Machine Learning for Child and Adolescent Health: A Systematic Review

Hoodbhoy, Zahra; Jeelani, Sarah Masroor; Aziz, Abeer; Habib, Muhammad Ibrahim; Iqbal, Bilal; Akmal, Waqaas; Siddiqui, Khan M.; Hasan, Babar; Leeflang, Mariska; Das, Jai K

doi:10.1542/peds.2020-011833

Cited by 23 publications

(15 citation statements)

References 29 publications

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“…In Supplementary Table S1 , we have complied recent review articles detailing emerging examples of how statistical and ML methods are being utilized for clinical outcome prediction in major medical specialities. Applications are found in the fields of Anesthesiology [ 32 , 33 , 34 ], Dermatology [ 35 , 36 , 37 ], Emergency Medicine [ 38 , 39 ], Family Medicine [ 40 , 40 ], Internal Medicine [ 41 , 42 , 43 ], Interventional Radiology [ 44 , 45 ], Medical Genetics [ 46 ], Neurological Surgery [ 47 ], Neurology [ 48 , 49 , 50 ], Obstetrics and Gynecology [ 51 , 52 ], Ophthalmology [ 53 , 54 , 55 ], Orthopaedic Surgery [ 56 ], Otorhinolaryngology [ 57 , 58 ], Pathology [ 59 , 60 , 61 ], Pediatrics [ 62 ], Physical Medicine and Rehabilitation [ 63 , 64 ], Plastic and Reconstructive Surgery [ 65 , 66 ], Psychiatry [ 67 , 68 ], Radiation Oncology [ 69 , 70 ], Radiology [ 71 , 72 ], General Surgery [ 73 , 74 ], Cardiothoracic Surgery [ 75 , 76 ], Urology [ 77 , 78 ], Vascular Surgery [ 79 , 80 ]. These papers introduce terms describing ML models as ‘supervised’ or ‘unsupervised’.…”

Section: Emerging Methods and Emerging Applicationsmentioning

confidence: 99%

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Pettit

Fullem

Cheng

et al. 2021

Emerging Topics in Life Sciences

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AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

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Section: Emerging Methods and Emerging Applicationsmentioning

confidence: 99%

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Pettit

Fullem

Cheng

et al. 2021

Emerging Topics in Life Sciences

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“…Despite the potential of Big Data strategies to improve child health and patient safety by trans-institutional identification of rare patient phenotypes, adverse patient events (e.g., sentinel pediatric events or adverse drug reactions), 78 and responses to therapies, significant barriers remain in the practical application of research strategies to real-world bedside care. [79][80][81] One of the most significant barriers is the lack of a universal, interoperable, modular system for capturing and sharing medical data. Proprietary and institutionally cloistered electronic health record systems limit discovery of critical components of best clinical and nursing practices 82 and of pediatric-and disease-specific patient characteristics, disease risk, and adverse events.…”

Section: Research Gaps and Future Directionsmentioning

confidence: 99%

Improving child health through Big Data and data science

2022

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Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. Impact Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

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“…The ability of Machine Learning algorithms to see and learn from patterns that are obscure to humans afford them the utilization in mass disease screening, with little or no input from qualified clinical personnel. The use of ML models to support diagnosis have evolved in the last three decades favoring deep learning and clustering facilitated by the adoption of electronic medical records ( 14 ). A study carried out in 2020 was able to predict the incidence of Alzheimer's disease in older people by training Machine Learning algorithms on MRI tests using the algorithm called “Support Vector Machines.” The ML algorithm could diagnose the disease with an accuracy, sensitivity, and specificity of 96.12, 94.94, and 98.23%, respectively ( 15 ).…”

Section: Emerging Technologies and Uhcmentioning

confidence: 99%