Event prediction by estimating continuously the completion of a single temporal pattern's instances.

OBJECTIVE: Develop a new method for continuous prediction that utilizes a single temporal pattern ending with an event of interest and its multiple instances detected in the temporal data. METHODS: Use temporal abstraction to transform time series, instantaneous events, and time intervals into a uniform representation using symbolic time intervals (STIs). Introduce a new approach to event prediction using a single time intervals-related pattern (TIRP), which can learn models to predict whether and when an event of interest will occur, based on multiple instances of a pattern that end with the event. RESULTS: The proposed methods achieved an average improvement of 5% AUROC over LSTM-FCN, the best-performed baseline model, out of the evaluated baseline models (RawXGB, Resnet, LSTM-FCN, and ROCKET) that were applied to real-life datasets. CONCLUSION: The proposed methods for predicting events continuously have the potential to be used in a wide range of real-world and real-time applications in diverse domains with heterogeneous multivariate temporal data. For example, it could be used to predict panic attacks early using wearable devices or to predict complications early in intensive care unit patients.

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality.

The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population-level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas.

Prediction of acute hypertensive episodes in critically ill patients.

Prevention and treatment of complications are the backbone of medical care, particularly in critical care settings. Early detection and prompt intervention can potentially prevent complications from occurring and improve outcomes. In this study, we use four longitudinal vital signs variables of intensive care unit patients, focusing on predicting acute hypertensive episodes (AHEs). These episodes represent elevations in blood pressure and may result in clinical damage or indicate a change in a patient's clinical situation, such as an elevation in intracranial pressure or kidney failure. Prediction of AHEs may allow clinicians to anticipate changes in the patient's condition and respond early on to prevent these from occurring. Temporal abstraction was employed to transform the multivariate temporal data into a uniform representation of symbolic time intervals, from which frequent time-intervals-related patterns (TIRPs) are mined and used as features for AHE prediction. A novel TIRP metric for classification, called coverage, is introduced that measures the coverage of a TIRP's instances in a time window. For comparison, several baseline models were applied on the raw time series data, including logistic regression and sequential deep learning models, are used. Our results show that using frequent TIRPs as features outperforms the baseline models, and the use of the coverage, metric outperforms other TIRP metrics. Two approaches to predicting AHEs in real-life application conditions are evaluated: using a sliding window to continuously predict whether a patient would experience an AHE within a specific prediction time period ahead, our models produced an AUC-ROC of 82%, but with low AUPRC. Alternatively, predicting whether an AHE would generally occur during the entire admission resulted in an AUC-ROC of 74%.