FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

[Virtual Presenter] Today, I'm here to talk about FGRL-Net, our work for clinical risk prediction based on Electronic Health Records. It is a Fine-Grained Personalized Patient Representation Learning System offering an effective and accurate approach for making predictions. This system is capable of learning patient-specific representations which enable us to improve the accuracy of risk predictions. We have tested the system on real-world datasets, and our experimental results show that it is doing well in predicting clinical risk..

[Audio] Clinical risk prediction is a valuable asset for healthcare professionals, providing them with an insight into what a patient's health is likely to be in a given period of time. For example, it can help determine the most suitable course of action for a patient, such as whether to keep them in the ICU or to move them elsewhere. Making use of these predictions can enable healthcare professionals to make decisions based on knowledge, potentially leading to improved health outcomes..

[Audio] Using fine-grained patient representation learning, FGRL-Net looks at Electronic Health Records to identify and predict risk of death during hospitalization. This helps health professionals assess the possibility of death during hospitalization and provides them with the tools to ensure appropriate treatment and better outcomes..

[Audio] Today, I'm going to talk about the findings of our study on risk prediction based on patient records, focusing on one specific example. We studied the risk of falls among disabled inpatients. We found that a personalized patient representation learning model is able to identify potential fall risks using recorded patient data and went on to prove that this system was more accurate than traditional methods. We believe that this type of system can be extremely helpful to medical professionals..

[Audio] We can use EHRs to predict the risk of a diabetic patient developing an eye disease. Combining the results of our methods with EHRs allows us to accurately estimate the risk of a diabetic patient developing eye conditions..

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[Audio] Electronic Health Records (EHRs) are widely adopted in hospitals and clinics for patient healthcare. To further improve the quality of care, we present a new fine-grained personalized representation learning method for clinical risk prediction based on EHRs, FGRL-Net. Comparing to existing models, our proposed method has demonstrated better accuracy for risk predicition. Therefore, clinicians can make better decisions for their patients to provide better care with FGRL-Net..

[Audio] We have proposed a personalized patient representation learning method for clinical risk prediction based on EHRs, which extracts feature information from EHRs and feeds it into a deep neural network. The model learns meaningful personalized patient representations and uses them for predicting potential health risks. A set of medical scenarios are further defined to validate the model's accuracy and efficacy..

[Audio] We investigated creating a personalized patient representation learning approach for precisely forecasting a patient's potential clinical risks according to their electronic health records. Our findings demonstrate the capacity of utilizing recent breakthroughs in deep learning for this purpose..

[Audio] When it comes to medical data, sparsity is a common occurrence. Examples demonstrate correlations between, for example, diabetes and eye diseases. Data can evolve rapidly and be irregular, which makes it hard for traditional machine learning approaches to deal with. Accordingly, it is necessary to decide the most suitable way of dealing with this data. To achieve a satisfactory outcome, utilizing simple and efficient models to handle primary illnesses is the key..

[Audio] We present a sparsity and correlated feature problem in clinical risk prediction. Medical data is usually highly correlated, for instance patients with diabetes tend to have eye diseases. To address this issue, we propose to use an irregular and progressive feature extraction method, as well as an insufficient extraction method, to create features of multiple correlations. This will help differentiate between an abnormal pattern of multiple features and a single independent abnormal feature, and calculate a patient's final risk score. This is illustrated by the tables, which show the different calculations when both two Features are abnormal at the same time-step..

[Audio] We will concentrate on the sparsity and correlation of medical features, as well as their irregular and progressive variations. We will consider how these qualities can cause an inadequate portrayal of data from Electronic Health Records (EHRs). This brings up the requirement for a more detailed personalized representation of patients that regards the shifting significance of patient features over time..

[Audio] Clinical risk prediction is a difficult task, made even more complicated by the lack of medical features, their correlation and changing patterns, and the limited amount of training data required for personalized results. To tackle this, Macao Polytechnic University is conducting research on FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs. Our aim is to provide more accurate risk predictions and guidance for better patient care..

[Audio] Current PPRL works based on EHR have not yet fully exploited feature correlation. To address this, various approaches have been proposed such as ConCare, which uses attention-based networks to calculate the importance of each feature, and StageNet, which utilizes an average method to calculate stage-weight of features. However, these approaches are limited in terms of the amount of information they can process, which can lead to information loss. To overcome this, AEFNet has employed 2D CNN networks with maxpolling, enabling it to capture and depict the essential correlations between features..

[Audio] Recognizing the variation pattern of dynamic features is essential to accurately predict risk in health care. Two typical representatives are AdaCare and AEFNet, with AdaCare using three single-layer dilated one-dimensional CNNs. However, AdaCare does not perform well enough and AEFNet is not suitable for medical features, as it uses 2D CNNs, with downsampling and up sampling. To address these challenges, FGRL-Net proposed a novel fine-grained personalized patient representation learning model..

[Audio] Two-stage embedding for representation learning is a method that typically involves two processes, namely, the feature-visit process and the visit-feature process. The feature-visit process embeds the time series of each feature separately, extracting the relationships between the embedded features. The visit-feature process, on the other hand, first embeds all features of each visit separately, then deals with the temporal visits. However, this method has a downside, as some important low-level information for risk prediction is lost after the first embedding stage..

[Audio] Widely used for patient representation learning, two-stage embedding processes have certain limitations, such as the feature-visit process being unable to differentiate well between two conditions such as Acute Kidney Injury and Chronic Kidney Disease. To address this issue, KA KIT CHIO from Macao Polytechnic University proposed Fine-Grained Personalized Patient Representation Learning as a means of improving the accuracy of risk predictions based on a patient's EHR data. This technique leverages a single stage embedding approach in order to detect slight variations in patient records while preserving the essential information. This results in more precise risk estimations and provides deeper insights into a patient's state of health..

[Audio] In order to capture both temporal information of visits and relationships between different features, KA KIT CHIO proposed the Fine-Grained Personalized Patient Representation Learning (FGRL-Net) network. This network combines the advantages of both visit-feature and feature-visit approaches to effectively capture the fine-grained temporal information of visits and the relationships between different features, thus providing an accurate and personalized representation for clinical risk prediction..

[Audio] KA KIT CHIO from Macao Polytechnic University has presented on FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data, which can lead to some low-level information important for risk prediction getting lost after the first embedding stage. Following that, we will discuss the economic and environmental benefits of investing in renewable energy sources, as well as the potential economic implications of ignoring these sources..

[Audio] We focus on the development of KA KIT CHIO, a fine-grained personalized patient representation learning model for clinical risk prediction based on electronic health records. The model takes demographic data, primary disease information, and dynamic features as inputs, and predicts if a patient will suffer from a certain health risk within a given time window, such as 48 hours. We believe KA KIT CHIO will be of great help to healthcare providers for making timely and accurate decisions for their patients..

[Audio] FGRL-Net, designed by Macao Polytechnic University as part of its research initiative, is a system that seeks to better predict potential risks for patients. To achieve this, FGRL-Net employs a method based on data preprocessing, patient representation learning, and risk prediction modelling. Through the use of this approach, FGRL-Net is able to provide more accurate and personalized predictions for patients, resulting in earlier and more effective treatments..

[Audio] Recent advances in deep learning have shown promising performance in many applications. Leveraging the power of deep learning, this work proposes an RNN-based model - FGRL-Net to effectively learn personalized patient representation from EHR. It is capable of capturing the complex correlations between numerical and categorical features, and classifying the numerical data in terms of generic medical knowledge. For example, the Respiratory Rate Classification Table helps to better understand patient conditions. The reference range of 12 to 20 is interpreted as 'high' when lower than 12, 'normal' when between 12 and 20, and 'low' when higher than 20. The corresponding one-hot vector is 001, 010 and 100 respectively. Thus, with the help of FGRL-Net, we can learn useful representation of the EHR and predict clinical risks more accurately." In this work, we propose an innovative deep learning model FGRL-Net to effectively learn personalized patient representation from Electronic Health Records (EHRs). FGRL-Net is capable of capturing complex correlations between numerical and categorical features, and classifying the numerical data with respect to generic medical knowledge. For instance, a Respiratory Rate Classification Table can precisely come to conclusions about the patient's condition based on a reference range of 12-20, with 'high' being lower than 12, 'normal' being between 12-20, and 'low' being higher than 20. These ranges are represented by a one-hot vector of 001, 010 and 100 respectively. With the help of FGRL-Net, we can learn meaningful representation of the EHR and more accurately predict clinical risks for patients..

[Audio] Making sense of complex medical data is no easy task. To address this issue, recurrent neural networks (RNN)-based approaches and their variants have been proposed. The recent work of KA KIT CHIO from Macao Polytechnic University seen in FGRL-Net, utilizes a Fine-Grained Representation Mechanism (FGRM) that seeks to maintain low-level useful information detected from both visit and feature dimensions. This is achieved by utilizing a Fine-Grained Multi-Layer 1D Convolutional Neural Network that uses an average-based approach. This approach is then applied to the challenge of clinical risk prediction in Electronic Health Records (EHRs)..

[Audio] Recent studies have focused on the ability to capture long-term dependencies in high-dimensional time series data. This work proposes fine-grained personalized patient representation learning to better leverage the longitudinal representation. A new neural network architecture is proposed to capture fine-grained patient-specific patterns from EHRs data. The model can learn the personalized representations across multiple levels of temporal granularity, utilizing both local and global temporal context information. The model is integrated into a comprehensive risk prediction framework and evaluated on three real-world datasets. Results show the effectiveness of the approach in terms of accuracy and robustness..

[Audio] Predicting the risk of certain diseases in patients is a major challenge in healthcare. Traditional statistical methods and machine learning models are not suitable for dealing with the complexity of Electronic Health Records (EHR). To address this challenge, researchers have suggested recurrent neural network-based approaches and their variations. This approach introduces a patient representation, REP, and combines REPf and REPt. After this, the GRU function is used for the patient representation REP, and a prediction result is generated with the aid of a binary cross entropy function to calculate the loss..

[Audio] From the table, there is a total of 33,678 patients and 2,202,114 records. Of all the patients, 2,797 died whilst in the hospital, giving an in-hospital mortality rate of 13.23%. Additionally, 4.2% decompensated. There are 12 demographic features, 128 primary diseases, and 76 dynamic features which include 12 numerical ones. Each patient and record is assigned one label each. This data can be used to develop personalized patient representation learning for clinical risk prediction based on electronic health records (EHRs)..

[Audio] We will delve into the performance of our model on a binary classification task by looking at two different measures: binary cross-entropy loss and the area under the receiver operating characteristic curve (AUROC). Additionally, we will examine the area under the precision-recall curve (AUPRC) and the minimum of precision and sensitivity (min(Se, P+))..

[Audio] Our FGRL-Net has demonstrated to be an advantageous option for clinical risk prediction utilizing EHRs. Evidence of its efficacy comes from experiments which have generated an increase in AUROC, AUPRC, and min(Se, P+) of respectively 0.24%, 3.09%, 8.39%, 138.47%, and 94.04%. Thanks to this improved accuracy when predicting clinical risk, FGRL-Net stands out as a trustworthy option that can be tailored to any EHR environment..

Ablation Study. 29.

[Audio] Our research at Macao Polytechnic University focused on FGRL-Net, a fine-grained personalized patient representation learning method for clinical risk prediction based on EHRs. Experiments showed that applying the general medical classification to each baseline model improved the performances of all baselines. This is a significant finding that could positively impact healthcare workers in providing better results when predicting risks in their patients..

[Audio] We will discuss an innovative way of predicting clinical risk, focusing on the case of a patient who suddenly suffered a cardiac arrest. This patient's condition took a turn for the worse and eventually led to their death. Our model proved to be effective in detecting the risk of death, accurately predicting it at the 150th time step. This shows that our model can be a useful tool in helping medical professionals predict clinical risk and provide appropriate care..

[Audio] We observe in case two that the patient was stable and alive during monitoring. Nonetheless, the baseline models failed to make accurate predictions. Our FGRL-Net model, however, was more successful in predicting the likelihood of sudden cardiac arrest. This indicates the usefulness of our model in offering a precise and personalized depiction of a patient's health condition to obtain more reliable clinical risk predictions..

[Audio] Our research has determined that FGRL-Net is a beneficial method for clinical risk prediction when using electronic health records (EHRs). FGRL-Net has the capability to identify correlations between features and differences in features, as well as the simpler information of the data. Moreover, FGRL-Net has the ability to classify numerical dynamic characteristics in a generic classification model. In conclusion, FGRL-Net provides a successful tool for clinical risk prediction using EHRs..

Thank you!. 34.