Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences

Characterizing soil COPs eco-risk in China

Although soil combustible organic pollutants (COPs) pose a serious threat to human well-being, their spatial distribution patterns, responses to environmental constraints, and areas of risk throughout China are still unclear. This knowledge gap hinders the control of soil COPs, causing us to overlook their impact on climate change and the environment. In this study, a total of 420 soil samples, distributed in typical regions of China, were tested for COPs content, including black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs). Interest points (POI) such as parking lots, gas stations, and car services have become the main factors that influence soil COPs enrichment, and can be considered new indicators in other organic pollution studies. By comparing various machine learning simulations and predictions, this study accurately predicted the content of soil COPs in China and pointed out that, as the "third pole of the world", the Qinghai Tibet Plateau will face an unprecedented crisis. We established a method for assessing the comprehensive risk of soil COPs and identified at-risk areas, which accounted for 38.9 % of China's total soil area. Our research findings emphasize the main driving factors for soil COPs and identify areas in China that require prioritized soil COPs control.

Identifying the key predictors of positive self-perceptions of aging using machine learning

This study aimed to identify key predictors of self-perceptions of aging (SPA) among older adults by examining a comprehensive set of potential predictors across physical, psychological, social, and demographic domains. Data from over 4000 American adults (mean age ≈ 70) from the Health and Retirement Study were used. A machine learning approach using Random Forest regression was employed to assess the relative importance of 49 potential predictors of SPA. The results revealed that health status, age, and psychological resources emerged as the strongest predictors of SPA. The psychological resources included the positive triad of self-esteem, life satisfaction, and optimism, as well as sense of mastery. Emotional tendencies and experiences, financial satisfaction, personality traits, and social factors had substantially lower predictive power. This study provides a comprehensive understanding of the factors that predict SPA and their relative importance, offering insights for both theory and practice. The results highlight the potential for designing targeted, evidence-based interventions that enhance psychological resources, address health and functional well-being, provide tailored support across the lifespan, and incorporate lifestyle changes to foster positive aging perceptions.

Around the EQUATOR With Clin-STAR: AI-Based Randomized Controlled Trial Challenges and Opportunities in Aging Research

The CONSORT 2010 statement is a guideline that provides an evidence-based checklist of minimum reporting standards for randomized trials. With the rapid growth of Artificial Intelligence (AI) based interventions in the past 10 years, the CONSORT-AI extension was created in 2020 to provide guidelines for AI-based randomized controlled trials (RCT). The Clin-STAR "Around the EQUATOR" series features existing reported standards while also highlighting the inherent complexities of research involving research of older participants. In this work, we propose that when designing AI-based RCTs involving older adults, researchers adopt a conceptual framework (CONSORT-AI-5Ms) designed around the 5Ms (Mind, Mobility, Medications, Matters most, and Multi-complexity) of Age-Friendly Healthcare Systems. Employing the 5Ms in this context, we provide a detailed rationale and include specific examples of challenges and potential solutions to maximize the impact and value of AI RCTs in an older adult population. By combining the original intent of CONSORT-AI with the 5Ms framework, CONSORT-AI-5Ms provides a patient-centered and equitable perspective to consider when designing AI-based RCTs to address the diverse needs and challenges associated with geriatric care.

CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction

BACKGROUND

Mortality prediction is critical in clinical care, particularly in intensive care units (ICUs), where early identification of high-risk patients can inform treatment decisions. While deep learning (DL) models have demonstrated significant potential in this task, most suffer from limited generalizability, which hinders their widespread clinical application. Additionally, the class imbalance in electronic health records (EHRs) complicates model training. This study aims to develop a causally-informed prediction model that incorporates underlying causal relationships to mitigate class imbalance, enabling more stable mortality predictions.

METHODS

This study introduces the CRISP model (Causal Relationship Informed Superior Prediction), which leverages native counterfactuals to augment the minority class and constructs patient representations by incorporating causal structures to enhance mortality prediction. Patient data were obtained from the public MIMIC-III and MIMIC-IV databases, as well as an additional dataset from the West China Hospital of Sichuan University (WCHSU).

RESULTS

A total of 69,190 ICU cases were included, with 30,844 cases from MIMIC-III, 27,362 cases from MIMIC-IV, and 10,984 cases from WCHSU. The CRISP model demonstrated stable performance in mortality prediction across the 3 datasets, achieving AUROC (0.9042-0.9480) and AUPRC (0.4771-0.7611). CRISP's data augmentation module showed predictive performance comparable to commonly used interpolation-based oversampling techniques.

CONCLUSION

CRISP achieves better generalizability across different patient groups, compared to various baseline algorithms, thereby enhancing the practical application of DL in clinical decision support.

TRIAL REGISTRATION

Trial registration information for the WCHSU data is available on the Chinese Clinical Trial Registry website ( http://www.chictr.org.cn ), with the registration number ChiCTR1900025160. The recruitment period for the data was from August 5, 2019, to August 31, 2021.

Integrative single-cell metabolomics and phenotypic profiling reveals metabolic heterogeneity of cellular oxidation and senescence

Emerging evidence has unveiled heterogeneity in phenotypic and transcriptional alterations at the single-cell level during oxidative stress and senescence. Despite the pivotal roles of cellular metabolism, a comprehensive elucidation of metabolomic heterogeneity in cells and its connection with cellular oxidative and senescent status remains elusive. By integrating single-cell live imaging with mass spectrometry (SCLIMS), we establish a cross-modality technique capturing both metabolome and oxidative level in individual cells. The SCLIMS demonstrates substantial metabolomic heterogeneity among cells with diverse oxidative levels. Furthermore, the single-cell metabolome predicted heterogeneous states of cells. Remarkably, the pre-existing metabolomic heterogeneity determines the divergent cellular fate upon oxidative insult. Supplementation of key metabolites screened by SCLIMS resulted in a reduction in cellular oxidative levels and an extension of C. elegans lifespan. Altogether, SCLIMS represents a potent tool for integrative metabolomics and phenotypic profiling at the single-cell level, offering innovative approaches to investigate metabolic heterogeneity in cellular processes.

Causal-inference machine learning reveals the drivers of China's 2022 ozone rebound

Ground-level ozone concentrations rebounded significantly across China in 2022, challenging air quality management and public health. Identifying the drivers of this rebound is crucial for designing effective mitigation strategies. Commonly used methods, such as chemical transport models and machine learning, provide valuable insights but face limitations-chemical transport models are computationally intensive, while machine learning often fails to address confounding factors or establish causality. Here we show that elevated temperatures and increased solar radiation, as primary meteorological drivers, collectively account for 57 % of the total ozone increase, based on an integrated analysis of ground-based monitoring data, satellite observations, and meteorological reanalysis information using explainable machine learning and causal inference techniques. Compared to the year 2021, 90 % of the stations reported an increase in the Formaldehyde to Nitrogen ratio, implying a growing sensitivity of ozone formation to nitrogen oxide levels. These findings highlight the significant causal role of meteorological changes in the ozone rebound, urging the adoption of targeted ozone mitigation strategies under climate warming, particularly through varied regional strategies that consider existing anthropogenic emission levels and the prospective increase in biogenic volatile organic compounds. This identification of causal relationships in air pollution dynamics can support data-driven and accurate decision-making.

Time-dependent interpretable survival prediction model for second primary NSCLC patients

OBJECTIVE

Accurate predictive models for second primary non-small cell lung cancer (SP-NSCLC) are limited. This study aimed to develop and validate overall survival (OS) prediction models for SP-NSCLC patients using time-dependent interpretable survival machine learning algorithms.

METHODS

This study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database, encompassing 8 and 12 registries, to extract data on patients aged 20-89 diagnosed with SP-NSCLC between 1988 and 2020. The dataset was divided into development, external temporal and spatial validation cohorts. Predictors included demographic, clinical, pathological and initial primary cancer-related features. Multiple survival machine learning algorithms were developed and validated, assessing model performance using C-index, time-dependent area under the receiver operating characteristic curve (time-AUC), and time-dependent Brier Score. The time-dependent interpretability analysis was employed to explore the time-dependent feature importance of key predictors.

RESULTS

The Blackboost model demonstrated excellent performance (C-index: 0.7517, and time-AUC: 0.8438), and good calibration (time-Brier Score of 0.0754). External validations and subgroup analyses demonstrated the robustness, generalizability, and fairness. Utilizing the optimal cutoff threshold, high-risk groups could be effectively identified. Surgery was the most critical predictor across the entire survival period. Combined stage (distant) and chemotherapy were the second most important predictors within 0 to 5 years, while age replaced from 5 to 20 years. Additionally, we developed an online visualization tool.

CONCLUSIONS

The Blackboost survival model achieved accurate, fair, and robust survival prediction for SP-NSCLC patients. Surgery, combined stage (distant), chemotherapy, and age contributed differently across various survival periods. The online visualization tool facilitated personalized survival predictions.

Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia

BACKGROUND

Low birth weight (LBW) is a critical global health issue that affects infants disproportionately, particularly in developing countries. This study adopted causal machine learning (CML) algorithms for predicting LBW in newborns, drawing from midwife-led continuity care (MLCC).

METHODS

A quasi-experimental study was carried out in the North Shoa Zone of Ethiopia from August 2019 to September 2020. A total of 1166 women were allocated into two groups. The first group, the MLCC group, received all their antenatal, labor, birth, and immediate post-natal care from a single midwife. The second group received care from various staff members at different times throughout their pregnancy and childbirth. In this study, CML was implemented to predict LBW. Data preprocessing, including data cleaning, was conducted. CML was then employed to identify the most suitable classifier for predicting LBW. Gradient boosting algorithms were used to estimate the causal effect of MLCC on LBW. Moreover, meta-learner algorithms were utilized to estimate the individual treatment effect (ITE), the average treatment effect (ATE), and performance. Moreover, meta-learner algorithms were utilized to estimate the individual treatment effect (ITE), the average treatment effect (ATE), and performance.

RESULTS

The study results revealed that Causal K-Nearest Neighbors (CKNN) was the most effective classifier based on accuracy and estimated LBW using a 94.52% accuracy, 90.25% precision, 92.57% recall, and an F1 score of 88.2%. Meconium aspiration, perinatal mortality, pregnancy-induced hypertension, vacuum babies in need of resuscitation, and previous surgeries on their reproductive organs were identified as the top five features affecting LBW. The estimated impact of MLCC versus other professional groups on LBW was analyzed using gradient boosting algorithms and was found to be 0.237. The estimated ATE for the S-learner was 0.284, which is lower than the true ATE of 0.216. Additionally, the estimated ITE for both the T-learner and X-learner was less than -0.5, indicating that mothers would not choose to participate in the MLCC program.

CONCLUSIONS

Based on these findings, the CKNN classifier demonstrated a higher accuracy and effectiveness. The S-learner and R-learner models, utilizing the XGBoost Regressor and BaseSRegressor, provided accurate estimations of ITE for assessing the impact of the MLCC program. Promoting the MLCC program could help stabilize LBW outcomes.

Common issues of data science on the eco-environmental risks of emerging contaminants

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response.

Unravelling nitrate transformation mechanisms in karst catchments through the coupling of high-frequency sensor data and machine learning

Nitrate dynamics within a catchment are critical to the earth's system process, yet the intricate details of its transport and transformation at high resolutions remain elusive. Hydrological effects on nitrate dynamics in particular have not been thoroughly assessed previously and this knowledge gap hampers our understanding and effective management of nitrogen cycling in watersheds. Here, machine learning (ML) models were employed to reconstruct the annual variation trend in nitrate dynamics and isotopes within a typical karst catchment. Random forest model demonstrates promising potential in predicting nitrate concentration and its isotopes, surpassing other ML models (including Long Short-term Memory, Convolutional Neural Network, and Support Vector Machine) in performance. The ML-modeled NO3--N concentrations, δ15N-NO3-, and δ18O-NO3- values were in close agreement with field data (NSE values of 0.95, 0.80, and 0.53, respectively), which are notably challenging to achieve for process models. During the transition from dry to wet period, approximately 23.0 % of the annual precipitation (∼269.1 mm) was identified as the threshold for triggering a rapid response in the wet period. The modeled nitrate isotope values were significantly supported by the field data, suggesting seasonal variations of nitrogen sources, with precipitation as the primary driving force for fertilizer sources. Mixing of multiple sources appeared to be the main control of the transport and transformation of nitrate during the rising limb in the wet period, whereas process control (denitrification) took precedence during the falling limb, and the fate of nitrate was controlled by biogeochemical processes during the dry period.

Healthy aging meta-analyses and scoping review of risk factors across Latin America reveal large heterogeneity and weak predictive models

Models of healthy aging are typically based on the United States and Europe and may not apply to diverse and heterogeneous populations. In this study, our objectives were to conduct a meta-analysis to assess risk factors of cognition and functional ability across aging populations in Latin America and a scoping review focusing on methodological procedures. Our study design included randomized controlled trials and cohort, case-control and cross-sectional studies using multiple databases, including MEDLINE, the Virtual Health Library and Web of Science. From an initial pool of 455 studies, our meta-analysis included 38 final studies (28 assessing cognition and 10 assessing functional ability, n = 146,000 participants). Our results revealed significant but heterogeneous effects for cognition (odds ratio (OR) = 1.20, P = 0.03, confidence interval (CI) = (1.0127, 1.42); heterogeneity: I2 = 92.1%, CI = (89.8%, 94%)) and functional ability (OR = 1.20, P = 0.01, CI = (1.04, 1.39); I2 = 93.1%, CI = (89.3%, 95.5%)). Specific risk factors had limited effects, especially on functional ability, with moderate impacts for demographics and mental health and marginal effects for health status and social determinants of health. Methodological issues, such as outliers, inter-country differences and publication bias, influenced the results. Overall, we highlight the specific profile of risk factors associated with healthy aging in Latin America. The heterogeneity in results and methodological approaches in studying healthy aging call for greater harmonization and further regional research to understand healthy aging in Latin America.

Greenness, air pollution, and temperature exposure effects in predicting premature mortality and morbidity: A small-area study using spatial random forest model

BACKGROUND

Although studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework.

METHODS

Years of potential life lost reflecting premature mortality and comparative illness and disability ratio reflecting chronic morbidity from 1673 small areas covering Greater Manchester for the year 2008-2013 obtained. Average annual levels of NO2 concentration, normalized difference vegetation index (NDVI) representing greenness, and annual average air temperature were utilized to assess exposure in each area. These exposures were linked to health outcomes using non-spatial and spatial random forest (RF) models while accounting for spatial autocorrelation.

RESULTS

Spatial-RF models provided the best predictive accuracy when accounted for spatial autocorrelation. Among the exposures considered, air pollution emerged as the most influential in predicting mortality and morbidity, followed by NDVI and temperature exposure. Nonlinear exposure-response relations were observed, and interactions between exposures illustrated specific ranges or sweet and sour spots of exposure thresholds where combined effects either exacerbate or moderate health conditions.

CONCLUSION

Air pollution exposure had a greater negative impact on health compared to greenness and temperature exposure. Combined exposure effects may indicate the highest influence of premature mortality and morbidity burden.

AI Through Ethical Lenses: A Discourse Analysis of Guidelines for AI in Healthcare

While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI's beneficial outputs and concerns about the challenges of human-computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.

Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement

BACKGROUND

Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets.

METHODS

We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups.

RESULTS

Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations.

CONCLUSIONS

This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.

REFORMS: Consensus-based Recommendations for Machine-learning-based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.

Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data

BACKGROUND

Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions.

OBJECTIVE

This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes.

METHODS

Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm's learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR.

RESULTS

In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days.

CONCLUSIONS

This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities.

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis

OBJECTIVE

This study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China.

METHODS

Longitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix.

RESULTS

Two physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group.

LIMITATION

This study didn't carry out external validation.

CONCLUSIONS

This study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.

A machine-learning-based prediction of non-home discharge among acute heart failure patients

BACKGROUND

Scarce data on factors related to discharge disposition in patients hospitalized for acute heart failure (AHF) were available, and we sought to develop a parsimonious and simple predictive model for non-home discharge via machine learning.

METHODS

This observational cohort study using a Japanese national database included 128,068 patients admitted from home for AHF between April 2014 and March 2018. The candidate predictors for non-home discharge were patient demographics, comorbidities, and treatment performed within 2 days after hospital admission. We used 80% of the population to develop a model using all 26 candidate variables and using the variable selected by 1 standard-error rule of Lasso regression, which enhances interpretability, and 20% to validate the predictive ability.

RESULTS

We analyzed 128,068 patients, and 22,330 patients were not discharged to home; 7,879 underwent in-hospital death and 14,451 were transferred to other facilities. The machine-learning-based model consisted of 11 predictors, showing a discrimination ability comparable to that using all the 26 variables (c-statistic: 0.760 [95% confidence interval, 0.752-0.767] vs. 0.761 [95% confidence interval, 0.753-0.769]). The common 1SE-selected variables identified throughout all analyses were low scores in activities of daily living, advanced age, absence of hypertension, impaired consciousness, failure to initiate enteral alimentation within 2 days and low body weight.

CONCLUSIONS

The developed machine learning model using 11 predictors had a good predictive ability to identify patients at high risk for non-home discharge. Our findings would contribute to the effective care coordination in this era when HF is rapidly increasing in prevalence.

Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease

BACKGROUND

Predicting time to renal replacement therapy (RRT) is important in patients at high risk for end-stage kidney disease. We developed and validated machine learning models for predicting the time to RRT and compared its accuracy with conventional prediction methods that uses the rate of estimated glomerular filtration rate (eGFR) decline.

METHODS

Data of adult chronic kidney disease (CKD) patients who underwent hemodialysis at Oita University Hospital from April 2016 to March 2021 were extracted from electronic medical records (N = 135). A new machine learning predictor was compared with the established prediction method that uses the eGFR decline rate and the accuracy of the prediction models was determined using the coefficient of determination (R2). The data were preprocessed and split into training and validation datasets. We created multiple machine learning models using the training data and evaluated their accuracy using validation data. Furthermore, we predicted the time to RRT using a conventional prediction method that uses the eGFR decline rate for patients who had measured eGFR three or more times in two years and evaluated its accuracy.

RESULTS

The least absolute shrinkage and selection operator regression model exhibited moderate accuracy with an R2 of 0.60. By contrast, the conventional prediction method was found to be extremely low with an R2 of -17.1.

CONCLUSIONS

The significance of this study is that it shows that machine learning can predict time to RRT moderately well with continuous values from data at a single time point. This approach outperforms the conventional prediction method that uses eGFR time series data and presents new avenues for CKD treatment.

Socio-ethical challenges and opportunities for advancing diversity, equity, and inclusion in digital medicine

Digitalization in medicine offers a significant opportunity to transform healthcare systems by providing novel digital tools and services to guide personalized prevention, prediction, diagnosis, treatment and disease management. This transformation raises a number of novel socio-ethical considerations for individuals and society as a whole, which need to be appropriately addressed to ensure that digital medical devices (DMDs) are widely adopted and benefit all patients as well as healthcare service providers. In this narrative review, based on a broad literature search in PubMed, Web of Science, Google Scholar, we outline five core socio-ethical considerations in digital medicine that intersect with the notions of equity and digital inclusion: (i) access, use and engagement with DMDs, (ii) inclusiveness in DMD clinical trials, (iii) algorithm fairness, (iv) surveillance and datafication, and (v) data privacy and trust. By integrating literature from multidisciplinary fields, including social, medical, and computer sciences, we shed light on challenges and opportunities related to the development and adoption of DMDs. We begin with an overview of the different types of DMDs, followed by in-depth discussions of five socio-ethical implications associated with their deployment. Concluding our review, we provide evidence-based multilevel recommendations aimed at fostering a more inclusive digital landscape to ensure that the development and integration of DMDs in healthcare mitigate rather than cause, maintain or exacerbate health inequities.

Predicting depression in old age: Combining life course data with machine learning

With ageing populations, understanding life course factors that raise the risk of depression in old age may help anticipate needs and reduce healthcare costs in the long run. We estimate the risk of depression in old age by combining adult life course trajectories and childhood conditions in supervised machine learning algorithms. Using data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we implement and compare the performance of six alternative machine learning algorithms. We analyse the performance of the algorithms using different life-course data configurations. While we obtain similar predictive abilities between algorithms, we achieve the highest predictive performance when employing semi-structured representations of life courses using sequence data. We use the Shapley Additive Explanations method to extract the most decisive predictive patterns. Age, health, childhood conditions, and low education predict most depression risk later in life, but we identify new predictive patterns in indicators of life course instability and low utilization of dental care services.

Artificial intelligence for dementia prevention

INTRODUCTION

A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.

METHODS

ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.

RESULTS

Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.

DISCUSSION

ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.

HIGHLIGHTS

Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective

Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.

Machine Learning Classification of Alzheimer's Disease Pathology Reveals Diffuse Amyloid as a Major Predictor of Cognitive Impairment in Human Hippocampal Subregions

Analyzing Alzheimer's disease (AD) pathology within anatomical subregions is a significant challenge, often carried out by pathologists using a standardized, semi-quantitative approach. To augment traditional methods, a high-throughput, high-resolution pipeline was created to classify the distribution of AD pathology within hippocampal subregions. USC ADRC post-mortem tissue sections from 51 patients were stained with 4G8 for amyloid, Gallyas for neurofibrillary tangles (NFTs) and Iba1 for microglia. Machine learning (ML) techniques were utilized to identify and classify amyloid pathology (dense, diffuse and APP (amyloid precursor protein)), NFTs, neuritic plaques and microglia. These classifications were overlaid within manually segmented regions (aligned with the Allen Human Brain Atlas) to create detailed pathology maps. Cases were separated into low, intermediate, or high AD stages. Further data extraction enabled quantification of plaque size and pathology density alongside ApoE genotype, sex, and cognitive status. Our findings revealed that the increase in pathology burden across AD stages was driven mainly by diffuse amyloid. The pre and para-subiculum had the highest levels of diffuse amyloid while NFTs were highest in the A36 region in high AD cases. Moreover, different pathology types had distinct trajectories across disease stages. In a subset of AD cases, microglia were elevated in intermediate and high compared to low AD. Microglia also correlated with amyloid pathology in the Dentate Gyrus. The size of dense plaques, which may represent microglial function, was lower in ApoE4 carriers. In addition, individuals with memory impairment had higher levels of both dense and diffuse amyloid. Taken together, our findings integrating ML classification approaches with anatomical segmentation maps provide new insights on the complexity of disease pathology in AD progression. Specifically, we identified diffuse amyloid pathology as being a major driver of AD in our cohort, regions of interest and microglial responses that might advance AD diagnosis and treatment.