A machine learning model for predicting ICU readmissions and key risk factors: analysis from a longitudinal health records

2019 ◽  
Vol 9 (3) ◽  
pp. 297-309
Author(s):  
Alvaro Ribeiro Botelho Junqueira ◽  
Farhaan Mirza ◽  
Mirza Mansoor Baig
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Learning Model ◽  
Health Records ◽  
Factors Analysis ◽  
Machine Learning Model

A Comparison of Machine Learning Methods to Predict Hospital Readmission of Diabetic Patient

2021 ◽  
Vol 7 (2) ◽  
pp. 164-168
Author(s):  
Cuong Le Dinh Phu ◽  
Dong Wang
Keyword(s):  
Machine Learning ◽  
Hospital Readmission ◽  
Random Search ◽  
Learning Model ◽  
The Body ◽  
Hyperparameter Optimization ◽  
Health Records ◽  
Search Optimization ◽  
Machine Learning Model ◽  
Public Dataset

Diabetes is a chronic disease whereby blood glucose is not metabolized in the body. Electronic health records (EHRs) (Yadav, P. et al., 2018). for each individual or a population have become important to standing developing trends of diseases. Machine learning helps provide accurate predictions higher than actual assessments. The main problem that we are trying to apply machine learning model and using EHRs that combines the strength of a machine learning model with various features and hyperparameter optimization or tuning. The hyperparameter optimization (Feurer, M., 2019) uses the random search optimization which minimizes a predefined loss function on given independent data. The evaluation on the method comparisons indicated that machine learning models has increased the ratio of metrics compared to previous models (Accuracy, Recall, F1 and AUC score) on the same public dataset that is reprocessed.

scholarly journals PPM8 A MACHINE LEARNING MODEL FOR CANCER BIOMARKER IDENTIFICATION IN ELECTRONIC HEALTH RECORDS

2019 ◽  
Vol 22 ◽  
pp. S334
Author(s):  
G. Ambwani ◽  
A. Cohen ◽  
M. Estévez ◽  
N. Singh ◽  
B. Adamson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Learning Model ◽  
Cancer Biomarker ◽  
Health Records ◽  
Biomarker Identification ◽  
Machine Learning Model ◽  
Electronic Health

scholarly journals Risk Identification, Assessments, and Prediction for Mega Construction Projects: A Risk Prediction Paradigm Based on Cross Analytical-Machine Learning Model

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 172
Author(s):  
Debalina Banerjee Chattapadhyay ◽  
Jagadeesh Putta ◽  
Rama Mohan Rao P
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Risk Management ◽  
High Risk ◽  
Risk Prediction ◽  
Learning Model ◽  
Risk Identification ◽  
Large Set ◽  
High Risk Factors ◽  
Machine Learning Model

Risk identification and management are the two most important parts of construction project management. Better risk management can help in determining the future consequences, but identifying possible risk factors has a direct and indirect impact on the risk management process. In this paper, a risk prediction system based on a cross analytical-machine learning model was developed for construction megaprojects. A total of 63 risk factors pertaining to the cost, time, quality, and scope of the megaproject and primary data were collected from industry experts on a five-point Likert scale. The obtained sample was further processed statistically to generate a significantly large set of features to perform K-means clustering based on high-risk factor and allied sub-risk component identification. Descriptive analysis, followed by the synthetic minority over-sampling technique (SMOTE) and the Wilcoxon rank-sum test was performed to retain the most significant features pertaining to cost, time, quality, and scope. Eventually, unlike classical K-means clustering, a genetic-algorithm-based K-means clustering algorithm (GA–K-means) was applied with dual-objective functions to segment high-risk factors and allied sub-risk components. The proposed model identified different high-risk factors and sub-risk factors, which cumulatively can impact overall performance. Thus, identifying these high-risk factors and corresponding sub-risk components can help stakeholders in achieving project success.

PredictMed: A Machine Learning Model for Identifying Risk Factors of Neuromuscular Hip Dysplasia: A Multicenter Descriptive Study

2020 ◽  
Author(s):  
Carlo M. Bertoncelli ◽  
Paola Altamura ◽  
Domenico Bertoncelli ◽  
Virginie Rampal ◽  
Edgar Ramos Vieira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Motor Function ◽  
Hip Dysplasia ◽  
Descriptive Study ◽  
Learning Model ◽  
Trunk Muscles ◽  
Lateral Migration ◽  
Machine Learning Model

AbstractNeuromuscular hip dysplasia (NHD) is a common and severe problem in patients with cerebral palsy (CP). Previous studies have so far identified only spasticity (SP) and high levels of Gross Motor Function Classification System as factors associated with NHD. The aim of this study is to develop a machine learning model to identify additional risk factors of NHD. This was a cross-sectional multicenter descriptive study of 102 teenagers with CP (60 males, 42 females; 60 inpatients, 42 outpatients; mean age 16.5 ± 1.2 years, range 12–18 years). Data on etiology, diagnosis, SP, epilepsy (E), clinical history, and functional assessments were collected between 2007 and 2017. Hip dysplasia was defined as femoral head lateral migration percentage > 33% on pelvic radiogram. A logistic regression-prediction model named PredictMed was developed to identify risk factors of NHD. Twenty-eight (27%) teenagers with CP had NHD, of which 18 (67%) had dislocated hips. Logistic regression model identified poor walking abilities (p < 0.001; odds ratio [OR] infinity; 95% confidence interval [CI] infinity), scoliosis (p = 0.01; OR 3.22; 95% CI 1.30–7.92), trunk muscles' tone disorder (p = 0.002; OR 4.81; 95% CI 1.75–13.25), SP (p = 0.006; OR 6.6; 95% CI 1.46–30.23), poor motor function (p = 0.02; OR 5.5; 95% CI 1.2–25.2), and E (p = 0.03; OR 2.6; standard error 0.44) as risk factors of NHD. The accuracy of the model was 77%. PredictMed identified trunk muscles' tone disorder, severe scoliosis, E, and SP as risk factors of NHD in teenagers with CP.

Machine learning model to predict mental health crisis from electronic health records

2021 ◽  
Author(s):  
Roger Garriga ◽  
Aleksandar Matić ◽  
Javier Mas ◽  
Semhar Abraha ◽  
Jon Nolan ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
At Risk ◽  
Electronic Health Records ◽  
Learning Model ◽  
Mental Health Crisis ◽  
Health Records ◽  
Health Crisis ◽  
Machine Learning Model ◽  
Electronic Health

Abstract Timely identification of patients who are at risk of mental health crises opens the door for improving the outcomes and for mitigating the burden and costs to the healthcare systems. Due to high prevalence of mental health problems, a manual review of complex patient records to make proactive care decisions is an unsustainable endeavour. We developed a machine learning model that uses Electronic Health Records to continuously identify patients at risk to experience a mental health crisis within the next 28 days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. The usefulness of our model was tested in clinical practice in a 6-month prospective study, where the predictions were considered clinically useful in 64% of cases. This study is the first one to continuously predict the risk of a wide range of mental health crises and to evaluate the usefulness of such predictions in clinical settings.

scholarly journals Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah Quiñones ◽  
Aditya Goyal ◽  
Zia U. Ahmed
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Spatial Heterogeneity ◽  
Learning Model ◽  
Machine Learning Model ◽  
Non Parametric

AbstractType 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013–2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.

scholarly journals Towards a multivariate prediction model of pharmacological treatment for women with gestational diabetes mellitus (Preprint)

2020 ◽  
Author(s):  
Carmelo Velardo ◽  
David Clifton ◽  
Steven Hamblin ◽  
Rabia Khan ◽  
Lionel Tarassenko ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Risk Factors ◽  
Blood Glucose ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Real Time ◽  
Pharmacological Treatment ◽  
Learning Model ◽  
Machine Learning Model

BACKGROUND Successful management of gestational diabetes mellitus (GDM) reduces the risk of morbidity in women and newborns. A woman’s BG readings and risk factors are used by clinical staff to make decisions regarding the initiation of pharmacological treatment in women with GDM. Mobile-Health (mHealth) solutions allow the real-time follow-up of women with GDM and allow timely treatment and management. Machine learning offers the opportunity to quickly analyse large quantities of data to automatically flag women at risk of requiring pharmacological treatment. OBJECTIVE We sought to assess whether data collected through a mHealth system can be analysed to automatically evaluate the switch to pharmacological treatment from diet-based management of GDM. METHODS We collected data from 3,029 patients to design a machine-learning model that can identify when a woman with GDM needs to switch to medications (Insulin or Metformin) by analysing the data related to blood glucose and other risk factors. RESULTS Through the analysis of 411,785 blood glucose (BG) readings we have designed a machine learning model that can predict the timing of initiation of pharmacological treatment. After one hundred experimental repetitions we have obtained an average performance of 0.80 AUC and an algorithm that allows the flexibility of setting the operating point rather than relying on a static heuristic method, currently used in clinical practice. CONCLUSIONS Using real-time data collected via a mHealth system may further improve the timeliness of intervention and potentially improve patient care. Further real-time clinical testing will enable validating our algorithm using real-world data.

scholarly journals Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

2020 ◽  
Author(s):  
Jiandong Zhou ◽  
Gary Tse ◽  
Sharen Lee ◽  
Tong Liu ◽  
William KK Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hong Kong ◽  
Intensive Care ◽  
Cell Count ◽  
Learning Model ◽  
Older Age ◽  
Superior Performance ◽  
Icu Admission ◽  
Machine Learning Model

ABSTRACTBackgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission.MethodsConsecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission.ResultsThis study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression.ConclusionsA machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

scholarly journals Development of glaucoma predictive model and risk factors assessment based on supervised models

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahyar Sharifi ◽  
Toktam Khatibi ◽  
Mohammad Hassan Emamian ◽  
Somayeh Sadat ◽  
Hassan Hashemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Ensemble Methods ◽  
Learning Model ◽  
Training Dataset ◽  
Data Sampling ◽  
Machine Learning Model ◽  
Average Accuracy ◽  
Under Sampling ◽  
Bagging Ensemble

Abstract Objectives To develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors. Method Data analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include data sampling, preprocessing, classification and evaluation and validation. Data sampling for providing the training dataset was performed with balanced sampling based on over-sampling and under-sampling methods. Data preprocessing steps were missing value imputation and normalization. For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. Moreover, in the classification step, a novel stacking ensemble model is designed and proposed using the superior classifiers. Results The data were from Shahroud Eye Cohort Study including demographic and ophthalmology data for 5190 participants aged 40-64 living in Shahroud, northeast Iran. The main variables considered in this dataset were 67 demographics, ophthalmologic, optometric, perimetry, and biometry features for 4561 people, including 4474 non-glaucoma participants and 87 glaucoma patients. Experimental results show that DTs and RFs trained based on under-sampling of the training dataset have superior performance for predicting glaucoma than the compared single classifiers and bagging ensemble methods with the average accuracy of 87.61 and 88.87, the sensitivity of 73.80 and 72.35, specificity of 87.88 and 89.10 and area under the curve (AUC) of 91.04 and 94.53, respectively. The proposed stacking ensemble has an average accuracy of 83.56, a sensitivity of 82.21, a specificity of 81.32, and an AUC of 88.54. Conclusions In this study, a machine learning model is proposed and developed to predict glaucoma disease among persons aged 40-64. Top predictors in this study considered features for discriminating and predicting non-glaucoma persons from glaucoma patients include the number of the visual field detect on perimetry, vertical cup to disk ratio, white to white diameter, systolic blood pressure, pupil barycenter on Y coordinate, age, and axial length.

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