Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm

Ye, Chengyin; Li, Jinmei; Hao, Shiying; Liu, Modi; Jin, Hua; Zheng, Le; Xia, Minjie; Jin, Bo; Zhu, Chunqing; Alfreds, Shaun T; Stearns, Frank; Kanov, Laura; Sylvester, Karl G.; Widen, Eric; McElhinney, Doff B.; Ling, Xuefeng B.

doi:10.1016/j.ijmedinf.2020.104105

Cited by 76 publications

(81 citation statements)

References 28 publications

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“…Predicting the risk of bone loss, osteoporotic fractures, falls, or comorbidities in osteoporotic patients over time was investigated in 14 studies (Table 4). ( 98–111 ) Two of them used unsupervised learning to identify fracture and comorbidity risk groups, respectively. ( 98,99 ) Kruse and colleagues developed a fracture risk clustering model to categorize subgroups of patients at risk.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

114

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Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12‐point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML‐based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model‐based decision‐making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).

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Section: Resultsmentioning

confidence: 99%

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

114

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“…Univariable analysis was performed on z-score-normalized features, and logistic regression was used to calculate the odds ratios and P values for feature filtering. For multivariate model building, a gradient boosting tree algorithm XGBoost was used for constructing a multivariable prediction model [10][11][12][13][14][15][16] . The baseline learner is the classification and regression tree and the number of trees is selected via cross-validation to avoid over-fitting.…”

Section: Statistical Analysis and Modelling To Predict Recurrence Of mentioning

confidence: 99%

Recurrence of SARS-CoV-2 PCR positivity in COVID-19 patients: a single center experience and potential implications

Huang

Zheng

et al. 2020

Preprint

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IMPORTANCE How to appropriately care for patients who become PCR-negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still not known.Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system if a subset were to be PCR-positive again with reactivated SARS-CoV-2.OBJECTIVE To characterize a single center COVID-19 cohort with and without recurrence of PCR positivity, and develop an algorithm to identify patients at high risk of retest positivity after discharge to inform health care policy and case management decision-making. DESIGN, SETTING, AND PARTICIPANTSA cohort of 414 patients with confirmed SARS-CoV-2 infection, at The Second Affiliated EXPOSURES Polymerase chain reaction (PCR) and IgM-IgG antibody confirmed SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES Univariable and multivariable statistical analysis of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data to develop an algorithm to predict patients at risk of recurrence of PCR positivity.RESULTS 16.7% (95CI: 13.0%-20.3%) patients retest PCR positive 1 to 3 times after discharge, despite being in strict quarantine. The driving factors in the recurrence prediction model included: age, BMI; lowest levels of the blood laboratory tests

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“…Disease duration may be an early predictor of falls, however, once a fall has occurred, the disease duration is less valuable in predicting recurrence, mainly because falls are reduced as patients become progressively immobilised [21]. In contrast, current age could be more important than number of falls for the prediction of recurrent falls, due to the ageing process of patients and multiple co-morbidities [22]. Consistent with prior studies, PIGD subtype was one of the contributing factors of falling regardless of the disease stage [3,4,11].…”

Section: Discussionmentioning

confidence: 99%