Machine learning for administrative health records: A systematic review of techniques and applications

Caruana, Adrian; Bandara, Madhushi; Musial, Katarzyna; Catchpoole, Daniel; Kennedy, Paul J.

doi:10.1016/j.artmed.2023.102642

Cited by 7 publications

(2 citation statements)

References 70 publications

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“…These technologies enable a self-service approach to patient check-in, allowing patients to access their information and receive pertinent documents and instructions. Additionally, AI aids the statistical unit by streamlining information gathering, record updating, and analysing the available data [ 49 ].…”

Section: Healthcare Managementmentioning

confidence: 99%

The use of artificial intelligence to improve mycetoma management

Ali,

Elkheir,

Fahal

2024

PLoS Negl Trop Dis

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Mycetoma is a serious, neglected tropical disease affecting the underprivileged populations in resource-constrained communities of the rural tropical and subtropical regions. It predominately affects individuals of low visibility and voice and continues to hinder and burden the poor, remote communities [1]. Furthermore, in endemic areas, the medical and health facilities are meagre and adequate treatment is lacking or inaccessible [2]. All these lead to a progressive disease with massive morbidities and serious medical, health, and socioeconomic consequences [3,4].Mycetoma is a chronic disabling subcutaneous granulomatous inflammatory disease of fungal and bacterial origin [5,6]. The inflammatory granuloma progressively spreads to affect the skin, deep tissues, and bones, leading to massive tissue damage, destruction, and serious morbidities and can be fatal [6][7][8]. A disease characteristic is the triad of subcutaneous mass, multiple sinuses discharging purulent and sero-purulent discharge frequently containing grains [9,10].The mycetoma development and the associated risk factors are still unclear. Likewise, the disease susceptibility, resistance, entry route, and incubation period are unclear [11][12][13]. Presently, the diagnosis of mycetoma is difficult and tedious [14]. The available diagnostic tests are invasive, time-consuming, of low sensitivity and specificity, and there is no point of care [15][16][17]. Furthermore, the available treatment of mycetoma is suboptimal, characterised by a low cure rate and high recurrence and follow-up rates, and the disease remains with patients for a while, if not for life [18,19].Artificial intelligence (AI) continues evolving swiftly, with ongoing research and development in various domains. It can potentially bring about significant advancements in science, technology, and society. AI has made substantial inroads into the medical and healthcare arenas, revolutionising healthcare delivery, disease diagnosis, and management. Nowadays, there are many applications for AI in medical and health practice, to mention but a few: its use in medical imaging, disease diagnosis, drug discovery, personalised medicine, data predictive analytics, electronic health records, telemedicine and virtual health assistance, robotics surgery, drug dosage, treatment recommendations, health monitoring, mental health, drug adverse event detection and healthcare operations, and resource management [20,21].Machine learning (ML) is a subset of AI that creates algorithms and models that enable computers to learn and make predictions or decisions based on data to improve performance without explicit programming. ML is essential for interpreting large datasets, automating tasks, and enhancing decision-making processes, applied across diverse domains like image and speech recognition, natural language processing, recommendation systems, autonomous PLOS NEGLECTED TROPICAL DISEASES

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Section: Healthcare Managementmentioning

confidence: 99%

The use of artificial intelligence to improve mycetoma management

Ali,

Elkheir,

Fahal

2024

PLoS Negl Trop Dis

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“…Machine learning is one of the fastest growing technology areas today and is widely used to enable evidence-based decision making in industries such as healthcare, manufacturing, and education ( 17 ). Machine learning is primarily based on large datasets to develop robust risk models and predict the type of person being studied ( 18 , 19 ). Prediction tools developed using machine learning can be a good predictor of vitamin D deficiency risk in participants.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based prediction of vitamin D deficiency: NHANES 2001-2018

Guo,

He,

2024

Front. Endocrinol.

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BackgroundVitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are no prediction tools for predicting the risk of vitamin D deficiency in the general community population, and this study aims to use machine learning to predict the risk of vitamin D deficiency using data that can be obtained through simple interviews in the community.MethodsThe National Health and Nutrition Examination Survey 2001-2018 dataset is used for the analysis which is randomly divided into training and validation sets in the ratio of 70:30. GBM, LR, NNet, RF, SVM, XGBoost methods are used to construct the models and their performance is evaluated. The best performed model was interpreted using the SHAP value and further development of the online web calculator.ResultsThere were 62,919 participants enrolled in the study, and all participants included in the study were 2 years old and above, of which 20,204 (32.1%) participants had vitamin D deficiency. The models constructed by each method were evaluated using AUC as the primary evaluation statistic and ACC, PPV, NPV, SEN, SPE, F1 score, MCC, Kappa, and Brier score as secondary evaluation statistics. Finally, the XGBoost-based model has the best and near-perfect performance. The summary plot of SHAP values shows that the top three important features for this model are race, age, and BMI. An online web calculator based on this model can easily and quickly predict the risk of vitamin D deficiency.ConclusionIn this study, the XGBoost-based prediction tool performs flawlessly and is highly accurate in predicting the risk of vitamin D deficiency in community populations.

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Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network

Liang,

Guo,

2024

Health Inf Sci Syst

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Machine learning for administrative health records: A systematic review of techniques and applications

Cited by 7 publications

References 70 publications

The use of artificial intelligence to improve mycetoma management

The use of artificial intelligence to improve mycetoma management

Machine learning-based prediction of vitamin D deficiency: NHANES 2001-2018

Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network

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