Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery

2021 ◽  
pp. 219256822097983
Author(s):  
Qiyi Li ◽  
Haoyan Zhong ◽  
Federico P. Girardi ◽  
Jashvant Poeran ◽  
Lauren A. Wilson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Value ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Improvement Program ◽  
Single Level ◽  
Laboratory Values ◽  
Same Day Discharge

Study Design: retrospective cohort study. Objectives: To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery. Methods: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures. Results: Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase. Conclusion: Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design.

scholarly journals Searching for improvements in predicting human eye colour from DNA

2021 ◽  
Author(s):  
Magdalena Kukla-Bartoszek ◽  
Paweł Teisseyre ◽  
Ewelina Pośpiech ◽  
Joanna Karłowska-Pik ◽  
Piotr Zieliński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sequencing Analysis ◽  
Learning Approaches ◽  
Human Eye ◽  
Software Analysis ◽  
Whole Exome ◽  
Eye Colour

AbstractIncreasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

Prediction of chronic obstructive pulmonary disease stages using machine learning algorithms

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Predictive Value ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Chronic Obstructive ◽  
Optimal Prediction ◽  
Obstructive Pulmonary Disease ◽  
Disease Stages

Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.

Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury

2019 ◽  
Vol 85 (7) ◽  
pp. 725-729 ◽  
Author(s):  
Joshua Parreco ◽  
Hahn Soe-Lin ◽  
Jonathan J. Parks ◽  
Saskya Byerly ◽  
Matthew Chatoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Learning Algorithms ◽  
Vital Signs ◽  
Kidney Injury ◽  
Machine Learning Algorithms ◽  
Laboratory Measurements ◽  
Icu Patients ◽  
Laboratory Values ◽  
F Measure

Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurements per patient. Patients with AKI were identified and trends in vital signs and laboratory values were determined by calculating the slope of the least-squares-fit linear equation using three days for each value. Different machine learning classifiers (gradient boosted trees [GBT], logistic regression, and deep learning) were trained to predict AKI using the laboratory values, vital signs, and slopes. There were 151,098 ICU stays identified and the rate of AKI was 5.6 per cent. The best performing algorithm was GBT with an AUC of 0.834 ± 0.006 and an F-measure of 42.96 per cent ± 1.26 per cent. Logistic regression performed with an AUC of 0.827 ± 0.004 and an F-measure of 28.29 per cent ± 1.01 per cent. Deep learning performed with an AUC of 0.817 ± 0.005 and an F-measure of 42.89 per cent ± 0.91 per cent. The most important variable for GBT was the slope of the minimum creatinine (30.32%). This study identifies the best performing machine learning algorithms for predicting AKI using trends in laboratory values in ICU patients. Early identification of these patients using readily available data indicates that incorporating machine learning predictive models into electronic medical record systems is an inevitable requisite for improving patient outcomes.

scholarly journals Predicting postoperative surgical site infection with administrative data: a random forests algorithm

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yelena Petrosyan ◽  
Kednapa Thavorn ◽  
Glenys Smith ◽  
Malcolm Maclure ◽  
Roanne Preston ◽  
...  
Keyword(s):  
Machine Learning ◽  
Administrative Data ◽  
Risk Score ◽  
Random Forests ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Improvement Program ◽  
Health Administrative Data ◽  
Administrative Datasets

Abstract Background Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms. Methods All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets. Results Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90–0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402). Conclusion We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.

scholarly journals Machine Learning Approaches on Diabetic Retinopathy Prediction

2020 ◽  
pp. 341-345
Author(s):  
Gowri Prasad ◽  
Vrinda Raveendran ◽  
Vidya B M ◽  
Tejavati Hedge
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Comparative Study ◽  
Blood Sugar ◽  
Efficient Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Early Age ◽  
Learning Approaches

Diabetic retinopathy is a eye disorder which is developed due to high blood sugar that affects the neurons in retina. A dangerous fact about this disease is that it can lead to blindness. The possible cure is through detection of disease at early age. This can be done using different machine learning algorithms. This paper does a comparative study on different machine learning algorithms that can be used for early detection of diabetic retinopathy. This study is done to find out the most efficient algorithm suitable for the process and to increase the efficiency of the particular algorithm.

scholarly journals A study of deep learning approaches for medication and adverse drug event extraction from clinical text

2019 ◽  
Vol 27 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Qiang Wei ◽  
Zongcheng Ji ◽  
Zhiheng Li ◽  
Jingcheng Du ◽  
Jingqi Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Joint Model ◽  
Machine Learning Algorithms ◽  
Entity Recognition ◽  
Superior Performance ◽  
Drug Event ◽  
Support Vector ◽  
Learning Approaches

AbstractObjectiveThis article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 National NLP Clinical Challenges (n2c2) shared task.Materials and MethodsThe clinical corpus used in this study was from the MIMIC-III database and the organizers annotated 303 documents for training and 202 for testing. Our system consists of 2 components: a named entity recognition (NER) and a relation classification (RC) component. For each component, we implemented deep learning-based approaches (eg, BI-LSTM-CRF) and compared them with traditional machine learning approaches, namely, conditional random fields for NER and support vector machines for RC, respectively. In addition, we developed a deep learning-based joint model that recognizes ADEs and their relations to medications in 1 step using a sequence labeling approach. To further improve the performance, we also investigated different ensemble approaches to generating optimal performance by combining outputs from multiple approaches.ResultsOur best-performing systems achieved F1 scores of 93.45% for NER, 96.30% for RC, and 89.05% for end-to-end evaluation, which ranked #2, #1, and #1 among all participants, respectively. Additional evaluations show that the deep learning-based approaches did outperform traditional machine learning algorithms in both NER and RC. The joint model that simultaneously recognizes ADEs and their relations to medications also achieved the best performance on RC, indicating its promise for relation extraction.ConclusionIn this study, we developed deep learning approaches for extracting medications and their attributes such as ADEs, and demonstrated its superior performance compared with traditional machine learning algorithms, indicating its uses in broader NER and RC tasks in the medical domain.

Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms

The Analyst ◽  
2020 ◽  
Vol 145 (21) ◽  
pp. 6955-6967
Author(s):  
Adam H. Agbaria ◽  
Guy Beck ◽  
Itshak Lapidot ◽  
Daniel H. Rich ◽  
Joseph Kapelushnik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ftir Spectroscopy ◽  
Blood Cells ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Infrared Microscopy

Rapid and objective diagnosis of the etiology of inaccessible infections by analyzing WBCs spectra, measured by FTIR spectroscopy, using machine-learning.

scholarly journals IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Nighat Bibi ◽  
Misba Sikandar ◽  
Ikram Ud Din ◽  
Ahmad Almogren ◽  
Sikandar Ali
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Network Resources ◽  
And Performance

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

scholarly journals Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6345
Author(s):  
Floriant Labarrière ◽  
Elizabeth Thomas ◽  
Laurine Calistri ◽  
Virgil Optasanu ◽  
Mathieu Gueugnon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Algorithms ◽  
Assistive Devices ◽  
Machine Learning Algorithms ◽  
Assistive Device ◽  
Learning Approaches ◽  
Study Characteristics ◽  
Locomotion Mode ◽  
Meta Analyses

Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

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