Explanatory predictive model for COVID-19 severity risk employing … – Nature.com
*The datasets used and/or analyzed during the current study are available from the corresponding author.
We used a casecontrol study for our research. All patients were recruited from Rabats Cheikh Zaid University Center Hospital. COVID-19 hospitalizations occurred between March 6, 2020, and May 20, 2020, and were screened using clinical features (fever, cough, dyspnea, fatigue, headache, chest pain, and pharyngeal discomfort) and epidemiological histology. Any patient admitted to Cheikh Zaid Hospital with a positive PCR-RT for SARS-CoV-2 was considered a COVID-19 case. According to the severity, the cases were divided into two categories: Cases with COVID symptoms and a positive RT-PCR test requiring oxygen therapy are considered severe. Case not requiring oxygen therapy: any case with or without COVID symptoms, normal lung CT with positive RT-PCR. The Controls were selected from Cheikh Zaid Hospital employees (two to three per week) who exhibited no clinical signs of COVID-19 and whose PCR-RT test was negative for the virus. People with chronic illnesses (high blood pressure, diabetes, cancer, and cardiovascular disease) and those who had used platelet-disrupting medications within the previous two weeks (Aspirin, Prasugrel, Clopidogrel, Ticagrelor, Cangrelor, Cilostazol, Dipyridamole, Abciximab, Eptifibatide, Tirofiban, Non-steroidal anti-inflammatory drugs) are excluded from our study (Fig.2).
Consequently, a total of 87 participants were selected for this study and divided as follows: 57 Patients infected with SARS-CoV-2: Thirty without severe COVID-19 symptoms, twenty-seven with severe symptoms requiring hospitalization, and thirty healthy controls. Table1 displays patients basic demographic and clinical information.
The cytokines investigated in our study are displayed in Table2, it consists of two panels, the first one contains 48 cytokines, while the second panel contains only 21 cytokines.
A data imputation procedure was considered for filling in missing values in entries. In fact, 29 individuals in our dataset had a missingness rate of more than 50 percent for their characteristics (cytokines), therefore our analysis will be significantly impacted by missing values. The most prevalent method for dealing with incomplete information is data imputation prior to classification, which entails estimating and filling in the missing values using known data.
There are a variety of imputation approaches, such as mean, k-nearest neighbors, regression, Bayesian estimation, etc. In this article, we apply the iterative imputation strategy Multiple imputation using chained equations Forest (Mice-Forest) to handle the issue of missing data. The reason for this decision is to employ an imputation approach that can handle any sort of input data and makes as few assumptions as possible about the datas structure55.the chained equation process is broken down into four core steps which are repeated until optimal results are achieved56. The first step involves replacing every missing data with the mean of the observed values for the variable. In the second phase, mean imputations are reset to missing. In the third step, the observed values of a variable (such as x) are regressed on the other variables, with x functioning as the dependent variable and the others as the independent variables. As the variables in this investigation are continuous, predictive mean matching (PPM) was applied.
The fourth stage involves replacing the missing data with the regression models predictions. This imputed value would subsequently be included alongside observed values for other variables in the independent variables. An Iteration is the recurrence of steps 2 through 4 for each variable with missing values. After one iteration, all missing values are replaced by regression predictions based on observed data. In the present study, we examined the results of 10 iterations.
The convergence of the regression coefficients is ideally the product of numerous iterations. After each iteration, the imputed values are replaced, and the number of iterations may vary. In the present study, we investigated the outcomes of 10 iterations. This is a single "imputation." Multiple imputations are performed by holding the observed values of all variables constant and just modifying the missing values to their appropriate imputation predictions. Depending on the number of imputations, this leads to the development of multiply imputed datasets (30, in this study). The number of imputations depends on the values that are missing. The selection of 30 imputations was based on the White et al.57 publication. The fraction of missing data was around 30%. We utilized the version 5.4.0 of the miceforest Python library to impute missing data. The values of the experiments hyper-parameters for the Mice-Forest technique are listed in Table3, and Fig.4 illustrates the distribution of each imputation comparing to original data (in red).
The distribution of each imputation compared to the original data (in red).
Machine learning frameworks have demonstrated their ability to deal with complex data structures, producing impressive results in a variety of fields, including health care. However, a large amount of data is required to train these models58. This is particularly challenging in this study because available datasets are limited (87 records and 48 attributes) due to acquisition accessibility and costs, such limited data cannot be used to analyze and develop models.
To solve this problem, Synthetic Data Generation (SDG) is one of the most promising approaches (SDG) and it opens up many opportunities for collaborative research, such as building prediction models and identifying patterns.
Synthetic Data is artificial data generated by a model trained or built to imitate the distributions (i.e., shape and variance) and structure (i.e., correlations among the variables) of actual data59,60. It has been studied for several modalities within healthcare, including biological signals61, medical pictures62, and electronic health records (EHR)63.
In this paper, a VAE network-based approach is suggested to generate 500 samples of synthetic cytokine data from real data. VAEs process consists of providing labeled sample data (X) to the Encoder, which captures the distribution of the deep feature (z), and the Decoder, which generates data from the deep feature (z) (Fig.1).
The VAE architecture preserved each samples probability and matched the column means to the actual data. Figure5 depicts this by plotting the mean of the real data column on the X-axis and the mean of the synthetic data column on the Y-axis.
Each point represents a column mean in the real and synthetic data. A perfect match would be indicated by all the points lying on the line y=x.
The cumulative feature sum is an extra technique for comparing synthetic and real data. The feature sum can be considered as the sum of patient diagnosis values. As shown in Fig.6, a comparison of the global distribution of feature sums reveals a significant similarity between the data distributions of synthetic and real data.
Plots of each feature in our actual dataset demonstrate the similarity between the synthesized and actual datasets.
Five distinct models are trained on synthetic data (Random Forest, XGBoost, Bagging Classifier, Decision Tree, and Gradient boosting Classifier). Real data is used for testing, and three metrics were applied to quantify the performance of fitting: precision, recall, F1 score, and confusion matrix.
As shown in Figs.7, 8, 9, 10 and 11 the performance of the Gradient Boosting Classifier proved to be superior to that of other models, with higher Precision, Recall, and F1 score for each class, and a single misclassification. Consequently, we expect that SHAP and LIMEs interpretation of the Gradient Boosting model for the testing set will reflect accurate and exhaustive information for the cytokines data set.
Matrix confusion and Report Classification of Random Forest.
Matrix confusion and Report Classification of Gradient Boosting.
Matrix confusion and Report Classification of XGB Classifier.
Matrix confusion and Report Classification of Bagging Classifier.
Matrix confusion and Report Classification of Decision Tree.
Explaining a prediction refers to the presentation of written or visual artifacts that enable qualitative knowledge of the relationship between the instances components and the models prediction. We suggest that if the explanations are accurate and understandable, explaining predictions is an essential component of convincing humans to trust and use machine learning effectively43. Figure12 depicts the process of explaining individual predictions using LIME and SHAP as approaches that resemble the classifiers black box to explain individual predictions. When explanations are provided, a doctor is clearly in a much better position to decide using a model. Gradient Boosting predicts whether a patient has an acute case of COVID-19 in our study, whereas LIME and SHAP highlight the cytokines that contributed to this prediction.
The Flow chart demonstrates how Machine learning can be used to make medical decisions. We entered cytokine data from severe, non-severe, and healthy patients, trained predictive models on cytokine data, and then used LIME and SHAP to explain the most important cytokine for each class of patients (Fig.12).
The SHAP explanation utilized in this study is the Kernel Explainer, a model-agnostic approach that produces a weighted linear regression depending on the data, predictions, and model64. It examines the contribution of a feature by evaluating the model output if the feature is removed from the input for various (theoretically all) combinations of features. The Kernel Explainer makes use of a backdrop dataset to demonstrate how missing inputs are defined, i.e., how a missing feature is approximated during the toggling process.
SHAP computes the impact of each characteristic on the learned systems predictions. Using gradient descent, SHAP values are created for a single prediction (local explanations) and multiple samples (resulting in global explanations).
Figure13 illustrates the top 20 SHAP value features for each class in the cytokine data prediction model (Healthy, Severe, and Non-Severe classes). The distribution of SHAP values for each feature is illustrated using a violin diagram. Here, the displayed characteristics are ordered by their highest SHAP value. The horizontal axis represents the SHAP value. The bigger the positive SHAP value, the greater the positive effect of the feature, and vice versa. The color represents the magnitude of a characteristic value. The color shifts from red to blue as the features value increases and decreases. For example, Mip-1b in Figure8, the positive SHAP value increases as the value of the feature increases. This may be interpreted as the probability of a patient developing COVID-19, severity increasing as MIP-1b levels rise.
Examples of SHAP values computed for individuals predictions (local explanations) for Healthy, Non-Sever, and Sever patients.
In the situation of a healthy patient, TNF, IL-22, and IL-27 are the most influential cytokines, as shown in Fig.14s first SHAP diagram (from left). The second diagram is for a patient with severity, and we can observe that the VEGF-A cytokines value is given greater weight. This can be viewed as an indication that the patient got a serious COVID-19 infection due to the increase in this cytokine.
SHAP diagrams of characteristics with varying conditions: Healthy, Severe, and Non-Severe, respectively.
The last SHAP diagram depicts an instance of a non-Severe patient, and we can see that the higher the feature value, the more positive the direction of IL-27. On the other hand, MDC, PDGF-AB/BB, and VEGF-A cytokines have a deleterious effect. The levels of MDC and PDGF-AB/BB cytokines suggest that the patient may be recovering, however, the presence of VEGF-A suggests that the patient may develop a severe case of COVID-19, despite being underweight.
LIME is a graphical approach that helps explain specific predictions. It can be applied to any supervised regression or classification model, as its name suggests. Behind the operation of LIME is the premise that every complex model is linear on a local scale and that it is possible to fit a simple model to a single observation that mimics the behavior of the global model at that locality. LIME operates in our context by sampling the data surrounding a prediction and training a simple interpretable model to approximate the black box of the Gradient Boosting model. The interpretable model is used to explain the predictions of the black-box model in a local region surrounding the prediction by generating explanations regarding the contributions of the features to these predictions. As shown in Fig.15, a bar chart depicts the distribution of LIME values for each feature, indicating the relative importance of each cytokine for predicting Severity in each instance. The order of shown features corresponds to their LIME value.
In the illustrations explaining various LIME predictions presented in Fig.16. We note that the model has a high degree of confidence that the condition of these patients is Severe, Non-Severe, or Healthy. In the graph where the predicted value is 2, indicating that the expected scenario for this patient is Severe (which is right), we can see for this patient that Mip-1b level greater than 41 and VEGF-A level greater than 62 have the greatest influence on severity, increasing it. However, MCP-3 and IL-15 cytokines have a negligible effect in the other direction.
Explaining individual predictions of Gradient descent classifier by LIME.
Alternatively, there are numerous cytokines with significant levels that influence non-Severity. For example, IL-27 and IL-9, as shown in the middle graph in Fig.14. and that IL-12p40 below a certain value may have the opposite effect on model decision-making. RANTES levels less than 519, on the other hand, indicate that the patient is healthy, as shown in Fig.16.
By comparing the individuals explanation of SHAP values to the individuals explanation of LIME values for the same patients, we may be able to determine how these two models differ in explaining the Severity results of the Gradient descent model. As a result, we can validate and gain insight into the impact of the most significant factors. To do so, we begin by calculating the frequency of the top ten features among all patients for each Explainer. We only consider features that appear in the top three positions, as we believe this signifies the features high value, and we only consider the highest-scoring features that appear at least ten times across all SHAP or LIME explanations (Tables 4, 5, and 6).
Table4 demonstrates that MIP-1b, VEGF-A, and IL-17A have Unanimous Importance according to the SHAP Value and LIME. In addition, we can remark that M-CSF is necessary for LIME but is ranks poor.
In the instance of non-Severity, Table5 reveals that IL-27 and IL-9 are essential in both explanatory models for understanding non-Severity in patients. We can see that IL-12p40 and MCP-3 are also essential for LIME and are highly ranked; hence, we add these two characteristics to the list of vital features for the non-Severity instance. RANTES, TNF, IL-9, IL-27, and MIP-1b are the most significant elements in the Healthy scenario, according to Table6.
The elements that explain the severity of the COVID-19 sickness are summarized in Table7.
See the rest here:
Explanatory predictive model for COVID-19 severity risk employing ... - Nature.com
- Open source machine learning systems are highly vulnerable to security threats - TechRadar - December 22nd, 2024 [December 22nd, 2024]
- After the PS5 Pro's less dramatic changes, PlayStation architect Mark Cerny says the next-gen will focus more on CPUs, memory, and machine-learning -... - December 22nd, 2024 [December 22nd, 2024]
- Accelerating LLM Inference on NVIDIA GPUs with ReDrafter - Apple Machine Learning Research - December 22nd, 2024 [December 22nd, 2024]
- Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis - BMC... - December 22nd, 2024 [December 22nd, 2024]
- Machine learning uncovers three osteosarcoma subtypes for targeted treatment - Medical Xpress - December 22nd, 2024 [December 22nd, 2024]
- From Miniatures to Machine Learning: Crafting the VFX of Alien: Romulus - Animation World Network - December 22nd, 2024 [December 22nd, 2024]
- Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning -... - December 22nd, 2024 [December 22nd, 2024]
- This AI Paper from Microsoft and Novartis Introduces Chimera: A Machine Learning Framework for Accurate and Scalable Retrosynthesis Prediction -... - December 18th, 2024 [December 18th, 2024]
- Benefits and Challenges of Integrating AI and Machine Learning into EHR Systems - Healthcare IT Today - December 18th, 2024 [December 18th, 2024]
- The History Of AI: How Machine Learning's Evolution Is Reshaping Everything Around Us - SlashGear - December 18th, 2024 [December 18th, 2024]
- AI and Machine Learning to Enhance Pension Plan Governance and the Investor Experience: New CFA Institute Research - Fintech Finance - December 18th, 2024 [December 18th, 2024]
- Address Common Machine Learning Challenges With Managed MLflow - The New Stack - December 18th, 2024 [December 18th, 2024]
- Machine Learning Used To Classify Fossils Of Extinct Pollen - Offworld Astrobiology Applications? - Astrobiology News - December 18th, 2024 [December 18th, 2024]
- Machine learning model predicts CDK4/6 inhibitor effectiveness in metastatic breast cancer - News-Medical.Net - December 18th, 2024 [December 18th, 2024]
- New Lockheed Martin Subsidiary to Offer Machine Learning Tools to Defense Customers - ExecutiveBiz - December 18th, 2024 [December 18th, 2024]
- How Powerful Will AI and Machine Learning Become? - International Policy Digest - December 18th, 2024 [December 18th, 2024]
- ChatGPT-Assisted Machine Learning for Chronic Disease Classification and Prediction: A Developmental and Validation Study - Cureus - December 18th, 2024 [December 18th, 2024]
- Blood Tests Are Far From Perfect But Machine Learning Could Change That - Inverse - December 18th, 2024 [December 18th, 2024]
- Amazons AGI boss: You dont need a PhD in machine learning to build with AI anymore - Fortune - December 18th, 2024 [December 18th, 2024]
- From Novice to Pro: A Roadmap for Your Machine Learning Career - KDnuggets - December 10th, 2024 [December 10th, 2024]
- Dimension nabs $500M second fund for 'still contrary' intersection of bio and machine learning - Endpoints News - December 10th, 2024 [December 10th, 2024]
- Using Machine Learning to Make A Really Big Detailed Simulation - Astrobites - December 10th, 2024 [December 10th, 2024]
- Driving Business Growth with GreenTomatos Data and Machine Learning Strategy on Generative AI - AWS Blog - December 10th, 2024 [December 10th, 2024]
- Unlocking the power of data analytics and machine learning to drive business performance - WTW - December 10th, 2024 [December 10th, 2024]
- AI and the Ethics of Machine Learning | by Abwahabanjum | Dec, 2024 - Medium - December 10th, 2024 [December 10th, 2024]
- Differentiating Cystic Lesions in the Sellar Region of the Brain Using Artificial Intelligence and Machine Learning for Early Diagnosis: A Prospective... - December 10th, 2024 [December 10th, 2024]
- New Amazon SageMaker AI Innovations Reimagine How Customers Build and Scale Generative AI and Machine Learning Models - Amazon Press Release - December 10th, 2024 [December 10th, 2024]
- What is Machine Learning? 18 Crucial Concepts in AI, ML, and LLMs - Netguru - December 5th, 2024 [December 5th, 2024]
- Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda - BMC Infectious Diseases - December 5th, 2024 [December 5th, 2024]
- Interdisciplinary Team Needed to Apply Machine Learning in Epilepsy Surgery: Lara Jehi, MD, MHCDS - Neurology Live - December 5th, 2024 [December 5th, 2024]
- A multimodal machine learning model for the stratification of breast cancer risk - Nature.com - December 5th, 2024 [December 5th, 2024]
- Machine learning based intrusion detection framework for detecting security attacks in internet of things - Nature.com - December 5th, 2024 [December 5th, 2024]
- Machine learning evaluation of a hypertension screening program in a university workforce over five years - Nature.com - December 5th, 2024 [December 5th, 2024]
- Vaultree Introduces VENum Stack: Combining the Power of Machine Learning and Encrypted Data Processing for Secure Innovation - PR Newswire - December 5th, 2024 [December 5th, 2024]
- Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics - pnas.org - December 5th, 2024 [December 5th, 2024]
- AI and Machine Learning - Maryland to use AI technology to manage traffic flow - SmartCitiesWorld - December 5th, 2024 [December 5th, 2024]
- Researchers make machine learning breakthrough in lithium-ion tech here's how it could make aging batteries safer - Yahoo! Voices - December 5th, 2024 [December 5th, 2024]
- Integrating IoT and machine learning: Benefits and use cases - TechTarget - December 5th, 2024 [December 5th, 2024]
- Landsat asks industry for artificial intelligence (AI) and machine learning for satellite operations - Military & Aerospace Electronics - December 5th, 2024 [December 5th, 2024]
- Machine learning optimized efficient graphene-based ultra-broadband solar absorber for solar thermal applications - Nature.com - December 5th, 2024 [December 5th, 2024]
- Polymathic AI Releases The Well: 15TB of Machine Learning Datasets Containing Numerical Simulations of a Wide Variety of Spatiotemporal Physical... - December 5th, 2024 [December 5th, 2024]
- Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China... - December 5th, 2024 [December 5th, 2024]
- Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes - Nature.com - November 30th, 2024 [November 30th, 2024]
- Predicting the time to get back to work using statistical models and machine learning approaches - BMC Medical Research Methodology - November 30th, 2024 [November 30th, 2024]
- AI and Machine Learning - US releases recommendations for use of AI in critical infrastructure - SmartCitiesWorld - November 30th, 2024 [November 30th, 2024]
- Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations -... - November 28th, 2024 [November 28th, 2024]
- Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques - Nature.com - November 28th, 2024 [November 28th, 2024]
- Evaluation and prediction of the physical properties and quality of Jatob-do-Cerrado seeds processed and stored in different conditions using machine... - November 28th, 2024 [November 28th, 2024]
- Researchers use fitness tracker data and machine learning to detect bipolar disorder mood swings - Medical Xpress - November 28th, 2024 [November 28th, 2024]
- Advances in AI and Machine Learning for Nuclear Applications - Frontiers - November 28th, 2024 [November 28th, 2024]
- Researchers make machine learning breakthrough in lithium-ion tech here's how it could make aging batteries safer - The Cool Down - November 28th, 2024 [November 28th, 2024]
- Svitla Systems Publishes Results of the Study on Machine Learning's Role in Credit Scoring - Newsfile - November 28th, 2024 [November 28th, 2024]
- Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study - Nature.com - November 28th, 2024 [November 28th, 2024]
- Quantum Machine Learning: Bridging the Future of AI and Quantum Computing - TechBullion - November 28th, 2024 [November 28th, 2024]
- AI and machine learning trends in healthcare - Healthcare Leader - November 28th, 2024 [November 28th, 2024]
- Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics... - November 28th, 2024 [November 28th, 2024]
- Revolutionizing Business Systems with Machine Learning: Practical Innovations for the Modern Era - TechBullion - November 28th, 2024 [November 28th, 2024]
- Can AI improve plant-based meats? Using mechanical testing and machine learning to mimic the sensory experience - Phys.org - November 16th, 2024 [November 16th, 2024]
- Machine Learning Reveals Impact of Microbial Load on Gut Health and Disease - Genetic Engineering & Biotechnology News - November 16th, 2024 [November 16th, 2024]
- Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective... - November 16th, 2024 [November 16th, 2024]
- Apple Researchers Propose Cut Cross-Entropy (CCE): A Machine Learning Method that Computes the Cross-Entropy Loss without Materializing the Logits for... - November 16th, 2024 [November 16th, 2024]
- Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy - Nature.com - November 16th, 2024 [November 16th, 2024]
- Facilitated the discovery of new / Co-based superalloys by combining first-principles and machine learning - Nature.com - November 16th, 2024 [November 16th, 2024]
- Thwarting Phishing Attacks with Predictive Analytics and Machine Learning in 2024 - Petri.com - November 16th, 2024 [November 16th, 2024]
- Optoelectronic performance prediction of HgCdTe homojunction photodetector in long wave infrared spectral region using traditional simulations and... - November 16th, 2024 [November 16th, 2024]
- A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine-learning classifiers and intraoral scanners (a... - November 16th, 2024 [November 16th, 2024]
- AI and Machine Learning - Google and National League of Cities develop AI toolkit - SmartCitiesWorld - November 16th, 2024 [November 16th, 2024]
- Machine learning for the physics of climate - Nature.com - November 14th, 2024 [November 14th, 2024]
- Red Hat acquires tech to lower the cost of machine learning - ComputerWeekly.com - November 14th, 2024 [November 14th, 2024]
- SUU Professor Receives Grant to Develop Machine Learning Certificate - Southern Utah University - November 14th, 2024 [November 14th, 2024]
- Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning -... - November 14th, 2024 [November 14th, 2024]
- SNPs and blood inflammatory marker featured machine learning for predicting the efficacy of fluorouracil-based chemotherapy in colorectal cancer -... - November 14th, 2024 [November 14th, 2024]
- Speech production under stress for machine learning: multimodal dataset of 79 cases and 8 signals - Nature.com - November 14th, 2024 [November 14th, 2024]
- Xbox Series X Machine Learning Hardware Has Some Use Cases, But Microsoft Never Showed Interest in Doing Anything With It - Wccftech - November 14th, 2024 [November 14th, 2024]
- Get An Introduction to Optimization: With Applications to Machine Learning, 5th Edition for FREE and save $106! - BetaNews - November 14th, 2024 [November 14th, 2024]
- New Study Uses fMRI and Machine Learning to Explore Brain Function - AZoRobotics - November 14th, 2024 [November 14th, 2024]
- Introduction to Machine Learning (ML) | by Venkat | Nov, 2024 - Medium - November 14th, 2024 [November 14th, 2024]
- The future of PC gaming will be AI-driven - AMD confirms machine learning FSR 4 for 2025, launching in Call of Duty: Black Ops 6 - TechRadar - November 4th, 2024 [November 4th, 2024]
- Machine-Learning Platform Gives DoD Ability To ID Threat Network Activity - Defense Innovation Unit - November 4th, 2024 [November 4th, 2024]
- Machine Learning Offers a Water Bill Discount to Wealthy Portlander - Willamette Week - November 4th, 2024 [November 4th, 2024]