System for machine learning-based predictive analytics in the management of chronic diseases

Abstract

A system for the treatment of chronic diseases, consisting of: a data acquisition unit configured to capture patient health information in real time from wearable or networked health monitoring devices, electronic health records, and patient-reported outcomes; a central processing unit configured to preprocess the captured data by normalizing, aggregating, and coding the data for further analysis; a feature extractor operationally connected to the central processing unit, the feature extractor being capable of extracting key features from preprocessed data, including demographic data, clinical history, biomarker levels, and lifestyle;a predictive modeling engine communicating with the feature extraction module, wherein the predictive modeling engine is configured to use machine learning techniques to generate recognition models from historical patient data, the models predicting disease progression, treatment outcomes, and potential complications; a visualization interface connected to the predictive modeling engine, wherein the visualization interface is configured to display the results of the predictive analytics to healthcare providers and patients to enable actionable insights for the management of chronic diseases; and a feedback mechanism integrated into the visualization interface, configured to allow users to input real-time feedback on the predicted outcomes, the feedback being used to refine the predictive models over time.

Description

Field of the InventionThe present invention relates to a system for predictive analytics using machine learning techniques for the treatment of chronic diseases.BACKGROUND OF THE INVENTIONThe patient's quality of life is impaired, as is the costs due to chronic diseases such as diabetes, heart diseases and airway diseases. Current screening and treatment methods for these diseases often depend on health care patient visits that are disturbed by delays in symptom detection, identification of abnormal physical signs by physicians during suboptimal routine static examinations, or out-of-standard test results. With the growth of portable technology and electronic health care records, we have the possibility of using real-time data more meaningfully for the treatment of chronic diseases. Nevertheless, current solutions are not equipped with powerful predictive analyses that can help use different patient data in real time to predict the disease course and better design treatment plans.Chronic diseases are one of the major causes of morbidity and mortality worldwide and lead to increasing health expenses and decreased quality of life of millions. Diseases such as diabetes, cardiovascular diseases, chronic airway diseases and cancer must be continuously studied in order to prevent the complications involved and improve their health. The traditional approach to treating chronic diseases is based rather on occasional visits to health providers to the patient, perhaps once or twice a year. In these visits, evaluations are made based on static health scales - symptoms reported by the patients themselves, as well as indirect clinical measurements obtained by laboratory testing. In this reactive system, patients normally wait for actions until their symptoms are exacerbated or new problems occur, resulting in hospital admission and emergency visit.Most solutions to treat chronic diseases use Electronic Health Records (EHRs) to ensure data collection and communication with various health providers. EHRs are bulky to improve the ability to obtain information about their patients and have rationalized many administrative processes, but for the most part EHRs serve as containers for past health data which can be better used for treatment. While EHR systems can send reminders of preventative care or compliance with drug ingestion, they are not as good in making predictive analyses of how disease will develop with regard to the effectiveness of the treatment. Therefore, it may be difficult for healthcare providers to target patients at the highest risk or to contextualize these data trends to make better interventions.Machine learning has provided a solution to improve predictive analytics in the area of treating chronic diseases. Current systems already use machine learning to process large amounts of data and recognize patterns that can direct clinical decision making. These include predicting risk factors for the course of the disease, complications that may occur, and adjusting treatment schedules to more closely conform them to a patient's specific profile. Nevertheless, most practical health care machine learning applications are still in the children's shoes and are subject to many limitations. Some of the current models are based on retrospective data that may not capture how complex patient encounters may be in real life. Moreover, these models are data controlled and inherently distorted because they use predetermined features that may not take into account person-specific context conditions, resulting in supposedly suboptimal decisions in predicting changes.Moreover, the lack of standardized protocols and interoperability between healthcare systems has also contributed to theto thetorawing of machine learning in the area of treating chronic diseases. Differences in data formats, definitions, and sources can lead to difficulties in aggregating and harmonizing the data required to build robust machine learning models. Moreover, they have prevented confidential health data from being passed on, since they must be kept and protected and potential machine learning capabilities can be used in proactive analyses. Many patients and even some healthcare providers fear that their data will be used against them, which is a barrier to the wide use of these technologies.We have long desired such a system - one that uses the necessary machine learning performance to provide real-time predictive analyses that not only allow patient specific personalized care, but also support proactive management strategies that require quick action if we do not want to optimize results and degrade quality of life.SUMMARY OF THE INVENTIONThe proposed system of the present invention overcomes these deficiencies by investigating the advantages of predictive analyses based on machine learning to adaptively assist in the management of chronic diseases. Data Acquisition The data acquisition portion of the system collects patient health information in real time from various sources such as portable health monitors and electronic health care records (EHRs). The central processing unit processes the data fed in from other nodes and performs preprocessing including normalizing, aggregating and encoding the information collected using techniques to keep it available for further analysis.A central processing unit is implemented in a feature extraction module that recognizes dependent parameters from the pre-processed data (e.g., demographics, medical history, and biomarker values), including factors associated with the lifestyle. This extracted information is then passed to a predictive modeling engine that uses sophisticated machine learning and generates predictive models from the existing patient history. They are also designed to be able to estimate the course of the disease, treatment results and undesirable events in order that the health care personnel can make important decisions for the correct chronic treatment.The system also has a visual communication interface that provides the predictive analysis results in an easily understood, human readable format, thus allowing health experts and patients to effectively analyze and use the data to make the correct decisions. This interface is coupled to an integrated feedback loop, via which users can provide live feedback for providing the predicted results. Such feedback is important to improve the prediction models used and ensure that they do not become aged out or otherwise false.The system also includes a notification unit that informs healthcare providers and patients when the predicted results change significantly. A predictive modeling engine that is regularly updated to provide accurate predictions and adapt models to changes in patient condition.The main object of the invention is to provide an integrated system for predictive analyses based on machine learning, specifically tailored to the treatment of chronic diseases. The system will use patient data from a variety of sources (including wearables, health databases, and self-offices) to predict possible future patient conditions and thus achieve improved precision medicine. The invention utilizes machine learning to improve predictive modeling techniques that can predict the progression of disease, treatment reactions, and complications - and allows healthcare facilities to make informed decisions in earlier treatment phases.Another important aspect of the invention is to provide a visually interactive interface that specifically visualizes complex results of predictive analyses so that both healthcare providers and patients can intuitively understand them. They are designed to improve understanding and interaction with health data by enabling a clear interpretation of the model results. The innovation therefore aims to provide this information and thus the possibility of making decisions jointly between patients, attending listeners / inspectors and medical professionals, which leads to better results in connection with the treatment of chronic diseases.Brief description of the Figure:FIG. 1 shows a block diagram of a system for machine learning-based predictive analyses in the management of chronic diseases.Detailed DescriptionReferring to Figure 1, a block diagram of a system for treating chronic diseases is provided. The system 100 includes: a data acquisition unit 102 configured to acquire real-time patient health information from portable or networked health monitors, EHRs, and patient reported results; a central processing unit 104 configured to pre-process the acquired data by normalizing, aggregating, and encoding the data for further analysis; a feature extraction unit 106 operatively connected to the central processing unit, wherein the feature extractor is configured to extract important features from pre-processed data including demographic data, medical history, biomarker values, lifestyle; a prediction modelling engine 108 in communication with the feature extraction module, the prediction modelling engine 108 being configured to employ machine learning to generate recognition models from historical patient data, the models predicting disease history, treatment results and potential complications; a visualization interface 110 connected to the prediction modelling engine 108, the visualization interface 110 being configured to display the results of the prediction analysis to healthcare providers and patients to enable implementable findings for chronic disease management; and a feedback mechanism 112 integrated with the visualization interface 110 and configured to enable users to input real-time feedback to the predicted results, the feedback being used to refine the prediction models over time.In one embodiment, the data acquisition unit 102 includes a plurality of sensor devices for continuously monitoring the vital signs such that these sensors continuously measure heart rate, blood pressure, and glucose levels, generate data, and then wirelessly transmit the same to the central processing controller.In one embodiment, the central processing unit 104 also includes a data clean-up module for removing any anomalies from the captured data and inconsistencies that occur in time, thereby performing this process required for more accurate prediction.In one embodiment, visualization interface 110 includes interactive dashboards that allow healthcare providers to filter relevant patient populations, disease states, and treatment interventions and obtain detailed information regarding them to make individual decisions.In one embodiment, the feedback mechanism 112 reports real-time therapy compliance monitoring to enable dynamic adjustment of treatment schedules by healthcare providers based on patient compliance and predicted results.In one embodiment, the system also includes a notification unit 114 configured to notify healthcare providers and patients of critical changes in the prognosis results, thereby enabling timely intervention in the treatment of chronic diseases.In one embodiment, the predictive modeling engine is also configured to periodically update its models using new patient data to make predictions at different times.In particular, the apparatus of the machine structure associated with this invention includes a data acquisition unit (which may consist of various sensors adapted for continuous monitoring of the vital functions). Such devices monitor heart rate, blood pressure and glucose levels and can wirelessly transmit these data to the central processing unit. The data purification module built into the central processing unit filters out any anomalies and inconsistencies found in the acquired radar-based dataset, such that only the best set of reliable and accurate data can serve as input for predictive analyses.The feature extraction module uses dimension reduction techniques that allow the predictive modeling engine to significantly improve its work by reducing its input features and output while maintaining the most important information for accurate predictions. Supervised and unsupervised learning such as decision trees, support vector machines, and clustering techniques are optimized for predicting disease outputs using the predictive modeling engine.Moreover, the visualization interface is equipped with interactive dashboards that allow medical professionals to adapt their analyses based on patient populations, disease states, and treatment interventions to support an individual decision process. The feedback loop in the system also includes real-time monitoring of therapy compliance, so that medical professionals can promptly remodel their treatment plans by obtaining an increase in patient compliance based on predicted predictions.In summary, the proposed machine learning-based prediction system for chronic disease management through its technology stack provides a comprehensive solution to better provide patients and achieve improved results. It allows healthcare providers to benefit from real-time data and predictive models that help them keep on the fly and treat chronic diseases.An integrated system invention is proposed that consists of a health management system that includes a data acquisition unit, a central processing unit, a feature extraction module, a predictive modeling engine, a visualization interface, and a feedback mechanism. The device is designed for use with various patient monitoring devices for integration into the system of data exchange and analysis predictions in real time.The apparatus also has an outer housing in which the CPU and other electronics are accommodated. The ports include high speed connection modules and may be used to connect various sensor devices to interact with the variety of health data. The output is displayed on a device via the visualization interface. On the screen, healthcare providers can access the results of the predictive analysis. The feedback mechanism includes an interactive touch input configured for user operation, thereby making the device more functional in view of patient needs.In summary, the presented system and apparatus provide a new way of treating chronic diseases through advanced machine learning in combination with real-time data acquisition and provide readily accessible interfaces for improving treatment results and providing health services.The invention relates to a complex machine learning based predictive analysis system for the management of chronic diseases, each claim clearly outlines an aspect thereof and its methodology.Systems according to the invention include, among other things, a data acquisition unit that receives patient-related health information from multiple sources in real time. These could include things such as wearable sensors that measure biometric data such as heart rate, blood pressure, and blood glucose. For example, a smart watch with biometric sensors would synchronize heart rate and motion activity data to the system via wireless technology. In addition, the system can download Electronic Health Records (EHRs) containing patient care history information - previous disease diagnoses and treatment methods, and laboratory results. Such a multi-view approach provides the system with a full insight into the patient's health, which in turn would improve the accuracy and reliability in predicting analyses.In each embodiment, the central processing unit is connected to the data acquisition unit and responsible for preprocessing the acquired information. This is a process for purifying, aggregating, and converting the data to be used for analysis purposes after normalization across different sources. In one example, the central processing unit may standardize data received from different devices into a common metric so that heart rate information measured and reported by a set of systems in different formats may be easily integrated into other systems. Preprocessing data is very important to maintain high quality and reliable inputs for predictive modeling efforts.In one embodiment, the feature extraction module identifies the features of relevant importance from preprocessed data (e.g., 4. in this module, various methods are used to browse the data and find out those important features that are helpful in disease treatment. The feature extraction module could analyze historical data to determine blood glucose level trends in diabetics, for example. This could allow for things such as average glucose levels, the occurrence of high or low values, and typical patterns occurring at certain times of day or after a certain type of activity. These attributes help the system understand what affects a patient's health, and in this way it can make more specific predictions.According to one embodiment, the predictive modeling engine uses machine learning techniques to generate predictive models by analyzing historical patient data. The engine uses supervised and unsupervised learning approaches to infer the detected features. For example, it may use supervised learning to build from previous data a model that relates certain patient attributes (e.g., age, coomorbidities, and treatment history) to results such as hospitalization or disease history. The predictive modeling engine also adjusts its techniques and becomes more accurate over time through the use of historical data, making it an even better tool for chronic disease management.In one embodiment, the visualization interface is configured to display user-friendly representations of the results of predictive analyses. This level of integration is of great importance because it allows more complex data to be explained by the medical professional, but also to be made somewhat comprehensible to the patients. This could include rental of interactive dashboards in which a physician can visually present certain patient trends over time, such as changes in vital signs or compliance with medication ingestion. These solutions allow healthcare providers to examine individual data points, compare results between populations, and visualize potential risks. The visualization interface enables intuitive graphics that allow easy interpretation of predictive models, aid in the selection of clinical treatments, and enhance patient compliance.According to at least one embodiment, a feedback mechanism is integrated via the visualization interface, which allows users to provide feedback to predicted results in real time. This feedback loop is decisive for improving the already existing prediction models which are based on real patient experiences. To name an example from healthcare: the system may have incorrectly predicted that a patient will be delivered to the hospital and cause it to return to his abdominal feel. This feedback was then analyzed by the prediction modelling engine, which adjusted its techniques accordingly for future predictions. The development phase improves the adaptability of the system, so that it remains useful in a context and does not overlook any nuances that are inherent in the patient care.A notification system may inform healthcare providers and patients about critical changes in predicted results. This system is very important and a central point in the control of chronic diseases. For example, if the predictive modeling engine determines a dramatic increase in risk associated with poor results for a patient based on current iteration data, magic automatically notifies both the patients and their health team. By timely communication, a quick intervention is possible-whether by changing the treatment or by agreement of a appointment for personal follow-up testing-which reduces the probability that something worse will happen.According to another embodiment, the system may receive current patient information that enables periodic updates of its prediction models. This part is crucial to maintain the accuracy and precision of the predictions as to new incoming data. For example, if a patient's health changes greatly, by introducing new medication or changing the lifestyle, these newly available data may be captured and the prediction modelling engine is invoked for recalibration. This is because the system can provide results to medical professionals that are certain to be derived from current data, thereby ensuring that they obtain the most accurate view possible for the treatment of chronic diseases.REFERENCES100 A System For Managing Chronic Diseases Is Provided. 102 Data acquisition unit 104 Central unit 106 Feature extractor 108 Predictive modeling engine 110 Visualization interface 112 Feedback mechanism 114 Notification unit

Claims

A system for treating chronic diseases, comprising: a data acquisition unit configured to acquire patient health information from portable or networked health monitors, electronic patient records, and patient reported results in real time; a central processing unit configured to pre-process the acquired data by normalizing, aggregating, and encoding the data for further analysis; a feature extractor operatively connected to the central processing unit, wherein the feature extractor is adapted to extract important features from pre-processed data including demographic data, clinical history, biomarker values, lifestyle; a predictive modelling engine in communication with the feature extraction module, the predictive modelling engine being configured to employ machine learning techniques to generate recognition models from historical patient data, the models predicting the history of disease, treatment results and possible complications; a visualization interface connected to the predictive modelling engine, the visualization interface being configured to display the results of the predictive analytics healthcare providers and patients to enable implementable findings for the management of chronic diseases; and a feedback mechanism integrated into the visualization interface configured to allow users to input feedback to the predicted results in real time, the feedback being used to refine the prediction models over time.The system of claim 1, wherein the data acquisition unit comprises a plurality of sensor devices for continuously monitoring the vital signs such that these sensors continuously measure heart rate, blood pressure and glucose levels, generate data, and then wirelessly transmit them to the central processing controller.The system of claim 1, wherein the central processing unit further comprises a data clean-up module to remove any anomalies and inconsistencies that occur in time from the captured data, wherein the processing required for a more accurate prediction occurs.The system of claim 1, wherein the visualization interface comprises interactive dashboards that enable healthcare providers to filter and digest relevant patient populations, disease states, and treatment interventions in detail to make individual decisions.The system of claim 1, wherein the feedback mechanism reports real-time therapy compliance monitoring to enable dynamic adjustment of the treatment schedules by healthcare providers based on patient compliance and predicted results.The system of claim 1 further comprising a notification unit configured to notify healthcare providers and patients of critical changes in the prognosis results, thereby enabling timely intervention in the treatment of chronic diseases.The system of claim 1, wherein the predictive modeling engine is further configured to periodically update its models using new patient data to make predictions at different times.

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