Using machine learning to predict post-operative depth of focus after cataract surgery with implantation of Tecnis Symfony

2021 ◽  
pp. 112067212199177
Author(s):  
Yang Liu ◽  
David Wei ◽  
Tao Bai ◽  
Jie Luo ◽  
Jennifer Wood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cataract Surgery ◽  
Predictive Accuracy ◽  
Strong Association ◽  
Anterior Chamber Depth ◽  
Penalized Regression ◽  
Machine Learning Techniques ◽  
Impact Factors ◽  
Apriori Algorithm ◽  
Depth Of Focus

Objective: To predict post-operative depth of focus (DoF) using machine learning techniques after cataract surgery with Tecnis Symfony implantation and determine associated impact factors. Methods: This was a retrospective cohort study among patients receiving Tecnis Symfony implantation, an extended-range-of-vision intraocular lens, during October 2016–January 2020 at Daqing Oilfield General Hospital, China. Four different predictive models were used to predict good post-operative DoF (⩾2.5 D): Extreme Gradient Boost (XGBoost), random forest (RF), LASSO penalized regression, and multivariable logistic regression (MLR). Apriori algorithm was employed to further explore the association between patient attributes and DoF. Results: A total of 182 unique cases (143 patients) were included. The XGBoost model produced the best predictive accuracy compared to RF, LASSO, and MLR models. Overall performance of the best fitting XGBoost model was as follows: accuracy = 70.3%, AUC = 80.2%, sensitivity = 65.5%, and specificity = 87.5%. The Apriori algorithm identified six preoparative attributes with substantial effects on good post-operative DoF: low anterior chamber depth (ACD) (1.9 to <2.5 mm), smaller pupil size (1.7 to <2.5 mm), low-to-mid axial length (21 to <23 mm), minimum astigmatism degree (−0.2 to 0 diopter), low IOP (9 to <12 mmHg), and medium lens target refractive error (−0.5 to <−0.25 diopter). Conclusions: Machine Learning models were able to predict good post-operative DoF among cataract patients receiving a Tecnis Symfony ocular lens implantation. The accuracy of the model was above 70%. The Apriori algorithm identified six preoperative attributes with a strong association with post-operative DoF.

scholarly journals Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9656
Author(s):  
Sugandh Kumar ◽  
Srinivas Patnaik ◽  
Anshuman Dixit
Keyword(s):  
Machine Learning ◽  
Expression Profiles ◽  
Disease Process ◽  
Penalized Regression ◽  
Functional Enrichment ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Sequencing Data ◽  
Therapeutic Modalities

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.

scholarly journals Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models

2020 ◽  
Vol 7 (4) ◽  
pp. 212-219 ◽  
Author(s):  
Aixia Guo ◽  
Michael Pasque ◽  
Francis Loh ◽  
Douglas L. Mann ◽  
Philip R. O. Payne
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Outcome Prediction ◽  
Predictive Accuracy ◽  
Imbalanced Data ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Patient Specific ◽  
Learning Models ◽  
Machine Learning Models

Abstract Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies.

scholarly journals GP.6 Improving Triaging of EEG Referrals for Rule out Infantile Spasms (ITERIS)

2021 ◽  
Vol 48 (s3) ◽  
pp. S13-S13
Author(s):  
D Djordjevic ◽  
J Tracey ◽  
M Alqahtani ◽  
J Boyd ◽  
C Go
Keyword(s):  
Machine Learning ◽  
Predictive Factors ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Infantile Spasms ◽  
Low Risk ◽  
Machine Learning Techniques ◽  
Risk Category ◽  
Point System ◽  
Machine Learning Model

Background: Infantile spasms (IS) is a devastating pediatric seizure disorder for which EEG referrals are prioritized at the Hospital for Sick Children, representing a resource challenge. The goal of this study was to improve the triaging system for these referrals. Methods: Part 1: descriptive analysis was performed retrospectively on EEG referrals. Part 2: prospective questionnaires were used to determine relative risk of various predictive factors. Part 3: electronic referral form was amended to include 5 positive predictive factors. A triage point system was tested by assigning EEGs as high risk (3 days), standard risk (1 week), or low risk (2 weeks). A machine learning model was developed. Results: Most EEG referrals were from community pediatricians with a low yield of IS diagnoses. Using the 5 predictive factors, the proposed triage system accurately diagnosed all IS within 3 days. No abnormal EEGs were missed in the low-risk category. The machine learning model had over 90% predictive accuracy and will be prospectively tested. Conclusions: Improving EEG triaging for IS may be possible to prioritize higher risk patients. Machine Learning techniques can potentially be applied to help with predictions. We hope that our findings will ultimately improve resource utilization and patient care.

scholarly journals INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Hooman Zabeti ◽  
Nick Dexter ◽  
Amir Hosein Safari ◽  
Nafiseh Sedaghat ◽  
Maxwell Libbrecht ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Predictive Accuracy ◽  
Group Testing ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Evaluation Metrics ◽  
Lower Accuracy ◽  
Unseen Data ◽  
The One

Abstract Motivation Prediction of drug resistance and identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Solving this problem requires a transparent, accurate, and flexible predictive model. The methods currently used for this purpose rarely satisfy all of these criteria. On the one hand, approaches based on testing strains against a catalogue of previously identified mutations often yield poor predictive performance; on the other hand, machine learning techniques typically have higher predictive accuracy, but often lack interpretability and may learn patterns that produce accurate predictions for the wrong reasons. Current interpretable methods may either exhibit a lower accuracy or lack the flexibility needed to generalize them to previously unseen data. Contribution In this paper we propose a novel technique, inspired by group testing and Boolean compressed sensing, which yields highly accurate predictions, interpretable results, and is flexible enough to be optimized for various evaluation metrics at the same time. Results We test the predictive accuracy of our approach on five first-line and seven second-line antibiotics used for treating tuberculosis. We find that it has a higher or comparable accuracy to that of commonly used machine learning models, and is able to identify variants in genes with previously reported association to drug resistance. Our method is intrinsically interpretable, and can be customized for different evaluation metrics. Our implementation is available at github.com/hoomanzabeti/INGOT_DR and can be installed via The Python Package Index (Pypi) under ingotdr. This package is also compatible with most of the tools in the Scikit-learn machine learning library.

Performance Analysis of Microarray Data Classification using Machine Learning Techniques

2015 ◽  
Vol 5 (2) ◽  
pp. 43-54
Author(s):  
Subhendu Kumar Pani ◽  
Bikram Kesari Ratha ◽  
Ajay Kumar Mishra
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Microarray Data ◽  
Predictive Accuracy ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Data Mining Technique ◽  
Single Experiment ◽  
Learning Techniques ◽  
Microarray Datasets

Microarray technology of DNA permits simultaneous monitoring and determining of thousands of gene expression activation levels in a single experiment. Data mining technique such as classification is extensively used on microarray data for medical diagnosis and gene analysis. However, high dimensionality of the data affects the performance of classification and prediction. Consequently, a key issue in microarray data is feature selection and dimensionality reduction in order to achieve better classification and predictive accuracy. There are several machine learning approaches available for feature selection. In this study, the authors use Particle Swarm Organization (PSO) and Genetic Algorithm (GA) to find the performance of several popular classifiers on a set of microarray datasets. Experimental results conclude that feature selection affects the performance.

scholarly journals An interpretable classification method for predicting drug resistance in M. tuberculosis

2020 ◽  
Author(s):  
Hooman Zabeti ◽  
Nick Dexter ◽  
Amir Hosein Safari ◽  
Nafiseh Sedaghat ◽  
Maxwell Libbrecht ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Compressed Sensing ◽  
Predictive Accuracy ◽  
Group Testing ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Sensitivity Specificity ◽  
Interpretable Classification

AbstractMotivationThe prediction of drug resistance and the identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Modern methods based on testing against a catalogue of previously identified mutations often yield poor predictive performance. On the other hand, machine learning techniques have demonstrated high predictive accuracy, but many of them lack interpretability to aid in identifying specific mutations which lead to resistance. We propose a novel technique, inspired by the group testing problem and Boolean compressed sensing, which yields highly accurate predictions and interpretable results at the same time.ResultsWe develop a modified version of the Boolean compressed sensing problem for identifying drug resistance, and implement its formulation as an integer linear program. This allows us to characterize the predictive accuracy of the technique and select an appropriate metric to optimize. A simple adaptation of the problem also allows us to quantify the sensitivity-specificity trade-off of our model under different regimes. We test the predictive accuracy of our approach on a variety of commonly used antibiotics in treating tuberculosis and find that it has accuracy comparable to that of standard machine learning models and points to several genes with previously identified association to drug resistance.Availabilityhttps://github.com/hoomanzabeti/[email protected]

scholarly journals Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249423
Author(s):  
Indy Man Kit Ho ◽  
Kai Yuen Cheong ◽  
Anthony Weldon
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Student Satisfaction ◽  
Multiple Regression ◽  
Predictive Accuracy ◽  
Machine Learning Techniques ◽  
Recursive Feature Elimination ◽  
Learning Models ◽  
Machine Learning Models ◽  
Remote Learning

Despite the wide adoption of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting student satisfaction for this novel learning environment in crisis. The present study investigated important predictors in determining the satisfaction of undergraduate students (N = 425) from multiple departments in using ERL at a self-funded university in Hong Kong while Moodle and Microsoft Team are the key learning tools. By comparing the predictive accuracy between multiple regression and machine learning models before and after the use of random forest recursive feature elimination, all multiple regression, and machine learning models showed improved accuracy while the most accurate model was the elastic net regression with 65.2% explained variance. The results show only neutral (4.11 on a 7-point Likert scale) regarding the overall satisfaction score on ERL. Even majority of students are competent in technology and have no obvious issue in accessing learning devices or Wi-Fi, face-to-face learning is more preferable compared to ERL and this is found to be the most important predictor. Besides, the level of efforts made by instructors, the agreement on the appropriateness of the adjusted assessment methods, and the perception of online learning being well delivered are shown to be highly important in determining the satisfaction scores. The results suggest that the need of reviewing the quality and quantity of modified assessment accommodated for ERL and structured class delivery with the suitable amount of interactive learning according to the learning culture and program nature.

Machine Learning in Fine Wine Price Prediction

2015 ◽  
Vol 10 (2) ◽  
pp. 151-172 ◽  
Author(s):  
Michelle Yeo ◽  
Tristan Fletcher ◽  
John Shawe-Taylor
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Predictive Accuracy ◽  
Time Series Prediction ◽  
Feature Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Price Prediction ◽  
Learning Techniques

AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

Comparing Attribute- and Form-Based Machine Learning Techniques for Component Prediction

2020 ◽  
Author(s):  
Glen Williams ◽  
Lucas Puentes ◽  
Jacob Nelson ◽  
Jessica Menold ◽  
Conrad Tucker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Learning Algorithms ◽  
3D Models ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Data Types ◽  
Online Data ◽  
Data Repositories

Abstract Online data repositories provide designers and engineers with a convenient source of data. Over time, the wealth and type of readily-available data within online repositories has greatly expanded. This data increase permits new uses for machine learning approaches which rely on large, high-dimensional datasets. However, a comparison of the efficacies of attribute-based data, which lends itself well to traditional machine learning algorithms, and form-based data, which lends itself to deep machine learning algorithms, has not fully been established. This paper presents one such comparison for an exemplar dataset. As the efficacy of different machine learning approaches may be dependent on the specific application, this method is intended to lay the groundwork for future studies that produce more extensive comparisons. Specifically, this work makes use of a manufactured gear dataset for sale price prediction. Two traditional machine learning algorithms, M5Rules and SMOreg, are selected due to their applicability to the gear attribute-based data. These algorithms are compared to a neural network model that is trained on a voxelized version of the gears’ 3D models, defined in this work as form-based data. Results show that both data types provide comparable predictive accuracy.

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