A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis

Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study

BACKGROUND

Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain regions due to immune escape and waning vaccine efficacy. Identifying high-risk patients to mitigate transmission and avert complications promptly is imperative. Nevertheless, the current diagnostic methods, including PCR and bacterial culture, are time-consuming and expensive. Some studies have attempted to develop risk prediction models based on multivariate data, but their performance can be improved. Therefore, this study aims to further optimize and expand the risk assessment tool to more efficiently identify high-risk individuals and compensate for the shortcomings of existing diagnostic methods.

OBJECTIVE

The aim of this study was to develop a pertussis risk prediction model that is both efficient and has good generalization ability, applicable to different datasets. The model was constructed using machine learning techniques based on multicenter data and screened for key features. The performance and generalization ability of the model were evaluated by deploying it on an online platform. At the same time, this study aims to provide a rapid and accurate auxiliary diagnostic tool for clinical practice to help identify high-risk patients in a timely manner, optimize early intervention strategies, reduce the risk of complications and reduce transmission, thereby improving the efficiency of public health management.

METHODS

First, data from 1085 suspected pertussis patients from 7 centers were collected, and ten key features were analyzed using the lasso regression and Boruta algorithm: PDW-MPV-RATIO, SII, white blood cells, platelet distribution width, mean platelet volume, lymphocytes, cough duration, vaccination, fever, and lytic lymphocytes.Eight models were then trained and validated to assess their performance and to confirm their generalization ability with external datasets based on these features. Finally, an online platform was constructed for clinicians to use the models in real time.

RESULTS

The random forest model demonstrated excellent discrimination ability in the validation set, with an AUC of 0.98, and an AUC of 0.97 in the external validation set. Calibration curve and decision curve analysis showed that the model had high accuracy in predicting low-to-medium risk patients, which could help clinicians avoid unnecessary interventions, especially in resource-limited settings. The application of this model can help optimize the early identification and management of high-risk patients and improve clinical decision-making.

CONCLUSION

The pertussis prediction model devised in this study was validated using multicenter data, exhibited high prediction performance, and was successfully implemented online. Future research should broaden the data sources and incorporate dynamic data to enhance the model's accuracy and applicability.

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.