Random forest modeling using socioeconomic distress predicts hernia repair approach

Tracy, Brett M.; Finnegan, Timothy; Smith, Randi N.; Senkowski, Christopher

doi:10.1007/s00464-020-07860-6

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…A retrospective review of 559 patients undergoing abdominal hernia was the basis for RF modeling to predict surgical approach and determine the importance of different socioeconomic variables in selecting the type of surgery (area under the receiver operating characteristic (AUROC) ≈0.82). [28] Data were obtained from a single institution, which limits the generalizability of findings. Psychotic and depressive symptom clusters in dementia were predicted using RF on the EHR records of 4,003 patients with dementia (AUROC 0.80).…”

Section: Methodsmentioning

confidence: 99%

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

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Machine learning (ML) models for analyzing medical data are critical for both accelerating development of novel diagnostic and treatment strategies and improving the accuracy of medical care delivery. Our objective was to comprehensively review supervised ML models for diagnosis or treatment prediction. Publications indexed in PubMed were reviewed to identify articles utilizing supervised predictive ML models in medicine. Articles published between 01/01/2020–01/01/2022 were included in this review. Initially, PubMed was searched using MeSH major terms, and if more extensive search results were needed, a broader search was applied (titles/abstracts).PubMed indexed 21,268 published articles (MeSH Major topic) describing ML methods implemented in medicine. Of those, 11,726 articles were published within the last 2 years. Most of the published ML models in medicine in the last two years were different types of deep learning models (about 75%). Fifty articles were included in this review.Almost all categories of disease were subjects of ML predictions. Positive and negative factors in each of the scenarios need to be evaluated before the most optimal ML model is selected. Domain knowledge and collaborations between physicians and ML experts can improve the selection and prediction performance of ML models in medicine and facilitate implementation in clinical practice. Predictive ML models could provide recommendations to recruit suitable patients for clinical trials. Prediction ML models may contribute to development of more effective diagnostic and therapeutic choices, founded on evidence-based medicine. A broad range of methodological approaches have been taken toward this goal, and those approaches are presented here with their various advantages and disadvantages.AUTHOR SUMMARYOver the last decade, there has been rapid development of Machine learning (ML) methods to analyze Big Data in medicine. ML is aimed to make the computer learn from past experiences and make predictions by recognizing patterns in medical data. We performed a comprehensive systematic literature review of recent publications (last two years), indexed in PubMed/MEDLINE that have described either traditional or deep supervised prediction ML models in medicine. We identified 21,268 articles describing ML implementation in medicine. 11,726 articles were published in the last 2 years. We presented the number of publications describing each of the most often ML methods to show current trends in development of these models. Most of the recently published ML models in medicine were deep learning models. We found that the understanding of disease is likely to lead to more accurate prediction. An important dilemma is the selection of optimal ML models for a specific task, considering amount and type of available data. Domain knowledge and collaborations between physicians and ML experts can improve the prediction performance of ML models, which could help clinicians to select the most effective diagnostic and therapeutic choices available and decrease medical errors.

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Section: Methodsmentioning

confidence: 99%

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Patients were divided into distinct comparison groups based on DCI quintile, as has been performed in previous studies utilizing this dataset [ 8 , 9 ]. DCI is a composite measure of seven unique economic and sociological factors available for each zip code in the USA, including rates of high school diplomas, housing vacancy, poverty, and unemployment, as well as changes in employment and business establishments and median income ratio.…”

Section: Methodsmentioning

confidence: 99%

Community distress as a predictor of early hernia recurrence for older adults undergoing ventral hernia repair (VHR)

et al. 2022

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Background Social cohesion and neighborhood support have been linked to improved health in a variety of fields, but is not well-studied among the elderly population. This is particularly evident in surgical populations. Therefore, this study sought to assess the potential role of community distress in predicting early hernia recurrence among older adults. Methods The Abdominal Core Health Quality Collaborative (ACHQC) was used to identify patients aged 65 or older undergoing elective ventral hernia repair with zip code data available. Patients were linked to the Distressed Communities Index (DCI), which is a national database that assigns a score of 0–100 to each zip code based on 7 measures of neighborhood prosperity. Quintiles were used to compare groups: prosperous (0–20), comfortable (21–40), mid-tier (41–60), at-risk (61–80), and distressed (81–100). Distressed (0–20), at-risk (21–40), mid-tier (41–60), comfortable (61–80), and prosperous (81–100). Time to recurrence for neighborhood distress quintiles was examined using a Cox proportional hazards model. Results In total, 9819 patients were included in the study, including 3056 (31.1%) prosperous, 2307 (23.5%) comfortable, 1795 (18.2%) mid-tier, 1390 (14.2%) at-risk, and 1271 (12.9%) distressed. Distressed communities had lower mean age and greater proportion of racial minorities ( p < 0.001). Open repairs were significantly more common among the distressed group (66.7%), as were all comorbidities ( p < 0.001). Recurrence-free survival was shorter for distressed communities compared to prosperous after adjusting for baseline characteristics (HR 1.3, 95% CI 1.07–1.67, p = 0.01). Mean time to recurrence was lowest for patients living in distressed communities, indicating the worst recurrence rates, while mean time to recurrence was greatest for those in prosperous zip codes ( p < 0.001). Conclusion Older VHR patients presenting from distressed zip codes, as identified by the Distressed Communities Index, experience hernia recurrence significantly sooner as compared to patients from prosperous zip codes. This study may provide evidence of the role of neighborhood and environmental factors in caring for older patients following VHR. Graphical abstract

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Spin is present in the majority of articles evaluating robot-assisted groin hernia repair: a systematic review

2022

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Random forest modeling using socioeconomic distress predicts hernia repair approach

Cited by 7 publications

References 33 publications

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Community distress as a predictor of early hernia recurrence for older adults undergoing ventral hernia repair (VHR)

Spin is present in the majority of articles evaluating robot-assisted groin hernia repair: a systematic review

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