Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness. MATERIALS AND METHODS: We used EHR data from an academic medical center (AMC) and a clinical research network database from 2014 to 2020 to evaluate the predictive performance and net benefit of the PPD risk model. We used area under the curve and sensitivity as predictive performance and conducted a decision curve analysis. In assessing model fairness, we employed metrics such as disparate impact, equal opportunity, and predictive parity with the White race being the privileged value. The model was also reviewed by multidisciplinary experts for clinical appropriateness. Lastly, we debiased the model by comparing 5 different debiasing approaches of fairness through blindness and reweighing. RESULTS: We determined the classification threshold through a performance evaluation that prioritized sensitivity and decision curve analysis. The baseline PPD model exhibited some unfairness in the AMC data but had a fair performance in the clinical research network data. We revised the model by fairness through blindness, a debiasing approach that yielded the best overall performance and fairness, while considering clinical appropriateness suggested by the expert reviewers. DISCUSSION AND CONCLUSION: The findings emphasize the need for a thorough evaluation of intervention-specific models, considering predictive performance, fairness, and appropriateness before clinical implementation.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.

OBJECTIVES: To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes). MATERIALS AND METHODS: We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome. RESULTS: Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%). DISCUSSION AND CONCLUSION: All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

Healthcare costs for legally adjudicated elder mistreatment victims in comparison to non-mistreated older adults.

BACKGROUND: Elder mistreatment (EM) is associated with adverse health outcomes and healthcare utilization patterns that differ from other older adults. However, the association of EM with healthcare costs has not been examined. Our goal was to compare healthcare costs between legally adjudicated EM victims and controls. METHODS: We used Medicare insurance claims to examine healthcare costs of EM victims in the 2 years surrounding initial mistreatment identification in comparison to matched controls. We adjusted costs using the Centers for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCC) risk score. RESULTS: We examined healthcare costs in 114 individuals who experienced EM and 410 matched controls. Total Medicare Parts A and B healthcare costs were similar between cases and controls in the 12 months prior to initial EM detection ($11,673 vs. $11,402, p = 0.92), but cases had significantly higher total healthcare costs during the 12 months after initial mistreatment identification ($15,927 vs. $10,805, p = 0.04). Adjusting for CMS-HCC scores, cases had, in the 12 months after initial EM identification, $5084 of additional total healthcare costs (95% confidence interval [$92, $10,077], p = 0.046) and $5817 of additional acute/subacute/post-acute costs (95% confidence interval [$1271, $10,362], p = 0.012) compared with controls. The significantly higher total costs and acute/sub-acute/post-acute costs among EM victims in the post-year were concentrated in the 120 days after EM detection. CONCLUSIONS: Older adults experiencing EM had substantially higher total costs during the 12 months after mistreatment identification, driven by an increase in acute/sub-acute/post-acute costs and focused on the period immediately after initial EM detection.

Automated classification of lay health articles using natural language processing: a case study on pregnancy health and postpartum depression.

Objective: Evidence suggests that high-quality health education and effective communication within the framework of social support hold significant potential in preventing postpartum depression. Yet, developing trustworthy and engaging health education and communication materials requires extensive expertise and substantial resources. In light of this, we propose an innovative approach that involves leveraging natural language processing (NLP) to classify publicly accessible lay articles based on their relevance and subject matter to pregnancy and mental health. Materials and methods: We manually reviewed online lay articles from credible and medically validated sources to create a gold standard corpus. This manual review process categorized the articles based on their pertinence to pregnancy and related subtopics. To streamline and expand the classification procedure for relevance and topics, we employed advanced NLP models such as Random Forest, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer model (gpt-3.5-turbo). Results: The gold standard corpus included 392 pregnancy-related articles. Our manual review process categorized the reading materials according to lifestyle factors associated with postpartum depression: diet, exercise, mental health, and health literacy. A BERT-based model performed best (F1 = 0.974) in an end-to-end classification of relevance and topics. In a two-step approach, given articles already classified as pregnancy-related, gpt-3.5-turbo performed best (F1 = 0.972) in classifying the above topics. Discussion: Utilizing NLP, we can guide patients to high-quality lay reading materials as cost-effective, readily available health education and communication sources. This approach allows us to scale the information delivery specifically to individuals, enhancing the relevance and impact of the materials provided.

USING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS.

The population of older adults, defined in this study as those 50 years of age or older, continues to increase every year. Substance misuse, particularly alcohol misuse, is often neglected in these individuals. To better identify older adults who might not be properly assessed for alcohol misuse, we have derived a risk assessment tool using patients from the United Kingdom Biobank (UKB), which was validated on patients in the Weill Cornell Medicine (WCM) electronic health record (EHR). The model and tooling created stratifies the risk of alcohol misuse in older adults using 10 features that are commonly found in most EHR systems. We found that the area under the receiver operating curve (AUROC) to correctly predict alcohol misuse in older adults for the UKB and WCM models were 0.84 and 0.78, respectively. We further show that of those who self-identified as having ongoing alcohol misuse in the UKB cohort, only 12.5% of these patients had any alcohol-related F.10 ICD-10 code. Extending this to the WCM cohort, we forecast that 7,838 out of 12,360 older adults with no F.10 ICD-10 code (63.4%) may be missed as having alcohol misuse in the EHR. Overall, this study importantly prioritizes the health of older adults by being able to predict alcohol misuse in an understudied population.

Effects of online support and social media communities on gestational diabetes: A systematic review.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common complication in pregnancy that can lead to negative maternal and fetal outcomes. Online support interventions have been suggested as a potential tool to improve the management of GDM. OBJECTIVE: This systematic review aimed to summarize the effectiveness of social media and online support interventions for the management of GDM. METHODS: We conducted a thorough systematic search across Web of Science, Scopus, and PubMed, following PRISMA guidelines, and supplemented it with a manual search. Our results included both qualitative and quantitative research. We rigorously assessed quantitative studies for bias using ROBINS-I and RoB 2 tools, ensuring the reliability of our findings. RESULTS: We incorporated a total of 22 studies, which were comprised of ten qualitative and twelve quantitative studies. Online support interventions were found to have a positive impact on promoting self-care and improving healthcare outcomes for women with GDM. Individualized diet and exercise interventions resulted in lower odds of weight gain and GDM diagnosis, while online prenatal education increased breastfeeding rates. In addition, telemedicine options reduced the need for in-person clinical visits and improved patient satisfaction. CONCLUSIONS: Online support interventions show potential to improve outcomes in patients with GDM in this small literature review. Future research is also necessary to determine the effectiveness of different types of online interventions and identify strategies to improve engagement and the quality of the information provided through online resources.

Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, healthcare systems, and the government responsible for harm. Most reported it was "very important" for them to understand AI output. More previously pregnant respondents reported being told AI played a small role in mental healthcare was "very important" versus those not previously pregnant (P = .03). We conclude that protections against harm, transparency around data use, preservation of the patient-clinician relationship, and patient comprehension of AI predictions may facilitate trust in AI-based technologies for mental healthcare among women.

Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models.

Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four online health information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and Reddit.com. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time, the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.

Construction and immunogenicity of a trypsin-independent porcine epidemic diarrhea virus variant.

Porcine epidemic diarrhea virus (PEDV) is a re-emerging enteropathogenic coronavirus that causes high mortality in neonatal piglets. The addition of trypsin plays a crucial role in the propagation of PEDV, but also increases the complexity of vaccine production and increases its cost. Previous studies have suggested that the S2' site and Y976/977 of the PEDV spike (S) protein might be the determinants of PEDV trypsin independence. In this study, to achieve a recombinant trypsin-independent PEDV strain, we used trypsin-dependent genotype 2 (G2) PEDV variant AJ1102 to generate three recombinant PEDVs with mutations in S (S2' site R894G and/or Y976H). The three recombinant PEDVs were still trypsin dependent, suggesting that the S2' site R894 and Y976 of AJ1102 S are not key sites for PEDV trypsin dependence. Therefore, we used AJ1102 and the classical trypsin-independent genotype 1 (G1) PEDV strain JS2008 to generate a recombinant PEDV carrying a chimeric S protein, and successfully obtained trypsin-independent PEDV strain rAJ1102-S2'JS2008, in which the S2 (amino acids 894-1386) domain was replaced with the corresponding JS2008 sequence. Importantly, immunization with rAJ1102-S2'JS2008 induced neutralizing antibodies against both AJ1102 and JS2008. Collectively, these results suggest that rAJ1102-S2'JS2008 is a novel vaccine candidate with significant advantages, including no trypsin requirement for viral propagation to high titers and the potential provision of protection for pigs against G1 and G2 PEDV infections.

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

Background: Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method: In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1st, 2020, and November 30th, 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results: We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions: This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Emergency Department and Hospital Utilization Among Older Adults Before and After Identification of Elder Mistreatment.

Importance: Elder mistreatment is common and has serious health consequences. Little is known, however, about patterns of health care utilization among older adults experiencing elder mistreatment. Objective: To examine emergency department (ED) and hospital utilization of older adults experiencing elder mistreatment in the period surrounding initial mistreatment identification compared with other older adults. Design, Setting, and Participants: This retrospective case-control study used Medicare insurance claims to examine older adults experiencing elder mistreatment initially identified between January 1, 2003, and December 31, 2012, and control participants matched on age, sex, race and ethnicity, and zip code. Statistical analysis was performed in April 2022. Main Outcomes and Measures: We used multiple measures of ED and hospital utilization patterns (eg, new and return visits, frequency, urgency, and hospitalizations) in the 12 months before and after mistreatment identification. Data were adjusted using US Centers for Medicare and Medicaid Services Hierarchical Condition Categories risk scores. Chi-squared tests and conditional logistic regression models were used for data analyses. Results: This study included 114 case patients and 410 control participants. Their median age was 72 years (IQR, 68-78 years), and 340 (64.9%) were women. Race and ethnicity were reported as racial or ethnic minority (114 [21.8%]), White (408 [77.9%]), or unknown (2 [0.4%]). During the 24 months surrounding identification of elder mistreatment, older adults experiencing mistreatment were more likely to have had an ED visit (77 [67.5%] vs 179 [43.7%]; adjusted odds ratio [AOR], 2.95 [95% CI, 1.78-4.91]; P < .001) and a hospitalization (44 [38.6%] vs 108 [26.3%]; AOR, 1.90 [95% CI, 1.13-3.21]; P = .02) compared with other older adults. In addition, multiple ED visits, at least 1 ED visit for injury, visits to multiple EDs, high-frequency ED use, return ED visits within 7 days, ED visits for low-urgency issues, multiple hospitalizations, at least 1 hospitalization for injury, hospitalization at multiple hospitals, and hospitalization for ambulatory care sensitive conditions were substantially more likely for individuals experiencing elder mistreatment. The rate of ED and hospital utilization for older adults experiencing elder mistreatment was much higher in the 12 months after identification than before, leading to more pronounced differences between case patients and control participants in postidentification utilization. During the 12 months after identification of elder mistreatment, older adults experiencing mistreatment were particularly more likely to have had high-frequency ED use (12 [10.5%] vs 8 [2.0%]; AOR, 8.23 [95% CI, 2.56-26.49]; P < .001) and to have visited the ED for low-urgency issues (12 [10.5%] vs 8 [2.0%]; AOR, 7.33 [95% CI, 2.54-21.18]; P < .001). Conclusions and Relevance: In this case-control study of health care utilization, older adults experiencing mistreatment used EDs and hospitals more frequently and with different patterns during the period surrounding mistreatment identification than other older adults. Additional research is needed to better characterize these patterns, which may be helpful in informing early identification, intervention, and prevention of elder mistreatment.

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Environmental regulation, foreign investment, and green innovation: a case study from China.

Green innovation (GI) has the dual attributes of green development and being innovation driven, and it has become an inevitable choice for solving the prisoner's dilemma of environmental protection and economic development under the action of the concept of sustainable development in the new era. This paper aims to clarify how environmental regulation (ER) can achieve a win‒win situation of GI and environmental protection by using data from prefecture-level cities in China and creating a dynamic panel model, quantile model, spatial econometric model, and panel threshold model to empirically analyze the dynamic effect and spatial effect of ER on GI as well as the nonlinear characteristics of the relationship between them and to examine the moderating effect of foreign direct investment (FDI). The results show that ER significantly promotes the development of the GI level and that FDI can play a positive moderating role. The impact has regional heterogeneity, time period heterogeneity, and resource endowment heterogeneity. After several robustness tests, the empirical conclusions are still credible. Based on the empirical conclusions, this paper makes policy suggestions on ER, foreign investment introduction, and the coordinated development of regional GI.

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications.

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.

Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.

Clinical EHR data is naturally heterogeneous, where it contains abundant sub-phenotype. Such diversity creates challenges for outcome prediction using a machine learning model since it leads to high intra-class variance. To address this issue, we propose a supervised pre-training model with a unique embedded k-nearest-neighbor positive sampling strategy. We demonstrate the enhanced performance value of this framework theoretically and show that it yields highly competitive experimental results in predicting patient mortality in real-world COVID-19 EHR data with a total of over 7,000 patients admitted to a large, urban health system. Our method achieves a better AUROC prediction score of 0.872, which outperforms the alternative pre-training models and traditional machine learning methods. Additionally, our method performs much better when the training data size is small (345 training instances).

Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach.

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO2 was 16.7 µg/m3, equals to a drop of 73% in NO2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO2 was 3.1 µg/m3, accounting for a marginal reduction of 18.6% of the overall NO2 change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 µg/m3 NO2 per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO2 concentration into compliance with the health-based limit.

Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative.

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated, or newly incident in the post-acute SARS-CoV-2 infection period of COVID-19 patients. Most studies have examined these conditions individually without providing concluding evidence on co-occurring conditions. To answer this question, this study leveraged electronic health records (EHRs) from two large clinical research networks from the national Patient-Centered Clinical Research Network (PCORnet) and investigated patients' newly incident diagnoses that appeared within 30 to 180 days after a documented SARS-CoV-2 infection. Through machine learning, we identified four reproducible subphenotypes of PASC dominated by blood and circulatory system, respiratory, musculoskeletal and nervous system, and digestive system problems, respectively. We also demonstrated that these subphenotypes were associated with distinct patterns of patient demographics, underlying conditions present prior to SARS-CoV-2 infection, acute infection phase severity, and use of new medications in the post-acute period. Our study provides novel insights into the heterogeneity of PASC and can inform stratified decision-making in the treatment of COVID-19 patients with PASC conditions.

Safety and efficacy of hemodialysis and peritoneal dialysis in treating end-stage diabetic nephropathy: a meta-analysis of randomized controlled trials.

BACKGROUND: Present investigation aims to elucidate safety and efficacy of hemodialysis as well as peritoneal dialysis in treating end-stage diabetic nephropathy. METHODS: We searched various databases for articles from the database starting date to October 2019. The analysis involved studies that contained outcomes of hemodialysis and peritoneal dialysis in the treatment of end-stage diabetic nephropathy. A total of 12 randomized controlled trials (RCTs) with 932 participants were collected. RESULTS: Meta-analysis results suggested that comparing with peritoneal dialysis group, hemodialysis group had a higher incidence of cardiovascular and cerebrovascular events and bleeding complications. There was no statistically significant difference regarding the infection (P = 0.71) or malnutrition (P = 0.53) incidence between the two forms of dialysis. Hemodialysis could better improve the levels of albumin [mean difference (MD) = 6.80, 95% CI = (4.17-9.44)] and hemoglobin [MD = 3.40, 95% CI = (0.94-5.86)] than peritoneal dialysis after 3 months or more. CONCLUSIONS: In treating end-stage diabetic nephropathy patients, peritoneal dialysis had a lower incidence of cardiovascular and cerebrovascular events, as well as bleeding complication than hemodialysis. However, hemodialysis could better improve albumin and hemoglobin levels than peritoneal dialysis after 3 months.