A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission.
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ABSTRACT:
SUBMITTER: Misic VV
PROVIDER: S-EPMC8203794 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveHealth care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model's potential to introduce bias.Materials and methodsOur methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist.ResultsWe selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical ways in which these algorithms can perpetuate health care inequalities. We found that LACE and HOSPITAL have the greatest potential for introducing bias, Johns Hopkins ACG has the most areas of uncertainty, and HATRIX has the fewest causes for concern.DiscussionOur approach gives model developers and health care providers a practical and systematic method for evaluating bias in predictive models. Traditional bias identification methods do not elucidate sources of bias and are thus insufficient for mitigation efforts. With our checklist, bias can be addressed and eliminated before a model is fully developed or deployed.ConclusionThe potential for algorithms to perpetuate biased outcomes is not isolated to readmission prediction models; rather, we believe our results have implications for predictive models across health care. We offer a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.
| S-EPMC9277650 | biostudies-literature
Project description:Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient's readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores.
| S-EPMC9286269 | biostudies-literature
Project description:Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.
| S-EPMC9880585 | biostudies-literature
Project description:ObjectiveOsteoporosis is a common skeletal disease that greatly increases the risk of pathologic fractures and accounts for approximately 700,000 vertebral compression fractures (VCFs) annually in the United States. Cement augmentation procedures such as balloon kyphoplasty (KP) and percutaneous vertebroplasty (VP) have demonstrated efficacy in the treatment of VCFs, however, some studies report rates of readmission as high as 10.8% following such procedures. The purpose of this study was to employ Machine Learning (ML) algorithms to predict 30-day hospital readmission following cement augmentation procedures for the treatment of VCFs using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database.MethodsACS-NSQIP was queried to identify patients undergoing either KP or VP from 2011 to 2014. Three ML algorithms were constructed and tasked with predicting post-operative readmissions within this cohort of patients. Results: Postoperative pneumonia, ASA Class 2 designation, age, partially-dependent functional status, and a history of smoking were independently identified as highly predictive of readmission by all ML algorithms. Among these variables postoperative pneumonia (p < 0.01), ASA Class 2 designation (p < 0.01), age (p = 0.002), and partially-dependent functional status (p < 0.01) were found to be statistically significant. Predictions were generated with an average AUC value of 0.757 and an average accuracy of 80.5%.ConclusionsPostoperative pneumonia, ASA Class 2 designation, partially-dependent functional status, and age are perioperative variables associated with 30-day readmission following cement augmentation procedures. The use of ML allows for quantification of the relative contributions of these variables toward producing readmission.
| S-EPMC10943990 | biostudies-literature
Project description:Genome-scale metabolic model (GEM) has been established as an important tool to study cellular metabolism at a systems level by predicting intracellular fluxes. With the advent of generic human GEMs, they have been increasingly applied to a range of diseases, often for the objective of predicting effective metabolic drug targets. Cancer is a representative disease where the use of GEMs has proved to be effective, partly due to the massive availability of patient-specific RNA-seq data. When using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). However, while MEMs have been thoroughly examined, MSMs have not been systematically examined, especially, when studying cancer metabolism. In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patient-specific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy observations were made from the evaluation, including the high performance of two MEMs, namely rank-based 'task-driven Integrative Network Inference for Tissues' (tINIT) or 'Gene Inactivity Moderated by Metabolism and Expression' (GIMME), paired with least absolute deviation (LAD) as a MSM, and relatively poorer performance of flux balance analysis (FBA) and parsimonious FBA (pFBA). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.
| S-EPMC9218235 | biostudies-literature
Project description:BackgroundHealthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment.MethodsThis study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics.ResultsOur best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings.ConclusionThe predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration.
| S-EPMC10765581 | biostudies-literature
Project description:Background:Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions. Objective:To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile. Materials:An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost. Methods:Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size. Results:Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms. Conclusions:We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.
| S-EPMC6500604 | biostudies-other
Project description:Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.
| S-EPMC8327376 | biostudies-literature
Project description:Ecosystem service (ES) spatial modelling is a key component of the integrated assessments designed to support policies and management practices aiming at environmental sustainability. ESTIMAP ("Ecosystem Service Mapping Tool") is a collection of spatially explicit models, originally developed to support policies at a European scale. We based our analysis on 10 case studies, and 3 ES models. Each case study applied at least one model at a local scale. We analyzed the applications with respect to: the adaptation process; the "precision differential" which we define as the variation generated in the model between the degree of spatial variation within the spatial distribution of ES and what the model captures; the stakeholders' opinions on the usefulness of models. We propose a protocol for adapting ESTIMAP to the local conditions. We present the precision differential as a means of assessing how the type of model and level of model adaptation generate variation among model outputs. We then present the opinion of stakeholders; that in general considered the approach useful for stimulating discussion and supporting communication. Major constraints identified were the lack of spatial data with sufficient level of detail, and the level of expertise needed to set up and compute the models.
| S-EPMC5821683 | biostudies-other
Project description:AimsThis study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.Methods and resultsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full-text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time-point comparisons, ML-derived c-indices were higher than CSM-derived c-indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in-hospital mortality to 1 year survival; of these nine, seven reported higher c-indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c-indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c-indices 0.913 vs. 0.835).ConclusionsML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML-based studies of prediction modelling. We suggest that ML-based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867.
| S-EPMC7835549 | biostudies-literature