Intelligent newborn risk assessment and intervention assistance method based on machine learning
By simulating the real delivery room environment and neonatal care scenarios, combining machine learning algorithms and real-time feedback systems, the problem of insufficient applicability and accuracy in neonatal health assessment is solved, doctors' skills and team collaboration capabilities are improved, and personalized risk assessment and real-time intervention are achieved.
Patent Information
- Application Number
- CN202510323106.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-19
- Publication Date
- 2025-07-08
AI Technical Summary
Existing machine learning models are inadequate in applicability and accuracy in neonatal health assessments, especially in resource-limited areas, and lack interpretability and long-term dynamic monitoring capabilities.
By simulating real delivery room environments and neonatal care scenarios, combining machine learning algorithms and real-time feedback systems, we provide personalized neonatal risk assessment and intervention assistance methods, including data collection, model construction, virtual reality training and multi-person collaborative simulation.
Improve doctors' understanding and operational ability of newborn care and first aid skills, enhance the accuracy and interpretability of the model, support personalized education and real-time intervention suggestions, and improve team collaboration capabilities.
Smart Images
Figure CN120280123A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of machine learning, and particularly relates to an intelligent neonatal risk assessment and intervention assistance method based on machine learning. Background Art
[0002] Many studies rely on limited datasets or data in specific environments, which may lead to insufficient generalization ability of the model. For example, some studies only model the data of a specific country or region, without fully considering the differences in neonatal health conditions between different regions. In addition, some studies mention that there are problems of incompleteness or inaccuracy in the data collection and processing process, which may affect the prediction performance of the model. Although machine learning models perform excellently in prediction accuracy, their "black box" characteristics make it difficult for doctors to understand the decision-making process of the model. For example, some studies point out that although the AdaBoost algorithm performs outstandingly in predicting medication errors, its results lack interpretability, which may hinder its widespread application in clinical practice. Current machine learning models mostly focus on risk assessment at a single time point, and there is less dynamic monitoring and assessment of the long-term health conditions of newborns. For example, some studies mention the need to study more deeply the changes in laboratory biomarkers over time to better understand the disease process. Existing machine learning models are often developed based on specific environments, such as neonatal data in high-income countries, and in regions with limited resources, the applicability and accuracy of these models are relatively low. Summary of the Invention
[0003] Aiming at the above technical problems of the low applicability and accuracy of existing machine learning models, the present invention provides an intelligent neonatal risk assessment and intervention assistance method based on machine learning. By simulating the real delivery room environment and neonatal conditions, doctors can perform practical operations under safe and controlled conditions, so as to deepen their understanding and mastery of neonatal care and first aid knowledge, including various simulation devices, scenario simulation software, and real-time feedback systems, to help doctors discover problems, improve skills, and enhance their ability to respond to emergencies in actual operations.
[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:
[0005] An intelligent neonatal risk assessment and intervention assistance method based on machine learning, comprising the following steps:
[0006] S1. Simulate the delivery room environment and various scenarios of neonatal care and first aid. By simulating the real operation environment and situation, doctors can perform practical operations in a safe and controlled environment, so as to deepen their understanding and mastery of neonatal care and first aid knowledge;
[0007] S2. The system is also equipped with a real-time feedback mechanism. When doctors perform simulated operations, the system tracks the operation process of doctors in real time and provides instant feedback. This feedback not only includes prompts on whether the operation is correct, but also includes deficiencies and suggestions during the operation process, thereby helping doctors discover and correct mistakes and improve their operation skills.
[0008] S3. By simulating scenarios of multi-person collaboration, doctors can learn how to communicate and cooperate effectively with other medical team members in practice, thus better ensuring the health and safety of newborns in actual work.
[0009] S4. With the continuous progress of medical technology and the update of neonatal care concepts, the system is upgraded and expanded as needed to meet the changing educational needs. At the same time, the system is also customized according to the specific needs of users to provide more personalized educational services.
[0010] The method of simulating the delivery room environment and various scenarios of neonatal care and first aid in S1 is as follows:
[0011] S1.1. Data collection and preprocessing: Collect clinical data related to newborns, including vital signs, medical history, and diagnosis results; clean, label, and normalize the data to ensure data quality and provide a reliable basis for subsequent model training.
[0012] S1.2. Machine learning model construction: Use supervised learning algorithms or deep learning models to predict risk factors and potential problems of newborns; develop prediction models for high-risk infants to identify key risk factors such as preterm birth, infection, and respiratory problems; use ensemble learning methods to improve the accuracy and robustness of the model.
[0013] S1.3. Simulation environment and interactive teaching: Construct virtual delivery rooms and neonatal care scenarios, including simulating the delivery process, neonatal resuscitation training, and vital sign monitoring; provide 3D animation demonstrations and interactive game tests to enable learners to observe and operate from different angles; combine tactile perception systems and virtual reality technologies to enhance the realism of the simulation.
[0014] The method of the system in S2 for tracking the operation process of doctors in real time is as follows:
[0015] S2.1. The system uses sensors and cameras to monitor the operation process of doctors in real time, including key indicators such as action trajectories and force changes, and transmits this data to the analysis module in real time; using Internet of Things technology, the data is uploaded to the cloud server in real time for analysis and prediction by machine learning models.
[0016] S2.2. Use the trained random forest, support vector machine or deep learning model to perform real-time analysis on the doctor's operation behavior to determine whether it conforms to the standard operation process; the model outputs the evaluation results, including the correctness score of the operation, the risk level and possible error prompts;
[0017] S2.3. According to the model prediction results, the system automatically generates feedback information;
[0018] S2.4. The system continuously optimizes the model parameters according to the doctor's feedback and operation performance to improve the prediction accuracy and feedback quality; as the doctor's skills improve, the system gradually reduces the intervention frequency to encourage the doctor to complete the operation independently.
[0019] The method for determining whether it conforms to the standard operation process in S2.2 is as follows: Use the training data set to train the selected model:
[0020] Random forest: Improve the robustness of the model by constructing multiple decision trees and performing voting or average prediction;
[0021] Support vector machine: Maximize the classification margin by optimizing the support vectors to improve the classification accuracy;
[0022] Deep learning: Use convolutional neural network CNN or recurrent neural network RNN to model time series data;
[0023] Use k-fold cross-validation to evaluate the model performance and adjust the hyperparameters to optimize the model.
[0024] The method for simulating the scenario of multi-person collaboration in S3 is as follows:
[0025] S3.1. Use virtual reality VR, augmented reality AR and high-fidelity simulation technology to create realistic neonatal care scenarios, which include emergency handling, daily care operations and multi-disciplinary team collaboration;
[0026] S3.2. Design a multi-disciplinary team collaboration module to simulate the role division and communication process of doctors, nurses, pharmacists and other medical staff in neonatal care. Through simulation training, participants can learn how to communicate and make decisions effectively under pressure;
[0027] S3.3. Evaluate the simulation training process through video recording and expert scoring to improve the team collaboration ability.
[0028] The method for designing the multidisciplinary team collaboration module in S3.2 is as follows: using simulation training, participants can learn how to divide roles and collaborate under pressure, thereby improving team efficiency and decision-making ability; using the SBAR model to standardize information exchange between team members; in a simulation environment, through role-playing and scenario exercises, training team members to quickly and accurately transmit information in emergency situations; with the help of the multidisciplinary team MDT model, integrating resources such as pediatricians, neonatologists, nutritionists, rehabilitation therapists, etc., to jointly develop intervention strategies; in simulation training, through case discussions and problem solving, enhance team members' understanding of each other's work and improve overall collaboration capabilities.
[0029] Compared with the prior art, the present invention has the following beneficial effects:
[0030] 1. The present invention uses advanced algorithms and data processing technologies. These algorithms are carefully designed and optimized, and can accurately identify risk factors that affect the health of newborns from a large amount of data and predict their potential impact. At the same time, data processing technology can efficiently process and analyze these data to ensure the accuracy and real-time nature of the prediction results.
[0031] 2. The present invention not only focuses on data processing and analysis, but also on building a complete intelligent risk assessment and intervention assistance system. The system can collect and process newborn-related data in real time, conduct risk assessment through algorithm models, and provide users with personalized intervention suggestions. The construction of the system involves the design and integration of multiple modules, including data collection modules, risk assessment modules, intervention suggestion modules, etc. The collaborative work between these modules enables the system to maximize its effectiveness in practical applications.
[0032] 3. The present invention also involves the development and optimization of mobile applications. The application not only provides an intuitive user interface and simple operation mode, but also has real-time synchronization and data update functions, ensuring that medical staff can obtain the latest risk assessment results and intervention recommendations at any time. At the same time, the application also takes into account user experience and interactive design, so that medical staff can perform practical operations and learning more conveniently. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.
[0034] The structures, proportions, sizes, etc. illustrated in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.
[0035] Figure 1 is the flowchart of the present invention. Detailed implementation manners
[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. These descriptions are only to further illustrate the features and advantages of the present invention, rather than a limitation on the claims of the present invention; based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.
[0037] The following will further describe in detail the specific implementation manners of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.
[0038] An intelligent neonatal risk assessment and intervention assistance method based on machine learning, as Figure 1 shown, includes the following steps:
[0039] Step 1. Simulate the delivery room environment and various scenarios of neonatal care and first aid. By simulating the real operation environment and situations, doctors can practice in a safe and controlled environment, thereby deepening their understanding and mastery of neonatal care and first aid knowledge.
[0040] Step 1.1. Data collection and preprocessing: Collect clinical data related to neonates, including vital signs, medical history, and diagnosis results. Clean, label, and normalize the data to ensure data quality and provide a reliable basis for subsequent model training.
[0041] Step 1.2. Machine learning model construction: Use supervised learning algorithms or deep learning models to predict the risk factors and potential problems of neonates. For high-risk infants, develop prediction models to identify key risk factors such as preterm birth, infection, and respiratory problems. Use ensemble learning methods to improve the accuracy and robustness of the model.
[0042] Step 1.3. Simulation Environment and Interactive Teaching: Construct virtual delivery room and neonatal care scenarios, including simulating the delivery process, neonatal resuscitation training, and vital sign monitoring. Provide 3D animation demonstrations and interactive game tests to enable learners to observe and operate from different perspectives. Combine the tactile perception system and virtual reality technology to enhance the realism of the simulation.
[0043] Step 2. The system is also equipped with a real-time feedback mechanism. When doctors perform simulation operations, the system tracks the operation process of doctors in real time and provides immediate feedback. This feedback includes not only prompts on whether the operation is correct, but also deficiencies and suggestions during the operation process, thereby helping doctors discover and correct errors and improve their operation skills.
[0044] Step 2.1. The system uses sensors and cameras to monitor the operation process of doctors in real time, including key indicators such as movement trajectories and force changes, and transmits this data to the analysis module in real time. Using Internet of Things technology, the data is uploaded to the cloud server in real time for analysis and prediction by machine learning models.
[0045] Step 2.2. Use trained random forest, support vector machine, or deep learning models to analyze the operation behavior of doctors in real time to determine whether it conforms to the standard operation process. The model outputs evaluation results, including the correctness score of the operation, risk level, and possible error prompts. Use the training dataset to train the selected model:
[0046] Random forest: Improve the robustness of the model by constructing multiple decision trees and performing voting or average prediction.
[0047] Support vector machine: Maximize the classification margin by optimizing support vectors to improve classification accuracy.
[0048] Deep learning: Use convolutional neural network CNN or recurrent neural network RNN to model time series data.
[0049] Use k-fold cross-validation to evaluate the model performance and adjust hyperparameters to optimize the model.
[0050] Step 2.3. According to the model prediction results, the system automatically generates feedback information.
[0051] Step 2.4. The system continuously optimizes the model parameters based on the feedback and operation performance of doctors to improve the prediction accuracy and feedback quality. As doctors' skills improve, the system gradually reduces the intervention frequency to encourage doctors to complete operations independently.
[0052] Step 3. By simulating scenarios of multi-person collaboration, doctors can learn how to communicate and cooperate effectively with other medical team members in practice, thereby better ensuring the health and safety of newborns in actual work.
[0053] Step 3.1: Create realistic neonatal care scenarios using virtual reality (VR), augmented reality (AR), and high-fidelity simulation technologies. These scenarios include emergency handling, daily care operations, and multidisciplinary team collaboration.
[0054] Step 3.2: Design a multidisciplinary team collaboration module to simulate the role division and communication processes among doctors, nurses, pharmacists, and other medical staff in neonatal care. Through simulation training, participants can learn how to communicate effectively and make decisions under pressure. Using simulation training, participants can learn how to divide roles and collaborate under pressure, thereby improving team efficiency and decision-making ability. Use the SBAR model to standardize information exchange among team members. In the simulation environment, through role-playing and scenario-based exercises, train team members to quickly and accurately transmit information in emergency situations. With the multidisciplinary team (MDT) model, integrate resources such as pediatricians, neonatologists, dietitians, and rehabilitation therapists to jointly develop intervention strategies. In the simulation training, through case discussions and problem-solving, enhance team members' understanding of each other's work and improve overall collaboration ability.
[0055] Step 3.3: Evaluate the simulation training process through video recording and expert scoring to improve team collaboration ability.
[0056] Step 4: As medical technology continues to advance and neonatal care concepts are updated, the system is upgraded and expanded as needed to meet changing educational requirements. At the same time, the system is also customized according to the specific needs of users to provide more personalized educational services.
[0057] Example 1: Application of the Intelligent Neonatal Risk Assessment and Intervention Assistance System Based on Machine Learning in the Neonatal Intensive Care Unit
[0058] With the continuous progress of medical technology and the increasing demand for efficient and accurate medical assistance tools in the neonatal intensive care unit, the intelligent neonatal risk assessment and intervention assistance system based on machine learning is expected to play an important role in this field. By real-time monitoring and analyzing the physiological parameters of neonates, the system can intelligently identify abnormal situations and issue early warnings, helping medical staff make decisions quickly. At the same time, the system can also combine medical knowledge bases and advanced computer technologies to provide personalized treatment suggestions for doctors, thereby improving the diagnosis and treatment efficiency and accuracy. In addition, the user-friendly human-computer interaction interface of the system can also enable medical staff to obtain the required information more conveniently and improve the overall work efficiency. Therefore, applying the intelligent neonatal risk assessment and intervention assistance system based on machine learning to the neonatal intensive care unit is expected to provide strong support for neonatal health management and medical decision-making.
[0059] Example 2: Premature Infant Feeding Recommendation System Based on Big Data Analysis
[0060] Feeding management for premature infants is a complex and critical task that requires comprehensive consideration of the physiological characteristics, nutritional needs, and various factors in the feeding process of premature infants. With the help of an intelligent neonatal risk assessment and intervention assistance system based on machine learning, we can build a premature infant feeding recommendation system based on big data analysis. The system collects and analyzes a large amount of premature infant feeding data, including feeding amount, feeding frequency, weight gain and other indicators, and uses machine learning algorithms to mine the correlation and regularity between the data. Based on these analysis results, the system can provide personalized feeding recommendations to medical staff to help premature infants obtain reasonable nutritional intake and healthy growth. Such a system can not only improve the scientificity and effectiveness of premature infant feeding, but also help reduce health problems caused by improper feeding and promote the rehabilitation and development of premature infants.
[0061] Example 3: Neonatal jaundice risk assessment system based on real-time physiological data monitoring
[0062] Neonatal jaundice is a common health problem, and early identification and intervention are crucial to preventing complications. We can use the intelligent neonatal risk assessment and intervention assistance system based on machine learning to build a system specifically for real-time monitoring of neonatal physiological data to assess the risk of jaundice. By continuously collecting and analyzing key data such as the bilirubin level, body temperature, heart rate, etc. of the newborn, the system can detect abnormal changes in time and automatically conduct risk assessment. Once the risk exceeds the preset threshold, the system will automatically issue an early warning notification to remind medical staff to take appropriate intervention measures. Such a system not only improves the accuracy and real-time nature of jaundice risk assessment, but also helps to reduce the workload of medical staff and improve the management level of neonatal jaundice.
[0063] Only the preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention, and various changes should be included in the protection scope of the present invention.
Claims
1. An intelligent neonatal risk assessment and intervention assistance method based on machine learning, characterized in that, Including the following steps: S1. Simulate the delivery room environment and various scenarios of neonatal care and first aid. By simulating the real operating environment and situations, doctors can practice in a safe and controlled environment, thus deepening their understanding and mastery of neonatal care and first aid knowledge; S2. The system is also equipped with a real-time feedback mechanism. When doctors perform simulated operations, the system tracks the operation process of doctors in real time and provides instant feedback. This feedback not only includes prompts on whether the operation is correct, but also includes deficiencies and suggestions during the operation process, thereby helping doctors discover and correct mistakes and improve their operation skills; S3. By simulating scenarios of multi-person collaboration, doctors can learn how to communicate and cooperate effectively with other medical team members in practice, thus better ensuring the health and safety of newborns in actual work; S4. With the continuous progress of medical technology and the update of neonatal care concepts, the system is upgraded and expanded as needed to meet the changing educational needs. At the same time, the system is also customized according to the specific needs of users to provide more personalized educational services.
2. The intelligent neonatal risk assessment and intervention assistance method based on machine learning according to claim 1, characterized in that The method of simulating the delivery room environment and various scenarios of neonatal care and first aid in S1 is as follows: S1.
1. Data collection and preprocessing: Collect clinical data related to newborns, including vital signs, medical history, and diagnosis results; clean, label, and normalize the data to ensure data quality and provide a reliable basis for subsequent model training; S1.
2. Machine learning model construction: Use supervised learning algorithms or deep learning models to predict the risk factors and potential problems of newborns; for high-risk infants, develop prediction models to identify key risk factors such as premature birth, infection, and respiratory problems; Utilize ensemble learning methods to improve the accuracy and robustness of the model. S1.
3. Simulated environment and interactive teaching: Construct a virtual delivery room and neonatal care scenarios, including simulating the delivery process, neonatal resuscitation training, and vital sign monitoring; provide 3D animation demonstrations and interactive game tests to enable learners to observe and operate from different angles; combine a tactile perception system and virtual reality technology to enhance the realism of the simulation.
3. The intelligent neonatal risk assessment and intervention assistance method based on machine learning according to claim 1, characterized in that The method for the system to track the operation process of doctors in real time in S2 is as follows: S2.
1. The system monitors the operation process of doctors in real time through sensors and cameras, including key indicators such as action trajectories and force changes, and transmits this data to the analysis module in real time; using Internet of Things technology, upload the data to the cloud server in real time for analysis and prediction by a machine learning model; S2.
2. Use trained random forest, support vector machine, or deep learning models to analyze the operation behavior of doctors in real time to determine whether it conforms to the standard operation process; the model outputs evaluation results, including the correctness score of the operation, the risk level, and possible error prompts; S2.
3. According to the model prediction results, the system automatically generates feedback information; S2.
4. The system continuously optimizes the model parameters according to the feedback and operation performance of doctors to improve the prediction accuracy and feedback quality; as doctors' skills improve, the system gradually reduces the intervention frequency to encourage doctors to complete operations independently.
4. An intelligent neonatal risk assessment and intervention assistance method based on machine learning according to claim 1, characterized in that, The method for determining whether it conforms to the standard operation process in S2.2 is as follows: Use the training dataset to train the selected model: Random forest: Improve the robustness of the model by constructing multiple decision trees and performing voting or average prediction; Support vector machine: Maximize the classification margin by optimizing the support vectors to improve classification accuracy; Deep learning: Use convolutional neural network CNN or recurrent neural network RNN to model time series data; Use k-fold cross-validation to evaluate the model performance and adjust the hyperparameters to optimize the model.
5. A method for intelligent neonatal risk assessment and intervention assistance based on machine learning according to claim 1, characterized in that, The method for simulating the scenario of multi-person collaboration in S3 is as follows: S3.
1. Utilize virtual reality VR, augmented reality AR, and high-fidelity simulation technology to create realistic neonatal care scenarios, including emergency handling, daily care operations, and multi-disciplinary team collaboration; S3.
2. Design a multi-disciplinary team collaboration module to simulate the role division and communication process of doctors, nurses, pharmacists, and other medical staff in neonatal care. Through simulation training, participants can learn how to communicate and make decisions effectively under pressure; S3.
3. Evaluate the simulation training process through video recording and expert scoring to improve the team collaboration ability.
6. The intelligent neonatal risk assessment and intervention assistance method based on machine learning according to claim 5, characterized in that, The method for designing the multi-disciplinary team collaboration module in S3.2 is as follows: Through simulation training, participants can learn how to divide roles and collaborate under pressure, thereby improving team efficiency and decision-making ability; Use the SBAR model to standardize the information exchange between team members; In the simulation environment, through role-playing and scenario-based training, train team members to quickly and accurately convey information in emergency situations; With the help of the multi-disciplinary team MDT model, integrate resources such as pediatricians, neonatologists, dietitians, and rehabilitation therapists to jointly develop intervention strategies; In the simulation training, through case discussions and problem-solving, enhance team members' understanding of each other's work and improve the overall collaboration ability.