Machine learning and artificial intelligence: applications in healthcare epidemiology

Machine learning decision support model for discharge planning in stroke patients

BACKGROUND/AIM

Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission.

DESIGN

Prospective observational study.

METHODS

A prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best-suited predictor for discharge disposition, dichotomized into home and non-home. To evaluate the accuracy, reliability and interpretability of the best-performing models, we identified and analysed the features that had the greatest impact on the predictions.

RESULTS

In total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non-home discharge. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia.

CONCLUSION

The ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non-home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision-making.

RELEVANCE TO CLINICAL PRACTICE

This study illustrates the characteristics and risk factors of non-home discharge in patients with stroke, potentially contributing to the improvement of the discharge process.

REPORTING METHOD

STROBE guidelines.

Exploring Prior Antibiotic Exposure Characteristics for COVID-19 Hospital Admission Patients: OpenSAFELY

Previous studies have demonstrated the association between antibiotic use and severe COVID-19 outcomes. This study aimed to explore detailed antibiotic exposure characteristics among COVID-19 patients. Using the OpenSAFELY platform, which integrates extensive health data and covers 40% of the population in England, the study analysed 3.16 million COVID-19 patients with at least two prior antibiotic prescriptions. These patients were compared to up to six matched controls without hospitalisation records. A machine learning model categorised patients into ten groups based on their antibiotic exposure history over the three years before their COVID-19 diagnosis. The study found that for COVID-19 patients, the total number of prior antibiotic prescriptions, diversity of antibiotic types, broad-spectrum antibiotic prescriptions, time between first and last antibiotics, and recent antibiotic use were associated with an increased risk of severe COVID-19 outcomes. Patients in the highest decile of antibiotic exposure had an adjusted odds ratio of 4.8 for severe outcomes compared to those in the lowest decile. These findings suggest a potential link between extensive antibiotic use and the risk of severe COVID-19. This highlights the need for more judicious antibiotic prescribing in primary care, primarily for patients with higher risks of infection-related complications, which may better offset the potential adverse effects of repeated antibiotic use.

Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach

BACKGROUND

Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity.

METHODS

To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-h temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011-2012 data collected from the noninstitutionalized civilian resident population of the United States. The number of participants that had the whole set of physical activity data collected by the accelerometer is 6613. Among 6613 participants, 4242 participants had complete demography and Patient Health Questionnaire-9 (PHQ-9) questionnaire, a tool to quantify depressive symptoms. The association between activity-count behaviors and depressive symptoms was analyzed using multivariable logistic regression to adjust for confounding factors in sequential models.

RESULTS

We identified four physical activity-count behaviors based on five physical activity-counting patterns classified by unsupervised machine learning. Regarding PHQ-9 scores, we found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group.

CONCLUSIONS

Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability of assessing depressive symptoms effectively.

Artificial intelligence for the optimal management of community-acquired pneumonia

PURPOSE OF REVIEW

This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes.

RECENT FINDINGS

Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality. The integration of AI-based risk scores in managing CAP has the potential to enhance the accuracy of predicting patients at higher risk, facilitating timely intervention and resource allocation. Moreover, AI algorithms reduce variability associated with subjective clinical judgment, promoting consistency in decision-making, and provide real-time risk assessments, aiding in the dynamic management of patients with CAP.

SUMMARY

The development and implementation of AI-tools for hospitalization in CAP represent a transformative approach to improving patient outcomes. The integration of AI into healthcare has the potential to revolutionize the way we identify and manage individuals at risk of severe outcomes, ultimately leading to more efficient resource utilization and better overall patient care.

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively, an integrated surveillance strategy needs to be applied to both human and animal health. Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat. It is not realistic to either invest only in efforts to detect emerging diseases, or to rely solely on event-based surveillance. A comprehensive strategy is needed, concurrently investigating and managing endemic zoonoses, studying evolving diseases which change their character and importance due to influences such as demographic and climatic change, and enhancing understanding of factors which are likely to influence the emergence of new pathogens. This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect. As yet there is no fully formed blueprint that can be applied in Asian countries. Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

Machine learning data sources in pediatric sleep research: assessing racial/ethnic differences in electronic health record-based clinical notes prior to model training

Introduction

Pediatric sleep problems can be detected across racial/ethnic subpopulations in primary care settings. However, the electronic health record (EHR) data documentation that describes patients' sleep problems may be inherently biased due to both historical biases and informed presence. This study assessed racial/ethnic differences in natural language processing (NLP) training data (e.g., pediatric sleep-related keywords in primary care clinical notes) prior to model training.

Methods

We used a predefined keyword features set containing 178 Peds B-SATED keywords. We then queried all the clinical notes from patients seen in pediatric primary care between the ages of 5 and 18 from January 2018 to December 2021. A least absolute shrinkage and selection operator (LASSO) regression model was used to investigate whether there were racial/ethnic differences in the documentation of Peds B-SATED keywords. Then, mixed-effects logistic regression was used to determine whether the odds of the presence of global Peds B-SATED dimensions also differed across racial/ethnic subpopulations.

Results

Using both LASSO and multilevel modeling approaches, the current study found that there were racial/ethnic differences in providers' documentation of Peds B-SATED keywords and global dimensions. In addition, the most frequently documented Peds B-SATED keyword rankings qualitatively differed across racial/ethnic subpopulations.

Conclusion

This study revealed providers' differential patterns of documenting Peds B-SATED keywords and global dimensions that may account for the under-detection of pediatric sleep problems among racial/ethnic subpopulations. In research, these findings have important implications for the equitable clinical documentation of sleep problems in pediatric primary care settings and extend prior retrospective work in pediatric sleep specialty settings.

Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation

BACKGROUND

From 2016 to 2021, the volume of peer-reviewed publications related to tobacco has experienced a significant increase. This presents a considerable challenge in efficiently summarizing, synthesizing, and disseminating research findings, especially when it comes to addressing specific target populations, such as the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two Spirit, and other persons who identify as part of this community) populations.

OBJECTIVE

In order to expedite evidence synthesis and research gap discoveries, this pilot study has the following three aims: (1) to compile a specialized semantic database for tobacco policy research to extract information from journal article abstracts, (2) to develop natural language processing (NLP) algorithms that comprehend the literature on nicotine and tobacco product use among sexual and gender diverse populations, and (3) to compare the discoveries of the NLP algorithms with an ongoing systematic review of tobacco policy research among LGBTQ+ populations.

METHODS

We built a tobacco research domain-specific semantic database using data from 2993 paper abstracts from 4 leading tobacco-specific journals, with enrichment from other publicly available sources. We then trained an NLP model to extract named entities after learning patterns and relationships between words and their context in text, which further enriched the semantic database. Using this iterative process, we extracted and assessed studies relevant to LGBTQ+ tobacco control issues, further comparing our findings with an ongoing systematic review that also focuses on evidence synthesis for this demographic group.

RESULTS

In total, 33 studies were identified as relevant to sexual and gender diverse individuals' nicotine and tobacco product use. Consistent with the ongoing systematic review, the NLP results showed that there is a scarcity of studies assessing policy impact on this demographic using causal inference methods. In addition, the literature is dominated by US data. We found that the product drawing the most attention in the body of existing research is cigarettes or cigarette smoking and that the number of studies of various age groups is almost evenly distributed between youth or young adults and adults, consistent with the research needs identified by the US health agencies.

CONCLUSIONS

Our pilot study serves as a compelling demonstration of the capabilities of NLP tools in expediting the processes of evidence synthesis and the identification of research gaps. While future research is needed to statistically test the NLP tool's performance, there is potential for NLP tools to fundamentally transform the approach to evidence synthesis.

Artificial Intelligence in Healthcare: Perception and Reality

Artificial intelligence (AI) has birthed the new "big thing" in modern medicine. It promises to bring about safer and improved care that will be beneficial to patients and become a helpful tool in the hands of a skilled physician. Despite its anticipation, however, the implementation and usage of AI are still in their elementary phases, particularly due to legal and ethical considerations that border on "data." These challenges should not be brushed aside but rather be recognized and resolved to enable acceptance by all relevant stakeholders without prejudice. Once these challenges can be overcome, AI will truly revolutionize the field of medicine with improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality. It is therefore paramount that AI be embraced by physicians and integrated into medical education in order to be well-prepared for our role in the future of medicine.

Artificial intelligence in acupuncture: A bibliometric study

This study aimed to provide a panorama of artificial intelligence (AI) in acupuncture by characterizing and visualizing the knowledge structure, hotspots and trends in global scientific publications. Publications were extracted from the Web of Science. Analyses on the number of publications, countries, institutions, authors, co-authorship, co-citation and co-occurrence were conducted. The USA had the highest volume of publications. Harvard University had the most publications among institutions. Dey P was the most productive author, while lczkowski KA was the most referenced author. The Journal of Alternative and Complementary Medicine was the most active journal. The primary topics in this field concerned the use of AI in various aspects of acupuncture. "Machine learning" and "deep learning" were speculated to be potential hotspots in acupuncture-related AI research. In conclusion, research on AI in acupuncture has advanced significantly over the last two decades. The USA and China both contribute significantly to this field. Current research efforts are concentrated on the application of AI in acupuncture. Our findings imply that the use of deep learning and machine learning in acupuncture will remain a focus of research in the coming years.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

Interorganizational health information exchange-related patient safety incidents: A descriptive register-based qualitative study

PURPOSE

The current literature related to patient safety of interorganizational health information is fragmented. This study aims to identify interorganizational health information exchange-related patient safety incidents occurring in the emergency department, emergency medical services, and home care. The research also aimed to describe the causes and consequences of these incidents.

METHODS

A total of sixty (n = 60) interorganizational health information exchange-related patient safety incident free text reports were analyzed. The reports were reported in the emergency department, emergency medical services, or home care between January 2016 and December 2019 in one hospital district in Finland.

RESULTS

The identified interorganizational health information exchange-related incidents were grouped under two main categories: "Inadequate documentation"; and "Inadequate use of information". The causes of these incidents were grouped under the two main categories "Factors related to the healthcare professional " and "Organizational factors", while the consequences of these incidents fell under the two main categories "Adverse events" and "Additional actions to prevent, avoid, and correct adverse events".

CONCLUSION

This study shows that the inadequate documentation and use of information is mainly caused by factors related to the healthcare professional and organization, including technical problems. These incidents cause adverse events and additional actions to prevent, avoid, and correct the events. The sociotechnical perspective, including factors related to health care professionals, organization, and technology, should be emphasized in patient safety development of inter-organizational health information exchange and it will be the focus of our future research. Continuous research and development work is needed because the processes and information systems used in health care are constantly evolving.

AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions

Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.