The infant health effects of doulas: Leveraging big data and machine learning to inform cost-effective targeting.

Doula services represent an underutilized maternal and child health intervention with the potential to improve outcomes through the provision of physical, emotional, and informational support. However, there is limited evidence of the infant health effects of doulas despite well-established connections between maternal and infant health. Moreover, because the availability of doulas is limited and often not covered by insurers, existing evidence leaves unclear if or how doula services should be allocated to achieve the greatest improvements in outcomes. We use unique data and machine learning to develop accurate predictive models of infant health and doula service participation. We then combine these predictive models within the double machine learning method to estimate the effects of doula services. We show that while doula services reduce risk on average, the benefits of doula services increase as the risk of negative infant health outcomes increases. We compare these benefits to the costs of doula services under alternative allocation schemes and show that leveraging the risk predictions dramatically increases the cost effectiveness of doula services. Our results show the potential of big data and novel analytic methods to provide cost-effective support to those at greatest risk of poor outcomes.

Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods.

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) has an extensive literature documenting positive effects on infant health outcomes, specifically preterm birth, low birthweight, small size for gestational age, and infant mortality. However, existing studies focus on average effects for these relatively infrequent outcomes, thus providing no evidence for how WIC affects those at greatest risk of negative infant health outcomes. Our study focuses on documenting how WIC's infant health effects vary by level of risk. In doing so, we leverage a uniquely rich database describing maternal and infant outcomes and risk factors. Additionally, we use high dimensional data to generate predictions of risk and combine these predictions with the novel double machine learning method to stratify the effects of WIC by predicted risk. Our estimates of WIC's average treatment effects align with those in the existing literature. More importantly, we document significant variation in the effects of WIC on infant health by predicted risk level. Our results show that WIC is most beneficial among those at greatest risk of poor outcomes.

A Narrative Review of Multiinstitutional Data Registries of Pediatric Congenital Heart Disease in Pediatric Cardiac Anesthesia and Critical Care Medicine.

Congenital heart disease (CHD) is one of the most common birth anomalies. While the care of children with CHD has improved over recent decades, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. Electronic health record systems have enabled institutions to combine data on the management and outcomes of children with CHD in multicenter registries. The application of descriptive analytics methods to these data can improve clinicians' understanding and care of children with CHD. This narrative review covers efforts to leverage multicenter data registries relevant to pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

Reducing the environmental impact of mask inductions in children: A quality improvement report.

BACKGROUND: Inhalational anesthetic agents are potent greenhouse gases with global warming potential that far exceed that of carbon dioxide. Traditionally, pediatric inhalation inductions are achieved with a volatile anesthetic delivered to the patient in oxygen and nitrous oxide at high fresh gas flows. While contemporary volatile anesthetics and anesthesia machines allow for a more environmentally conscious induction, practice has not changed. We aimed to reduce the environmental impact of our inhalation inductions by decreasing the use of nitrous oxide and fresh gas flows. METHODS: Through a series of four plan-do-study-act cycles, the improvement team used content experts to demonstrate the environmental impact of the current inductions and to provide practical ways to reduce this, by focusing on nitrous oxide use and fresh gas flows, with visual reminders introduced at point of delivery. The primary measures were the percentage of inhalation inductions that used nitrous oxide and the maximum fresh gas flows/kg during the induction period. Statistical process control charts were used to measure improvement over time. RESULTS: 33 285 inhalation inductions were included over a 20-month period. nitrous oxide use decreased from 80% to <20% and maximum fresh gas flows/kg decreased from a rate of 0.53 L/min/kg to 0.38 L/min/kg, an overall reduction of 28%. Reduction in fresh gas flows was greatest in the lightest weight groups. Induction times and behaviors remained unchanged over the duration of this project. CONCLUSIONS: Our quality improvement group decreased the environmental impact of inhalation inductions and created cultural change within our department to sustain change and foster the pursuit of future environmental efforts.

Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing.

OBJECTIVE: Social determinants of health (SDOH) are non-medical factors that can profoundly impact patient health outcomes. However, SDOH are rarely available in structured electronic health record (EHR) data such as diagnosis codes, and more commonly found in unstructured narrative clinical notes. Hence, identifying social context from unstructured EHR data has become increasingly important. Yet, previous work on using natural language processing to automate extraction of SDOH from text (a) usually focuses on an ad hoc selection of SDOH, and (b) does not use the latest advances in deep learning. Our objective was to advance automatic extraction of SDOH from clinical text by (a) systematically creating a set of SDOH based on standard biomedical and psychiatric ontologies, and (b) training state-of-the-art deep neural networks to extract mentions of these SDOH from clinical notes. DESIGN: A retrospective cohort study. SETTING AND PARTICIPANTS: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. The corpus comprised 3,504 social related sentences from 2,670 clinical notes. METHODS: We developed a framework for automated classification of multiple SDOH categories. Our dataset comprised narrative clinical notes under the "Social Work" category in the MIMIC-III Clinical Database. Using standard terminologies, SNOMED-CT and DSM-IV, we systematically curated a set of 13 SDOH categories and created annotation guidelines for these. After manually annotating the 3,504 sentences, we developed and tested three deep neural network (DNN) architectures - convolutional neural network (CNN), long short-term memory (LSTM) network, and the Bidirectional Encoder Representations from Transformers (BERT) - for automated detection of eight SDOH categories. We also compared these DNNs to three baselines models: (1) cTAKES, as well as (2) L2-regularized logistic regression and (3) random forests on bags-of-words. Model evaluation metrics included micro- and macro- F1, and area under the receiver operating characteristic curve (AUC). RESULTS: All three DNN models accurately classified all SDOH categories (minimum micro-F1 = 0.632, minimum macro-AUC = 0.854). Compared to the CNN and LSTM, BERT performed best in most key metrics (micro-F1 = 0.690, macro-AUC = 0.907). The BERT model most effectively identified the "occupational" category (F1 = 0.774, AUC = 0.965) and least effectively identified the "non-SDOH" category (F = 0.491, AUC = 0.788). BERT outperformed cTAKES in distinguishing social vs non-social sentences (BERT F1 = 0.87 vs. cTAKES F1 = 0.06), and outperformed logistic regression (micro-F1 = 0.649, macro-AUC = 0.696) and random forest (micro-F1 = 0.502, macro-AUC = 0.523) trained on bag-of-words. CONCLUSIONS: Our study framework with DNN models demonstrated improved performance for efficiently identifying a systematic range of SDOH categories from clinical notes in the EHR. Improved identification of patient SDOH may further improve healthcare outcomes.

Preoperative Fluid Fasting Times and Postinduction Low Blood Pressure in Children: A Retrospective Analysis.

BACKGROUND: Children are required to fast before elective general anesthesia. This study hypothesized that prolonged fasting causes volume depletion that manifests as low blood pressure. This study aimed to assess the association between fluid fasting duration and postinduction low blood pressure. METHODS: A retrospective cohort study was performed of 15,543 anesthetized children without preinduction venous access who underwent elective surgery from 2016 to 2017 at Children's Hospital of Philadelphia. Low blood pressure was defined as systolic blood pressure lower than 2 standard deviations below the mean (approximately the 2.5th percentile) for sex- and age-specific reference values. Two epochs were assessed: epoch 1 was from induction to completion of anesthesia preparation, and epoch 2 was during surgical preparation. RESULTS: In epoch 1, the incidence of low systolic blood pressure was 5.2% (697 of 13,497), and no association was observed with the fluid fasting time groups: less than 4 h (4.6%, 141 of 3,081), 4 to 8 h (6.0%, 219 of 3,652), 8 to 12 h (4.9%, 124 of 2,526), and more than 12 h (5.0%, 213 of 4,238). In epoch 2, the incidence of low systolic blood pressure was 6.9% (889 of 12,917) and varied across the fasting groups: less than 4 h (5.6%, 162 of 2,918), 4 to 8 h (8.1%, 285 of 3,531), 8 to 12 h (5.9%, 143 of 2,423), and more than 12 h (7.4%, 299 of 4,045); after adjusting for confounders, fasting 4 to 8 h (adjusted odds ratio, 1.33; 95% CI, 1.07 to 1.64; P = 0.009) and greater than 12 h (adjusted odds ratio, 1.28; 95% CI, 1.04 to 1.57; P = 0.018) were associated with significantly higher odds of low systolic blood pressure compared with the group who fasted less than 4 h, whereas the increased odds of low systolic blood pressure associated with fasting 8 to 12 h (adjusted odds ratio, 1.11; 95% CI, 0.87 to 1.42; P = 0.391) was nonsignificant. CONCLUSIONS: Longer durations of clear fluid fasting in anesthetized children were associated with increased risk of postinduction low blood pressure during surgical preparation, although this association appeared nonlinear.

A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine.

Congenital heart disease (CHD) is one of the most common birth anomalies, and the care of children with CHD has improved over the past 4 decades. However, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. The proliferation of electronic health record systems and sophisticated patient monitors affords the opportunity to capture and analyze large amounts of CHD patient data, and the application of novel, effective analytics methods to these data can enable clinicians to enhance their care of pediatric CHD patients. This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends.

BACKGROUND: Electrocardiogram (ECG) signal, an important indicator for heart problems, is commonly corrupted by a low-frequency baseline wander (BW) artifact, which may cause interpretation difficulty or inaccurate analysis. Unlike current state-of-the-art approach using band-pass filters, wavelet transforms can accurately capture both time and frequency information of a signal. However, extant literature is limited in applying wavelet transforms (WTs) for baseline wander removal. In this study, we aimed to evaluate 5 wavelet families with a total of 14 wavelets for removing ECG baseline wanders from a semi-synthetic dataset. METHODS: We created a semi-synthetic ECG dataset based on a public QT Database on Physionet repository with ECG data from 105 patients. The semi-synthetic ECG dataset comprised ECG excerpts from the QT database superimposed with artificial baseline wanders. We extracted one ECG excerpt from each of 105 patients, and the ECG excerpt comprised 14 s of randomly selected ECG data. Twelve baseline wanders were manually generated, including sinusoidal waves, spikes and step functions. We implemented and evaluated 14 commonly used wavelets up to 12 WT levels. The evaluation metric was mean-square-error (MSE) between the original ECG excerpt and the processed signal with artificial BW removed. RESULTS: Among the 14 wavelets, Daubechies-3 wavelet and Symlets-3 wavelet with 7 levels of WT had best performance, MSE = 0.0044. The average MSEs for sinusoidal waves, step, and spike functions were 0.0271, 0.0304, 0.0199 respectively. For artificial baseline wanders with spikes or step functions, wavelet transforms in general had lower performance in removing the BW; however, WTs accurately located the temporal position of an impulse edge. CONCLUSIONS: We found wavelet transforms in general accurately removed various baseline wanders. Daubechies-3 and Symlets-3 wavelets performed best. The study could facilitate future real-time processing of streaming ECG signals for clinical decision support systems.

Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method.

Objectives: To assess the performance of a Bayesian case detector (BCD) for influenza surveillance and clinical diagnosis. Methods: BCD uses a Bayesian network classifier to compute the posterior probability of a patient having influenza based on 31 findings from narrative clinical notes. To assess the potential for disease surveillance, we calculated area under the receiver operating characteristic curve (AUC) to indicate BCD's ability to differentiate between influenza and non-influenza encounters in emergency department settings. To assess the potential for clinical diagnosis, we measured AUC for diagnosing influenza cases among encounters having influenza-like illnesses. We also evaluated the performance of BCD using dynamically estimated influenza prevalence, and measured sensitivity, specificity and positive predictive value. Results: For influenza surveillance, BCD differentiated between influenza and non-influenza encounters well with an AUC of 0.90 and 0.97 with dynamic influenza prevalence (P < 0.0001). For clinical diagnosis, the addition of dynamic influenza prevalence to BCD significantly improved AUC from 0.63 to 0.85 to distinguish influenza from other causes of influenza-like illness. Conclusions and policy implications: BCD can serve as an influenza surveillance and a differential diagnosis tool via our dynamic prevalence approach. It enhances the communication between public health and clinical practice.

A Bayesian system to detect and characterize overlapping outbreaks.

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza. Clinical findings are extracted from patient care reports using natural language processing. These findings are analyzed by a case detection system to create disease likelihoods that are passed to a multiple outbreak detection system. We evaluated the system using real and simulated outbreaks. The results show that this approach can recognize and characterize overlapping outbreaks of influenza. We describe several extensions that appear promising.

Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.

In response to the challenges set forth by the CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing, we describe a framework to automatically classify initial psychiatric evaluation records to one of four positive valence system severities: absent, mild, moderate, or severe. We used a dataset provided by the event organizers to develop a framework comprised of natural language processing (NLP) modules and 3 predictive models (two decision tree models and one Bayesian network model) used in the competition. We also developed two additional predictive models for comparison purpose. To evaluate our framework, we employed a blind test dataset provided by the 2016 CEGS N-GRID. The predictive scores, measured by the macro averaged-inverse normalized mean absolute error score, from the two decision trees and Naïve Bayes models were 82.56%, 82.18%, and 80.56%, respectively. The proposed framework in this paper can potentially be applied to other predictive tasks for processing initial psychiatric evaluation records, such as predicting 30-day psychiatric readmissions.

A method for detecting and characterizing outbreaks of infectious disease from clinical reports.

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.

Comparison of machine learning classifiers for influenza detection from emergency department free-text reports.

Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic Emergency Department (ED) free-text reports can be of value to improve influenza detection in real time. This paper studies seven machine learning (ML) classifiers for influenza detection, compares their diagnostic capabilities against an expert-built influenza Bayesian classifier, and evaluates different ways of handling missing clinical information from the free-text reports. We identified 31,268 ED reports from 4 hospitals between 2008 and 2011 to form two different datasets: training (468 cases, 29,004 controls), and test (176 cases and 1620 controls). We employed Topaz, a natural language processing (NLP) tool, to extract influenza-related findings and to encode them into one of three values: Acute, Non-acute, and Missing. Results show that all ML classifiers had areas under ROCs (AUC) ranging from 0.88 to 0.93, and performed significantly better than the expert-built Bayesian model. Missing clinical information marked as a value of missing (not missing at random) had a consistently improved performance among 3 (out of 4) ML classifiers when it was compared with the configuration of not assigning a value of missing (missing completely at random). The case/control ratios did not affect the classification performance given the large number of training cases. Our study demonstrates ED reports in conjunction with the use of ML and NLP with the handling of missing value information have a great potential for the detection of infectious diseases.

The Risk of Aircraft-Acquired SARS-CoV-2 Transmission during Commercial Flights: A Systematic Review.

The aircraft-acquired transmission of SARS-CoV-2 poses a public health risk. Following PRISMA guidelines, we conducted a systematic review and analysis of articles, published prior to vaccines being available, from 24 January 2020 to 20 April 2021 to identify factors important for transmission. Articles were included if they mentioned index cases and identifiable flight duration, and excluded if they discussed non-commercial aircraft, airflow or transmission models, cases without flight data, or that were unable to determine in-flight transmission. From the 15 articles selected for in-depth review, 50 total flights were analyzed by flight duration both as a categorical variable-short (<3 h), medium (3-6 h), or long flights (>6 h)-and as a continuous variable with case counts modeled by negative binomial regression. Compared to short flights without masking, medium and long flights without masking were associated with 4.66-fold increase (95% CI: [1.01, 21.52]; p < 0.0001) and 25.93-fold increase in incidence rates (95% CI: [4.1, 164]; p < 0.0001), respectively; long flights with enforced masking had no transmission reported. A 1 h increase in flight duration was associated with 1.53-fold (95% CI: [1.19, 1.66]; p < 0.001) increase in the incidence rate ratio (IRR) of cases. Masking should be considered for long flights.

Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.

AIM OF THE REVIEW: The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models. METHODS: Systematic search of medical literature from PubMed and engineering literature from Compendex up to June 2, 2023. One reviewer screened studies that used EEG-based ML models to predict the neurologic outcomes after cardiac arrest. Four reviewers validated that the studies met selection criteria. Nine variables were manually extracted. The top-five common EEG features were calculated. We evaluated each study's risk of bias using the Quality in Prognosis Studies guideline. RESULTS: Out of 351 identified studies, 17 studies met the inclusion criteria. Random Forest (RF) (n = 7) was the most common ML model in the conventional ML category (n = 11), followed by Convolutional Neural Network (CNN) (n = 4) in the DNN category (n = 6). The AUCs for RF ranged between 0.8 and 0.97, while CNN had AUCs between 0.7 and 0.92. The top-three commonly used EEG features were band power (n = 12), Shannon's Entropy (n = 11), burst-suppression ratio (n = 9). CONCLUSIONS: RF and CNN were the two most common ML models with the highest AUCs for predicting the neurologic outcomes after cardiac arrest. Using a multimodal model that combines EEG features and electronic health record data may further improve prognostic performance.

Predictive Validity of the K-CAT-SS in High-Risk Adolescents and Young Adults.

OBJECTIVE: Suicide is a leading cause of death in adolescents and young adults and has increased substantially in the past 15 years. Accurate suicide risk stratification based on rapid screening can help reverse these trends. This study aimed to assess the ability of the Kiddie Computerized Adaptive Test Suicide Scale (K-CAT-SS), a brief computerized adaptive test of suicidality, to predict suicide attempts (SAs) in high-risk youth. METHOD: A total of 652 participants (age range, 12-24 years), 78% of whom presented with suicidal ideation or behavior, were recruited within 1 month of mental health care contact. The K-CAT-SS, scaled from 0 to 100, was administered at baseline, and participants were assessed at about 1, 3, and 6 months after intake. Weekly incidence of SAs was assessed using the Adolescent Longitudinal Interval Follow-up Evaluation and Columbia-Suicide Severity Rating Scale. A secondary outcome was suicidal behavior, including aborted, interrupted, and actual SAs. RESULTS: The K-CAT-SS showed a 4.91-fold increase in SAs for every 25-point increase in the baseline score (95% CI 2.83-8.52) and a 3.51-fold increase in suicidal behaviors (95% CI 2.32-5.30). These relations persisted following adjustment for prior attempts; demographic variables including age, sex, gender identity, sexual orientation, and race/ethnicity; and other measures of psychopathology. No moderating effects were identified. At 3 months, area under the receiver operating characteristic curve was 0.83 (95% CI 0.72-0.93) for 1 or more SAs. CONCLUSION: The K-CAT-SS is an excellent tool for suicide risk stratification, particularly in higher-risk populations where other measures have shown lower predictive validity.

A method for estimating from thermometer sales the incidence of diseases that are symptomatically similar to influenza.

Early detection and accurate characterization of disease outbreaks are important tasks of public health. Infectious diseases that present symptomatically like influenza (SLI), including influenza itself, constitute an important class of diseases that are monitored by public-health epidemiologists. Monitoring emergency department (ED) visits for presentations of SLI could provide an early indication of the presence, extent, and dynamics of such disease in the population. We investigated the use of daily over-the-counter thermometer-sales data to estimate daily ED SLI counts in Allegheny County (AC), Pennsylvania. We found that a simple linear model fits the data well in predicting daily ED SLI counts from daily counts of thermometer sales in AC. These results raise the possibility that this model could be applied, perhaps with adaptation, in other regions of the country, where commonly thermometer sales data are available, but daily ED SLI counts are not.

Extracting social determinants of health events with transformer-based multitask, multilabel named entity recognition.

OBJECTIVE: Social determinants of health (SDOH) are nonclinical, socioeconomic conditions that influence patient health and quality of life. Identifying SDOH may help clinicians target interventions. However, SDOH are more frequently available in narrative notes compared to structured electronic health records. The 2022 n2c2 Track 2 competition released clinical notes annotated for SDOH to promote development of NLP systems for extracting SDOH. We developed a system addressing 3 limitations in state-of-the-art SDOH extraction: the inability to identify multiple SDOH events of the same type per sentence, overlapping SDOH attributes within text spans, and SDOH spanning multiple sentences. MATERIALS AND METHODS: We developed and evaluated a 2-stage architecture. In stage 1, we trained a BioClinical-BERT-based named entity recognition system to extract SDOH event triggers, that is, text spans indicating substance use, employment, or living status. In stage 2, we trained a multitask, multilabel NER to extract arguments (eg, alcohol "type") for events extracted in stage 1. Evaluation was performed across 3 subtasks differing by provenance of training and validation data using precision, recall, and F1 scores. RESULTS: When trained and validated on data from the same site, we achieved 0.87 precision, 0.89 recall, and 0.88 F1. Across all subtasks, we ranked between second and fourth place in the competition and always within 0.02 F1 from first. CONCLUSIONS: Our 2-stage, deep-learning-based NLP system effectively extracted SDOH events from clinical notes. This was achieved with a novel classification framework that leveraged simpler architectures compared to state-of-the-art systems. Improved SDOH extraction may help clinicians improve health outcomes.