Postpartum depression monitoring method and system
By constructing a postpartum depression monitoring system that integrates multi-dimensional data collection and tiered intervention, the system addresses the screening bias and data security issues caused by reliance on subjective patient responses in existing technologies. This enables accurate screening and personalized treatment of postpartum depression, while also improving data security and management standardization.
Patent Information
- Application Number
- CN202511293512.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-11
- Publication Date
- 2025-12-30
AI Technical Summary
Existing postpartum depression screening systems rely on patients' subjective responses, which are insufficient in terms of accuracy and timeliness. They also lack systematic management and data security, and do not fully consider the influence of cultural background and education level, leading to biased assessment results.
By integrating multi-dimensional data collection, tiered intervention, and intelligent analysis, a closed-loop management system is constructed, encompassing online questionnaire assessment, personal information file creation, health status grading, clinical intervention plan generation, and follow-up feedback. Personalized video and article educational content is provided, and distributed storage and hierarchical access control are employed to ensure data security.
It has improved the accuracy and timeliness of postpartum depression screening, ensured timely intervention for critically ill patients, provided personalized treatment plans, enhanced data security and management standardization, and supported regional epidemiological analysis.
Smart Images
Figure CN121237402A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical monitoring technology, and in particular to a method and system for monitoring postpartum depression. Background Technology
[0002] Postpartum depression (PPD), a common perinatal mental disorder, has a global incidence rate of 10%-20%, with the latest data in my country showing an incidence rate of 14.7%-30%. Traditional screening mainly relies on paper-based questionnaires (such as the EPDS scale) and outpatient consultations, resulting in high missed diagnosis rates (detection rate less than 40%) and poor timeliness. With the development of digital healthcare, mobile-based intelligent screening systems have emerged since 2015, combining biomarker detection (such as the magnitude of estrogen drop and oxytocin receptor gene polymorphism) to improve predictive accuracy. A 2025 WHO multinational cohort study confirms the need to develop localized assessment tools in collectivist cultural contexts. Existing technological solutions can be divided into three categories: 1. Electronic scale system: such as the Chinese version of the EPDS mobile application developed by the Peking University team, which has been verified by 138 hospitals and has increased the detection rate by 40%.
[0003] 2. Biosignal monitoring system: South Korean smart bracelets predict mood fluctuations through skin conductance response (accuracy rate 79%), and Canadian AI psychological companion robots achieve a 67% relief rate for mild symptoms.
[0004] 3. Comprehensive management platform: such as the "Prenatal and Postnatal Health Management System", but it lacks real-time data collection function.
[0005] While electronic questionnaire systems, biosignal monitoring systems, and integrated management platforms are widely used in related technologies, they all have significant drawbacks. Essentially, they remain screening tools reliant on subjective patient completion. This makes it difficult to guarantee the accuracy and timeliness of questionnaire completion; some mothers may conceal their true psychological state due to privacy concerns or a lack of willingness to express themselves, leading to biased initial assessment results. Furthermore, traditional questionnaires are often generic versions that fail to adequately consider the impact of different cultural backgrounds, education levels, and family environments on the mother's psychological state, resulting in insufficient localization and further reducing the accuracy of the assessment. Summary of the Invention
[0006] The purpose of this invention is to provide a postpartum depression monitoring method and system that can effectively solve the problems of existing technologies, such as reliance on patient subjective completion, lack of systematic management, and insufficient data security. By integrating functions such as multi-dimensional data collection, graded intervention, intelligent analysis, and security management, a closed-loop management system covering the entire process of screening, assessment, intervention, and follow-up is constructed.
[0007] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for monitoring postpartum depression, comprising the following steps: Obtain assessment data from online questionnaires for postpartum women; Integrate personal information from online questionnaires into a database system to establish personal information profiles; A preliminary assessment of the mothers was conducted based on the evaluation data, and the questionnaire assessment results were obtained. Based on the personal information files and the questionnaire assessment results, the mothers' health status was classified. For target pregnant women classified as severe cases, a clinical intervention plan is generated and sent to the responsible physician, and test and examination reports during the treatment process are recorded; Provide educational videos and articles related to postpartum depression for target mothers to access; Postpartum follow-up was conducted on the target mothers after treatment to obtain follow-up results on the intervention effect. The follow-up results were correlated with questionnaire assessment data and clinical intervention plan to output optimized intervention strategies.
[0008] Optionally, obtaining the mother's assessment data on the online questionnaire includes: The Edinburgh Postpartum Depression Scale was used to collect information on the psychological state of postpartum women, and the test scores were received and stored in real time.
[0009] Optionally, the integration of personal information from online questionnaires into a database system to establish personal information profiles includes: By exporting the personal information form in Excel format generated by the online questionnaire platform, the personal information is batch entered into the database system through the data import interface, and the personal information is automatically associated with the mother's unique identifier to establish a personal information file.
[0010] Optionally, the process of classifying the health status of postpartum women based on personal information files and questionnaire assessment results includes: Set up multi-level scoring thresholds. When the questionnaire score exceeds the preset critical illness threshold, the mother will be marked as a critical patient and a high-risk warning will be triggered.
[0011] Optionally, generating a clinical intervention plan and sending it to the attending physician includes: Based on the target pregnant woman's age, gestational age, medical history, and questionnaire scores, a pre-set intervention strategy database is accessed to generate a clinical intervention list that includes psychological counseling suggestions and drug treatment reference plans. This list is then sent to the responsible physician's mobile and PC backend via a push notification module.
[0012] Optionally, the test and examination reports recorded during the treatment process include: A report upload interface is provided for doctors to upload or for the database system to retrieve biomarker test results from the hospital's laboratory system, and to store them in a timeline-linked manner with the clinical intervention plan; the biomarkers include serum corticotropin-releasing hormone and serotonin.
[0013] Optionally, the provision of educational content related to postpartum depression, including videos and articles, includes: The video resource library is categorized and organized, sorted by play count and update time, and the access history of the target mothers is recorded; the video resource library covers emotion regulation techniques, family support methods, and treatment case sharing.
[0014] Optionally, the postpartum follow-up of the target mothers after treatment includes: The system sets follow-up time points and automatically generates follow-up reminders at certain postpartum time periods. It records the feedback information of the target mothers through a database system. The feedback information includes emotional state, sleep quality, and recovery of social function.
[0015] Optionally, the method further includes: distributing and localizing sensitive data within the database system, and establishing a hierarchical access permission system for the database system.
[0016] Secondly, embodiments of the present invention provide a postpartum depression monitoring system, the system comprising: The online questionnaire module is used to obtain evaluation data from mothers on the online questionnaire; The personal information maintenance and management module is used to integrate personal information from online questionnaires into the database system and establish personal information files; The questionnaire assessment management module is used to conduct a preliminary assessment of postpartum women based on the assessment data and obtain the questionnaire assessment results; based on personal information files and questionnaire assessment results, the postpartum women's health status is classified. The health intervention module is used to generate clinical intervention plans for target pregnant women classified as critically ill and push them to the responsible doctor, and record test and examination reports during the treatment process; The video and article education module is used to provide video and article education content related to postpartum depression for target mothers to access; The postpartum follow-up module is used to conduct postpartum follow-ups on target mothers after treatment, obtain follow-up results on the intervention effect, and perform correlation analysis with the follow-up results, questionnaire assessment data, and clinical intervention plan to output optimized intervention strategies.
[0017] The beneficial effects of this invention are as follows: This invention discloses a method and system for monitoring postpartum depression. Firstly, it achieves preliminary assessment through online questionnaires, simultaneously establishing personal files containing individual information to provide a data foundation for subsequent health status grading. The health status grading mechanism can accurately identify severely ill patients, ensuring that high-risk groups receive timely clinical intervention, while the generation and delivery of intervention plans enables the targeted allocation of medical resources. The recording of test and examination reports and the follow-up feedback mechanism during treatment form a complete chain for tracking treatment effectiveness. By correlating initial assessment data, intervention plans, and follow-up results, intervention strategies can be continuously optimized to improve treatment effectiveness. Simultaneously, the educational content provided by the system meets the self-learning needs of postpartum women, distributed storage and localized processing ensure the security of sensitive data, hierarchical access control further strengthens the standardization of data management, and the regionalized epidemiological analysis function can provide data support for public health decision-making, achieving an organic combination of individual health management and group disease prevention and control. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A flowchart of a postpartum depression monitoring method according to an embodiment of the present invention is shown.
[0020] Figure 2 A schematic diagram of the postpartum depression monitoring system in an embodiment of the present invention is shown.
[0021] Figure 3 A schematic diagram of the online questionnaire module in an embodiment of the present invention is shown.
[0022] Figure 4 A schematic diagram of a postpartum depression monitoring system according to an embodiment of the present invention is shown.
[0023] Figure 5 A schematic diagram of the personal information maintenance and management module in an embodiment of the present invention is shown.
[0024] Figure 6 A schematic diagram of the questionnaire assessment management module in an embodiment of the present invention is shown.
[0025] Figure 7 A schematic diagram of the health intervention management module in an embodiment of the present invention is shown.
[0026] Figure 8A schematic diagram of the education module in an embodiment of the present invention is shown.
[0027] Figure 9 A schematic diagram of the postpartum follow-up module in an embodiment of the present invention is shown. Detailed Implementation
[0028] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.
[0029] The related technologies have the following limitations: Limitations of electronic scale systems: Highly subjective dependence: Although mobile applications have increased the detection rate by 40%, they still rely on the subjective information provided by the mother and are easily affected by social expectation bias (such as deliberately concealing negative emotions).
[0030] The accuracy of information provided by people with lower levels of education or language barriers was significantly reduced, and the rate of missed diagnoses in rural western regions was 8.3 percentage points higher than that in eastern regions.
[0031] The scales require regular, proactive completion and cannot capture sudden mood swings. For example, the 3-7 days postpartum period is a high-risk window for depression, but traditional scales typically only screen for it around 6 weeks postpartum.
[0032] Limitations of biosignal monitoring technology: Low reliability of single signals: Korean smart bracelets predict emotions through skin conductance (79% accuracy), but interference such as changes in body surface humidity during breastfeeding and the act of holding the baby can easily lead to misjudgment.
[0033] Lack of application of biomarkers: Abnormal serum corticotropin-releasing hormone to serotonin ratio has been shown to be strongly correlated with PPD (sensitivity 92%), but it has not yet been included in clinical monitoring systems.
[0034] Systemic flaws of the integrated management platform: Data silo problem: Psychological assessment, physiological data (body temperature / blood pressure), and medical care collaboration modules are independent of each other, and a multimodal data correlation analysis mechanism has not been established.
[0035] Intervention and risk mismatch: Interventions are not matched to biomarkers (such as cortisol levels) or clinical symptom grading, and patients with mild or severe illness receive the same standardized recommendations.
[0036] To address the limitations of the three types of systems mentioned above, this invention provides a postpartum depression monitoring method and system that integrates postpartum mental health education, psychological assessment, and clinical intervention modules. It emphasizes user experience and functional practicality, ensuring that postpartum women can easily access mental health services. The postpartum depression monitoring module includes a questionnaire assessment module, a clinical intervention module, and a post-intervention follow-up module. Clinicians can assess postpartum women based on their questionnaire scores. For patients with severe postpartum depression, clinical intervention treatment can be provided, followed by follow-up to understand the effectiveness of the clinical intervention. From questionnaire assessment to clinical intervention and post-intervention follow-up, a closed-loop management system is formed, providing postpartum women with a new mental health service platform that can effectively alleviate psychological stress and improve self-adjustment abilities.
[0037] See Figure 1 This invention provides a method for monitoring postpartum depression, comprising: S100, obtain the evaluation data of the mothers on the online questionnaire; S200 integrates personal information from online questionnaires into a database system to establish personal information profiles; S300 conducts a preliminary assessment of postpartum women based on evaluation data and obtains questionnaire assessment results; based on personal information files and questionnaire assessment results, postpartum women's health status is classified. S400 generates clinical intervention plans for target pregnant women classified as critically ill and pushes them to the attending physician, while also recording test and examination reports during the treatment process; S500 provides educational videos and articles related to postpartum depression for target mothers to access; S600 involves postpartum follow-up of target mothers after treatment to obtain follow-up results on the intervention effect. The follow-up results are then correlated with questionnaire assessment data and clinical intervention plans to output optimized intervention strategies.
[0038] To address the issues of screening bias caused by reliance on subjective patient responses and the lack of dynamic monitoring and personalized intervention in existing technologies, this method integrates multi-dimensional data collection, intelligent hierarchical management, closed-loop intervention tracking, and data security mechanisms to construct a comprehensive management system from risk screening to intervention optimization. Specifically, the data collection phase includes not only traditional online questionnaires but also the establishment of personal information profiles for data integration, laying the foundation for subsequent analysis. The health status grading process utilizes multi-source data for quantitative assessment, ensuring the scientific validity and accuracy of the grading results. The generation and delivery of clinical intervention plans for critically ill patients enables precise allocation of medical resources. Data recording and follow-up feedback mechanisms during treatment form a complete intervention loop, facilitating timely strategy adjustments. The provision of educational content meets the self-learning needs of postpartum women, enhancing their health management awareness. Simultaneously, through distributed storage, localized processing, and hierarchical access control, regional epidemiological research is supported while ensuring data security, providing data support for public health decision-making.
[0039] In some embodiments, obtaining the mother's assessment data on the online questionnaire includes: The Edinburgh Postpartum Depression Scale was used to collect information on the psychological state of postpartum women, and the test scores were received and stored in real time.
[0040] In this embodiment, the Edinburgh Postnatal Depression Scale (EPDS) is used as an internationally recognized postpartum depression screening tool. It has 10 items covering core symptoms such as low mood, anxiety, and insomnia. The total score ranges from 0 to 30 points, with a score ≥13 indicating a risk of depression. The system integrates the EPDS scale as a core module of the online questionnaire. Mothers can access it through multiple terminals, including WeChat mini-programs and hospital HIS system portals. An automatic progress saving function is set during the completion process, supporting completion in different time slots to avoid data loss due to fatigue or interruption. After completion, the system immediately calculates the scale score and synchronizes it to the database, while generating a visual assessment report. The report uses a radar chart to intuitively display the scores for each dimension, helping mothers quickly understand their psychological state. For mothers with a score ≥13, the system automatically triggers an orange alert and pushes it to the corresponding obstetrician's workstation; for high-risk mothers with a score ≥15, a red alert is triggered, simultaneously notifying the obstetrics director and the psychological intervention team to ensure that high-risk individuals receive priority attention.
[0041] In some embodiments, integrating the personal information from the online questionnaire into a database system to establish a personal information profile includes: By exporting the personal information form in Excel format generated by the online questionnaire platform, the personal information is batch entered into the database system through the data import interface, and the personal information is automatically associated with the mother's unique identifier to establish a personal information file.
[0042] In this embodiment, a preset data import interface supports seamless integration with the Hospital Information System (HIS), automatically capturing the mother's basic information (age, gestational age, delivery method, etc.), past medical history (such as gestational hypertension, thyroid dysfunction, etc.), and obstetric examination data (such as fetal heart rate monitoring results, postpartum hemorrhage, etc.), avoiding duplicate data entry. The personal information file adopts a distributed storage architecture, encrypting and storing sensitive information (such as ID number and contact information) on the local server, while non-sensitive information (such as gestational age and questionnaire scores) is synchronized to the cloud database, achieving hierarchical data management. The system assigns a unique identification code (ID) to each mother, linking questionnaire assessment data, clinical intervention records, and follow-up information through this ID to form a complete personal health data chain. Simultaneously, the database supports multi-dimensional retrieval by timeline, data type, and other dimensions, allowing doctors to quickly access the mother's full-cycle health records via ID, providing data support for hierarchical assessment.
[0043] In some embodiments, classifying the health status of postpartum women based on personal information profiles and questionnaire assessment results includes: Set up multi-level scoring thresholds. When the questionnaire score exceeds the preset critical illness threshold, the mother will be marked as a critical patient and a high-risk warning will be triggered.
[0044] In this embodiment, a three-dimensional grading model is constructed by combining the Edinburgh Postpartum Depression Scale score with key indicators in the personal information file. Specifically, it includes a basic threshold layer, a dynamic adjustment layer, and a clinical correction layer. The basic threshold layer sets three scoring standards: a score <10 indicates low risk, 10-14 indicates medium risk, and ≥15 indicates the severe risk threshold. The dynamic adjustment layer introduces a correction coefficient; for example, the correction coefficient is 1.2 for mothers who complete the questionnaire within 72 hours postpartum (due to significant mood fluctuations during this period), and 1.3 for mothers with a family history of depression. The final grading score is calculated using the formula: Corrected score = Original score × Correction coefficient. The clinical correction layer allows doctors to manually adjust the risk level upwards based on actual symptoms (such as positive signs like self-harm tendencies or hallucinations). When the corrected score exceeds the critical illness threshold, the system immediately marks the patient as a "critical patient" in the database, displays a red highlight warning on the hospital's intranet system, and sends a high-risk warning message to the responsible doctor via SMS and APP push, which includes the mother's ID, corrected score, and key risk factors (such as family history and recent major life events). The warning message also includes a quick access link to view the complete personal file with one click.
[0045] In some embodiments, generating a clinical intervention plan and sending it to the attending physician includes: Based on the target pregnant woman's age, gestational age, medical history, and questionnaire scores, a pre-set intervention strategy database is accessed to generate a clinical intervention list that includes psychological counseling suggestions and drug treatment reference plans. This list is then sent to the responsible physician's mobile and PC backend via a push notification module.
[0046] In this embodiment, a clinical intervention protocol generation engine is constructed, comprising a basic protocol library and dynamic matching rules. The basic protocol library is divided into a psychological intervention sub-library (such as structured courses of cognitive behavioral therapy (CBT) and mindfulness meditation audio resources), a drug intervention sub-library (such as starting doses and contraindications of SSRI drugs such as sertraline and fluvoxamine), and a physical intervention sub-library (such as treatment parameter recommendations for repetitive transcranial magnetic stimulation (rTMS)). The dynamic matching rules perform multi-condition screening based on the maternal health status classification results, key variables in the personal information file (such as whether the mother is breastfeeding—paroxetine is contraindicated during lactation, liver and kidney function indicators—adjusting drug metabolism dosage), and core symptoms in the questionnaire assessment results (such as prioritizing sedative antidepressants for those with insomnia as the chief complaint). For example, for a critically ill postpartum woman aged 28, 10 days postpartum, with a revised EPDS score of 18, no history of drug allergies, and not breastfeeding, the system retrieves a regimen from the drug sub-database of "sertraline 50mg / day starting, increasing by 25mg weekly to 100mg maintenance" and matches it from the psychological intervention sub-database of "twice-weekly CBT remote consultation + 15 minutes of breathing relaxation training daily," automatically excluding drug options that may affect milk secretion. The generated clinical intervention list is presented in a structured document format, including the intervention name, implementation frequency, precautions, and expected effect evaluation indicators (such as a ≥30% reduction in EPDS score after 2 weeks). It is pushed to the responsible physician's mobile terminal via an internal encrypted communication protocol and forms a pending task list on the PC backend. The physician can edit and modify the regimen online, and the system automatically records the modification history and synchronizes it to the database.
[0047] In some embodiments, the test and examination reports recorded during the treatment process include: A report upload interface is provided for doctors to upload or for the database system to retrieve biomarker test results from the hospital's laboratory system, and to store them in a timeline-linked manner with the clinical intervention plan; the biomarkers include serum corticotropin-releasing hormone and serotonin.
[0048] In this embodiment, a standardized data interface protocol is developed to achieve real-time data interaction with the hospital's laboratory LIS system. This automatically retrieves serum corticotropin-releasing hormone (CRH) and serotonin (5-HT) biomarkers, as well as routine blood tests and liver and kidney function tests, from the parturient during treatment. The system stores this data categorized by sampling time and test item, and uses a timeline visualization function to link it to implementation nodes of the clinical intervention plan (such as medication adjustment time). For example, after the parturient has been taking sertraline for two weeks, the system automatically retrieves the 5-HT concentration test results from the same period, generating a comparison curve with the baseline data before intervention to assist doctors in assessing the drug's efficacy. For abnormal biomarker test values (such as persistently elevated CRH levels), the system triggers a yellow alert, suggesting that doctors adjust the intervention plan based on clinical symptoms. Simultaneously, all test reports are stored encrypted in PDF format, supporting online viewing, downloading, and electronic signature confirmation by doctors, ensuring the traceability of the treatment process.
[0049] In some embodiments, providing educational content related to postpartum depression through videos and articles includes: The video resource library is categorized and organized, sorted by play count and update time, and the access history of the target mothers is recorded; the video resource library covers emotion regulation techniques, family support methods, and treatment case sharing.
[0050] In this embodiment, a video resource library with a three-level classification system is constructed. The first-level classification consists of three modules: "Basic Cognition," "Skills Training," and "Case Reference." The second-level classification further subdivides "Skills Training" into sub-items such as "Emotion Regulation," "Family Communication," and "Stress Management." The third-level classification intelligently recommends content based on the postpartum woman's health status classification, targeting specific scenarios. For example, videos on "How to Prevent and Treat Postpartum Depression" (the top 5 most-viewed resources) are prioritized for postpartum women at medium risk. Simultaneously, a user behavior analysis module records data such as postpartum women's access duration, completion rate, and repeated viewing segments. When the weekly increase in access to a certain type of content (such as "Family Support Methods") exceeds 30%, a special recommendation section is automatically added to the homepage. The article education content adopts a dual mode of "text + audio reading," providing an "audiobook" function for users with lower levels of education, and includes interactive buttons such as "one-click collection of key content" and "marking questions for consultation." Collected content is synchronized to personal information files, facilitating targeted answers from doctors during follow-up visits.
[0051] In some embodiments, postpartum follow-up of the target mothers after treatment includes: The system sets follow-up time points and automatically generates follow-up reminders at certain postpartum time periods. It records the feedback information of the target mothers through a database system. The feedback information includes emotional state, sleep quality, and recovery of social function.
[0052] In this embodiment, a three-tiered follow-up timeline is established: short-term follow-up (2 weeks after intervention), mid-term follow-up (1 month after intervention), and long-term follow-up (3 months after intervention). The system automatically calculates the time for each tier based on the mother's discharge date and sends follow-up reminders 3 days in advance via SMS and app push notifications. The reminders include the core focus dimensions of this follow-up (e.g., short-term follow-up focuses on assessing emotional state fluctuations, while long-term follow-up focuses on the recovery of social function). Follow-up data collection uses a combination of scales and structured questions. Short-term follow-up uses a simplified version of the EPDS scale (containing 5 core items), while mid-term and long-term follow-up include key questions from the Pittsburgh Sleep Quality Index (PSQI) and the Social Disability Screening Scale (SDSS). Mothers can quickly provide feedback via voice input or by selecting preset options. The system compares the feedback information with baseline data before intervention and changes in biomarkers during treatment in real time. For example, when the follow-up shows a PSQI score decrease of ≥4 points compared to before intervention, a preliminary assessment conclusion of "effective sleep improvement" is automatically generated. For postpartum women who fail to complete follow-up visits within the stipulated time, the system activates a tiered reminder mechanism: a gentle reminder is sent if the first delay is 3 days, a follow-up call is made by the responsible nurse if the delay is 7 days, and the delay is marked as "follow-up out of contact" and included in the hospital's priority care list if the delay is 14 days. All follow-up data is integrated into the individual's profile in a timeline format, allowing doctors to visually view the dynamic changes in the intervention's effectiveness through data dashboards.
[0053] In some embodiments, the method further includes: performing distributed storage and localization processing on sensitive data within the database system, and establishing a hierarchical access permission system for the database system.
[0054] Specifically, blockchain technology is used to encrypt and store sensitive data such as the mother's ID number and medical records. A copy of the core data is stored on a local hospital server, and only anonymized statistical data is uploaded to the regional management platform. In this embodiment, the distributed ledger characteristic of blockchain enables decentralized storage of sensitive data. Each data node is independently backed up and cannot be tampered with, avoiding the risk of data leakage due to single point of failure. The localized processing stage utilizes edge computing nodes to restrict the analysis and computation of core data such as personal biometric information and medical records to the hospital's internal network environment. Only anonymized statistical indicators (such as the incidence of postpartum depression and intervention effectiveness within the region) are uploaded to the regional public health platform. Hierarchical access control adopts a role-based access control (RBAC) model, dividing user permissions into four levels: system administrator, clinician, researcher, and the mother herself. System administrators have data architecture configuration permissions, clinicians can only access the complete files of the mothers they are responsible for, researchers can only retrieve anonymized summary data, and the mother herself can view her personal health records but cannot modify them. Meanwhile, all data access operations generate audit logs, recording the access time, operation content, and user identity. These logs are encrypted and cannot be deleted, ensuring full traceability of data flow. For cross-institutional data sharing scenarios, federated learning technology is employed to complete model training and parameter updates without requiring data to leave the local machines of each hospital, enabling collaborative research while protecting data privacy.
[0055] See Figure 2 This invention provides a postpartum depression monitoring system, comprising: The online questionnaire module is used to obtain evaluation data from mothers on the online questionnaire; The personal information maintenance and management module is used to integrate personal information from online questionnaires into the database system and establish personal information files; The questionnaire assessment management module is used to conduct a preliminary assessment of postpartum women based on the assessment data and obtain the questionnaire assessment results; based on personal information files and questionnaire assessment results, the postpartum women's health status is classified. The health intervention module is used to generate clinical intervention plans for target pregnant women classified as critically ill and push them to the responsible doctor, and record test and examination reports during the treatment process; The video and article education module is used to provide video and article education content related to postpartum depression for target mothers to access; The postpartum follow-up module is used to conduct postpartum follow-ups on target mothers after treatment, obtain follow-up results on the intervention effect, and perform correlation analysis with the follow-up results, questionnaire assessment data, and clinical intervention plan to output optimized intervention strategies.
[0056] Specifically, the postpartum depression monitoring system consists of two main modules: an online questionnaire module and a postpartum depression monitoring module.
[0057] refer to Figure 3 The online questionnaire module is mainly used for postpartum women to scan online questionnaire scores (the assessment form is the Edinburgh Postpartum Depression Scale), which is a preliminary assessment of postpartum women.
[0058] refer to Figure 4 The overall system functionality includes a postpartum depression monitoring module comprising a personal information maintenance and management module, a questionnaire assessment and management module, a health intervention module (clinical intervention), a video and article education module, and a postpartum follow-up module.
[0059] refer to Figure 5 The personal information maintenance and management module is used for entering and modifying maternal information, as well as importing information from online questionnaires.
[0060] refer to Figure 6 The questionnaire assessment management module allows users to take the Edinburgh Postnatal Depression Scale and query their scores within the system.
[0061] refer to Figure 7 The health intervention management module is mainly used for critically ill patients with high questionnaire scores, allowing doctors to conduct clinical intervention treatments and view relevant test and examination reports.
[0062] refer to Figure 8 The education module allows pregnant and postpartum women to access videos and articles about postpartum depression treatment, providing them with insights into mental health.
[0063] refer to Figure 9 The postpartum follow-up module is mainly used to follow up with critically ill patients after clinical intervention and record the effects of treatment.
[0064] Compared with related technologies, the present invention has the following advantages: Questionnaire Information Integration: Traditional screening mainly relies on paper forms or online questionnaires, often resulting in pregnant women receiving no follow-up or being unable to receive further treatment after completing the questionnaires. This invention integrates online questionnaire participant information. Participant information can be imported into the system.
[0065] Achieving closed-loop management of patients: The postpartum depression monitoring module combines questionnaire information and further processes the questionnaire scores to prevent critically ill patients from being lost or not receiving timely treatment.
[0066] Clinical intervention mechanism: This invention automatically switches intervention strategies based on the user's status (prenatal / postnatal), providing personalized emotional support, clinical intervention, and emergency suicide risk warning functions.
[0067] Integrated clinical treatment system: Allows users to view patient treatment plans, medication status, and test and examination results.
[0068] It employs distributed storage to enable localized processing of sensitive data while supporting regionalized epidemiological analysis. It also implements anonymized data aggregation and hierarchical access control.
[0069] Clinical Collaboration Network: Supports the automatic push of screening results to the responsible physician.
[0070] Achieving closed-loop management of patients: After conducting a questionnaire survey, intelligent classification is carried out based on the scores, and psychological counseling and appropriate clinical intervention treatment are provided for critically ill patients.
[0071] Online video and article education: Relevant videos and articles on postpartum depression have been added for patients to browse.
[0072] Data security: The system manages backend data with different access permissions, and physically separates the local area network (LAN) from the Internet. LAN users need administrator permission to access the data.
[0073] The above is a detailed description of the preferred embodiments of this disclosure. However, this disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this disclosure. All such equivalent modifications or substitutions are included within the scope defined by the claims of this disclosure.
Claims
1. A method of monitoring postpartum depression, the method comprising: The method comprises the following steps: Obtaining evaluation data of the questionnaire of the parturient woman; Integrating personal information of the online questionnaire into a database system to establish a personal information file; Based on the personal information file and the questionnaire evaluation result, the health status of the parturient woman is graded; For the target parturient woman classified as a serious patient, a clinical intervention plan is generated and pushed to the responsible doctor, and the test and examination reports in the treatment process are recorded; Postpartum depression-related video and article education content is provided for the target parturient woman to access; The target parturient woman after treatment is followed up after childbirth to obtain follow-up results of intervention effect feedback, and the follow-up results are associated with questionnaire evaluation data and clinical intervention plans for correlation analysis to output optimized intervention strategies.
2. The method of claim 1, wherein, The method comprises the following steps: Collecting the psychological state information of the parturient woman through the Edinburgh Postpartum Depression Scale, and receiving and storing the evaluation score in real time.
3. The method of claim 1, wherein the method further comprises: The method comprises the following steps: Through the Excel format personal information table generated by the online questionnaire platform, the personal information is batched into the database system through the data import interface, and the personal information is associated with the unique identification of the parturient woman to establish the personal information file.
4. The method of claim 1, wherein the method further comprises: The method comprises the following steps: Set multiple score thresholds, when the questionnaire score exceeds the preset severe threshold, mark the parturient woman as a severe patient, and trigger a high-risk warning.
5. The method of claim 1, wherein the method further comprises: The method comprises the following steps: According to the age, gestational age, medical history and questionnaire score of the target parturient woman, the preset intervention strategy database is called to generate a clinical intervention list containing psychological counseling suggestions and drug treatment reference schemes, and sent to the mobile terminal and PC terminal background of the responsible doctor through the message push module.
6. The method of claim 1, wherein the method further comprises: The method comprises the following steps: Provide a report upload interface for doctors to upload or the database system to grab biomarker detection results in the hospital test system, and store the biomarker detection results in the time axis associated with the clinical intervention plan; The biomarkers include serum corticotropin-releasing hormone and 5-hydroxytryptamine.
7. The method of claim 1, wherein the method further comprises: The method comprises the following steps: Classify and arrange the video resource library, sort the content by play quantity and update time, and record the access history of the target parturient woman; The video resource library covers emotional regulation skills, family support methods and treatment case sharing.
8. The method of claim 1, wherein the method further comprises: The method comprises the following steps: Set the follow-up time node to automatically generate follow-up reminders at certain time periods after childbirth, and record the feedback information of the target parturient woman through the database system; The feedback information includes emotional state, sleep quality and social function recovery.
9. The method of claim 1, wherein, The method further comprises: performing distributed storage and localized processing on sensitive data in the database system, and establishing a permission hierarchy for accessing the database system.
10. A postpartum depression monitoring system, characterized by, The system comprises: An online questionnaire module for obtaining evaluation data of the questionnaire of the parturient woman; The personal information maintenance management module is used for integrating the personal information of the online questionnaire into a database system to establish a personal information file; The questionnaire evaluation management module is used for preliminarily evaluating the puerpera according to the evaluation data to obtain a questionnaire evaluation result; and classifying the health state of the puerpera based on the personal information file and the questionnaire evaluation result; The health intervention module is used for generating a clinical intervention scheme for the target puerpera classified as a critical patient and pushing the clinical intervention scheme to a responsible doctor, and recording the test and examination reports in the treatment process; The video and article propaganda module is used for providing video and article propaganda contents related to postpartum depression for the target puerpera to access; The postpartum follow-up module is used for postpartum follow-up of the target puerpera after treatment to obtain follow-up results of intervention effect feedback, and correlatively analyzes the follow-up results with the questionnaire evaluation data and the clinical intervention scheme to output an optimized intervention strategy.