one of the following from the patient's 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.

[0019] The analysis unit may also 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 person receiving treatment, using AI including a machine learning model.

[0020] Another aspect of the present invention is a method for evaluating the effectiveness of a treatment as an alternative medicine for a person receiving treatment, comprising: a data collection step of collecting the person receiving treatment's biological data and lifestyle data, as well as environmental data relating to the treatment environment for the person receiving treatment; an analysis step of obtaining analysis results including the effectiveness of the treatment by analyzing the biological data, lifestyle data, and environmental data using AI including a machine learning model; and an output step of outputting the analysis results obtained in the analysis step. The analysis step is characterized by instructing the AI ​​to extract improved biological data before and after the treatment, or in the case of continuous treatment, over a series of treatments, and to obtain analysis results that include positive words regarding the continuation of the treatment.

[0021] Another aspect of the present invention is a program for causing a computer to function as a treatment effect evaluation system for evaluating the effects of a treatment as an alternative medicine on a person receiving treatment, wherein the program causes the computer to perform a process including: a data collection step of collecting the person receiving treatment's biological data and lifestyle data, as well as environmental data relating to the treatment environment for the person receiving treatment; an analysis step of obtaining an analysis result including the effects of the treatment by analyzing the biological data, lifestyle data, and environmental data using an AI including a machine learning model; and an output step of outputting the analysis result obtained in the analysis step, wherein the analysis step instructs the AI ​​to extract improved biological data before and after the treatment, or in the case of continuous treatment, over a series of treatments, and to obtain an analysis result that includes positive words regarding the continuation of the treatment. [Effects of the Invention]

[0022] According to the present invention, it is possible to provide a technique capable of appropriately evaluating the effect of a treatment and providing the evaluation result to the subject or the practitioner in an easy-to-understand manner.

[0023] Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of the Drawings

[0024] [Figure 1] FIG. 1 is a diagram showing an example of the biological data of a subject. [Figure 2] FIG. 2 is a diagram showing an example of the biological data of a subject. [Figure 3] FIG. 3 is a diagram showing an example of the lifestyle data of a subject. [Figure 4] FIG. 4 is a diagram showing an example of the lifestyle data of a subject. [Figure 5] FIG. 5 is a diagram showing an example of the environmental data representing the treatment environment. [Figure 6] FIG. 6 is a diagram showing an example of the environmental data representing the treatment environment. [Figure 7] FIG. 7 is a diagram showing a configuration example of a treatment effect evaluation system according to an embodiment of the present invention. [Figure 8] FIG. 8 is a diagram showing a configuration example of a terminal device. [Figure 9] FIG. 9 is a diagram showing a configuration example of an analysis device. [Figure 10] FIG. 10 is a flowchart for explaining an example of the treatment effect evaluation process by the treatment effect evaluation system. [Figure 11] FIG. 11 is a diagram showing an example of the state of red blood cells in the blood as a treatment effect. [Figure 12] FIG. 12 is a diagram showing 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 showing an example of behavioral psychology data. [Figure 15]Figure 15 shows an example of life event and personal history data. [Figure 16] Figure 16 shows an example of digital health data. [Figure 17] Figure 17 shows an example of risk factor data. [Figure 18] Figure 18 shows an example of lifelong learning and knowledge data. [Figure 19] Figure 19 shows a fractal correspondence map of the human body. [Figure 20] Figure 20 shows a fractal correspondence map of the human body. [Figure 21] Figure 21 shows a fractal correspondence map of the human body. [Modes for carrying out the invention]

[0025] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In all drawings used to describe the embodiment, the same reference numerals will be used for identical components, and repeated descriptions will be omitted. In addition, in the following embodiment, the components (including element steps, etc.) are not necessarily essential unless otherwise explicitly stated, or unless they are clearly essential in principle. Furthermore, when referring to "consisting of A," "being made of A," "having A," or "including A," other elements are not excluded unless otherwise explicitly stated. Similarly, in the following embodiment, when referring to the shape, positional relationship, etc., of the components, etc., it includes those that are substantially similar or approximate to their shape, etc., unless otherwise explicitly stated, or unless otherwise clearly stated in principle.

[0026] <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 effects of treatment performed by a practitioner on a patient.

[0027] In this embodiment, the term "treatment" refers to alternative medicine methods that lack evidence and are the opposite of evidence-based Western medicine (modern medicine), such as manual therapy and osteopathic therapy, and includes, for example, a variety of therapies listed below. Manual therapy: Traditional massage, acupressure, chiropractic, osteopathy, myofascial release, etc. • Manual physical therapy: A method of selecting the most appropriate treatment technique after evaluating and diagnosing each system (sensory system, connective tissue, muscular system, nervous system, joint system, circulatory system, visceral system, etc.). • Energy therapies: Qigong therapy, Reiki, healing touch, innate healing, etc. • Brain and nervous system adjustment therapy: Cerebrospinal fluid adjustment (craniosacral therapy), autonomic nervous system adjustment therapy, neurofeedback, etc. • Therapies utilizing body movement: Sotaiho, Feldenkrais Method, Rolfing, etc. • Osteopathic therapy: Skeletal correction, pelvic correction, spinal correction, etc. • Therapies based on Oriental medicine: acupuncture, moxibustion, meridian therapy, herbal acupressure, etc. • Relaxation therapies: Aromatherapy, reflexology, hot stone therapy, etc.

[0028] • Five Senses Therapy: Methods of adjusting and restoring the body through the senses of touch, sight, hearing, smell, and taste. Touch: For example, pleasant touch (light massage, warmth, hugs, etc.) stimulates the secretion of oxytocin and serotonin, stabilizing the autonomic nervous system. Visual aspects: For example, mild skin stimulation has been reported to suppress inflammatory responses via the vagus nerve (Harvard Medical School, 2015, etc.). Hearing: For example, heart rate variability (HRV) improves with music listening. Examples include the shinobue (bamboo flute), singing bowls, and natural sound therapy, which are applications of this. Sense of smell: For example, it's the key to the body, not the mind, remembering what the "correct state" is. Taste: Resetting rhythm and autonomic nervous system through "tasting" The five senses do not function independently; they are all interconnected through a neural network, serving as gateways to activating the body's "inner intelligence." • Therapies utilizing fractal structures: "Fractal structures (self-similarity)" in the human body can be observed at various levels, not only in appearance and structure, but also in physiological functions and neural networks.

[0029] As shown in Figure 19, the fractal correspondence map of the human body, the above-mentioned fractal theory × integrated care method of the five senses: theoretical background - the human body is a "multilayered self-similar system". The human body contains structural fractals (skeleton, fascia, nerves) and informational fractals (sensation, thought, emotion). The five senses (touch, sight, hearing, smell, taste) are each "local windows" but are similarly connected to the whole body. Therefore, by adjusting one sense, a ripple effect is created on other senses, the musculoskeletal system, internal organs, and the mind. Other therapies: Trigger point therapy, balance therapy, lymphatic drainage, sports massage, etc.

[0030] Furthermore, Figure 20 shows the objectives of the treatment as a cross-professional model, and Figure 21 shows adjustment methods and approaches in neutral language that can be used across professions, and also shows the physical function adjustment method as a five-layer structure model.

[0031] The alternative medical treatments mentioned above are said to be effective for health management and improving physical condition. However, their effectiveness is often evaluated based on the practitioner's experience and the patient's subjective feelings (such as pain reduction and improvement of fatigue), and objective and scientific evaluation criteria have not been established.

[0032] On the other hand, the patient's biometric data is an important indicator that reflects the patient's internal health status. In addition, lifestyle data and environmental data representing the treatment environment are important factors that influence the patient's health status.

[0033] Therefore, in this embodiment, the treatment effect is analyzed using objective and scientific evaluation criteria based on the patient's biological data and lifestyle data, as well as environmental data of the treatment environment.

[0034] Figures 1 and 2 show examples of biometric data of a patient. Biometric data includes at least one of the following: basic physical data (height, weight, body fat percentage, BMI, waist circumference, etc.), vital sign data (body temperature, blood pressure, heart rate, blood oxygen saturation, etc.), internal physiological data (blood data, urine data, hormone levels, etc.), genetic data (genomic information, epigenome data, etc.), microbiome-related data (gut flora, skin flora, oral flora, etc.), neurological and psychological data (electroencephalogram, autonomic nervous system data, emotional and psychological state, etc.), and behavioral data (activity level, sleep data, dietary habits data, etc.).

[0035] Figures 3 and 4 show examples of lifestyle data of the treatment recipient. Lifestyle data includes at least one of the following: dietary data (calorie intake, nutritional balance, meal timing, meal content, etc.), exercise and physical activity data (amount of physical activity, type of exercise, frequency and timing of exercise, daily activities, etc.), sleep data (sleep duration, sleep rhythm, sleep quality, naps, etc.), work and learning data (working hours, work content and posture, study time, etc.), mental health data (stress level, emotions and mood, relaxation, etc.), leisure product usage data (amount of alcohol consumed, amount of smoking, caffeine intake, etc.), digital and lifestyle rhythm data (screen time, time of day of digital device use, etc.), social activity data (frequency of socializing and interaction, volunteer activities, etc.), and daily habit data (housework and childcare, frequency of going out, etc.).

[0036] Figures 5 and 6 show examples of environmental data for the treatment recipient. Environmental data includes at least one of the following: physical data (electric and magnetic fields, noise, vibration, air environment, 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 / periodic data (day-night rhythm, seasonal changes, busy schedule, etc.), and special environmental data (climate change, natural disasters, urban environment characteristics, etc.).

[0037] <Example of the configuration of the treatment effect evaluation system 10 according to one embodiment of the present invention> Figure 7 shows an example configuration of a treatment effect evaluation system 10 according to one embodiment of the present invention. The treatment effect evaluation system 10 comprises a sensor group 20, a terminal device 30, and an analysis device 40.

[0038] 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 to after treatment by the practitioner 1, 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.

[0039] The biometric data acquisition device 21 measures and acquires the biometric data of the patient 2 using a non-invasive measurement method.

[0040] The biometric data acquisition device 21 specifically consists of, for example, wearable sensors (IMU (Inertial Measurement Unit), EMG (Surface Electromyography) sensors, pressure sensors, etc.), physiological sensors (ECG sensors, heart rate sensors, oxygen saturation sensors, respiration sensors, body temperature sensors, blood pressure sensors, blood glucose sensors, body composition sensors, electroencephalogram sensors, activity sensors, electromyogram sensors, galvanic skin response sensors, etc.), and facial expression recognition systems (used to detect facial expressions (pain, discomfort, pleasure, stress levels, manic-depressive states, etc.)). The attachment and operation of each sensor may be performed by the practitioner 1 or the patient 2, and for those requiring specialized expertise, it should be performed by a specialist other than the practitioner 1 or patient 2. Furthermore, tests such as electroencephalograms and genetic testing information may be outsourced. Furthermore, the biometric data acquisition device 21 may acquire data such as the location and severity of pain at the time, based on input from the practitioner, using, for example, the main examination methods used in chiropractic treatment (see the table for classification of examination methods). For example, in a method called SLR (Straight Leg Raise), where the patient lies on their back and raises one leg straight up, the biometric data acquisition device 21 acquires pre-treatment biometric data such as how high the leg can be raised comfortably, where cramping and pain occur at that point, and what symptoms occur when the leg is raised further (e.g., cramping in the opposite side of the lower back, or the lower back being pulled back and lifting). Furthermore, symptoms related to muscle spasms may also be acquired as biometric data. In addition, if the equipment is available on-site, the biometric data acquisition device 21 may also perform blood status (red blood cell) tests and acquire the results. The biometric data acquisition device 21 may also acquire data from additional tests that can be performed at a later date (e.g., blood tests or MRI scan data).

[0041] [Table 1]

[0042] [Table 2]

[0043] The following are examples of typical tests for different body parts. Cervical region: Spurling test, Jackson test, Cervical Distraction test, Upper Limb Tension Test (ULTT) Shoulder: Hawkins-Kennedy test, Neer test, Empty / Full Can test, Apprehension / Relocation test, Elbow / Hand Cozen test (Tennis Elbow), Tinel sign, Phalen test Lumbar spine and pelvic girdle: Straight leg raise test (SLR), slump test, prone instability test, vertebral PA spring test (PA spring), sacroiliac joint tests (thigh thrust, compression / distraction, Gaenslen) Hip joint: FABER test, FADIR test, Scour test, Log Roll test Knee: Lachman test, Anterior / Posterior Drawer test, McMurray test, Apley test Ankle and foot: Anterior drawer test, talar tilt test, Thompson test, Windlass test

[0044] The following is an example of how the inspection should be conducted. • Screening: Identify red flags through interviews and determine the baseline pain level using NRS / VAS. • Static and palpation: Observe posture. Check for tenderness, tension, trigger points, and edema through palpation. • Range of motion and joint function: AROM, pain location assessment, PROM, end-feel, segment evaluation The following is an example of a functional test. • Select target areas: movement, muscle strength, flexibility, and balance. • Specialized tests • Neurology: Based on suspected findings, narrow down to a few tests with high sensitivity / specificity. • Integrated Iris Diagnosis: Oriental Iridology × Multilayer Integrated Model (Hawkins Theory × Integrated Iris Analysis) "An integrated model that maps the structure, color, and reflection patterns of the iris as multilayered information (matter to consciousness) based on the Oriental energy hierarchy (Qi, Spirit, and Mind)." This theory layers the iris into 12 layers (material to soul) and 36 layers in total, simultaneously analyzing the balance of the Five Elements, Qi, Blood, and Fluids, the nerve axis, and the consciousness vibration hierarchy. As a result, it becomes possible to grasp "body, mind, consciousness, and mission" in a unified way. In short, it is "an approach to reading a comprehensive map of human existence that fuses Eastern philosophy and consciousness science."

[0045] The biometric data of patient 2 can be used to understand the patient's basic health status and assess disease risk. In other words, while each biometric data point is an important health indicator on its own, a comprehensive analysis can lead to the prevention and early detection of lifestyle-related diseases.

[0046] Furthermore, the biometric data of patient 2 can be used for personalized medicine (precision medicine). In other words, the genomic information and gut flora information contained in the biometric data will enable the selection of treatment methods and medications tailored to each individual patient 2.

[0047] Furthermore, the biometric data of patient 2 can be used for preventive medicine. Specifically, by routinely monitoring the vital signs and behavioral data included in patient 2's biometric data, early signs of illness can be detected.

[0048] Furthermore, the biometric data of patient 2 can be used to improve psychological health and quality of life. Specifically, the neuropsychological and behavioral data included in patient 2's biometric data can serve as indicators for stress management, sleep improvement, and quality of life improvement.

[0049] The lifestyle data acquisition device 22 acquires lifestyle data from the patient 2 by receiving input via a questionnaire, including information such as dietary content, exercise history, exercise frequency, sleep status, stress level, alcohol consumption, smoking history, and subjective changes in the patient 2's symptoms.

[0050] The attribute data acquisition device 23 acquires attribute data such as age, gender, and occupation by receiving input from the person receiving treatment 2.

[0051] The lifestyle data of patient 2 can be used to maintain and improve the patient's healthy lifestyle and prevent disease. In other words, by monitoring daily lifestyle habits such as exercise, sleep, and diet included in patient 2's lifestyle data, the risk of developing lifestyle-related diseases and mental illnesses can be reduced.

[0052] Furthermore, the lifestyle data of patient 2 can be used to provide personalized care for patient 2. In other words, by understanding the individual behavior and preference patterns from patient 2's lifestyle data, it is possible to provide patient 2 with advice and treatment plans that are individually suited to them.

[0053] Furthermore, the lifestyle data of patient 2 can be used to support behavioral change. Specifically, by setting concrete improvement goals based on patient 2's current lifestyle data, motivation for behavioral change can be increased.

[0054] Furthermore, the lifestyle data of patient 2 can be used to improve their quality of life. Specifically, by analyzing the mental health data and social activity data included in patient 2's lifestyle data, it is possible to maintain physical and mental health and enhance their sense of well-being.

[0055] Furthermore, by combining the lifestyle data of patient 2 with basic physical data and genetic data included in patient 2's biometric data, more comprehensive health management becomes possible.

[0056] The lifestyle data acquisition device 22 and the attribute data acquisition device 23 consist of terminal devices, etc., into which the patient 2 inputs age, gender, occupation, diet, exercise history, exercise frequency, sleep status, stress level, alcohol consumption, smoking history, etc. The patient 2 may also use their smartphone or similar device (not shown) for input.

[0057] The environmental data acquisition device 24 consists of sensors that measure environmental data of the treatment environment. The environmental data includes at least one piece of information from among temperature, atmospheric pressure, humidity, weather, noise level, light intensity, air quality (VOC concentration, etc.), scent, illuminance, and ambient color (e.g., the color of the room walls or curtains, the color of clothing, etc.). The environmental data may also include temperature, atmospheric pressure, humidity, weather, etc. for the most recent few days leading up to the treatment day. The environmental data acquisition device 24 may also acquire environmental data from an external database.

[0058] Environmental data can be used to evaluate the impact of external factors on health and to improve living environments and work styles.

[0059] Furthermore, the collection and use of data using the sensor group 20 will be carried out with the consent of the patient 2, the privacy of the patient 2 will be protected, the analysis results will be accurately recorded, and feedback will be provided to the practitioner 1 and the patient 2.

[0060] 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, to collect biometric data, lifestyle data, attribute data, and environmental data.

[0061] Alternatively, the biometric data, lifestyle data, attribute data, and environmental data acquired by each device in the sensor group 20 may be recorded on a detachable portable recording medium, and the terminal device 30 may read the biometric data, lifestyle data, attribute data, and environmental data recorded on the portable recording medium.

[0062] Furthermore, the terminal device 30 connects to the analysis device 40 via a network N, which is a bidirectional communication network such as the Internet. The terminal device 30 transmits the collected biometric data, lifestyle data, attribute data, treatment data (described later), and environmental data to the analysis device 40 and requests analysis. In addition, the terminal device 30 receives the evaluation results of the treatment performed on the patient 2 obtained from the analysis device 40 and presents them to the user (practitioner 1) and the patient 2. Alternatively, the terminal device 30 may be connected directly 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.

[0063] 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 patient 2, and transmits the evaluation results to the terminal device 30.

[0064] Figure 8 shows an example of the configuration of the functional blocks of the terminal device 30. The terminal device 30 has the following functional blocks: a data acquisition unit 31, an analysis request unit 32, a storage unit 33, a communication unit 34, and an output unit 35.

[0065] The terminal device 30 consists of a computer such as a smartphone, tablet PC (personal computer), notebook PC, desktop PC, or dedicated terminal, which includes, for example, 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, or touch panel, output devices such as a display, and a communication module such as a NIC (Network Interface Card) (all not shown).

[0066] The data acquisition unit 31 and the analysis request unit 32 are realized by the computer's processor executing a predetermined program.

[0067] The data acquisition unit 31 connects to each device of the sensor group 20 via the communication unit 34 and collects measured biological data, lifestyle data, attribute data, and environmental data, storing them in the storage unit 33. The data acquisition unit 31 also collects treatment data from practitioner 1, etc. Treatment data includes the date and time of treatment, treatment duration, type of treatment, treatment tools, treatment frequency, and the skill level of practitioner 1. The treatment data is input into the terminal device 30 by practitioner 1 or patient 2.

[0068] The analysis request unit 32 transmits the biological data (long-term time-series data (described later)), lifestyle 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 to request analysis.

[0069] The memory unit 33 consists of the computer's memory and storage. The memory unit 33 stores biometric data, lifestyle data, attribute data, environmental data, and treatment data collected from each device of the sensor group 20 by the data acquisition unit 31. The memory unit 33 stores the biometric data of each patient 2 as long-term time-series data, linking it with biometric data collected during past treatments.

[0070] The communication unit 34 consists of the computer's communication module. 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.

[0071] The output unit 35 consists of the computer's output device. The output unit 35 displays an operation screen to the user (practitioner 1). The operation screen may display instructions to the user, and may also provide instructions using voice or sound. Furthermore, it may be configured to accept responses from the user to the instructions.

[0072] Figure 9 shows an example of the configuration of the functional blocks of the analysis device 40. The analysis device 40 has functional blocks consisting of a data analysis unit 41 and a communication unit 42. The analysis device 40 consists of a computer such as a server computer, which includes, for example, a processor such as a CPU, memory such as DRAM, storage such as an HDD or SSD, input devices such as a keyboard, mouse, or touch panel, output devices such as a display, and a communication module such as a NIC (none of which are shown).

[0073] The data analysis unit 41 is realized by the computer's processor executing a predetermined program. The data analysis unit 41 requests the AI ​​(Artificial Intelligence) 411 to analyze biometric data (short-term and long-term time-series data), lifestyle data, attribute data, environmental data, and treatment data transmitted from the terminal device 30. As a result of the analysis, it obtains an evaluation of the treatment for the patient 2, the treatment content suitable for the patient 2, and the predicted health status of the patient 2 after the treatment. By treating the biometric data as time-series data, it is possible to objectively grasp the changes in the patient 2's condition and quantitatively evaluate the physical changes in the patient 2 caused by the treatment. The data analysis unit 41 can use AI that includes a machine learning model that analyzes the effects of the treatment and corrects for factors other than the treatment.

[0074] For example, AI411 uses LSTM (Long Short-Term Memory) for analyzing biometric data, and a causal inference model for analyzing the causal relationships between attribute data, environmental data, and treatment data to construct a model for evaluating treatment effects. The model is then trained by dividing it into training data and validation data to evaluate treatment effects. In the case of alternative medicine, where scientific reproducibility is difficult to confirm, the effects often vary greatly depending on the individual. By clarifying the causal relationships using a data analysis model, the effectiveness of alternative medicine can be proven and reproducibility can be improved.

[0075] Furthermore, AI411 analyzes whether there are statistically significant differences in biometric data before and after treatment, eliminating the influence of the placebo effect and natural healing to determine whether alternative medicine is contributing to the patient's health. This addresses the inherent problem in alternative medicine where it is difficult to distinguish between the placebo effect, natural healing, and therapeutic effects, and helps to address skeptical views towards alternative medicine.

[0076] Furthermore, if the effectiveness of a treatment is confirmed, AI411 will suggest optimization of the treatment method and environmental conditions, and if the treatment is ineffective, it will specifically suggest alternative treatment methods or supportive therapies. In alternative medicine, there have been problems such as the misconception that it is a "panacea" and the ambiguity of how to respond when it is ineffective. By specifically presenting treatment improvement and alternative options, AI411 can improve the reliability and practicality of alternative medicine.

[0077] Furthermore, AI411 periodically retrains its model using newly collected biometric data to improve its accuracy. AI411 also visualizes the decision-making process using the model and provides the results to practitioner 1 in an explainable format.

[0078] AI411 may be a so-called generative AI. For example, a generative AI is a system connected to a network N that uses machine learning and natural language processing to generate and output various documents, images, videos, audio, program code, etc., in response to instructions (prompts). If an API (Application Programming Interface) is provided to 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 it may be accessed via a WAN (Wide Area Network). Examples of generative AIs that can be used include OpenAI's CHAT-GPT and Google's Gemini.

[0079] Alternatively, instead of AI411, advanced AI technologies such as AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) may be adopted. The AI ​​analysis function and time-series data utilization of the present invention have high applicability in growing fields such as medical technology currently in the demonstration stage, psychotherapy and neurofeedback, and digital health analysis using wearable devices.

[0080] When using a generative AI, the response is derived from prompts that include, for example, the following commands. For example, the data analysis unit 41 can evaluate the effectiveness of the treatment by giving the generative AI the following instructions and obtaining the analysis results. When giving instructions to the generative AI, all of the biometric data, lifestyle data, attribute data, environmental data, and treatment data may be provided, or one or more selected from these may be provided. Past data linked to time series may also be provided.

[0081] Example of instructions: "We analyze multiple data points from the patient before and after treatment to quantitatively evaluate the effects of the treatment." "Based on the patient's biological condition, attribute information, and treatment environment, the system analyzes the changes caused by the treatment and outputs the evaluation results." "When evaluating the effects of a treatment, the treatment effect should be calculated objectively while taking into account fluctuations caused by factors other than the treatment itself." "Refer to the patient's past treatment data and propose the most suitable treatment method." "Predict how the effects of the treatment will change over time and provide the results." "Based on the patient's characteristics and treatment history, recommend conditions to maximize the effectiveness of the treatment." "Based on the evaluation results of the treatment, we generate information that can be used as evidence and output data demonstrating the effectiveness of the treatment." "We will predict improvements in health after treatment and provide feedback, including suggestions for lifestyle changes." "To compare the effectiveness of multiple treatment methods and establish criteria for determining the most suitable treatment for each patient." "The analysis results will be output in a format that is easy for users to understand, and this will help in explaining the effects of the treatment." Furthermore, considering the problems specific to alternative medicine lacking evidence, the following guidelines may be adopted. "To scientifically evaluate the effectiveness of the treatment, we will conduct statistical and causal analyses using indicators such as placebo, natural healing, and reproducibility." "When evaluating the effects of treatment, the placebo effect and the effects of natural healing are statistically excluded, and the pure effect of the treatment is calculated." "By comparing the results with past patient data, the reproducibility of the effects of the same treatment is evaluated, and statistical significance is determined." "We analyze whether changes in the patient's biometric data are due to the treatment or other factors (diet, sleep, exercise, etc.), and calculate the contribution rate." "While taking into account individual differences among patients, the general effectiveness of the treatment is evaluated, and the confidence interval for the results is calculated." "When explaining the effects of a treatment, we analyze its correlation with medical fields that have medical evidence to reinforce the scientific explanation." "Generate a statistical model for the effectiveness of treatments, perform comparative evaluations of different treatment methods, and create a highly reliable list of recommendations." "If the effectiveness of the treatment is confirmed, we will propose ways to optimize the factors related to the treatment (treatment method, environmental conditions, patient attributes, etc.) to achieve higher reproducibility." "In alternative medicine, we will evaluate the differences between generally believed treatment effects and data analysis results, and analyze the factors behind those differences." "Extract objective data demonstrating the effectiveness of alternative medicine treatments and provide scientific explanations to both practitioners and patients." "We will analyze whether the treatment effects are consistent with medical knowledge by comparing them with past statistical data and databases of medical papers." "When a new, previously undiscovered factor is found, or when it can be presumed that one has been found, the new factor item will be added, modified, or deleted from the analysis items." These instructions may be given individually or in combination. In this way, the data analysis unit 41 instructs the generating AI to perform the necessary processing to objectively evaluate the treatment effect and obtains the analysis results, thereby enabling quantitative evaluation of the treatment's impact and addressing issues such as the placebo effect and reproducibility.

[0082] Such generative AI may leverage expertise in medicine, biometric data analysis, statistics, and machine learning to evaluate the effects of treatments, and apply various scientific methods necessary for analyzing treatment effectiveness. Specifically, statistical analysis and causal inference algorithms can be used to distinguish between the effects of treatment and the placebo effect or the effects of natural healing.

[0083] Furthermore, the AI ​​generates data and performs time-series analysis to predict changes and improvement trends in health after treatment, and evaluates the sustainability of the treatment's effects. This allows the AI ​​to provide recommended conditions for optimizing the effectiveness of the treatment while taking into account individual differences among patients.

[0084] Furthermore, this generating AI supports not only the analysis of text data but also the analysis of image data and time-series data, enabling the visual presentation of changes in the body's state before and after treatment. This makes it possible to visually demonstrate the effects of treatment in an easy-to-understand way and strengthens feedback for both the practitioner and the patient.

[0085] Furthermore, the AI ​​generates data using statistical models to evaluate the effectiveness of each treatment, analyzing the reproducibility of the effects for each treatment. This allows for the comparison of data from multiple patients who received the same treatment, and by verifying statistical significance, the scientific basis of the treatment can be clarified.

[0086] This AI-generating system visualizes the estimation process of treatment results and outputs analysis results in a format that can be explained to both the practitioner and the patient. This allows for the scientific demonstration of the basis for treatment effectiveness and improves the reliability of the treatment.

[0087] Furthermore, this generating AI can continuously learn based on accumulated treatment data, and by reflecting new treatment data and the patient's biometric information, it can improve the accuracy of evaluating treatment effectiveness. In addition, by comparing it with the treatment history, it can propose the optimal treatment method for each patient.

[0088] Furthermore, the generating AI can employ federated learning and edge AI processing to ensure the protection of personal information and data security. This makes it possible to safely analyze patient data without centrally managing it on the cloud.

[0089] Furthermore, the generating AI can compare the results with past medical research and academic data to evaluate the scientific validity of the treatment's effects. This makes it possible to provide objective evidence regarding the effectiveness of alternative medicine treatments and to verify their efficacy.

[0090] The communication unit 42 consists of the computer's communication module. 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, the patient's 2 biological data, attribute data, and environmental data from the terminal device 30. The communication unit 42 also transmits to the terminal device 30 an evaluation of the effectiveness of the treatment on the patient 2, which has been analyzed by the AI ​​411, and a treatment plan suitable for the patient 2.

[0091] <Treatment effect evaluation process by treatment effect evaluation system 10> Figure 10 is a flowchart illustrating an example of the treatment effect evaluation process performed by the treatment effect evaluation system 10. This treatment effect evaluation process is initiated in response to a predetermined operation by the user to the terminal device 30.

[0092] As a prerequisite, it is assumed that the treatment on subject 2 has already been completed, and that the devices constituting the sensor group 20 have acquired subject 2's biological data, lifestyle data, attribute data, and environmental data from before the treatment to after the treatment.

[0093] First, the data acquisition unit 31 of the terminal device 30 collects biological 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 practitioner 1, etc., and stores it in the storage unit 33 (step S1).

[0094] Next, the analysis request unit 32 of the terminal device 30 reads the biological data, lifestyle data, attribute data, environmental data, and treatment data stored in the storage unit 33, and transmits them to the analysis device 40 via the communication unit 34 and the network N to request analysis (step S2).

[0095] Next, the data analysis unit 41 of the analysis device 40 performs integration processing and data preprocessing on the biological data, lifestyle data, attribute data, and environmental data (step S3). In the integration processing, different types of data contained in the biological data, lifestyle data, attribute data, and environmental data are integrated and converted into an analyzable format. In the data preprocessing, data normalization, interpolation of missing values, and noise reduction are performed. This minimizes the impact of anomalous data due to mismeasurements, etc., and improves the accuracy of subsequent analyses.

[0096] 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 then analyzes the biometric data, lifestyle data, attribute data, environmental data, and treatment data after integration processing and data preprocessing (step S4), and outputs a determination of the treatment effect on the patient 2 and a personalized treatment plan for the patient 2 (step S5). Specifically, for example, the AI ​​411 analyzes the correlation between biometric data, lifestyle data, attribute data, environmental data, and treatment data to quantify the treatment effect. The AI ​​411 also learns patterns of treatment effects from past biometric data and compares them with the latest biometric data to predict the health status and improvement effect after treatment. Based on the changes in biometric data before and after treatment, it quantifies the treatment effect and proposes personalized treatment content and lifestyle improvement plans for the patient 2 based on the patient 2's health status.

[0097] To quantify the effects of the treatment, for example, we calculate the rate of blood flow improvement, the rate of stress reduction, and the balance of the autonomic nervous system. The rate of blood flow improvement can be calculated, for example, by comparing the blood condition (fluidity and aggregation state of red blood cells) before and after the treatment.

[0098] Figure 11 shows an example of images captured by a phase-contrast microscope of the state of red blood cells in the blood before and after treatment, specifically, the aggregation state and fluidity of red blood cells, after data preprocessing. The left side of the figure shows the image before treatment, and the right side shows the image after treatment. Before treatment, some red blood cells have lost their original oval shape, have spine-like protrusions around them, or are linked together. After treatment, however, the shape distortion and linkage between red blood cells are resolved, and fluidity is improved.

[0099] Stress reduction rates can be calculated, for example, based on changes in electroencephalogram (EEG) data (alpha waves, beta waves, etc.). Autonomic nervous system balance can be calculated, for example, by analyzing heart rate variability data to determine improvements in autonomic nervous system balance.

[0100] Regarding individualized treatment content, for example, we can propose the most suitable treatment method (e.g., osteopathy, Qigong, cerebrospinal fluid adjustment, etc.) based on the condition of the patient 2. Regarding lifestyle improvements, we can propose things like extending sleep time, increasing intake of specific nutrients, and increasing exercise.

[0101] Next, the communication unit 42 of the analysis device 40 transmits the determination of the treatment effect on the patient 2 and a personalized treatment plan for patient 2 to the terminal device 30. The output unit 35 of the terminal device 30 visualizes and displays the determination of the treatment effect on patient 2 and the personalized treatment plan for patient 2 on the screen and presents it to the user (practitioner 1) and patient 2 (step S6). Alternatively, the data analysis unit 41 may generate a report on the determination of the treatment effect on patient 2 and the personalized treatment for patient 2, and the communication unit 42 may transmit this report to the terminal device 30, which will then output the report. With this, the treatment effect evaluation process by the treatment effect evaluation system 10 is completed.

[0102] The treatment effect evaluation process described above allows for the objective and quantitative evaluation of the effects of the treatment performed on patient 2 based on the quantification of specific sensor data and AI analysis, enabling the "utilization of time-series data" and "statistical elimination of the placebo effect." Furthermore, practitioner 1 can modify the treatment given to patient 2 to individualize the treatment content for patient 2. In addition, practitioner 1 can suggest improvements to patient 2's lifestyle.

[0103] In particular, this embodiment focuses on treatments performed using alternative medicine, and addresses 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.

[0104] <Variation> 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 using only the terminal device 30. Alternatively, if the terminal device 30 is a smartphone or tablet computer, an application program may be installed on the smartphone or tablet computer acting as the terminal device 30 to realize each of the above-mentioned functional units. Furthermore, all functional units may be held by the analysis device 40 (server), and information may be presented to the user and instructions may be accepted via a general-purpose browser running on the terminal device 30.

[0105] Furthermore, the treatment effectiveness evaluation system 10 may communicate and cooperate with external AI systems (such as AI servers or robot systems with built-in AI). It may also have interfaces and protocols for cooperation (such as APIs, messaging protocols, gRPC, WebSocket, and file sharing). This allows the AI ​​411 (self-AI) of the treatment effectiveness evaluation system 10 to have expert AIs knowledgeable in the necessary fields (AIs excelling in medicine, law, the latest information, etc.) provide answers. In this case, the expert AI becomes the primary AI, and the self-AI becomes secondary. Conversely, for example, the self-AI may become the primary AI and give instructions to a work AI robot. The self-AI may then output answers to requests (questions) from the work AI robot.

[0106] In this embodiment, biometric data, lifestyle data, and environmental data were used in the analysis to evaluate the treatment content and identify important factors that affect human 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 also affect human health may be added to the analysis.

[0107] Figure 12 shows an example of medical data. Medical data includes at least one of the following: medical history data (disease history, surgical history, family medical history, vaccination status, etc.), diagnostic data (physician's diagnosis, medical certificate, test results, etc.), prescription data (past and present medication history, allergy medications, dosage, etc.), and vaccination record data (vaccination records, vaccination timing, type of vaccination, etc.). By including medical data in the analysis, it becomes possible to formulate appropriate treatment and prevention plans utilizing past medical history.

[0108] Figure 13 shows an example of social and economic factor data. Social and economic factor data includes at least one of the following: economic status data (income, savings, income inequality, etc.), education level data (educational attainment, knowledge literacy, etc.), employment status data (job security, employment type, work environment, etc.), housing status data (homeownership, rental, size of space, housing environment, etc.), and community connection data (frequency of interaction with neighbors, participation in community activities, etc.). By including social and economic factor data in the analysis, health disparities and mental health risks can be analyzed from a social background perspective.

[0109] Figure 14 shows an example of behavioral psychology data. Behavioral psychology data includes at least one of the following: personality traits (such as introversion or extroversion), stress response data (such as behavior and coping mechanisms in stressful situations), motivation data (such as the willingness to continue healthy behaviors), and hobby / recreation data (such as the frequency of hobby activities and relaxation). By including behavioral psychology data in the analysis, it becomes possible to design mental care and behavior improvement programs.

[0110] Figure 15 shows an example of life event and personal history data. This 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 preference and values ​​data (values ​​regarding health and beauty, social influences). By including life event and personal history data in the analysis, the impact of life events on health and adaptation can be analyzed.

[0111] Figure 16 shows an example of digital health data. Digital health data includes at least one of the following: data from wearable devices (data obtained from smartwatches, fitness trackers, etc.), data from health apps (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 lifestyle, etc.). By including digital health data in the analysis, it can be applied to health status monitoring and behavioral management.

[0112] Figure 17 shows an example of risk factor data. Risk factor data includes at least one of the following: family history data (medical history of family members and relatives, etc.), body mass index data (degree of obesity, fat distribution, etc.), smoking status data (number of cigarettes smoked, smoking history, etc.), and drinking habit data (frequency of drinking, amount of alcohol consumed, etc.). Including risk factor data in the analysis can be useful for identifying disease risks and facilitating early intervention.

[0113] Figure 18 shows an example of lifelong learning and knowledge data. This data includes at least one of the following: health literacy data (ability to gather health-related information, knowledge level, etc.) and learning activity data (learning as a hobby, reading, skill acquisition, etc.). Including lifelong learning and knowledge data in the analysis can enhance the understanding and utilization of health information and contribute to promoting healthy behaviors.

[0114] Further modifications of this embodiment will be described. Continuing treatment requires patience and willpower. Traditionally, when the effects of treatment were difficult to discern, motivation to continue treatment decreased, and people did not continue for long. Furthermore, for practitioners, if the effects are not easily discernible, they may feel hesitant to encourage patients to continue treatment, and their own motivation to continue treatment also decreases. For example, alternative medicine can be difficult to understand, its effects may be hard to grasp, and it may not be sustainable in the long run. Therefore, by extracting the symptoms (biological data values) that have improved through treatment, and using positive words to maintain motivation for both the patient and the practitioner to continue treatment, the positive words work on the subconscious mind to enhance the natural healing ability and create a sense of expectation. Even if rapid improvement is not expected, it promotes maintaining the current state and slowing the worsening of symptoms.

[0115] Therefore, the data analysis unit 41 of the analysis device 40 may instruct the AI ​​to extract improved biological data before and after the treatment (or throughout the series of treatments in the case of continuous treatment) and to obtain analysis results that include positive words regarding the continuation of the treatment.

[0116] Specifically, the data analysis unit 41 generates instructions that "extract up to 5 indicators from pre- and post-treatment biometric data that show a statistically significant improvement trend (mean difference, effect size, confidence interval) or clinically meaningful improvement, and for each indicator, list the 'indicator name / pre-treatment value / post-treatment value / change amount / statistical significance (or trend) / simple interpretation', and then generate (a) a positive message for the patient (30-60 characters, no exaggeration or definitive statements, including one suggestion for continued action), and (b) professional feedback for the practitioner (1 sentence, including next intervention plan and points to note). If the improvement is not statistically significant, replace it with expressions that provide reassurance and encourage the next step from the perspective of 'maintenance and prevention of deterioration,' and do not give excessive expectations." and input these instructions into the AI. Then, responses such as, "(For the patient) 'Your heart rate variability (HRV) has stabilized somewhat. Continue deep breathing before bed and maintain the same sleep rhythm.' / (For the practitioner) 'HRV +8ms, p=0.04. Continue thoracic mobility intervention and add 10 minutes of low-intensity aerobic exercise.'; (For the patient) 'Systolic blood pressure is showing a stabilizing trend. Continue hydration and light walks as before.' / (For the practitioner) 'SBP -6mmHg (95%CI -1 to -11). Maintain breathing guidance aimed at sympathetic inhibition.'; (For the patient: when not statistically significant) 'The numbers haven't changed significantly, but deterioration has been prevented. Continue the same habits within a reasonable range.' / (For the practitioner) 'No significant difference. Pain NRS is trending at -0.5. Next time, add low-load soft tissue intervention and self-stretching.'" are elicited.

[0117] Furthermore, the data analysis unit 41 may instruct the AI ​​to obtain analysis results that explain why improvement in symptoms can be expected, even if it cannot be determined that there has been a clear improvement before and after the treatment, by linking the changes in biometric data before and after the treatment.

[0118] Specifically, the Data Analysis Department 41 is tasked with: "Even if no statistically significant difference is found in the biological data before and after treatment, extract minute changes and trends (mean difference, coefficient of variation, autocorrelation trend, etc.) and estimate the possibility that these may lead to future improvements. Based on the extracted change indicators, generate (a) a short sentence (for the patient, approximately 30-60 characters) explaining that the body is in the process of adaptation and recovery, and (b) a supplementary sentence (for the practitioner, 1 sentence) that technically explains which physiological and psychological mechanisms may be related to the change. If the data shows a worsening trend, express the possibility of adjustment and recovery positively without causing excessive anxiety." This command is generated and input into the AI. For example, AI can arrive at the following answer: "(For the patient) 'No major changes are visible yet, but your body temperature and heart rate are becoming more stable. Let's continue to help your body adjust at this pace.' / (For the practitioner) 'The HRV coefficient of variation is +2.5%, which is estimated to be an adaptive response of the autonomic nervous system. Maintain the current treatment load.'" or "(For patients) 'There are no significant changes in the numbers, but your body is showing signs of gradual improvement. Continue without overexerting yourself.' / (For practitioners) 'Average EMG amplitude +3%, fluctuation range decreased. Estimated to be in the process of adjusting muscle tension. Next time, we will add a mild intervention to increase range of motion.'" In this way, even when the effects of treatment are not statistically clear, interpreting and presenting even slight changes positively can support the motivation of both the patient and the practitioner to continue treatment.

[0119] Furthermore, the data analysis unit 41, If the patient's biometric data indicates a potential deterioration, the AI ​​may be instructed to produce analysis results that visually or verbally present the risk of further deterioration of the patient's health (including cascading deterioration) that may occur if treatment is neglected, delayed, or reduced. Furthermore, even if no improvement is achieved, the AI ​​may be instructed to present analysis results that show that maintaining the current condition or slowing the progression would be of greater benefit to the patient.

[0120] Specifically, the data analysis unit 41 is: "Visualize the results obtained from continuing treatment, not only from the perspective of improvement, but also from the perspectives of 'preventing deterioration,' 'maintaining the current condition,' and 'delaying progression.' Compare this with the deterioration prediction curve in the case of neglect, and present the difference effect of continuous treatment (including the patient's efforts) using (a) visual information such as charts, graphs, and videos, and (b) positive messages (approximately 30-60 characters) that enhance a sense of security and motivation to continue. In particular, the expression should alleviate the anxiety that is common among the elderly, who fear that their condition will rapidly worsen, and contribute to suppressing mental illness (worsening of symptoms due to anxiety)." This instruction is generated and input to the AI.

[0121] For example, the AI ​​can produce the following output: "(Visual information presentation) 'If left untreated: Knee joint function predicted to decrease by 18% after 6 months / If treatment is continued: Predicted to stabilize at -2 to +3%'" "(To the person receiving treatment) 'The fact that you are able to maintain your current condition is a great achievement in itself. Let's gradually improve your body without rushing.'" "(For patients) 'This treatment has been shown to alleviate anxiety about the condition worsening and to slow its progression. Your body is responding well.'" "(For practitioners) 'Confirmed suppression of the rate of decline in range of motion. Combining with exercise therapy may increase the maintenance effect. Continue with the same load next time.'"

[0122] Thus, according to this embodiment, in addition to presenting improvement results, objectively demonstrating the important treatment effect of preventing deterioration reduces anxiety about whether the condition will be cured, and is expected to reduce the risk of treatment discontinuation, especially among the elderly. Furthermore, continued treatment and positive efforts toward lifestyle habits can maximize long-term health maintenance and preventative effects.

[0123] Furthermore, the data analysis unit 41, Even if it is not possible to determine that there has been a clear improvement before and after the treatment, the AI ​​may be instructed to obtain analysis results that include words that have the effect of stimulating the body's natural healing ability through the placebo effect. Specifically, the data analysis unit 41 is: "Even if the changes in biological data before and after treatment are not statistically significant, generate a short sentence that provides a positive psychological suggestion. The sentence should enhance the recipient's self-efficacy and evoke feelings of reassurance, anticipation, and motivation to continue, while avoiding expressions that definitively state medical effects. The sentence should be written in natural Japanese, approximately 30-60 characters long, and should include (a) elements that positively express signs of improvement and the body's adaptation process, and (b) action suggestions that encourage positive engagement with the next treatment or lifestyle changes." This command is generated and input into the AI. For example, AI can arrive at the following answer: "(To the person receiving treatment) 'Your body is gradually starting to adjust. Let's continue at this pace without rushing.'" "(For the person receiving treatment) 'You're finding it easier to breathe deeply. Your body is learning to relax and is trying to recover.'" (To the person receiving treatment) "The changes are slow, but your inner balance is improving. Take care of your body today." Examples include: "(To the person receiving treatment) 'Even a slight difference will definitely cause your body to react. It will improve even further with the next treatment.'" In this way, AI can incorporate psychological elements into the results of biometric data and transform them into positive expressions, thereby playing a supporting role in drawing out the body's natural healing abilities through the placebo effect. This allows for an integrated evaluation of both the objective and psychological effects of treatment, supporting the patient's continuous recovery.

[0124] Furthermore, the data analysis unit 41, The AI ​​may be instructed to obtain analysis results that include the effect of performing preventive treatments for symptoms that have not yet appeared in the patient. Specifically, the data analysis unit 41 will: "Analyze the patient's current biometric data, lifestyle data, environmental data, and historical statistical data to estimate the body parts and functions that are potentially at high risk. Then, calculate the expected effects (improvement rate, duration of prevention, stability of health indicators, etc.) of preventive treatment for these risk factors, and (a) provide a positive message (approximately 30-60 characters) for the patient to prevent future health problems, and (b) provide a specific preventive treatment plan or lifestyle guidance (1 sentence) for the practitioner." This command is generated and input into the AI.

[0125] For example, AI can arrive at the following answer: "(For patients) 'By strengthening your core now, you can reduce your risk of future lower back pain. Continue with light stretching.' / (For practitioners) 'Muscle activity of postural muscles is decreasing. Gentle myofascial release is recommended to stabilize the lower back.'" "(For patients) 'The movement around your shoulder is a little stiff. Let's prevent it from becoming chronic with early care.' / (For practitioners) 'Right shoulder abduction ROM 82° → Preventive intervention to prevent contracture. Add suprascapular nerve gliding.'" Examples include: "(For patients) 'Even if you don't feel pain now, muscle tension is a sign. Let's improve circulation with some light exercise.' / (For practitioners) 'Muscle hardness measurement has increased. We recommend using low-impact massage and heat therapy to promote blood flow.'" In this way, AI can estimate the preventative effects of treatments based on risk indicators that have not yet materialized, and generate policies and action suggestions to prevent future health problems. As a result, the treatment effect evaluation system 10 can be applied not only to therapeutic treatments but also to the field of preventive medicine.

[0126] The present invention is not limited to the embodiments and modifications described above, and various further modifications are possible. For example, the embodiments and modifications described above are explained in detail for the purpose of clearly illustrating the present invention, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of one modification with other modifications or to combine modifications.

[0127] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations and functions may be implemented in software by a processor interpreting and executing programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, recording devices such as hard disks and SSDs, or recording media such as IC cards, SD cards, and DVDs. Also, control lines and information lines are shown only if deemed necessary for explanation, and not all control lines and information lines are necessarily shown in the actual product. In practice, it can be assumed that almost all configurations are interconnected.

[0128] The present invention can be provided in various forms, not limited to systems or devices, but also including methods, computer-readable programs, and so on. [Explanation of Symbols]

[0129] 1...Practitioner, 2...Patient, 10...Treatment effect evaluation system, 20...Sensor group, 21...Biometric data acquisition device, 22...Lifestyle 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...Storage unit, 34...Communication unit, 35...Output unit, 40...Analysis device, 41...Data analysis unit, 411...AI, 42...Communication unit

Claims

1. A treatment effectiveness evaluation system that evaluates the effects of treatment on the patient, A data collection unit collects the subject's biological data and lifestyle data, as well as environmental data related to the treatment environment for the subject. An analysis unit that obtains analysis results including the effects of the treatment by analyzing the aforementioned biological data, lifestyle data, and environmental data using AI including a machine learning model, It has an output unit that outputs the analysis results from the analysis unit, The aforementioned analysis unit, The AI ​​is instructed to extract improved biological data before and after the treatment, or, in the case of continuous treatment, during a series of treatments, and to obtain analysis results that include positive statements encouraging the continuation of the treatment. A treatment effectiveness evaluation system characterized by the following features.

2. A treatment effect evaluation system according to claim 1, The aforementioned analysis unit, A treatment effectiveness evaluation system characterized by instructing the AI ​​to obtain analysis results that explain the expectation of symptom improvement, even if it cannot be determined that there has been a clear improvement before and after the treatment, or in the case of continuous treatment, after a series of treatments, by linking it to changes in biometric data before and after the treatment.

3. A treatment effect evaluation system according to claim 1, The aforementioned analysis unit, Before and after treatment, or in the case of continuous treatment, during a series of treatments, if the patient's biometric data indicates a potential deterioration, the AI ​​is instructed to obtain analysis results that visually or verbally present the risk of deterioration in health (including cascading deterioration) that may occur if treatment is neglected, delayed, or reduced in frequency. A treatment effectiveness evaluation system characterized by the following features.

4. A treatment effect evaluation system according to claim 1, The aforementioned analysis unit, The AI ​​is instructed to obtain analysis results that include words that have the effect of stimulating the body's natural healing ability through the placebo effect, even if it is not possible to determine that there has been a clear improvement before and after the treatment, or in the case of continuous treatment, after a series of treatments. A treatment effectiveness evaluation system characterized by the following features.

5. A treatment effect evaluation system according to claim 1, The aforementioned analysis unit, The AI ​​is instructed to obtain analysis results that include the effects of performing preventive treatments for symptoms that are not present in the patient. A treatment effectiveness evaluation system characterized by the following features.

6. A treatment effect evaluation system according to claim 1, The aforementioned analysis unit, The AI ​​is instructed to obtain analysis results that include words that will encourage the patient to continue the treatment. A treatment effectiveness evaluation system characterized by the following features.

7. A treatment effect evaluation system according to claim 1, The aforementioned analysis unit, The AI ​​is instructed to obtain analysis results that include words that will encourage the practitioner to continue the treatment. A treatment effectiveness evaluation system characterized by the following features.

8. A treatment effect evaluation system according to claim 1, The biological data of the person receiving treatment includes at least one of the following: basic physical data, vital sign data, internal physiological data, genetic data, microbiological data, neurological / psychological data, and behavioral data. A treatment effectiveness evaluation system characterized by the following features.

9. A treatment effect evaluation system according to claim 1, The aforementioned lifestyle data of the person receiving treatment includes at least one of the following: dietary data, exercise and physical activity data, sleep data, work and learning data, mental health data, usage data of luxury goods, digital and lifestyle rhythm data, social activity data, and daily habit data. A treatment effectiveness evaluation system characterized by the following features.

10. A treatment effect evaluation system according to claim 1, The aforementioned environmental data includes at least one of the following: physical data, chemical data, biological data, psychological data, social / periodic data, and special environmental data. A treatment effectiveness evaluation system characterized by the following features.

11. A treatment effect evaluation system according to claim 1, The data collection unit collects at least one of the following from the patient's 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. The analysis unit analyzes at least one of the collected patient's 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 using AI, including a machine learning model. A treatment effectiveness evaluation system characterized by the following features.

12. A method for evaluating the effectiveness of treatment as an alternative medicine for a patient, using a treatment effectiveness evaluation system, A data collection step for collecting the subject's biological data and lifestyle data, as well as environmental data related to the treatment environment for the subject, An analysis step to obtain analysis results including the effects of the treatment by analyzing the aforementioned biological data, lifestyle data, and environmental data using AI including a machine learning model, The system includes an output step that outputs the analysis results obtained from the analysis step, The aforementioned analysis step is, The AI ​​is instructed to extract improved biological data before and after the treatment, or, in the case of continuous treatment, during a series of treatments, and to obtain analysis results that include positive statements encouraging the continuation of the treatment. A method for evaluating the effectiveness of a treatment, characterized by the features described above.

13. This is a program that enables a computer to function as a treatment effectiveness evaluation system for evaluating the effects of alternative medicine treatments on the patient. To the aforementioned computer, A data collection step for collecting the subject's biological data and lifestyle data, as well as environmental data related to the treatment environment for the subject, The process includes an analysis step of obtaining an analysis result including the effect of the treatment by analyzing the aforementioned biological data, the aforementioned lifestyle data, and the aforementioned environmental data using AI including a machine learning model, and an output step of outputting the analysis result obtained from the analysis step. The aforementioned analysis step is, The AI ​​is instructed to extract improved biological data before and after the treatment, or, in the case of continuous treatment, during a series of treatments, and to obtain analysis results that include positive statements encouraging the continuation of the treatment. A program characterized by the following features.

Images