An explainable machine learning framework for lung cancer hospital length of stay prediction.
Ontology highlight
ABSTRACT:
SUBMITTER: Alsinglawi B
PROVIDER: S-EPMC8755804 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectivesThe aim of this study is to determine whether the clinical features including blood markers can establish an explainable machine learning model to predict epidermal growth factor receptor (EGFR) mutation in lung cancer.MethodsWe retrospectively analyzed 7,413 patients with lung adenocarcinoma (LA) diagnosed by gene sequencing in West China Hospital of the Sichuan University from April 2015 to June 2019. The machine learning algorithms (MLAs) included logistic regression (LR), random forest (RF), LightGBM, support vector machine (SVM), multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and decision tree (DT). Demographic characteristics, personal history, and blood markers were taken into. The area under the receiver operating characteristic curve (AUC) and SHapley Additive exPlanation (SHAP) value were used to explain the prediction models.ResultsOf the 7,413 patients with LA (47.6%), 3,527 were identified with EGFR mutation; RF achieved greatest performance in predicting EGFR mutation AUC [0.771, 95% confidence interval (CI): 0.770, 0.772], which was like XGBoost with AUC (0.740, 95% CI: 0.739, 0.741). The five most influential features were smoking consumption, sex, cholesterol, age, and albumin globulin ratio. The SHAP summary and dependence plot have been used to explain the affection of the 12 features to this model and how a single feature influences the output, respectively.ConclusionWe established EGFR mutation prediction models by MLAs and revealed that the RF was preferred, AUC (0.771, 95% CI: 0.770, 0.772), which was better than the traditional models. Therefore, the artificial intelligence-based MLA predicting model may become a practical tool to guide in diagnosis and therapy of LA.
| S-EPMC9259982 | biostudies-literature
Project description:BackgroundPostoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information.ObjectiveThe objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach.MethodsIn our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type.ResultsIn the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables.ConclusionsA machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.
| S-EPMC7939945 | biostudies-literature
Project description:Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes. Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945 (95% confidence interval [CI] 0.922-0.977). In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 (95% CI 0.876-0.908) and 0.889 (95% CI 0.849-0.936), respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality. The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.
| S-EPMC9334583 | biostudies-literature
Project description:BackgroundThe aim of this study was to improve understanding of hospital length of stay (LOS) in patients undergoing total joint arthroplasty (TJA) in a high-efficiency, hospital-based pathway.MethodsWe retrospectively reviewed 1401 consecutive primary and revision TJA patients across 67 patient and preoperative care characteristics from 2016 to 2019 from the institutional electronic health records. A machine learning approach, testing multiple models, was used to assess predictors of LOS.ResultsThe median LOS was 1 day; outpatients accounted for 16.5%, 1-day inpatient stays for 38.0%, 2-day stays for 26.4%, and 3-days or more for 19.1%. Patients characteristically fell into 1 of 3 broad categories that contained relatively similar characteristics: outpatient (0-day LOS), short stay (1- to 2-day LOS), and prolonged stay (3 days or greater). The random forest models suggested that a lower Risk Assessment and Prediction Tool score, unplanned admission or hospital transfer, and a medical history of cardiovascular disease were associated with an increased LOS. Documented narcotic use for surgery preparation prior to hospitalization and preoperative corticosteroid use were factors independently associated with a decreased LOS.ConclusionsAfter TJA, most patients have either an outpatient or short-stay hospital episode. Patients who stay 2 days do not differ substantially from patients who stay 1 day, while there is a distinct group that requires prolonged admission. Our machine learning models support a better understanding of the patient factors associated with different hospital LOS categories for TJA, demonstrating the potential for improved health policy decisions and risk stratification for centers caring for complex patients.
| S-EPMC10372176 | biostudies-literature
Project description:ObjectivesThe study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five different chronic conditions.MethodsAn administrative claim dataset (2008-2012) of a regional network of nine hospitals in the Tampa Bay area, Florida, USA, was used to develop the prediction models. Features were extracted from the dataset using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Five learning algorithms, namely, decision tree C5.0, linear support vector machine (LSVM), k-nearest neighbors, random forest, and multi-layered artificial neural networks, were used to build the model with semi-supervised anomaly detection and two feature selection methods. Issues with the unbalanced nature of the dataset were resolved using the Synthetic Minority Over-sampling Technique (SMOTE).ResultsLSVM with wrapper feature selection performed moderately well for all patient cohorts. Using SMOTE to counter data imbalances triggered a tradeoff between the model's sensitivity and specificity, which can be masked under a similar area under the curve. The proposed aggregate rank selection approach resulted in a balanced performing model compared to other criteria. Finally, factors such as comorbidity conditions, source of admission, and payer types were associated with the increased risk of a prolonged LOS.ConclusionsProlonged LOS is mostly associated with pre-intraoperative clinical and patient socioeconomic factors. Accurate patient identification with the risk of prolonged LOS using the selected model can provide hospitals a better tool for planning early discharge and resource allocation, thus reducing avoidable hospitalization costs.
| S-EPMC7010949 | biostudies-literature
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.We conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.Outcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.The MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.NCT03015454.
| S-EPMC5687546 | biostudies-literature
Project description:The coronavirus is considered this century's most disruptive catastrophe and global concern. This disease has prompted extreme social, psychological and economic impacts affecting millions of people around the globe. COVID-19 is transmitted from one infected person's body to another through respiratory droplets. This virus proliferates when people breathe in air-contaminated space with droplets and microscopic airborne particles. This research aims to analyze automatic COVID-19 detection using machine learning techniques to build an intelligent web application. The dataset has been preprocessed by dropping null values, feature engineering, and synthetic oversampling (SMOTE) techniques. Next, we trained and evaluated different classifiers, i.e., logistic regression, random forest, decision tree, k-nearest neighbor, support vector machine (SVM), ensemble models (adaptive boosting and extreme gradient boosting) and deep learning (artificial neural network, convolutional neural network and long short-term memory) techniques. Explainable AI with the LIME framework has been applied to interpret the prediction results. The hybrid CNN-LSTM algorithm with the SMOTE approach performed better than the other models on the employed open-source dataset obtained from the Israeli Ministry of Health website, with 96.34% accuracy and a 0.98 F1 score. Finally, this model was chosen to deploy the proposed prediction system to a website, where users may acquire an instantaneous COVID-19 prognosis based on their symptoms.
| S-EPMC9876019 | biostudies-literature
Project description:Hospital length of stay (LOS) after burn injury is commonly estimated as 1 day per percent burn, but LOS often exceeds that estimate. The purpose of this study is to develop a novel method for estimating burn hospital LOS at any time during hospitalization. The authors used the American Burn Association National Burn Repository from 2000 to 2009 to directly estimate the median residual LOS (MRLOS) of patients hospitalized for burn injuries and who survived to discharge. The MRLOS is the median of how many more days a burn patient will be hospitalized given that the person has been in hospital for a specified time period. The authors also estimated the 25th and 75th percentiles of residual life and quantified the relationship between MRLOS and LOS with ordinary least squares for all burn patients, by burn size and by presence of inhalation injury. MRLOS increased with increasing LOS, confirming that discharge estimates change over time. Patients with inhalation injury had longer MRLOS than patients without inhalation injury in the first 100 hospital days. Patients with large burns (>25%) had large MRLOS consistent with prolonged hospitalization, but patients with small burns (<25%) also had steadily increasing MRLOS during hospitalization (ie, the longer the patient was in the hospital, the longer the predicted LOS regardless of initial LOS estimate). Estimating remaining hospital LOS at any time during admission has been problematic; MRLOS can be used to provide an estimate of remaining hospital LOS and resource utilization to families, administrators, and other medical professionals.
| S-EPMC5014656 | biostudies-literature
Project description:The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.
| S-EPMC10333394 | biostudies-literature