CN-121983247-A - Psychoelastic dynamic image method for coupling chemotherapy physical sign and emotion entropy value

CN121983247ACN 121983247 ACN121983247 ACN 121983247ACN-121983247-A

Abstract

The invention relates to the technical field of medical information processing, and particularly discloses a psychoelastic dynamic image method for coupling chemotherapy physical signs with emotion entropy values, which comprises the steps of obtaining continuous time sequence physical sign data and emotion state data of a chemotherapy patient, calculating emotion entropy change rate, and constructing an individual emotion-physiology coupling dynamics model so as to predict the psychological state of the patient in real time; the method comprises the steps of introducing a psychological toughness probe micro-experiment mechanism, quantitatively evaluating the psychological adjustment capability of a patient to generate an immediate elastic recovery spectrum when the emotion-physiological resonance state is identified, combining a resonance state identification result and the immediate elastic recovery spectrum to generate a dynamic psychological elastic portrait, providing personalized nursing advice, optimizing model parameters by using feedback signals to ensure the long-term accuracy and effectiveness of a system, and realizing dynamic, accurate and personalized monitoring and support of the psychological elasticity of the patient and improving the initiative and timeliness of psychological intervention.

Inventors

WANG QING
YANG WEIJUAN
WANG LILI

Assignees

江苏省人民医院（南京医科大学第一附属医院）

Dates

Publication Date: 20260505
Application Date: 20260206

Claims (10)

1. A psychoelastic dynamic image method for coupling chemotherapy physical signs with emotion entropy values, which is characterized by comprising the following steps: s1, acquiring continuous time sequence sign data of a target patient in a chemotherapy process, synchronously acquiring time sequence emotion state data of the target patient, and calculating emotion entropy change rate data of the target patient based on the time sequence emotion state data; S2, constructing an individual emotion-physiology coupling dynamics model for representing a dynamic association relationship between physiological fluctuation and emotion stability of a target patient based on continuous time sequence sign data and emotion entropy change rate data; S3, inputting continuous time sequence sign data into an individualized emotion-physiology coupling dynamics model in real time, generating emotion state prediction data, and comparing the emotion state prediction data with emotion entropy change rate data acquired in real time to obtain prediction deviation data; S4, analyzing the time sequence characteristics of the predicted deviation data to identify whether a collaborative amplification mode related to the physiological or therapeutic rhythm exists, and generating a resonance state identification result comprising a resonance intensity index and a destabilization proximity index when the collaborative amplification mode is identified; S5, responding to the generation of the resonance state identification result, initiating a psychological toughness probe micro-experiment for probing the effectiveness of a specific psychological adjustment path to a target patient through terminal equipment, and collecting emotion state response data after the psychological toughness probe micro-experiment is executed; s6, analyzing the emotion state response data to quantify the dynamic characteristics of the emotion restoration process, and calculating an instant elastic restoration spectrum containing multiple psychology adjustment path efficacy scores; S7, coupling the resonance state identification result and the immediate elastic recovery spectrum to generate a dynamic psychological elastic portrait of the target patient; S8, updating parameters of the personalized emotion-physiology coupling dynamics model by using the resonance state identification result and the instantaneous elasticity recovery spectrum as feedback signals, and obtaining the optimized personalized emotion-physiology coupling dynamics model.
2. The method for psychoelastic dynamic representation of chemo-therapeutic sign-to-emotion entropy coupling of claim 1, wherein the step of calculating the rate of change of emotion entropy data of the target patient based on time-series emotion state data comprises: Periodically collecting emotion state self-evaluation data or voice data of a target patient through an embedded micro-interactive interface; carrying out emotion dimension analysis on emotion state self-evaluation data or voice data to generate a time sequence emotion state vector; calculating probability distribution of the time sequence emotion state vector in the sliding time window, and calculating time sequence emotion entropy based on the probability distribution; and performing time differential processing on the sequential emotion entropy to obtain emotion entropy change rate data of the target patient.
3. The method for psychoelastic dynamic imaging of chemo-therapeutic sign-to-emotion entropy coupling according to claim 1, wherein the specific step of constructing an individualized emotion-to-physiology coupling dynamics model for characterizing dynamic association between physiological fluctuation and emotion stability of a target patient comprises: Processing the continuous time sequence sign data into a system input vector, and processing the emotion entropy change rate data into a system output response; performing dynamic relation fitting on the system input vector and the system output response by adopting a system identification method to generate a group of preliminary coupling model parameters; And verifying and calibrating the parameters of the preliminary coupling model by using a historical data window, and when the prediction error of the model is lower than an error threshold value, confirming the parameters of the preliminary coupling model to form an individualized emotion-physiology coupling dynamics model.
4. The method for psychoelastic dynamic image coupling of chemo-therapeutic signs and emotion entropy according to claim 1, wherein the specific steps of generating emotion state prediction data and comparing the emotion state prediction data with emotion entropy change rate data acquired in real time to obtain prediction deviation data comprise: Constructing a real-time data buffer pool for storing continuous time sequence sign data and historical emotion entropy change rate data of the last N sampling periods, and performing time alignment and resampling processing on heterogeneous data sources; based on an autoregressive structure of the individualized emotion-physiology coupling dynamics model, performing recursive prediction operation by utilizing data in a real-time data buffer pool, and calculating an emotion state predicted value at the current moment; calculating an instantaneous difference value between the emotion state predicted value and emotion entropy change rate data acquired in real time, and smoothing the instantaneous difference value by adopting an exponential moving average algorithm to generate predicted deviation data.
5. A chemo-sign and emotion entropy coupled psycho-elastic dynamic representation method according to claim 1, wherein said analyzing the time-series characteristics of the predictive deviation data to identify if there is a co-amplification pattern associated with a physiological or therapeutic rhythm comprises: performing spectrum analysis on the predicted deviation data to obtain deviation spectrum distribution; extracting physiological rhythm signals from continuous time sequence physical sign data or acquiring external treatment period data to form rhythm reference signals; And calculating a coherence coefficient between the deviation spectrum distribution and the spectrum of the rhythm reference signal, and confirming the existence of the collaborative amplification mode when the coherence coefficient exceeds a coherence threshold value in a specific frequency band, the amplitude of the predicted deviation data shows a monotonically increasing trend, and the increasing rate exceeds a preset increasing rate threshold value.
6. The method for psychoelastic dynamic imaging of chemotherapy physical signs coupled with emotion entropy values according to claim 1, wherein the specific step of initiating a psycho-ductile probe micro-experiment for exploring the efficacy of a specific psycho-regulatory pathway to a target patient through a terminal device comprises: constructing a probe task library comprising a plurality of probe tasks, wherein each probe task is associated with one or more psychological regulation pathways; according to the characteristics of the resonance state identification result, matching and selecting a target probe task from a probe task library; And sending a guide instruction of the target probe task to the terminal equipment of the target patient, and monitoring task execution interaction data of the target patient to ensure effective completion of the task.
7. The psychoelastic dynamic image method of claim 6 wherein the chemo-therapeutic sign is coupled with the emotion entropy value, the method is characterized in that the specific steps of calculating the instantaneous elastic recovery spectrum containing a plurality of psychological regulation path efficacy scores comprise the following steps: drawing an emotion restoration curve based on the emotion state response data; Performing function fitting on the emotion restoration curve to extract a group of morphological characteristic parameters comprising a restoration time constant and a curve attenuation type; and mapping morphological characteristic parameters into performance scores of corresponding psychological regulation paths according to psychological regulation path types associated with the target probe task, and synthesizing all performance scores into an instant elastic recovery spectrum.
8. The method for dynamically representing psychoelastic coupled with entropy values of emotion as recited in claim 1, wherein the step of generating the dynamically psychoelastic representation of the target patient by coupling the resonance state identification result and the immediate elastic recovery spectrum comprises: fusing the resonance intensity index and the instability proximity index, and calculating to obtain a system vulnerability index; Analyzing the efficiency scores of all psychological adjustment paths from the instantaneous elastic recovery spectrum to form an adjustment strategy efficiency profile; the system vulnerability index and regulation strategy performance profile is integrated with the historical portrait data to output dynamic psychological elasticity portrait in a visual form comprising trend curve and multidimensional chart.
9. The method of psychoelastic dynamic imaging of a chemotherapeutic sign coupled with an emotional entropy value according to claim 8, further comprising, after generating the dynamic psychoelastic image of the target patient: constructing a hierarchical intervention strategy library containing different intervention levels; Matching and selecting a recommended intervention strategy from a hierarchical intervention strategy library according to the numerical value of the vulnerability index of the system; combining the recommended intervention strategy with the dynamic psychological elasticity portrayal, generating a personalized nursing suggestion report for assisting the medical staff in deciding and pushing the personalized nursing suggestion report to the medical staff terminal.
10. The method for psychoelastic dynamic imaging of chemo-therapeutic sign and emotion entropy coupling according to claim 1, wherein the specific step of updating parameters of the personalized emotion-physiology coupling dynamics model to obtain the optimized personalized emotion-physiology coupling dynamics model comprises: Marking a data segment corresponding to the resonance state identification result as a reinforcement learning sample; Weighting and fusing the efficacy scores in the instant elastic recovery spectrum, and calculating to obtain a reward signal serving as the feedback of the event; And adopting a reinforcement learning algorithm, and iteratively adjusting parameters of the personalized emotion-physiology coupling dynamics model by using reinforcement learning samples and reward signals to minimize prediction deviation under future similar situations, thereby obtaining an optimized personalized emotion-physiology coupling dynamics model.

Description

Psychoelastic dynamic image method for coupling chemotherapy physical sign and emotion entropy value Technical Field The invention relates to the technical field of medical information processing, in particular to a psychoelastic dynamic image method for coupling chemotherapy physical signs and emotion entropy values. Background Chemotherapy is one of the important means for treating malignant tumor, but is often accompanied by a series of physiological toxic side effects while killing tumor cells, and causes huge psychological stress on patients. Psychological elasticity, i.e. a good adaptation process when an individual is subjected to severe stress such as stress, trauma, tragedy, etc., is a key factor affecting the quality of life and therapeutic compliance of chemotherapy patients. Therefore, effective monitoring and support of psychoelasticity of patients with chemotherapy is an essential part of modern tumor care. Digital health technology, particularly methods for health data processing and diagnostic support using information communication technology, offer new possibilities for achieving this goal. Currently, psychological state monitoring for chemotherapy patients is clinically dependent mainly on regularly filled psychological assessment scales, such as the hospital anxiety depression scale HADS or the cancer patient quality of life core scale EORTC QLQ-C30. Some advanced digital health schemes attempt to collect physiological sign data such as heart rate, skin electricity, etc. through wearable devices, or collect daily emotional self-assessment of patients through mobile applications, and perform independent threshold alarms or trend analysis on these data in hopes of finding abnormal emotional fluctuations. However, the prior art approaches have significant technical drawbacks. Firstly, the evaluation mode based on the scale has low frequency and strong subjectivity, and is difficult to capture the instantaneous change of emotion, so that obvious hysteresis exists in monitoring. Secondly, the method of splitting physiological data and psychological data ignores complex interactions between body and mind, and cannot reveal deep coupling relation between emotion fluctuation and physiological stress response. In addition, the widely adopted fixed or group threshold alarm mechanism does not consider the huge individual difference among patients, so that the monitoring specificity and sensitivity are insufficient, and false alarm or false alarm is easy to generate. Disclosure of Invention In view of this, in order to solve the above-mentioned problems in the background art, a psychoelastic dynamic image method of coupling chemotherapy physical signs with emotion entropy values is now proposed. The invention provides a psychological elasticity dynamic image method for coupling chemotherapy physical signs and emotion entropy values, which comprises the following steps of S1, acquiring continuous time sequence physical sign data of a target patient in a chemotherapy process, synchronously acquiring time sequence emotion state data of the target patient, and calculating emotion entropy change rate data of the target patient based on the time sequence emotion state data. S2, constructing an individual emotion-physiology coupling dynamics model for representing the dynamic association relationship between the physiological fluctuation and the emotion stability of the target patient based on the continuous time sequence sign data and the emotion entropy change rate data. S3, inputting continuous time sequence sign data into the individualized emotion-physiology coupling dynamics model in real time, generating emotion state prediction data, and comparing the emotion state prediction data with emotion entropy change rate data acquired in real time to obtain prediction deviation data. S4, analyzing the time sequence characteristics of the predicted deviation data to identify whether a collaborative amplification mode related to the physiological or therapeutic rhythm exists, and generating a resonance state identification result comprising a resonance intensity index and a destabilization proximity index when the collaborative amplification mode is identified. S5, responding to the generation of the resonance state identification result, initiating a psychological toughness probe micro-experiment for exploring the effectiveness of a specific psychological adjustment path to a target patient through terminal equipment, and collecting emotion state response data after the psychological toughness probe micro-experiment is executed. S6, analyzing the emotion state response data to quantify the dynamic characteristics of the emotion restoration process, and calculating an immediate elastic restoration spectrum containing multiple psychology adjustment path efficacy scores. S7, coupling the resonance state identification result and the immediate elastic recovery spectrum to generate a dynamic psychological elasti