WO2023157596A1 - Procédé de traitement d'informations, dispositif de traitement d'informations, programme, et système de traitement d'informations - Google Patents

Procédé de traitement d'informations, dispositif de traitement d'informations, programme, et système de traitement d'informations Download PDF

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WO2023157596A1
WO2023157596A1 PCT/JP2023/002369 JP2023002369W WO2023157596A1 WO 2023157596 A1 WO2023157596 A1 WO 2023157596A1 JP 2023002369 W JP2023002369 W JP 2023002369W WO 2023157596 A1 WO2023157596 A1 WO 2023157596A1
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information
user
information processing
patients
sensing data
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PCT/JP2023/002369
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English (en)
Japanese (ja)
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健治 山根
咲湖 安川
拓 田中
乃愛 金子
律子 金野
能宏 脇田
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ソニーグループ株式会社
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Publication of WO2023157596A1 publication Critical patent/WO2023157596A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance

Definitions

  • the present disclosure relates to an information processing method, an information processing device, a program, and an information processing system.
  • the information entered by the patient as described above often contains non-objective information, and it is difficult to recommend a suitable medical institution for the patient based on such information.
  • objective information such as test values
  • the disease is a disease other than a disease that can be quantitatively diagnosed based on such information
  • the conventional technology cannot find a suitable medical institution. It can be difficult to find.
  • the present disclosure proposes an information processing method, an information processing device, a program, and an information processing system capable of recommending a suitable medical institution to a patient (user).
  • an information processing device acquires sensing data obtained from a sensor attached to a part of a user's body, and based on the sensing data and treatment performance information of a plurality of patients, extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients;
  • An information processing method includes determining the medical institution to be recommended to the user from among the institutions, and transmitting information on the determined medical institution to an information processing terminal.
  • an acquisition unit that acquires sensing data obtained from a sensor attached to a part of a user's body, the sensing data and treatment performance information of a plurality of patients;
  • the plurality of patients having the sensing data similar to the sensing data of the user is extracted from among the patients, and based on the extracted treatment performance information of the plurality of patients, among the plurality of medical institutions,
  • An information processing apparatus comprising a recommendation unit that determines the medical institution to recommend to the user.
  • the computer has a function of acquiring sensing data obtained from a sensor attached to a part of the user's body, and based on the sensing data and treatment performance information of a plurality of patients, A function of extracting, from among the plurality of patients, the plurality of patients having the sensing data similar to the sensing data of the user;
  • a program is provided for executing a function of determining the medical institution to be recommended to the user from among the institutions and a function of transmitting information on the determined medical institution to an information processing terminal.
  • an information processing system including a sensor attached to a part of a user's body, an information processing device, and an information processing terminal, wherein the information processing device is attached to a part of the user's body. and obtaining the sensing data obtained from the sensors, and selecting the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients.
  • a medical institution to be recommended to the user is determined from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients, and the determined medical institution
  • An information processing system is provided for transmitting information to the information processing terminal.
  • FIG. 1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to an embodiment of the present disclosure
  • FIG. 1 is a block diagram showing an example functional configuration of a wearable device 100 according to an embodiment of the present disclosure
  • FIG. 1 is an explanatory diagram showing an example of the appearance of a wearable device 100 according to an embodiment of the present disclosure
  • FIG. 2 is a block diagram showing an example of a functional configuration of a user terminal 200 according to an embodiment of the present disclosure
  • FIG. 3 is a block diagram showing an example of a functional configuration of a server 300 according to an embodiment of the present disclosure
  • FIG. 4 is a sequence diagram of an information processing method according to an embodiment of the present disclosure
  • FIG. 10 is a diagram showing an example of a medical institution information table 362 according to an embodiment of the present disclosure
  • FIG. 1 is a flowchart (Part 1) of an information processing method according to an embodiment of the present disclosure
  • FIG. 4 is an explanatory diagram showing an example of display on the user terminal 200 according to the embodiment of the present disclosure
  • FIG. 2 is a flowchart (part 2) of an information processing method according to an embodiment of the present disclosure
  • FIG. 13 is an explanatory diagram for explaining an information processing method in the importance calculation unit 334 according to the embodiment of the present disclosure
  • FIG. 3 is a flowchart (part 3) of an information processing method according to an embodiment of the present disclosure
  • FIG. 10 is an explanatory diagram (Part 1) for explaining an information processing method in the recommendation degree calculation unit 336 according to the embodiment of the present disclosure
  • FIG. 11 is an explanatory diagram (Part 2) for explaining the information processing method in the recommendation degree calculation unit 336 according to the embodiment of the present disclosure
  • 7 is a flowchart of an information processing method according to Modification 1 of the embodiment of the present disclosure
  • FIG. 13 is an explanatory diagram for explaining an information processing method in the recommendation degree calculation unit 336 according to Modification 1 of the embodiment of the present disclosure
  • FIG. 10 is a flowchart of an information processing method according to Modification 2 of the embodiment of the present disclosure
  • FIG. FIG. 11 is an explanatory diagram for explaining an information processing method in a recommendation degree calculation unit 336 according to Modification 2 of the embodiment of the present disclosure
  • 3 is a block diagram showing an example of hardware configuration
  • Psychiatric disorders are often caused by disorders of brain function, and unlike hypertension, diabetes, etc., it is said to be difficult to detect early by conducting regular and quantitative examinations.
  • it is difficult to establish a consistent treatment protocol for psychiatric disorders because the causes, symptoms, and treatment methods differ for each individual patient. It is not guaranteed that you will receive it. Therefore, it is difficult for patients and their families to find a suitable medical institution for the patient, and in addition, it is difficult for patients with mental illness to make appropriate and calm decisions. Because there are cases, it is still difficult to find a suitable medical institution.
  • the present inventors have developed information capable of recommending a suitable medical institution for treatment of mental illness to a patient using biological information detected by such a sensor.
  • Embodiments of the present disclosure related to processing methods, information processing apparatuses, programs, and information processing systems have been created. The details of such embodiments of the present disclosure will be sequentially described below.
  • a user means a person for whom a medical institution is recommended in the embodiment of the present disclosure.
  • biomarkers mean biometric information that can be routinely detected by noninvasive sensor devices and is considered to be correlated with the user's mental state. Biomarkers are, for example, directly by the sensor device (specifically, the sensing data itself acquired by the sensor device), or indirectly (specifically, the sensing data itself acquired by the sensor device). ).
  • biomarkers are, for example, heart rate, heart rate variability, pulse rate, pulse variability, blood flow, blood oxygen concentration, blood pressure, respiratory volume, respiratory rate, brain waves, perspiration, body temperature, muscle condition, posture , activity level, exercise state, number of steps, distance traveled, sleep time, sleep state (specifically, REM sleep, non-REM sleep, etc.), basal metabolic calorie consumption, calorie consumption due to exercise, biological information such as facial and pupil movements can be
  • interview information means all the information collected from specialists, etc. for diagnosis of patients with mental illness.
  • the interview information includes information such as age, gender, occupation, smoking history, degree of drinking, activity hours, working hours, meal content, family structure, preferences, hobbies, and growth history.
  • the patient's background information on mental status means the subjective evaluation results obtained from specialists for diagnosis of patients with mental illness.
  • the background information is, for example, PHQ-9 (Patient Health Questionnaire-9), GAD-7 (General Anxiety Disorder-7), etc. It means the result of scoring the patient's subjective evaluation of
  • the treatment results mean, for example, information as to whether or not the patient is in remission, treatment period until remission, treatment method, medication information, medical institution in charge, doctor in charge, etc. and
  • FIG. 1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to an embodiment of the present disclosure.
  • an information processing system 10 includes a user terminal (information processing terminal) 200 communicably connected to a wearable device 100, a server (information processing device) 300, and a medical institution terminal. 400 , which are communicably connected to each other via a network 500 .
  • the user terminal 200, the server 300, and the medical institution terminal 400 are connected to the network 500 via a base station (for example, a mobile phone base station, a wireless LAN (Local Area network) access point, etc.) (not shown).
  • a base station for example, a mobile phone base station, a wireless LAN (Local Area network) access point, etc.
  • the communication method used in the network 500 can be any method regardless of whether it is wired or wireless (for example, WiFi (registered trademark), Bluetooth (registered trademark), etc.), but stable operation is maintained. It is desirable to use a communication method that can Below, an outline of each device included in the information processing system 10 according to the present embodiment will be described.
  • Wearable device 100 can be a device that can be worn on a user's body part (face, earlobe, neck, arm, wrist, ankle, etc.). More specifically, the wearable device 100 is a head mounted display (HMD) type, eyeglass type, ear device type, anklet type, bracelet (wristband) type, collar type, eyewear type, pad type, batch type, clothes It can be a wearable device of various types such as a type, a hat type, and a mask type.
  • HMD head mounted display
  • eyeglass type eyeglass type
  • ear device type anklet type
  • bracelet bracelet
  • collar type eyewear type
  • pad type a wearable device of various types
  • clothes can be a wearable device of various types such as a type, a hat type, and a mask type.
  • the wearable device 100 has a sensor section 140 (see FIG. 2), which is a non-invasive sensor device capable of acquiring the user's biomarkers.
  • the sensor unit 140 includes, for example, a blood flow sensor that detects the user's pulse, heart rate, blood flow, intermittent oxygen, etc., an ECG (Electrocardiogram) sensor that detects the user's electrocardiogram, a blood pressure sensor that detects the user's blood pressure, a user A perspiration sensor that detects the perspiration of the user, an electroencephalogram sensor that detects the user's electroencephalogram (and can indirectly detect the user's state of sleep, relaxation, etc.
  • ECG Electrocardiogram
  • a body temperature sensor that detects the user's body temperature
  • a user's A myoelectric potential sensor that detects the tension state of muscles
  • a respiration sensor that detects the user's respiration rate and respiration volume, and the like may be provided.
  • the sensor unit 140 may also include a motion sensor for detecting the user's posture and movement, that is, the state of exercise and the state of activity.
  • the motion sensor for example, acquires sensing data indicating changes in acceleration that occur with the motion of the user, and performs analysis processing as necessary to determine the user's exercise state, posture, number of steps, distance traveled, and amount of activity. , sleep state, sleep time, energy consumption, basal metabolic energy consumption, and the like can be detected.
  • the sensor unit 140 includes an imaging device (not shown) that captures the user's expression, face, line of sight, and pupil movement, and a microphone that acquires the user's voice (hereinafter referred to as a microphone). (illustration omitted) etc. may be included.
  • the wearable device 100 is assumed to be, for example, a bracelet (wristband) type wearable device. Details of the wearable device 100 will be described later.
  • the user terminal 200 is a terminal that the user uses on a daily basis, and is capable of transmitting biomarkers from the wearable device 100 to the server 300 described below and receiving information from the server 300 .
  • the user terminal 200 can be a device such as a tablet, a smart phone, a mobile phone, a laptop PC (Personal Computer), a desktop PC, or a Head Mounted Display (HMD).
  • the user terminal 200 shall be a smart phone. Details of the user terminal 200 will be described later.
  • the server 300 Based on the biomarkers obtained from the wearable device 100 via the user terminal 200 and the information input by the user using the user terminal 200, the server 300 determines and determines the medical institution or the like to be recommended to the user. Information can be provided to the user.
  • the server 300 is configured by, for example, a computer. A detailed configuration of the server 300 will be described later.
  • the medical institution terminal 400 is a terminal used by medical personnel such as doctors at the medical institution, and can transmit information to the server 300 via the network 500 .
  • the medical institution terminal 400 can be a device such as a tablet, smart phone, mobile phone, laptop PC, or desktop PC. In the following description, medical institution terminal 400 is assumed to be a desktop PC. Details of the medical institution terminal 400 will be described later.
  • FIG. 1 shows the information processing system 10 according to the present embodiment as including a pair of wearable device 100 and user terminal 200
  • the present embodiment is not limited to this. do not have.
  • the information processing system 10 according to the present embodiment may include one or more pairs of multiple wearable devices 100 and one or multiple user terminals 200 .
  • the information processing system 10 may include a plurality of medical institution terminals 400 .
  • the information processing system 10 according to the present embodiment includes other communication devices such as relay devices for transmitting and receiving information between the plurality of user terminals 200 and the plurality of medical institution terminals 400 and the server 300, for example. etc. may be included.
  • FIG. 2 is a block diagram showing an example of the functional configuration of the wearable device 100 according to this embodiment.
  • the wearable device 100 mainly has an input unit 110, an output unit 120, a control unit 130, a sensor unit 140, a communication unit 150, and a storage unit 160, as shown in FIG. Details of each functional unit of the wearable device 100 will be described below.
  • the input unit 110 receives input of data and commands from the user to the wearable device 100 . More specifically, the input unit 110 is implemented by a touch panel, buttons, a microphone, and the like.
  • the output unit 120 is a device for presenting information to the user, and for example, outputs various information to the user using images, sounds, lights, vibrations, or the like. More specifically, the output unit 120 can display information provided from the server 300, which will be described later, on the screen.
  • the output unit 120 is implemented by a display, a speaker, earphones, a light-emitting element (for example, a Light Emitting Diode (LED)), a vibration module, and the like. Note that part of the functions of the output unit 120 may be provided by the user terminal 200 .
  • control unit 130 The control unit 130 is provided in the wearable device 100, controls each functional unit of the wearable device 100, acquires sensing data (biomarkers) output from the sensor unit 140 described later, and analyzes the sensing data. can be processed.
  • the control unit 130 is realized by hardware such as a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Note that part of the functions of the control unit 130 may be provided by the user terminal 200, which will be described later.
  • the sensor unit 140 is provided in the wearable device 100 attached to the user's body, and has various sensors that detect the user's biological information.
  • the sensor unit 140 is, for example, a PPG sensor (pulse sensor) (not shown) that detects the pulse or heartbeat of the user and acquires time-series data of the heartbeat or pulse, or a sensor that detects the movement of the user. It has a motion sensor (not shown) and the like.
  • the PPG sensor is a biosensor worn on a part of the body such as the user's skin (for example, both arms, wrists, ankles, etc.) in order to detect the user's pulse wave signal.
  • the pulse wave signal refers to the contraction of the heart muscle at a constant rhythm (pulsation, the number of heart beats per unit time is called the heart rate), and blood is sent throughout the body through the arteries.
  • a change in pressure occurs on the inner wall of the artery, and it is a waveform due to the pulsation of the artery that appears on the body surface.
  • the PPG sensor In order to acquire a pulse wave signal, the PPG sensor irradiates light on the blood vessels in the measurement site of the user, such as a hand, arm, leg, etc. Detect scattered light. Since the irradiated light is absorbed by red blood cells in the blood vessel, the amount of light absorption is proportional to the amount of blood flowing through the blood vessel in the measurement site. Therefore, the PPG sensor can detect changes in the amount of flowing blood by detecting the intensity of the scattered light. Furthermore, a pulsation waveform, that is, a pulse wave signal can be detected from this change in blood flow. Such a method is called PhotoPlethysmoGraphy (PPG) method.
  • PPG PhotoPlethysmoGraphy
  • the PPG sensor incorporates a small laser or LED (Light Emitting Diode) (not shown) that can irradiate coherent light, and irradiates light with a predetermined wavelength such as around 850 nm, for example. .
  • the wavelength of the light emitted by the PPG can be selected as appropriate.
  • the PPG sensor incorporates, for example, a photodiode (Photo Detector: PD), and acquires a pulse wave signal by converting the intensity of the detected light into an electrical signal.
  • the PPG sensor may incorporate a CCD (Charge Coupled Devices) type sensor, a CMOS (Complementary Metal Oxide Semiconductor) type sensor, or the like instead of the PD.
  • the PPG sensor may also include optical mechanisms such as lenses and filters to detect light from the user's measurement site.
  • the PPG sensor can detect the pulse wave signal as time-series data having multiple peaks.
  • a peak interval between a plurality of peaks appearing in a pulse wave signal is called a pulse rate interval (PPI).
  • PPI pulse rate interval
  • the PPI value can be obtained by processing the pulse wave signal detected by the PPG sensor. It is known that each PPI value fluctuates over time, but is approximately normally distributed while the user's state is maintained constant. Therefore, by statistically processing the PPI value data group (for example, calculating the standard deviation of the PPI value), various HRV (Heart Rate Variability) indicators of the user's mental state (for example, Stre's state) An index can be calculated. Therefore, time-series data of heartbeat or pulse can serve as an indicator of the user's mental state.
  • HRV Heart Rate Variability
  • the present embodiment is not limited to obtaining pulse wave signals using the PPG method described above, and pulse wave signals may be obtained by other methods.
  • the sensor unit 140 may detect pulse waves using a laser Doppler blood flow measurement method.
  • the laser Doppler blood flow measurement method is a method of measuring blood flow using the following phenomena. Specifically, when the user's measurement site is irradiated with laser light, scattering substances (mainly red blood cells) present in the user's blood vessels move, causing scattered light with a Doppler shift. . Then, the scattered light accompanied by the Doppler shift interferes with the scattered light from non-moving tissue present in the measurement site of the user, and a beat-like intensity change is observed. Therefore, the laser Doppler blood flow measurement method can measure blood flow by analyzing the intensity and frequency of the beat signal.
  • an ECG sensor that detects the electrocardiogram of the target user via electrodes (not shown) attached to the user's body may be provided.
  • the RR interval which is the interval between heart beats
  • the HRV index which is an index indicating the mental state of the user
  • the sensor unit 140 may be provided with a perspiration sensor (not shown).
  • a perspiration sensor (not shown).
  • thermal perspiration is sweating that is done to regulate body temperature.
  • mental sweating is sweating caused by human emotions such as tension and emotions. It is sweating that occurs instantaneously and in a small amount on the palms and soles of the feet at room temperature compared to thermal sweating.
  • mental sweating is sweating caused by tension when giving a presentation.
  • Such mental sweating is known to occur frequently when the sympathetic nervous system is dominant, and is generally considered to be an indicator of mental state.
  • the perspiration sensor is attached to the user's skin and detects the voltage or resistance between two points on the skin that changes due to perspiration.
  • information such as the amount of perspiration, the frequency of perspiration, and changes in the amount of perspiration is obtained based on the sensing data detected by the perspiration sensor, thereby obtaining one index indicating the mental state of the user. be able to.
  • the sensor unit 140 may include other various biosensors (not shown).
  • the various biosensors may include one or more sensors attached directly or indirectly to a part of the user's body and measuring the target user's electroencephalogram, respiration, myoelectric potential, skin temperature, etc. can.
  • sensing data obtained by an electroencephalogram sensor unit (not shown) that measures the electroencephalogram of the user is analyzed to determine the type of electroencephalogram (e.g., alpha waves, beta waves, etc.), it is possible to obtain an index indicating the mental state of the user (for example, the degree of relaxation of the user, etc.).
  • the sensor unit 140 may include an imaging device (not shown) that captures the user's facial expression, as described above.
  • the imaging device detects, for example, the user's eye movement, pupil diameter, gaze time, and the like. It is said that the muscle that controls the human pupil diameter is influenced by the sympathetic/parasympathetic nerves. Therefore, in this embodiment, by detecting the user's pupil diameter with the imaging device, it is possible to obtain one index indicating the user's mental state, such as the user's sympathetic/parasympathetic state. .
  • the sensor unit 140 may also include a motion sensor for detecting the user's posture and movement, that is, the motion state.
  • the motion sensor detects the motion state of the user by acquiring sensing data indicating changes in acceleration that occur with the motion of the user.
  • the user's exercise state acquired by the motion sensor can be used when estimating the user's emotion.
  • the posture affects the depth of breathing and the like, and furthermore, the depth of breathing is said to be highly related to the state of tension (degree of tension) of a person. Therefore, in the present embodiment, it is possible to detect the user's posture and obtain one index indicating the user's mental state from the detected posture.
  • the motion sensor includes an acceleration sensor, a gyro sensor, a geomagnetic sensor, etc. (not shown).
  • the motion sensor may be an imaging device (not shown) that captures an image of the user.
  • the motion sensor may include an infrared sensor, an ultrasonic sensor, or the like (not shown) capable of detecting user motion. Note that such an imaging device, an infrared sensor, and the like may be installed around the user as separate devices from the wearable device 100 .
  • the sensor unit 140 may include a positioning sensor (not shown) together with the motion sensor.
  • the positioning sensor is a sensor that detects the position of the user wearing the wearable device 100, and specifically can be a GNSS (Global Navigation Satellite System) receiver or the like.
  • the positioning sensor can generate sensing data indicating the latitude and longitude of the user's current location based on signals from GNSS satellites.
  • RFID Radio Frequency Identification
  • Wi-Fi since it is possible to detect the relative positional relationship of the user from the information of the wireless base station, such It is also possible to use a communication device as the positioning sensor.
  • the sensor unit 140 may include a microphone (not shown) that detects the user's uttered voice.
  • a microphone not shown
  • the result obtained by extracting a specific voice (for example, a specific phrase uttered by the user) from the sound detected by the microphone is used as one index indicating the mental state of the user. can be obtained as
  • the sensor unit 140 can include various sensors. Furthermore, the sensor unit 140 may incorporate a clock mechanism (not shown) that grasps the correct time, and associate the time when the sensing data (biomarker) is acquired with the acquired sensing data. Further, as described above, the various sensors may not be provided in the sensor unit 140 of the wearable device 100. For example, they may be provided separately from the wearable device 100. may be provided in a device or the like used by
  • the communication unit 150 is provided within the wearable device 100 and can transmit and receive information to and from an external device such as the user terminal 200 .
  • the communication unit 150 can be said to be a communication interface having a function of transmitting and receiving data.
  • the communication unit 150 is implemented by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 160 is provided in the wearable device 100 and stores programs, information, etc. for the above-described control unit 130 to execute various processes, and information obtained by the processes.
  • the storage unit 160 is realized by, for example, a nonvolatile memory such as a flash memory.
  • FIG. 3 shows an example of the appearance of the wearable device 100 according to this embodiment.
  • the wearable device 100 is a bracelet-type wearable device worn on the user's wrist.
  • the wearable device 100 has a belt-shaped band portion 170 and a control unit 180 .
  • the band part 170 is worn around the user's wrist, it is made of a soft material such as silicone gel so as to have a ring-like shape that matches the shape of the wrist.
  • the control unit 180 is a portion where the above-described control section 130, sensor section 140, and the like are provided.
  • the sensor unit 140 is provided at a position such that when the wearable device 100 is worn on a part of the user's body, it contacts or faces the user's body.
  • the wearable device 100 according to the present embodiment is not limited to the configuration example shown in FIG. 2 or the appearance shown in FIG. .
  • FIG. 4 is a block diagram showing an example of the functional configuration of the user terminal 200 according to this embodiment.
  • the user terminal 200 according to this embodiment mainly includes an input unit 210, an output unit 220, a control unit 230, a communication unit 250, and a storage unit 260, as shown in FIG.
  • Each functional unit of the user terminal 200 will be described below.
  • the input unit 210 can accept input of data and commands to the user terminal 200 . More specifically, the input unit 210 is implemented by a touch panel, keyboard, microphone, or the like.
  • the output unit 220 is composed of, for example, a display, a speaker, a lamp, a video output terminal, an audio output terminal, etc., and can output various information to the user by means of images, flashes, sounds, and the like.
  • control unit 230 The control unit 230 is provided in the user terminal 200 and can control each functional unit of the user terminal 200 and acquire biomarkers (sensing data) from the wearable device 100 .
  • the control unit 230 is realized by, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Also, the control unit 230 may perform analysis processing on sensing data from the wearable device 100 .
  • the communication unit 250 can transmit and receive information to and from external devices such as the wearable device 100 and the server 300 .
  • the communication unit 250 can be said to be a communication interface having a function of transmitting and receiving data.
  • the communication unit 250 is implemented by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 260 can store programs, information, etc. for the above-described control unit 230 to execute various processes, and information obtained by the processes.
  • the storage unit 260 is realized by, for example, a magnetic recording medium such as a hard disk (HD), a nonvolatile memory such as a flash memory, or the like.
  • the user terminal 200 according to the present embodiment is not limited to the configuration example shown in FIG. 4, and may further include other functional units, for example.
  • FIG. 5 is a block diagram showing an example of the functional configuration of the server 300 according to this embodiment.
  • the server 300 mainly includes an input unit 310, an output unit 320, a processing unit 330, a communication unit 350, and a storage unit 360, as shown in FIG.
  • Each functional unit of the server 300 will be described below.
  • the input unit 310 can accept input of data and commands to the server 300 . More specifically, the input unit 310 is implemented by a touch panel, keyboard, or the like.
  • the output unit 320 is configured by, for example, a display or the like, and can output various information in the form of images or the like.
  • the processing unit 330 can control each functional unit of the server 300 .
  • the processing unit 330 is realized by hardware such as CPU, ROM, and RAM, for example.
  • the processing unit 330 can determine a medical institution to recommend to the user based on the biomarkers and the like from the wearable device 100 .
  • the processing unit 330 can also extract items with high importance.
  • the processing unit 330 functions as an acquisition unit 332, an importance calculation unit 334, a recommendation calculation unit (recommendation unit) 336, and an output control unit 338 in order to realize these functions described above.
  • the processing unit 330 may also perform analysis processing on sensing data from the wearable device 100 . Details of these functions of the processing unit 330 according to the present embodiment will be described below.
  • the acquisition unit 332 acquires biomarkers transmitted from the wearable device 100 via the user terminal 200, interview information, background information (scores), and the like from the user terminal 200.
  • the acquiring unit 332 acquires information (biomarkers, interview information, background information, treatment results, etc.) of a plurality of patients from each medical institution terminal 400 .
  • the acquisition unit 332 can output the acquired information to the importance calculation unit 334 and the recommendation calculation unit 336, which will be described later.
  • the importance calculation unit 334 uses a technique such as multivariate analysis for a plurality of patient information (biomarkers, medical interview information, background information, treatment results, etc.) obtained from a plurality of medical institutions, for each information , the degree of importance, which is an index indicating the degree of high correlation (relevance) to remission (more specifically, whether or not remission has occurred) can be calculated. Furthermore, the importance calculation unit 334 can search for information items (types) highly correlated with remission based on the calculated importance.
  • the importance calculation unit 334 outputs information such as the importance of each information (item) and the ranking of the information (items) arranged according to the importance to the output control unit 338 and the storage unit 360, which will be described later. can be done.
  • the information output by the importance calculation unit 334 can be used when requesting the user to input information, or used when determining the number of patients to be extracted by the recommendation calculation unit 336, which will be described later. can. Details of the operation of the importance calculation unit 334 will be described later.
  • the recommendation degree calculation unit 336 analyzes biomarkers (sensing data) transmitted from the wearable device 100 via the user terminal 200, treatment results obtained from a plurality of medical institutions, etc., and provides the user with treatment for mental illness. can decide which medical institution to recommend for Then, the recommendation degree calculation unit 336 can output information on the determined medical institution to the output control unit 338 described later. For example, the recommendation degree calculation unit 336 selects a user's A degree of similarity indicating the degree of similarity to the biomarker is calculated, and a predetermined number of patients are extracted in descending order of the calculated degree of similarity, thereby extracting patients similar to the user.
  • the recommendation degree calculation unit 336 calculates the recommendation degree of each medical institution based on the treatment results of a plurality of extracted patients (for example, consultation rate, remission rate, etc.), and sends the highly recommended medical institution to the user. Decide as a recommended medical institution. Details of the operation of the recommendation degree calculation unit 336 will be described later.
  • Output control unit 338 controls the communication unit 350, which will be described later, to transmit the information output by the importance calculation unit 334 and the recommendation calculation unit 336, information selected based on the information, and the like to the user terminal 200. can do.
  • the communication unit 350 can transmit and receive information to and from external devices such as the user terminal 200 and the medical institution terminal 400 .
  • the communication unit 350 can be said to be a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 360 can store programs, information, and the like for the processing unit 330 described above to execute various types of processing, and information obtained by the processing. Specifically, as shown in FIG. 5, the storage unit 360 stores the treatment results obtained from each medical institution (for example, information on whether the patient is in remission, treatment period until remission, treatment method, medication information, A medical institution information table 362 can be stored that stores information such as doctors in charge. The storage unit 360 can also store an importance ranking table 364 that stores the ranking of information arranged according to the importance calculated by the recommendation calculation unit 336 described above. Furthermore, the storage unit 360 can store a user information table 366 that stores various types of user information (eg, biomarkers, interview information, background information) obtained from the user terminal 200 .
  • user information table 366 that stores various types of user information (eg, biomarkers, interview information, background information) obtained from the user terminal 200 .
  • the storage unit 360 is implemented by, for example, a magnetic recording medium such as a hard disk, or a non-volatile memory such as a flash memory.
  • the above information includes information related to the privacy of the user and the patient, in the present embodiment, the above information is temporarily stored in the storage unit 360 only when processing is performed by the processing unit 330, It is preferable to erase immediately after the processing is finished.
  • information processing for example, using identification information consisting of a simple character string instead of naming a patient is performed so that individual patients cannot be identified.
  • the server 300 is not limited to the configuration example shown in FIG. 5, and may further include other functional units, for example. Furthermore, the server 300 may be composed of a plurality of information processing devices communicably connected to each other via the network 500 . At least part of the functions of the server 300 may be executed by the user terminal 200 described above, or the server 300 may be configured as a device integrated with the user terminal 200 or the medical institution terminal 400. .
  • FIG. 6 is a sequence diagram of the information processing method according to this embodiment
  • FIG. 7 is a diagram showing an example of the medical institution information table 362 according to this embodiment.
  • 8, 10 and 12 are flowcharts of the information processing method according to this embodiment
  • FIG. 9 is an explanatory diagram showing an example of display on the user terminal 200 according to this embodiment.
  • FIG. 11 is an explanatory diagram for explaining the information processing method in the importance calculation unit 334 according to this embodiment
  • FIGS. It is an explanatory view for explaining an information processing method.
  • the information processing method according to this embodiment includes a plurality of steps from step S100 to step S1100. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the user terminal 200 acquires user information including background information (score) about the user, interview information, biomarker data, etc., and transmits the acquired information to the server 300 (step S100). Details of step S100 will be described later.
  • the server 300 receives the user information from the user terminal 200 (step S200).
  • the medical institution terminal 400 receives patient treatment results (for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information of the doctor in charge, etc.), biomarkers at the start of the medical examination, background information (score), interview information, etc. are acquired (step S300).
  • patient treatment results for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information of the doctor in charge, etc.
  • biomarkers at the start of the medical examination for example, background information (score), interview information, etc.
  • background information background information
  • medical interview information for example, information on whether the patient is in remission, treatment period until remission, treatment method, medication information, doctor in charge, etc. information
  • the treatment results may also include patient subjective information, such as the patient's degree of satisfaction with treatment and the degree of recovery after treatment.
  • patient's subjective information can be collected by a medical institution, an operating agency of the information processing system according to the present embodiment, or the like, through a regular or non-periodic questionnaire to the patient.
  • the biomarkers stored in the medical institution information table 362 are not limited to two types of biomarkers as shown in FIG. good.
  • the background information (score) and medical inquiry information stored in the medical institution information table 362 are limited to one type of background information (score) and medical inquiry information as shown in FIG. It may be multiple types of background information (scores) and interview information.
  • the timing of the processing in step S300 is not particularly limited, and may be executed each time the treatment information is updated at the medical institution, or may be executed periodically at predetermined intervals. .
  • the server 300 acquires the medical institution information table 362 described above from the medical institution terminal 400 (step S400). As described above, the above-described information includes a lot of information related to patient privacy. It is preferable to acquire the copy data of the information table 362 and delete the copy data after the processing is completed.
  • the server 300 may directly acquire the medical institution information table 362 or various information contained therein without going through the medical institution terminal 400 (more specifically, the server 300 may information is entered directly).
  • the server 300 uses techniques such as multivariate analysis on the treatment information obtained from a plurality of medical institutions in step S400 described above to determine remission (specifically, remission or not) for each type (item) of information. or not) is calculated (step S500). Furthermore, the server 300 generates an importance ranking table 364 that ranks information (items) arranged according to importance.
  • the timing of the processing in step S500 is not particularly limited, but may be performed each time treatment information is updated at a medical institution, or may be performed periodically at predetermined intervals. good too. Details of step S500 will be described later.
  • Server 300 receives the types (items) of information included in the top B% of importance ranking table 364 generated in step S500 described above, that is, information with high importance, from user terminal 200 in step S200 described above. It is determined whether it is included in the information (whether it is available) (step S600). If server 300 determines that it is available (step S600: Yes), the process proceeds to step S1000. On the other hand, when the server 300 determines that it is not available (step S600: No), it proceeds to the process of step S600.
  • the server 300 transmits to the user terminal 200 an instruction requesting the input of information (items) of high importance (step S700). Specifically, the server 300 requests information that has not been received from the user terminal 200 in step S200 among the information included in the top B% of the importance ranking table 364 . In this embodiment, by making such a request, the server 300 can acquire information with a high degree of importance, and therefore can recommend a more suitable medical institution to the user based on the acquired information. . In the present embodiment, it is assumed that the operator can appropriately set and change the upper range of information in the importance ranking table 364 in which information with a high degree of importance is to be placed.
  • the user terminal 200 receives the request from the server 300 (step S800). Further, based on the request, the user terminal 200 prompts the user to input the relevant information by using highlighted display, output of an alarm sound, or the like. Then, the user terminal 200 acquires the additional information input by the user and transmits it to the server 300 (step S900).
  • the server 300 determines a medical institution to recommend to the user based on various information acquired from the user terminal 200 in the steps executed so far, and transmits the determined information to the user terminal 200 (step S1000). Details of step S1000 will be described later. Then, the user terminal 200 displays the information (recommendation information) of the medical institution recommended to the user, received from the server 300, to the user (step S1100).
  • the server 300 sends the name of the actually received medical institution to the user via the user terminal 200 after a predetermined period of time has passed from step S1100, A questionnaire may be conducted asking about the degree of satisfaction with the treatment, the degree of recovery after the treatment is completed, and the like. At this time, the information obtained is accumulated in the server 300 as treatment performance information.
  • the information processing method (in detail, step S100 in FIG. 6) performed by the user terminal 200 according to the present embodiment will be described.
  • the information processing method according to this embodiment includes a plurality of steps from step S101 to step S104. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the user terminal 200 presents the contents of the inquiry and prompts the user to input (step S101). Further, the user terminal 200 presents a question about the patient's background information regarding the mental state as shown in FIG. 9 and prompts the user to input, as in step S101 described above (step S102). For example, the user can answer the question 600 by pressing a button 602 displayed simultaneously with the question 600 .
  • the user terminal 200 uploads biomarker data from the wearable device 100 (step S103). Then, the user terminal 200 transmits the information acquired in steps S101 to S103 described above to the server 300 (step S104).
  • the information processing method (Information processing method performed by the importance calculation unit 334 of the server 300 according to the present embodiment) Next, the information processing method (in detail, step S500 in FIG. 6) performed by the importance calculation unit 334 according to the present embodiment will be described with reference to FIGS. 10 and 11.
  • FIG. 10 the information processing method according to this embodiment includes a plurality of steps from step S501 to step S504. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the server 300 can more reliably acquire information that is strongly correlated (highly important) with remission from the user, and thus can recommend a more suitable medical institution to the user. can.
  • the server 300 collects treatment information of all medical institutions (patient treatment results (for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information on the doctor in charge, etc.) , biomarkers, background information (score), and interview information at the start of medical examination) are acquired (step S501).
  • patient treatment results for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information on the doctor in charge, etc.
  • biomarkers for example, information on whether or not the patient is in remission, treatment period until remission, treatment method, medication information, information on the doctor in charge, etc.
  • biomarkers background information
  • background information interview information at the start of medical examination
  • the server 300 sets, as an inference result, information on whether or not the patient's mental illness is in remission, among the treatment information acquired in step S501 (step S502).
  • the server 300 uses the information on each item other than the information on whether or not the patient is in remission as a variable, and uses a machine learning technique to calculate the importance of each item (step S503).
  • a machine learning technique For example, methods such as random forest, LightGBM, and the like can be used as methods for calculating the degree of importance.
  • the present embodiment is not limited to a random forest or the like, and is not particularly limited as long as it is a method that can perform supervised learning and calculate the importance of variables.
  • the importance calculation unit 334 of the server 300 is assumed to be a supervised learning device such as support vector regression or deep neural network. Then, for example, as shown in FIG. 11, an information group consisting of various biomarkers 1-a, 2-a, scores (background information) 1-a, 2-a, etc. of the patient a is stored in the importance calculation unit 334. , and information on whether or not patient a is in remission (remission a). Furthermore, in the same way, input information groups of multiple patients (biomarkers 1-n, 2-n, scores 1-n, 2-n, etc.) and information on whether each patient is in remission (remission n) . Then, the importance calculation unit 334 performs machine learning on the relationship between each piece of information in the information group and remission information according to a predetermined rule, and calculates the degree of correlation with remission, that is, the importance.
  • a supervised learning device such as support vector regression or deep neural network.
  • the server 300 Based on the importance calculated in step S503, the server 300 generates an importance ranking table 364 in which each piece of information (item) is arranged according to its importance (step S504).
  • FIG. 12 the information processing method according to this embodiment includes a plurality of steps from step S901 to step S906. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the server 300 determines the target medical institution in advance. For example, it may be all medical institutions, it may be limited to medical institutions located within a range that the user can visit, or conditions specified in advance by the user (for example, size, number of affiliated doctors, The target medical institutions may be limited according to the number of affiliated medical professionals, facilities, etc.).
  • the server 300 selects one medical institution from the target medical institutions (step S901).
  • the server 300 calculates the degree of similarity with the user regarding the interview information or background information for the patients in the data table of the target medical institution, and patients are extracted and their remission rate is calculated (step S902). Specifically, the server 300 uses patient interview information included in the treatment information of the medical institution selected in step S901 described above, for example, to generate a similarity index indicating how similar each patient is to the user's interview information. Calculate degrees. Furthermore, the server 300 selects M patients in descending order of similarity, and calculates the remission rate of the selected patients.
  • M persons can be arbitrarily set, for example, may be determined based on the degree of importance calculated by the importance degree calculation unit 334, or may be set in advance by the user.
  • similarity can be calculated using, for example, a difference in Euclidean distance, a cosine similarity, or the like, but is not particularly limited. For example, as shown in FIG. 13, assuming that M is 100, the remission rate is 10% if 10 of them are in remission, as shown in FIG.
  • step S902 may be performed for both the medical interview information and the background information, or the process of step S902 may be omitted.
  • the server 300 calculates the similarity with the user in terms of biomarkers for patients in the data table of the target medical institution, extracts L patients with high similarity, and calculates the remission rate. Calculate (step S903). Specifically, the server 300 uses the patient's biomarkers included in the treatment data of the medical institution selected in step S901 described above to calculate the degree of similarity that indicates how similar each patient is to the user's biomarkers. calculate. Furthermore, the server 300 selects L patients in descending order of similarity and calculates the remission rate of the selected patients.
  • the L persons can be arbitrarily set, and for example, may be determined based on the importance calculated by the importance calculation unit 334, or may be set in advance by the user.
  • the similarity can be calculated using, for example, a difference in Eugrid distance, a cosine similarity, or the like, but is not particularly limited. For example, as shown in FIG. 13, assuming that L is 100 and 90 of them are in remission, the remission rate is 90%.
  • the server 300 calculates the recommendation level of the medical institution selected in step S901 (step S904). For example, as shown in FIG. 13, when the degree of remission of three items (interview information, background information, and biomarker) is calculated, the average value thereof may be used as the degree of recommendation. Specifically, in the medical institution A shown in FIG. 13, the average value is 40%, so the recommendation level is set to 40. Note that in the present embodiment, the calculation of the recommendation level is not limited to the average value, and the average value may be calculated after weighting each item.
  • the server 300 determines whether the degrees of recommendation for all target medical institutions have been calculated (step S905). If the server 300 determines that the recommendation levels of all target medical institutions have been calculated (step S905: Yes), the process proceeds to step S906. On the other hand, when the server 300 determines that the recommendation levels of all target medical institutions have not been calculated (step S905: No), the process returns to step S901. In this embodiment, the recommendation degrees of all target medical institutions are calculated in this way.
  • the server 300 recommends medical institutions with a high degree of recommendation to the user (step S906). For example, in the example shown in FIG. 14, the server 300 recommends the medical institution B, which has the highest recommendation degree of 70, because the medical institution B has the highest recommendation degree.
  • biomarkers that can be easily and routinely detected, it is possible to recommend a medical institution suitable for treatment of mental illness to the user. can.
  • step S906 if a plurality of medical institutions have the same recommendation level, a plurality of medical institutions may be recommended, or the number of patients to be extracted (M person, L person, etc.) may be reset to a different value, and then the above-described information processing method may be performed again.
  • the information processing method performed by the recommendation degree calculation unit 336 is based on the similarity between all patients and users at all medical institutions, instead of calculating the remission rate for each medical institution.
  • a medical institution that has a track record of treating patients similar to the user may be recommended to the user. Therefore, the details of the modified example 1 of the embodiment of the present disclosure will be described below with reference to FIGS. 15 and 16.
  • FIG. FIG. 15 is a flowchart of the information processing method according to Modification 1 of the present embodiment
  • FIG. 16 is an explanation for explaining the information processing method in the recommendation level calculation unit 336 according to Modification 1 of the present embodiment. It is a diagram.
  • the information processing method according to Modification 1 includes a plurality of steps from step S911 to step S915. Details of each step included in the information processing method according to Modification 1 will be described below.
  • the server 300 acquires treatment information from all medical institutions, and based on the user's interview information, calculates the degree of similarity with the user regarding the interview information for patients in the data tables of all medical institutions. Further, the server 300 extracts M patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S911).
  • the server 300 acquires treatment information of all medical institutions, and based on the user's background information, calculates the degree of similarity with the user in terms of background information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts L patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S912).
  • the server 300 acquires the treatment information of all medical institutions, and based on the user's biomarkers, calculates the degree of similarity with the user in terms of biomarkers for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts A patients with a high degree of similarity, and calculates the consultation rate of the extracted patients for each medical institution (step S913).
  • the server 300 uses the consultation rates calculated in steps S911 to S913 described above to extract the medical institution with the highest consultation rate for each interview information, background information, and biomarker (step S914).
  • the server 300 recommends to the user the medical institution with the highest consultation rate among the medical institutions extracted in step S914 described above (step S915).
  • the consultation rate of medical institution B which has the highest consultation rate in the medical interview information items
  • the consultation rate of medical institution A which has the highest consultation rate in the biomarker items
  • the medical institution to be recommended may be determined according to a preset priority item. You may make it
  • the server 300 is preset to give priority to medical interview information, so it compares consultation rates in the medical interview information and selects medical institution B with the highest consultation rate (recommendation level). do.
  • FIG. 17 is a flowchart of the information processing method according to Modification 2 of the present embodiment
  • FIG. 18 is an explanation for explaining the information processing method in the recommendation degree calculation unit 336 according to Modification 2 of the present embodiment. It is a diagram.
  • the information processing method according to Modification 2 includes a plurality of steps from step S921 to step S925. Details of each step included in the information processing method according to Modification 2 will be described below.
  • the server 300 acquires treatment information from all medical institutions, and based on the user's interview information, calculates the degree of similarity with the user regarding the interview information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts M patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S921).
  • the server 300 acquires treatment information of all medical institutions, and based on the user's background information, calculates the degree of similarity with the user in terms of background information for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts L patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S922).
  • the server 300 acquires the treatment information of all medical institutions, and based on the user's biomarkers, calculates the degree of similarity with the user in terms of biomarkers for patients in the data tables of all medical institutions. Furthermore, the server 300 extracts A patients with a high degree of similarity, and calculates the remission rate of the extracted patients for each medical institution (step S923).
  • the server 300 calculates the average remission rate of each medical institution using the remission rates calculated in steps S921 to S923 described above, and uses the calculated average as the recommendation level (step S924). For example, in the example shown in FIG. 18, the average remission rate of medical institution A is 40%, and the average remission rate of medical institution A is 40%.
  • the server 300 recommends a medical institution with a high degree of recommendation to the user (step S925). For example, in the example shown in FIG. 18, the server 300 recommends the medical institution B, which has the highest recommendation degree of 60.
  • a medical institution that has a track record of remission for patients similar to the user can be recommended as a suitable medical institution for the user.
  • the medical institution is recommended to the user, but the present invention is not limited to this.
  • individual specialists may be recommended, or specialists are classified based on attribute information (sex, age, years of treatment experience, number of clinical trials, specialty, etc.), and the class may recommend a specialist included in to the user.
  • FIG. 19 is a block diagram showing an example of hardware configuration.
  • the server 300 will be described below as an example. The same explanation can be given for the user terminal 200 and the medical institution terminal 400 as well. Various types of processing by the server 300 are implemented by cooperation of software and hardware described below.
  • the server 300 has a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, and a host bus 904a.
  • the server 300 also has a bridge 904 , an external bus 904 b , an interface 905 , an input device 906 , an output device 907 , a storage device 908 , a drive 909 , a connection port 911 and a communication device 913 .
  • the server 300 may have a processing circuit such as a DSP (Digital Signal Processor) or an ASIC (Application Specific Integrated Circuit) in place of or in addition to the CPU 901 .
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • the CPU 901 functions as an arithmetic processing device and a control device, and controls overall operations within the server 300 according to various programs.
  • the CPU 901 may be a microprocessor.
  • the ROM 902 stores programs, calculation parameters, and the like used by the CPU 901 .
  • the RAM 903 temporarily stores programs used in the execution of the CPU 901, parameters that change as appropriate during the execution, and the like.
  • the CPU 901 can embody the processing unit 330 of the server 300, for example.
  • the CPU 901, ROM 902 and RAM 903 are interconnected by a host bus 904a including a CPU bus and the like.
  • the host bus 904a is connected via a bridge 904 to an external bus 904b such as a PCI (Peripheral Component Interconnect/Interface) bus.
  • PCI Peripheral Component Interconnect/Interface
  • host bus 904a, bridge 904 and external bus 904b need not necessarily have separate configurations from each other and may be implemented in a single configuration (eg, one bus).
  • the input device 906 is implemented by a device such as a mouse, keyboard, touch panel, button, microphone, switch, lever, etc., through which information is input by the practitioner.
  • the input device 906 may be, for example, a remote control device using infrared rays or other radio waves, or may be an external connection device such as a mobile phone or PDA (Personal Digital Assistant) compatible with the operation of the server 300.
  • the input device 906 may include, for example, an input control circuit that generates an input signal based on information input by the practitioner using the above input means and outputs the signal to the CPU 901 . By operating the input device 906, the practitioner can input various data to the server 300 and instruct processing operations.
  • the output device 907 is formed by a device capable of visually or audibly notifying the practitioner of the acquired information.
  • Such devices include display devices such as CRT (Cathode Ray Tube) display devices, liquid crystal display devices, plasma display devices, EL (Electro Luminescent) display devices and lamps, acoustic output devices such as speakers and headphones, and printer devices. etc.
  • the storage device 908 is a device for storing data.
  • the storage device 908 is realized by, for example, a magnetic storage device such as a HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like.
  • the storage device 908 may include a storage medium, a recording device that records data on the storage medium, a reading device that reads data from the storage medium, a deletion device that deletes data recorded on the storage medium, and the like.
  • the storage device 908 stores programs executed by the CPU 901, various data, and various data acquired from the outside.
  • the storage device 908 can embody the storage unit 360 of the server 300, for example.
  • the drive 909 is a reader/writer for storage media, and is either built into the server 300 or externally attached.
  • the drive 909 reads information recorded on a removable storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and outputs the information to the RAM 903 .
  • Drive 909 can also write information to a removable storage medium.
  • connection port 911 is an interface connected to an external device, and is a connection port with an external device capable of data transmission by, for example, USB (Universal Serial Bus).
  • USB Universal Serial Bus
  • the communication device 913 is, for example, a communication interface formed by a communication device or the like for connecting to the network 920 .
  • the communication device 913 is, for example, a communication card for wired or wireless LAN (Local Area Network), LTE (Long Term Evolution), Bluetooth (registered trademark), or WUSB (Wireless USB).
  • the communication device 913 may be a router for optical communication, a router for ADSL (Asymmetric Digital Subscriber Line), a modem for various types of communication, or the like.
  • the communication device 913 can transmit and receive signals to and from the Internet and other communication devices in accordance with a predetermined protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).
  • the communication device 913 can embody the communication unit 350 of the server 300, for example.
  • the network 920 is a wired or wireless transmission path for information transmitted from devices connected to the network 920 .
  • the network 920 may include a public network such as the Internet, a telephone network, a satellite communication network, various LANs (Local Area Networks) including Ethernet (registered trademark), WANs (Wide Area Networks), and the like.
  • Network 920 may also include a dedicated line network such as IP-VPN (Internet Protocol-Virtual Private Network).
  • the above-described embodiments of the present disclosure include, for example, an information processing method executed by an information processing apparatus or an information processing system as described above, a program for operating the information processing apparatus, and a program in which the program is recorded. may include non-transitory tangible media that have been processed. Also, the program may be distributed via a communication line (including wireless communication) such as the Internet.
  • each step in the information processing method according to the embodiment of the present disclosure described above does not necessarily have to be processed in the described order.
  • each step may be processed in an appropriately changed order.
  • each step may be partially processed in parallel or individually instead of being processed in chronological order.
  • the processing of each step does not necessarily have to be processed in accordance with the described method, and may be processed by another method by another functional unit, for example.
  • each component of each device illustrated is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.
  • the present technology can also take the following configuration.
  • the information processing device Acquiring sensing data obtained from a sensor attached to a part of a user's body; Extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients; Determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients; transmitting the determined information of the medical institution to an information processing terminal;
  • a method of processing information comprising: (2) The information processing method according to (1) above, wherein the sensor is a non-invasive sensor device.
  • the non-invasive sensor device is directly attached to a part of the user's body and measures the user's heart rate, pulse, blood flow, blood pressure, perspiration, electroencephalogram, respiration, respiration volume, myoelectric potential, skin temperature,
  • the information processing method according to (2) above wherein at least one of posture, motion state, number of steps, amount of activity, sleep state, sleep time, calorie consumption, facial expression, voice, and line of sight is detected.
  • the information on the treatment results includes information on whether or not the patient is in remission.
  • the information processing device calculating the degree of recommendation of each of the medical institutions based on the extracted information on the treatment results of the plurality of patients; recommending the medical institution with the high recommendation degree to the user;
  • the information processing device calculating a degree of similarity between the sensing data of each patient and the sensing data of the user; Extracting a predetermined number of the patients in descending order of the calculated similarity;
  • the information processing method according to (5) or (6) above comprising: (8) The information processing device Acquiring interview information about mental state from a user terminal used by the user; Acquiring the interview information and the treatment performance information of a plurality of patients from a plurality of medical institutions; calculating a degree of similarity indicating a degree of similarity between the interview information of each patient and the interview information of the user; Extracting a predetermined number of the patients in descending order of the calculated similarity;
  • the information processing method according to (7) above comprising: (9) The information processing device obtaining background information about mental state from a user terminal used by the user; Acquiring the background information and the treatment performance information of a plurality of patients from a plurality of medical institutions; calculating a degree of similarity indicating the degree of similarity between the background information of each patient
  • the information processing device Calculating the consultation rate of the medical institution in the plurality of extracted patients; Setting the calculated consultation rate as the recommendation degree;
  • the information processing method according to any one of (5) to (10) above, including (12) The information processing device Calculating the remission rate by each medical institution in the plurality of extracted patients; setting the calculated remission rate to the recommendation level;
  • the information processing method according to any one of (5) to (10) above, including (13) The information processing device From the plurality of medical institutions, Acquiring at least one of the sensing data, the interview information, and the background information of the plurality of patients; Acquiring information on whether or not the plurality of patients are in remission; Using the acquired information, for each piece of information, calculating the degree of importance indicating the degree of relevance to the information on whether or not remission has occurred;
  • the information processing device is requesting the user terminal to input information based on the calculated importance;
  • an acquisition unit that acquires sensing data obtained from a sensor attached to a part of a user's body, and information on the sensing data and treatment results of a plurality of patients;
  • the plurality of patients having the sensing data similar to the sensing data of the user are extracted from the plurality of patients, and based on the extracted treatment performance information of the plurality of patients, the treatment of the plurality of medical institutions is performed.
  • a recommendation unit that determines the medical institution to recommend to the user from among them;
  • An information processing device An information processing device.
  • the acquisition unit acquires at least one of interview information and background information of the plurality of patients from the plurality of medical institutions, and acquires information as to whether or not the plurality of patients are in remission.
  • the information processing device (10) to the computer, A function of acquiring sensing data obtained from a sensor attached to a part of the user's body; A function of extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients; a function of determining the medical institution to be recommended to the user from among a plurality of medical institutions based on the extracted information on the treatment performance of the plurality of patients; a function of transmitting information on the determined medical institution to an information processing terminal; The program that causes the to run.
  • An information processing system including a sensor attached to a part of a user's body, an information processing device, and an information processing terminal, The information processing device acquires sensing data obtained from a sensor attached to a part of a user's body, extracting the plurality of patients having the sensing data similar to the sensing data of the user from among the plurality of patients based on the sensing data and treatment performance information of the plurality of patients; determining a medical institution to recommend to the user from among a plurality of medical institutions based on the extracted information on the treatment results of the plurality of patients; transmitting the determined information of the medical institution to the information processing terminal; Information processing system.

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  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne un procédé de traitement d'informations, réalisé par un dispositif de traitement d'informations (300), consistant : à acquérir des données de détection obtenues d'un capteur (140) monté sur une partie du corps d'un utilisateur ; à extraire, parmi une pluralité de patients, une pluralité de patients ayant des données de détection similaires aux données de détection de l'utilisateur, sur la base de données de détection et d'informations de résultat de traitement de la pluralité de patients ; à déterminer, sur la base d'informations concernant des résultats de traitement de la pluralité de patients extraits, une institution médicale à recommander à l'utilisateur, parmi une pluralité d'institutions médicales ; et à transmettre, à un terminal de traitement d'informations (200), des informations concernant l'institution médicale déterminée.
PCT/JP2023/002369 2022-02-15 2023-01-26 Procédé de traitement d'informations, dispositif de traitement d'informations, programme, et système de traitement d'informations WO2023157596A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008146170A (ja) * 2006-12-06 2008-06-26 Fujifilm Corp 医療施設検索装置及び方法
JP6566409B1 (ja) * 2018-08-24 2019-08-28 株式会社鈴康 情報処理装置、プログラム及び情報処理方法
JP2020194346A (ja) * 2019-05-28 2020-12-03 キヤノンメディカルシステムズ株式会社 医療機関選定支援装置
JP2021068396A (ja) * 2019-10-28 2021-04-30 パナソニックＩｐマネジメント株式会社 生体情報管理システム
WO2021140731A1 (fr) * 2020-01-10 2021-07-15 オリンパス株式会社 Dispositif et procédé de transmission d'informations

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008146170A (ja) * 2006-12-06 2008-06-26 Fujifilm Corp 医療施設検索装置及び方法
JP6566409B1 (ja) * 2018-08-24 2019-08-28 株式会社鈴康 情報処理装置、プログラム及び情報処理方法
JP2020194346A (ja) * 2019-05-28 2020-12-03 キヤノンメディカルシステムズ株式会社 医療機関選定支援装置
JP2021068396A (ja) * 2019-10-28 2021-04-30 パナソニックＩｐマネジメント株式会社 生体情報管理システム
WO2021140731A1 (fr) * 2020-01-10 2021-07-15 オリンパス株式会社 Dispositif et procédé de transmission d'informations

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