scholarly journals Machine Learning for Predictive Modelling of Ambulance Calls

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 482
Author(s):  
Miao Yu ◽  
Dimitrios Kollias ◽  
James Wingate ◽  
Niro Siriwardena ◽  
Stefanos Kollias
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Latent Variables ◽  
Predictive Modelling ◽  
Forward Model ◽  
Data Set ◽  
Explanatory Variables ◽  
East Midlands ◽  
Emergency Ambulance ◽  
One Year

A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom.

scholarly journals Machine Learning for Predictive Modelling of Ambulance Calls

2021 ◽  
Author(s):  
Miao Yu ◽  
Dimitrios Kollias ◽  
James Wingate ◽  
Niro Siriwardena ◽  
Stefanos Kollias
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Latent Variables ◽  
Predictive Modelling ◽  
Forward Model ◽  
Data Set ◽  
Explanatory Variables ◽  
East Midlands ◽  
Emergency Ambulance ◽  
One Year

A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom.

One- year mortality in patients with advanced hepatocellular carcinoma on immunotherapy: Prediction using machine learning models (Preprint)

2021 ◽  
Author(s):  
Thomas Ka-Luen Lui ◽  
Ka Shing, Michael Cheung ◽  
Wai Keung Leung
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
False Negative ◽  
False Negative Rate ◽  
Absolute Error ◽  
Advanced Hepatocellular Carcinoma ◽  
Data Set ◽  
One Year ◽  
Related Mortality

BACKGROUND Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. OBJECTIVE This study aimed to evaluate the role of machine learning (ML) models in predicting the one-year cancer-related mortality in advanced HCC patients treated with immunotherapy METHODS 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) in 2014 - 2019 in Hong Kong were included. The whole data set were randomly divided into training (n=316) and validation (n=79) set. The data set, including 45 clinical variables, was used to construct six different ML models in predicting the risk of one-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and the mean absolute error (MAE) using calibration analysis. RESULTS The overall one-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.93 (95%CI: 0.86-0.98), which was better than logistic regression (0.82, p=0.01) and XGBoost (0.86, p=0.04). RF also had the lowest false positive (6.7%) and false negative rate (2.8%). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models. CONCLUSIONS ML models could predict one-year cancer-related mortality of HCC patients treated with immunotherapy, which may help to select patients who would most benefit from this new treatment option.

scholarly journals The impact of capital structure and certain firm specific variables on the value of the firm: Empirical evidence from Kuwait

2015 ◽  
Vol 13 (1) ◽  
pp. 1191-1200
Author(s):  
Ahmad Mohammad Obeid Gharaibeh ◽  
Adel Mohammed Sarea
Keyword(s):  
Capital Structure ◽  
Stock Exchange ◽  
Influential Factor ◽  
Annual Reports ◽  
Growth Opportunities ◽  
Data Set ◽  
Explanatory Variables ◽  
Size Growth ◽  
One Year ◽  
The Impact

The main objective of this study is to empirically examine the impact of leverage and certain firm-characteristics that are believed to have significant effects on the decision to use debt and on the value of the firm. The sample is composed of 48 companies listed in the Kuwait Stock Exchange (KSE) representing four different sectors. The study uses actual and historical panel data set obtained from the published annual reports of individual firms in addition to the publications of KSE. The study was accomplished using 8 years of data with a total of 239 observations representing the study period 2006-2013. The study uses descriptive statistics, correlation, and multiple-regression analyses to examine the impact of explanatory variables on the value of the firm. The study findings lead to the conclusion that capital structure (leveraging) is the most influential factor on firm’s value. Business risk, previous year’s value (one-year lagged ROA), dividends payout ratio, size, growth opportunities and liquidity of the firm are found to have significant influence on the firm’s value in Model 1 (where ROA is used as a proxy for the value of the firm). In model 2 (i.e., where ROE is used as a proxy of the firm’s value), the findings reveal that capital structure (leveraging); firm’s size, growth opportunities and liquidity of the firm are significant influential of the firm’s value. The study is valuable to academicians, finance managers, policy makers and other stakeholders as it fills the gap of literature by providing up-to-date evidence of the impact of capital structure and other firm specific variables on the value of the firm in Kuwait.

scholarly journals Decision making by NICE: examining the influences of evidence, process and context

2013 ◽  
Vol 9 (2) ◽  
pp. 119-141 ◽  
Author(s):  
Karin H. Cerri ◽  
Martin Knapp ◽  
Jose-Luis Fernandez
Keyword(s):  
Decision Making ◽  
New Technologies ◽  
Multinomial Logistic Regression ◽  
Clinical Excellence ◽  
Outcome Variable ◽  
Data Set ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Relative Contribution ◽  
The Impact

AbstractThe National Institute for Health and Clinical Excellence (NICE) provides guidance to the National Health Service (NHS) in England and Wales on funding and use of new technologies. This study examined the impact of evidence, process and context factors on NICE decisions in 2004–2009. A data set of NICE decisions pertaining to pharmaceutical technologies was created, including 32 variables extracted from published information. A three-category outcome variable was used, defined as the decision to ‘recommend’, ‘restrict’ or ‘not recommend’ a technology. With multinomial logistic regression, the relative contribution of explanatory variables on NICE decisions was assessed. A total of 65 technology appraisals (118 technologies) were analysed. Of the technologies, 27% were recommended, 58% were restricted and 14% were not recommended by NICE for NHS funding. The multinomial model showed significant associations (p ⩽ 0.10) between NICE outcome and four variables: (i) demonstration of statistical superiority of the primary endpoint in clinical trials by the appraised technology; (ii) the incremental cost-effectiveness ratio (ICER); (iii) the number of pharmaceuticals appraised within the same appraisal; and (iv) the appraisal year. Results confirm the value of a comprehensive and multivariate approach to understanding NICE decision making. New factors affecting NICE decision making were identified, including the effect of clinical superiority, and the effect of process and socio-economic factors.

scholarly journals A Novel Fast Searching Algorithm Based on Least Square Regression

2021 ◽  
Vol 35 (1) ◽  
pp. 93-98
Author(s):  
Ratna Kumari Challa ◽  
Siva Prasad Chintha ◽  
B. Reddaiah ◽  
Kanusu Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Predictive Modelling ◽  
Binary Search ◽  
Least Square ◽  
Search Performance ◽  
Worst Case ◽  
Data Set ◽  
Least Square Regression ◽  
Interpolation Search ◽  
Fast Searching

Currently, the machine learning group is well-understood and commonly used for predictive modelling and feature generation through linear methodologies such as reversals, principal analysis and canonical correlation analyses. All these approaches are typically intended to capture fascinating subspaces in the original space of high dimensions. These methods have all a closed-form approach because of its simple linear structures, which makes estimation and theoretical analysis for small datasets very straightforward. However, it is very common for a data set to have millions or trillions of samples and features in modern machine learning problems. We deal with the problem of fast estimation from large volumes of data for ordinary squares. The search operation is a very important operation and it is useful in many applications. Some applications when the data set size is large, the linear search takes the time which is proportional to the size of the data set. Binary search and interpolation search performs good for the search of elements in the data set in O(logn) and ⋅O(log(⋅logn)) respectively in the worst case. Now, in this paper, an effort is made to develop a novel fast searching algorithm based on the least square regression curve fitting method. The algorithm is implemented and its execution results are analyzed and compared with binary search and interpolation search performance. The proposed model is compared with the traditional methods and the proposed fast searching algorithm exhibits better performance than the traditional models.

A rule-based automated machine learning approach in the evaluation of recommender engine

2020 ◽  
Vol 27 (10) ◽  
pp. 2721-2757
Author(s):  
Rajat Kumar Behera ◽  
Pradip Kumar Bala ◽  
Rashmi Jain
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Small Data ◽  
Data Set ◽  
Rule Based ◽  
Content Type ◽  
Online Evaluation ◽  
Phase Study ◽  
Best Fitting ◽  
Automated Machine Learning

PurposeAny business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.Design/methodology/approachAn experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.FindingsThe rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.Research limitations/implicationsA small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.Practical implicationsThe best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.Originality/valueThe proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

scholarly journals A Review of Current Applications of Artificial Intelligence and Machine Learning in Medical Science

2021 ◽  
pp. 316-325
Author(s):  
Jayant Kumar A Rathod ◽  
Naveen Bhavani ◽  
Prenita Prinsal Saldanha ◽  
Preethi M Rao ◽  
Prasad Patil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Medical Science ◽  
Clinical Decision ◽  
Medical Sciences ◽  
Data Set ◽  
Time Problem ◽  
The Impact

Artificial Intelligence and Machine Learning are two fields that are causing substantial development in every field specifically in the field of medical sciences; for the stupendous potential that it can provide to assist the clinicians, researchers, in clinical decision making, automate time consuming procedures, medical imaging, and more. Most implementations of AI/ML rely on static data set, and this where the big data steps in. That is, these models are developed and trained on a data set that is already recorded and have been diligently reviewed for accuracy; leading to a precise decision-making process. Experts foresee that AI/ML based overarching care system will develop high-quality patient care and innovative research, aiding advanced decision support tools. In this paper we shall realize what are the current devices that are build and are being used for real time problem solving, also discuss the impact of Software as a Medical Device (SAMD) in future of medical sciences. [2,3,11]

scholarly journals Computing the Hazard Ratios Associated With Explanatory Variables Using Machine Learning Models of Survival Data

2021 ◽  
pp. 364-378
Author(s):  
Sameer Sundrani ◽  
James Lu
Keyword(s):  
Machine Learning ◽  
Survival Data ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Explanatory Variables ◽  
Cancer Data ◽  
Pan Cancer

PURPOSE The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models have been developed and applied to the survival analysis, no methodology exists for computing HRs associated with explanatory variables from such models. We describe a novel way to compute HRs from tree-based ML models using the SHapley Additive exPlanation values, which is a locally accurate and consistent methodology to quantify explanatory variables’ contribution to predictions. METHODS We used three sets of publicly available survival data consisting of patients with colon, breast, or pan cancer and compared the performance of CoxPH with the state-of-the-art ML model, XGBoost. To compute the HR for explanatory variables from the XGBoost model, the SHapley Additive exPlanation values were exponentiated and the ratio of the means over the two subgroups was calculated. The CI was computed via bootstrapping the training data and generating the ML model 1,000 times. Across the three data sets, we systematically compared HRs for all explanatory variables. Open-source libraries in Python and R were used in the analyses. RESULTS For the colon and breast cancer data sets, the performance of CoxPH and XGBoost was comparable, and we showed good consistency in the computed HRs. In the pan-cancer data set, we showed agreement in most variables but also an opposite finding in two of the explanatory variables between the CoxPH and XGBoost result. Subsequent Kaplan-Meier plots supported the finding of the XGBoost model. CONCLUSION Enabling the derivation of HR from ML models can help to improve the identification of risk factors from complex survival data sets and to enhance the prediction of clinical trial outcomes.

Diagnosis Rule Extraction from Patient Data for Chronic Kidney Disease Using Machine Learning

2016 ◽  
Vol 5 (2) ◽  
pp. 64-72 ◽  
Author(s):  
Alexander Arman Serpen
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Learning Algorithm ◽  
Primary Care Providers ◽  
Patient Data ◽  
Care Providers ◽  
Data Set ◽  
Actual Patient

This research study employed a machine learning algorithm on actual patient data to extract decision making rules that can be used to diagnose chronic kidney disease. The patient data set entails a number of health-related attributes or indicators and contains 250 patients positive for chronic kidney disease. The C4.5 decision tree algorithm was applied to the patient data to formulate a set of diagnosis rules for chronic kidney disease. The C4.5 algorithm utilizing 3-fold cross validation achieved 98.25% prediction accuracy and thus correctly classified 393 instances and incorrectly classified 7 instances for a total patient count of 400. The extracted rule set highlighted the need to monitor serum creatinine levels in patients as the primary indicator for the presence of disease. Secondary indicators were pedal edema, hemoglobin, diabetes mellitus and specific gravity. The set of rules provides a preliminary screening tool towards conclusive diagnosis of the chronic kidney disease by nephrologists following timely referral by the primary care providers or decision-making algorithms.

Data set quality in Machine Learning: Consistency measure based on Group Decision Making

2021 ◽  
pp. 107366
Author(s):  
Giuseppe Fenza ◽  
Mariacristina Gallo ◽  
Vincenzo Loia ◽  
Francesco Orciuoli ◽  
Enrique Herrera-Viedma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Data Set ◽  
Consistency Measure
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