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

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

Development of a Machine Learning Model for the Estimation of Hip and Lumbar Angles in Ballet Dancers

2021 ◽  
Vol 36 (2) ◽  
pp. 61-71
Author(s):  
Danica Hendry ◽  
Kathryn Napier ◽  
Richard Hosking ◽  
Kevin Chai ◽  
Paul Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lumbar Spine ◽  
Motion Capture ◽  
Sagittal Plane ◽  
Elevation Angle ◽  
Learning Model ◽  
The Body ◽  
Plane Angle ◽  
Study Objective ◽  
Machine Learning Model

OBJECTIVE: Accurate field-based assessment of dance kinematics is important to understand the etiology, and thus prevention and management, of hip and back pain. The study objective was to develop a machine learning model to estimate thigh elevation and lumbar sagittal plane angles during ballet leg lifting tasks, using wearable sensor data. METHODS: Female dancers (n=30) performed ballet-specific leg lifting tasks to the front, side, and behind the body. Dancers wore six wearable sensors (100 Hz). Data were simultaneously collected using an 18-camera motion analysis system (250 Hz). Due to synchronization and hardware malfunction issues, only 23 dancers had usable data. Using leave-one-out cross-validation, machine learning models were compared with the optic motion capture system using root mean square error (RMSE) in degrees and correlation coefficients (r) over the complete movement profile of each leg lift and mean absolute error (MAE) and Bland Altman plots for peak angle accuracy. RESULTS: The average RMSE for model estimation was 6.8 for thigh elevation angle and 5.6 for lumbar spine sagittal plane angle, with respective MAE of 6 and 5.7. There was a strong correlation between the machine learning model and optic motion capture for peak angle values (thigh r=0.86, lumbar r=0.96). CONCLUSION: The models developed demonstrated an acceptable degree of accuracy for the estimation of thigh elevation angle and lumbar spine sagittal plane angle during dance-specific leg lifting tasks. This provides potential for a near-real-time, field-based measurement system.

scholarly journals Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units

2021 ◽  
Vol 8 ◽  
Author(s):  
Qin-Yu Zhao ◽  
Huan Wang ◽  
Jing-Chao Luo ◽  
Ming-Hao Luo ◽  
Le-Ping Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Spontaneous Breathing Trial ◽  
Extubation Failure ◽  
Learning Model ◽  
Hyperparameter Optimization ◽  
Machine Learning Model ◽  
Key Features ◽  
Predicted Risk

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs).Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model.Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction.Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.

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 A cost-effectiveness analysis of the number of samples to collect and test from a sexual assault

2020 ◽  
Vol 117 (24) ◽  
pp. 13421-13427
Author(s):  
Zhengli Wang ◽  
Kevin MacMillan ◽  
Mark Powell ◽  
Lawrence M. Wein
Keyword(s):  
Machine Learning ◽  
Cost Effectiveness ◽  
Sexual Assault ◽  
The United States ◽  
Learning Model ◽  
The Body ◽  
Sexual Assaults ◽  
Dna Profile ◽  
Dna Database ◽  
Machine Learning Model

Although the backlog of untested sexual assault kits in the United States is starting to be addressed, many municipalities are opting for selective testing of samples within a kit, where only the most probative samples are tested. We use data from the San Francisco Police Department Criminalistics Laboratory, which tests all samples but also collects information on the samples flagged by sexual assault forensic examiners as most probative, to build a standard machine learning model that predicts (based on covariates gleaned from sexual assault kit questionnaires) which samples are most probative. This model is embedded within an optimization framework that selects which samples to test from each kit to maximize the Combined DNA Index System (CODIS) yield (i.e., the number of kits that generate at least one DNA profile for the criminal DNA database) subject to a budget constraint. Our analysis predicts that, relative to a policy that tests only the samples deemed probative by the sexual assault forensic examiners, the proposed policy increases the CODIS yield by 45.4% without increasing the cost. Full testing of all samples has a slightly lower cost-effectiveness than the selective policy based on forensic examiners, but more than doubles the yield. In over half of the sexual assaults, a sample was not collected during the forensic medical exam from the body location deemed most probative by the machine learning model. Our results suggest that electronic forensic records coupled with machine learning and optimization models could enhance the effectiveness of criminal investigations of sexual assaults.

Prediction of Hospital Readmission Combining Rule-based and Machine Learning Model

2020 ◽  
Author(s):  
Kareen Teo ◽  
Khin Wee Lai ◽  
Ching Wai Yong ◽  
Belinda Pingguan-Murphy ◽  
Joon Huang Chuah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Readmission ◽  
Learning Model ◽  
Rule Based ◽  
Machine Learning Model
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