Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation

Rajendran, Suraj; Pan, Weishen; Sabuncu, Mert R.; Chen, Yong; Zhou, Jiayu; Wang, Fei

doi:10.1016/j.patter.2023.100913

Cited by 3 publications

(2 citation statements)

References 120 publications

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“…EHRs, wearables and other technologies have expanded modalities [21] and volume of health data [22]. Primary care has subsequently emerged as a major location for patient and population health data, especially in the context of chronic disease management.…”

Section: Augmenting Shared Heuristic Decision Making and Population H...mentioning

confidence: 99%

Primary Care Informatics: Vitalizing the Bedrock of Healthcare (Preprint)

You,

Leung,

Pandita

et al. 2024

Preprint

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UNSTRUCTURED Primary care informatics professionals address workflow and technology solutions in a wide spectrum of health, ranging from optimizing the experience of the individual patient in the clinic room to supporting the health of populations. Primary care informatics overlaps uniquely with two key disciplines with immense impact on societal health - primary care and health informatics. Primary care is often a hub or gateway to accessing healthcare, and has a primary aim of synthesizing and coordinating numerous and potentially complex elements of patients’ health and medical care in a holistic manner. However, over the past 25 years, primary care has become a specialty in crisis: in a post-COVID world, worsening workforce shortages, clinician burnout, and continuing challenges in healthcare access all contribute to difficulties in sustaining primary care. informatics professionals are poised to change this trajectory. In this viewpoint, we aim to inform readers of the discipline of primary care informatics and its importance in the design, support, and maintenance of essential primary care services. Although the focus of this work is on primary care in the United States, which includes general internal medicine, family medicine, and pediatrics, many of the principles outlined can also be applied to comparable healthcare services and settings in other countries. We highlight the challenges in primary care and how primary care informatics professionals can address these challenges. We discuss current and emerging technologies in primary care informatics. In summary, primary care informatics offers important contributions to healthcare and to the informatics field, and there are many opportunities for informatics professionals to enhance the primary care experience for patients, families, and their care teams.

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Section: Augmenting Shared Heuristic Decision Making and Population H...mentioning

confidence: 99%

Primary Care Informatics: Vitalizing the Bedrock of Healthcare (Preprint)

You,

Leung,

Pandita

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…As AI/ML techniques continue to evolve, novel algorithms and architectures may offer improved predictive performance and interpretability. For example, advances in areas such as federated learning, transfer learning, and multi-task learning could enable more efficient model development and deployment across different healthcare systems and patient populations [ 34 ]. Additionally, the integration of causal reasoning and XAI techniques could enhance the transparency and trustworthiness of AI/ML models in clinical decision-making.…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review

Sinha,

Khan,

Awan

et al. 2024

Cureus

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Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.

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Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework

Tong,

Shen,

et al. 2024

Journal of the American Medical Informatics Association

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Objectives Racial disparities in kidney transplant access and posttransplant outcomes exist between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United States, with the site of care being a key contributor. Using multi-site data to examine the effect of site of care on racial disparities, the key challenge is the dilemma in sharing patient-level data due to regulations for protecting patients’ privacy. Materials and Methods We developed a federated learning framework, named dGEM-disparity (decentralized algorithm for Generalized linear mixed Effect Model for disparity quantification). Consisting of 2 modules, dGEM-disparity first provides accurately estimated common effects and calibrated hospital-specific effects by requiring only aggregated data from each center and then adopts a counterfactual modeling approach to assess whether the graft failure rates differ if NHB patients had been admitted at transplant centers in the same distribution as NHW patients were admitted. Results Utilizing United States Renal Data System data from 39 043 adult patients across 73 transplant centers over 10 years, we found that if NHB patients had followed the distribution of NHW patients in admissions, there would be 38 fewer deaths or graft failures per 10 000 NHB patients (95% CI, 35-40) within 1 year of receiving a kidney transplant on average. Discussion The proposed framework facilitates efficient collaborations in clinical research networks. Additionally, the framework, by using counterfactual modeling to calculate the event rate, allows us to investigate contributions to racial disparities that may occur at the level of site of care. Conclusions Our framework is broadly applicable to other decentralized datasets and disparities research related to differential access to care. Ultimately, our proposed framework will advance equity in human health by identifying and addressing hospital-level racial disparities.

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Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation

Cited by 3 publications

References 120 publications

Primary Care Informatics: Vitalizing the Bedrock of Healthcare (Preprint)

Primary Care Informatics: Vitalizing the Bedrock of Healthcare (Preprint)

Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review

Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework

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