Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach

Kuo, Kuang-Ming; Talley, Paul C.; Huang, Chi-Hsien; Cheng, Liang Chih

doi:10.1186/s12911-019-0792-1

Cited by 44 publications

(28 citation statements)

References 31 publications

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“…Several methods have been developed for identifying community-acquired pneumonia using neural networks and genetic algorithms [ 37 – 39 ] and one study predicted hospital-acquired pneumonia in patients with schizophrenia. [ 40 ] Concerning VAP, studies have examined the accuracy of electronic nose (e-nose) sniffers for screening potential VAP cases. These devices use ML methods to analyze exhaled breath for metabolites that may be suggestive of VAP, and some have demonstrated strong discrimination for identifying the presence of VAP.…”

Section: Discussionmentioning

confidence: 99%

Predicting ventilator-associated pneumonia with machine learning

Giang¹,

Calvert²,

Rahmani³

et al. 2021

Medicine

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Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will be diagnosed during the patient stay. A retrospective study examined data from 6126 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different ML models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to the area under the receiver operating characteristic (AUROC) curve on a 20% hold-out test set. Feature importance was measured in terms of Shapley values. The highest performing model achieved an AUROC value of 0.854. The most important features for the best-performing model were the length of time on mechanical ventilation, the presence of antibiotics, sputum test frequency, and the most recent Glasgow Coma Scale assessment. Supervised ML using patient EHR data is promising for VAP diagnosis and warrants further validation. This tool has the potential to aid the timely diagnosis of VAP.

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Section: Discussionmentioning

confidence: 99%

Predicting ventilator-associated pneumonia with machine learning

Giang¹,

Calvert²,

Rahmani³

et al. 2021

Medicine

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“…ML is more accurate in respect to statistical methods to predict acute kidney injury in older people [19] and hospitalacquired pneumonia in people with high risk, i.e. mentally ill people, treated with neuroleptic medication [20], functional fall risk [21] and other poor outcomes in older adults, including delirium [22] and the overall risk of emergency admission [23]. The described algorithms have a potential to provide a more complete and accurate assessment of effects of ageing and risk for physical illnesses and falls in older people while providing a clinically useful predictive capability for earlier intervention in those patients at greatest risk of developing them.…”

Section: Examples Of ML and Dl Implementation In The Medical Care Of mentioning

confidence: 99%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

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Clinical applications of Artifi cial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis infl uencing general healthcare have not materialized, with few exceptions, and then predominantly in the fi eld of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient's care, as well as streamline workfl ow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people. Key points: 1. Artifi cial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care. 2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations. 3. Telemedicine and robot assisted technology are well received by older service users. 4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting. seem to face a number of barriers in contacting communitybased primary health care, such as limited access, lack of standardized information systems and care pathways [3], all necessary to address their complex health care and social care needs. Indeed, older adults have much higher prevalence of nearly all major chronic and long-term conditions. In addition, they are more likely to succumb to adverse health events, such as a falls or infections, and these can lead to dramatic changes in their physical and mental wellbeing even after an apparently minor incident [4]. However, person and familyfocused care, self-management resources, and successful collaborative practice have been all highlighted as facilitators of good health care provision both by older people and their families [3]. All the above places the importance of diagnosis, monitoring of disease risks and their prevention, as well as management and optimizing of geriatric syndromes in the community for both older people living independenly or in 24h care facilities. In particular, identi ication of health issues/ diagnosis, support/treatment needs evaluation, development

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“…In addition, to prevent the overfitting phenomenon, the method of 10‐fold cross‐validation was used, which will also be helpful for the application of the model. Compared with the logistic regression (AUC 88.9%), GBDT (AUC 85.8%) and RF (AUC 88.5%) models, the deep learning model (AUC 90.0%) was the best in the prediction of SA performance; deep learning is a complex non‐linear system and is flexible, with proprietary properties that include dealing with noisy or incomplete input, a mode of robust performance and high fault tolerance, and an inductive ability based on the input data …”

Section: Discussionmentioning

confidence: 99%

Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests

Cai

Long

Kuang

et al. 2020

Geriatrics Gerontology Int

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Aim The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests. Methods A total of 3657 community‐dwelling adults aged ≥60 years from Nanchang city were recruited in this study. A 3‐year follow‐up test was carried out for all the participants to determine whether they turn to non‐SA. Developed questionnaires and physical fitness tests were used to obtain overall health condition, balance, agility, speed, reactions and gait. Four machine learning models (logistic regression, deep learning, random forest and gradient boosting decision tree) were applied to develop the prediction models, the analyzed sample was 890. Results The baseline prevalence of successful aging was 26.99%, The average annual incidence rate of SA to non‐SA was 11.04%. There were significant differences between the SA and non‐SA groups for all physical fitness tests at baseline. The accuracy and area under the curve of all four machine learning models was >85%, the positive predictive value and sensitivity was >75%, and the specificity was >86% on the average. The deep learning model outperformed the other model, with area under the curve 90.00%, accuracy 89.3%, positive predictive value 85.8% and specificity 93.1%, respectively. Compared with other models, the logistic regression model performed best in sensitivity. Age, arm curl, 30‐s sit‐to‐stand and reaction time were important predictors in all models. Conclusion The deep learning model is ideal in the prediction of SA maintenance, and the corresponding physical fitness interventions are essential to ensuring SA. Geriatr Gerontol Int 2020; ••: ••–••.

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Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach

Cited by 44 publications

References 31 publications

Predicting ventilator-associated pneumonia with machine learning

Predicting ventilator-associated pneumonia with machine learning

Artificial Intelligence in the healthcare of older people

Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests

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