The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed by critical care needs. Existing research faces challenges such as feature extraction difficulties, low accuracy, and resource-intensive features. Some studies have explored deep learning models that utilize raw clinical inputs. However, these models are considered non-interpretable black boxes, which prevents their wide application. The objective of the study is to develop a new method using stochastic signal analysis and machine learning techniques to effectively extract features with strong predictive power from ICU patients' real-time time series of vital signs for accurate and timely ICU outcome prediction. The results show the proposed method extracted meaningful features and outperforms baseline methods, including APACHE IV (AUC = 0.750), deep learning-based models (AUC = 0.732, 0.712, 0.698, 0.722), and statistical feature classification methods (AUC = 0.765) by a large margin (AUC = 0.869). The proposed method has clinical, management, and administrative implications since it enables healthcare professionals to identify deviations from prognostications timely and accurately and, therefore, to conduct proper interventions.

Background: Efficient allocation of health care resources is essential for long-term hospital operation. Effective intensive care unit (ICU) management is essential for alleviating the financial strain on health care systems. Accurate prediction of length-of-stay in ICUs is vital for optimizing capacity planning and resource allocation, with the challenge of achieving early, real-time predictions. Objective: This study aimed to develop a predictive model, namely wavelet long short-term memory model (WT-LSTM), for ICU length-of-stay using only real-time vital sign data. The model is designed for urgent care settings where demographic and historical patient data or laboratory results may be unavailable; the model leverages real-time inputs to deliver early and accurate ICU length-of-stay predictions. Methods: The proposed model integrates discrete wavelet transformation and long short-term memory (LSTM) neural networks to filter noise from patients’ vital sign series and improve length-of-stay prediction accuracy. Model performance was evaluated using the electronic ICU database, focusing on 10 common ICU admission diagnoses in the database. Results: The results demonstrate that WT-LSTM consistently outperforms baseline models, including linear regression, LSTM, and bidirectional long short-term memory, in predicting ICU length-of-stay using vital sign data, achieving significant improvements in mean square error. Specifically, the wavelet transformation component of the model enhances the overall performance of WT-LSTM. Removing this component results in an average decrease of 3.3% in mean square error; such a phenomenon is particularly pronounced in specific patient cohorts. The model’s adaptability is highlighted through real-time predictions using only 3-hour, 6-hour, 12-hour, and 24-hour input data. Using only 3 hours of input data, the WT-LSTM model delivers competitive results across the 10 most common ICU admission diagnoses, often outperforming Acute Physiology and Chronic Health Evaluation IV, the leading ICU outcome prediction system currently implemented in clinical practice. WT-LSTM effectively captures patterns from vital signs recorded during the initial hours of a patient’s ICU stay, making it a promising tool for early prediction and resource optimization in the ICU. Conclusions: Our proposed WT-LSTM model, based on real-time vital sign data, offers a promising solution for ICU length-of-stay prediction. Its high accuracy and early prediction capabilities hold significant potential for enhancing clinical practice, optimizing resource allocation, and supporting critical clinical and administrative decisions in ICU management.

Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care. For example, an echocardiogram is a type of laboratory test that is extremely important and not easily accessible. The increasing demand for echocardiograms underscores the imperative for more efficient scheduling protocols. Despite this pressing need, limited research has been conducted in this area. Objective: The study aims to develop an interpretable machine learning model for determining the urgency of patients requiring echocardiograms, thereby aiding in the prioritization of scheduling procedures. Furthermore, this study aims to glean insights into the pivotal attributes influencing the prioritization of echocardiogram appointments, leveraging the high interpretability of the machine learning model. Methods: Empirical and predictive analyses have been conducted to assess the urgency of patients based on a large real-world echocardiogram appointment dataset (ie, 34,293 appointments) sourced from electronic health records encompassing administrative information, referral diagnosis, and underlying patient conditions. We used a state-of-the-art interpretable machine learning algorithm, the optimal sparse decision tree (OSDT), renowned for its high accuracy and interpretability, to investigate the attributes pertinent to echocardiogram appointments. Results: The method demonstrated satisfactory performance (F1-score=36.18% with an improvement of 1.7% and F2-score=28.18% with an improvement of 0.79% by the best-performing baseline model) in comparison to the best-performing baseline model. Moreover, due to its high interpretability, the results provide valuable medical insights regarding the identification of urgent patients for tests through the extraction of decision rules from the OSDT model. Conclusions: The method demonstrated state-of-the-art predictive performance, affirming its effectiveness. Furthermore, we validate the decision rules derived from the OSDT model by comparing them with established medical knowledge. These interpretable results (eg, attribute importance and decision rules from the OSDT model) underscore the potential of our approach in prioritizing patient urgency for echocardiogram appointments and can be extended to prioritize other laboratory test appointments using electronic health record data.

BACKGROUND Efficient allocation of healthcare resources is essential for sustainable hospital operation. Effective intensive care unit (ICU) management is essential for alleviating the financial strain on healthcare systems. Accurate prediction of length-of-stay in ICUs is vital for optimizing capacity planning and resource allocation, with the challenge of achieving early, real-time predictions. OBJECTIVE This study aims to develop a predictive model, namely WT-LSTM, for ICU length-of-stay using only real-time vital sign data. The model is designed for urgent care settings where demographic and historical patient data or lab results may be unavailable; the model leverages real-time inputs to deliver early and accurate ICU length-of-stay predictions. METHODS The proposed model integrates discrete wavelet transformation and Long Short-Term Memory (LSTM) neural networks to filter noise from patients’ vital sign series and improve length-of-stay prediction accuracy. Model performance was evaluated using the eICU database, focusing on ten common ICU admission diagnoses in the database. RESULTS The results demonstrate that WT-LSTM consistently outperforms baseline models, including linear regression, LSTM, and BiLSTM, in predicting ICU length-of-stay using vital sign data, achieving significant improvements in Mean Squared Error (MSE). Specifically, the wavelet transformation component of the model enhances the overall performance of WT-LSTM. Removing this component results in an average decrease of 3.3% in MSE; such a phenomenon is particularly pronounced in specific patient cohorts. The model's adaptability is highlighted through real-time predictions using only 3-hour, 6-hour, 12-hour, and 24-hour input data. Using only three hours of input data, the WT-LSTM model delivers competitive results across the ten most common ICU admission diagnoses, often outperforming APACHE IV, the leading ICU outcome prediction system currently implemented in clinical practice. WT-LSTM effectively captures patterns from vital signs recorded during the initial hours of a patient’s ICU stay, making it a promising tool for early prediction and resource optimization in the ICU. CONCLUSIONS Our proposed WT-LSTM model, based on real-time vital sign data, offers a promising solution for ICU length-of-stay prediction. Its high accuracy and early prediction capabilities hold significant potential for enhancing clinical practice, optimizing resource allocation, and supporting critical clinical and administrative decisions in ICU management.

The increased utilization of echocardiography in clinical practice has witnessed a substantial rise, underscoring its pivotal role as a diagnostic tool for various cardiovascular conditions. However, due to the relative scarcity of echocardiography tests, challenges persist in efficiently prioritizing patients for echocardiographic assessments. In this study, we develop a model to assess the urgency of appointments by considering both clinical and administrative variables extracted from Electronic Health Record data. We use double machine learning techniques to analyze these variables and improve our predictions of patient urgency. Traditional methods for estimating variable effects have limitations, particularly in our research context, where clinical and administrative variables may influence one another while also directly impacting the outcome (i.e., the urgency of appointments). In this work, we address this issue by developing an urgency stratification model using double machine learning, which disentangles the complex relationships between variables. Our evaluations demonstrate that the proposed model not only outperforms traditional machine learning methods in predicting appointment urgency but also provides robust estimations of variable effects. Specifically, our results underscore the critical roles of administrative variables and cancer-related comorbidity variables in patient prioritization and appointment urgency prediction. By leveraging double machine learning techniques, our method can enhance the efficiency and effectiveness of echocardiography utilization in clinical practice. It provides clinicians with actionable insights for patient prioritization, facilitating the timely identification of urgent cases and the optimal allocation of resources. Our work contributes to the advancement of healthcare practices by leveraging sophisticated analytics to improve patient care delivery and streamline clinical workflows in echocardiography laboratories. A similar research design can also be extended to other advanced yet limited laboratory tests to help prioritize medical resources.

Aging disrupts sleep, but how these changes are structured across circadian time, vigilance states, and sex remains poorly understood, because most prior studies used single-sex cohorts and few days of recordings. We continuously recorded 14 days of EEG/EMG in 24 C57BL/6J mice using a balanced 2 × 2 design (young vs. old; male vs. female; n = 6/group). A comprehensive multiscale analysis of the extended dataset enabled detailed reconstruction of 24 h sleep–wake architecture, better characterization of natural day-to-day variability including across multiple estrous cycles, and detection of rare bouts and transition events. Across seven levels of analysis, from circadian profiles to EEG spectral parameterization, the strongest aging effect was a dark-phase-specific 17–18% loss of theta-dominant active wake (TDW) in both sexes, with reciprocal increases in quiet wake (nTDW) and NREM sleep. We also identified a recurring N-shaped structural motif at the dark-to-light transition, where age-related and several sex-associated differences were most apparent. Broadly, old mice exhibited (i) shorter TDW bouts; (ii) a shift in NREM exit kinetics toward wakefulness; (iii) more brief and poorly consolidated “out-block” NREM episodes; and (iv) a slowing of waking theta and higher low-frequency TDW power. Variance decomposition indicated that statistical power depends more on sample size than on recording length. Together, aging reflects a coordinated, circadian-phase-specific reorganization of sleep–wake architecture. Sex-related and interaction findings should be interpreted as hypothesis-generating pending larger cohorts.