Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

An efficient catalyst screening strategy combining machine learning and causal inference

Due to the diversity of catalyst synthesis methods, the optimization of catalysts by traditional experimental methods have brought greater challenges. This study presents a new strategy for determining catalyst performance by substituting causal inference results as prior knowledge into machine learning models, which was used to explore the correlation between the ratio of nitrogen functional groups in catalysts and degradation performance, so as to solve the problem of low efficiency in catalyst screening. A dataset comprising 14 critical parameters, including the physicochemical properties of catalysts and reaction conditions, was established through the analysis of 182 experimental results. The analysis results based on real data show that CatBoost model performs best (R2 = 0.953, MAE = 3.277, RMSE = 5.615). SHAP analysis showed that pyridinic N was a key N-functional group that affects the degradation performance of BPA. DoWhy causal inference further verified the positive effect of pyridinic N, with causal effect estimate of 0.4388. This strategy reduces the selection range of the best catalyst through causal inference pre-screening, and used CatBoost model to accurately evaluate the performance of its catalyst, which can reduce the catalyst screening process from multiple processes to a single process, and significantly improve the catalyst selection efficiency.

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study

BACKGROUND

The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions.

OBJECTIVE

This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy.

METHODS

We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling.

RESULTS

We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths.

CONCLUSIONS

The study's results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups.

Mechanisms through which befriending services may impact the health of older adults: A dyadic qualitative investigation

Befriending services are often delivered to older adults with a view to improving social connectedness, but they may also lead to improved health. The objective of the current study was to explore potential mechanisms through which befriending services might impact the health of older adults. Data were collected from 13 befriendee-befriender dyads (n = 26), using a constructivist grounded theory and dyadic analytic approach. Potential mechanisms were described, using a realist evaluative framework of mechanistic processes in a complex intervention context. Five mechanisms of action triggered by the intervention were identified: supporting health behaviours; providing emotional support; improving mood; getting cognitive stimulation and novelty; and providing opportunities for socialising. We identified five potential mechanisms through which befriending services might impact health for older people. Our results suggest potential mechanisms through which befriending might positively impact the health of older people, and which should be evaluated empirically in future research.

Large Language Models for Epidemiological Research via Automated Machine Learning: Case Study Using Data From the British National Child Development Study

Background

Large language models have had a huge impact on natural language processing (NLP) in recent years. However, their application in epidemiological research is still limited to the analysis of electronic health records and social media data.

objectives

To demonstrate the potential of NLP beyond these domains, we aimed to develop prediction models based on texts collected from an epidemiological cohort and compare their performance to classical regression methods.

Methods

We used data from the British National Child Development Study, where 10,567 children aged 11 years wrote essays about how they imagined themselves as 25-year-olds. Overall, 15% of the data set was set aside as a test set for performance evaluation. Pretrained language models were fine-tuned using AutoTrain (Hugging Face) to predict current reading comprehension score (range: 0-35) and future BMI and physical activity (active vs inactive) at the age of 33 years. We then compared their predictive performance (accuracy or discrimination) with linear and logistic regression models, including demographic and lifestyle factors of the parents and children from birth to the age of 11 years as predictors.

Results

NLP clearly outperformed linear regression when predicting reading comprehension scores (root mean square error: 3.89, 95% CI 3.74-4.05 for NLP vs 4.14, 95% CI 3.98-4.30 and 5.41, 95% CI 5.23-5.58 for regression models with and without general ability score as a predictor, respectively). Predictive performance for physical activity was similarly poor for the 2 methods (area under the receiver operating characteristic curve: 0.55, 95% CI 0.52-0.60 for both) but was slightly better than random assignment, whereas linear regression clearly outperformed the NLP approach when predicting BMI (root mean square error: 4.38, 95% CI 4.02-4.74 for NLP vs 3.85, 95% CI 3.54-4.16 for regression). The NLP approach did not perform better than simply assigning the mean BMI from the training set as a predictor.

Conclusions

Our study demonstrated the potential of using large language models on text collected from epidemiological studies. The performance of the approach appeared to depend on how directly the topic of the text was related to the outcome. Open-ended questions specifically designed to capture certain health concepts and lived experiences in combination with NLP methods should receive more attention in future epidemiological studies.

Broad Trends in Public Health and Epidemiology Informatics

OBJECTIVES

The objective of this study is to highlight innovative research and contemporary trends in the area of Public Health and Epidemiology Informatics (PHEI).

METHODS

Following a similar approach to last year's edition, a meticulous search was conducted on PubMed (with keywords including topics related to Public Health, Epidemiological Surveillance and Medical Informatics), examining a total of 2,022 scientific publications on Public Health and Epidemiology Informatics (PHEI). The resulting references were thoroughly examined by the three section editors. Subsequently, 10 papers were chosen as potential candidates for the best paper award. These selected papers were then subjected to peer-review by six external reviewers, in addition to the section editors and two chief editors of the IMIA yearbook of medical informatics. Each paper underwent a total of five reviews.

RESULTS

Out of the 539 references retrieved from PubMed, only two were deemed worthy of the best paper award, although four papers had the potential to qualify in total. The first best paper by pertains to a study about the need for a new annotation framework due to inadequacies in existing methods and resources. The second paper elucidates the use of Weibo data to monitor the health of Chinese urbanites. The correlation between air pollution and health sensing was measured via generalized additive models.

CONCLUSIONS

One of the primary findings of this edition is the dearth of studies identified for the PHEI section, which represents a significant decline compared to the previous edition. This is particularly surprising given that the post-COVID period should have led to an increased use of information and communication technology for public health issues.