scholarly journals The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Yujuan Shang ◽  
Kui Jiang ◽  
Lei Wang ◽  
Zheqing Zhang ◽  
Siwei Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Data Preprocessing ◽  
Diabetic Patients ◽  
Multiple Risk Factors ◽  
Performance Indexes ◽  
Major Chronic Disease ◽  
Admission Type

Abstract Background and objectives Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. Methods The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. Results A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. Conclusion The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and  deserves further validation in clinical trials.

scholarly journals Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018

2021 ◽  
Vol 8 ◽  
Author(s):  
Sri Astuti Thamrin ◽  
Dian Sidik Arsyad ◽  
Hedi Kuswanto ◽  
Armin Lawi ◽  
Sudirman Nasir
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Health Research ◽  
Health Data ◽  
Basic Health ◽  
Multiple Risk Factors ◽  
Obesity Status ◽  
Address Data ◽  
Multiple Risk

Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.

scholarly journals An approach to predicting patient experience through machine learning and social network analysis

2020 ◽  
Vol 27 (12) ◽  
pp. 1834-1843
Author(s):  
Vitej Bari ◽  
Jamie S Hirsch ◽  
Joseph Narvaez ◽  
Robert Sardinia ◽  
Kevin R Bock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Patient Experience ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Hospital Consumer Assessment ◽  
Consumer Assessment

Abstract Objective Improving the patient experience has become an essential component of any healthcare system’s performance metrics portfolio. In this study, we developed a machine learning model to predict a patient’s response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey’s “Doctor Communications” domain questions while simultaneously identifying most impactful providers in a network. Materials and Methods This is an observational study of patients admitted to a single tertiary care hospital between 2016 and 2020. Using machine learning algorithms, electronic health record data were used to predict patient responses to Hospital Consumer Assessment of Healthcare Providers and Systems survey questions in the doctor domain, and patients who are at risk for responding negatively were identified. Model performance was assessed by area under receiver-operating characteristic curve. Social network analysis metrics were also used to identify providers most impactful to patient experience. Results Using a random forest algorithm, patients’ responses to the following 3 questions were predicted: “During this hospital stay how often did doctors. 1) treat you with courtesy and respect? 2) explain things in a way that you could understand? 3) listen carefully to you?” with areas under the receiver-operating characteristic curve of 0.876, 0.819, and 0.819, respectively. Social network analysis found that doctors with higher centrality appear to have an outsized influence on patient experience, as measured by rank in the random forest model in the doctor domain. Conclusions A machine learning algorithm identified patients at risk of a negative experience. Furthermore, a doctor social network framework provides metrics for identifying those providers that are most influential on the patient experience.

Development and Validation of a Risk Prediction Model for Diabetic Retinopathy in Type 2 Diabetic Patients (Preprint)

2021 ◽  
Author(s):  
Chengjun Zhu ◽  
Jiaxi Zhu ◽  
Lei Wang ◽  
Shizheng Xiong ◽  
Yijian Zou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Prediction Model ◽  
Risk Prediction ◽  
Characteristic Curve ◽  
Risk Prediction Model ◽  
Lipid Lowering ◽  
Diabetic Patients ◽  
Type 2 Diabetic Patients

BACKGROUND Diabetes mellitus (DM) has become one of the most serious public health problems in the 21st century. chronic complications associated with type 2 DM (T2DM) increase the rate of disability, leading to untimely death and reduce the quality of life. In these complications, diabetic retinopathy (DR) is the most common one and could lead to secondary blindness. Despite retinal screening is first-of-choice for DR diagnosis, the limits of such screening equipments and experienced image readers restricted its applications, especially in those rural areas where DR risks even higher. Therefore, it’s essential to construct an easy-to-implement predictive model of the risk of DR in order to help predict individual morbidity and identify the risk factors of DR. OBJECTIVE Diabetic retinopathy (DR) has a high incidence rate in diabetic patients, the quality of life of whom will be seriously affected if not treated in time. This study aims to develop a risk prediction model for DR in type 2 diabetic patients. METHODS According to the retrieval strategy, inclusion and exclusion criteria, the relevant Meta analyses on DR risk factors were searched and evaluated. The pooled odds ratio (OR) or relative risk (RR) of each risk factor was obtained and calculated for β coefficients using logistic regression (LR) model. Besides, an electronic patient-reported outcome questionnaire was developed and 60 cases of DR and non-DR T2DM patients were investigated to validate the developed model. Receiver operating characteristic curve (ROC) was drawn to verify the prediction accuracy of the model. RESULTS After retrieving, eight Meta analysis with a total of 15654 cases and 12 risk factors associated with the onset of DR in T2DM, including weight loss surgery, myopia, lipid-lowing drugs, blood glucose control, course of T2DM, glycosylated hemo-globin, fasting blood glucose, hypertension, gender, insulin treatment, residence, and smoking were included for LR modeling. These factors, followed by the respective β coefficient was bariatric surgery(-0.942), myopia(-0.357), lipid-lowering drug follow-up <3y(-0.994), lipid-lowering drug follow-up >3y(-0.223), course of T2DM(0.174), glycated hemoglobin (0.372), fasting blood sugar(0.223), insulin therapy(0.688), rural residence(0.199), smoking(-0.083), hypertension(0.405), male(0.548), blood sugar control(-0.400) with constant term α = -0.949 in the constructed model. The area under receiver operating characteristic curve (AUC) of ROC curve of the model in the external validation was 0.912. An application was presented as an example of use. CONCLUSIONS In this study, the risk prediction model of DR was developed, which make individualized assessment for the susceptible DR population feasible and need to be further verified with large sample size application.

scholarly journals Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 1173
Author(s):  
Hafiz Farooq Ahmad ◽  
Hamid Mukhtar ◽  
Hesham Alaqail ◽  
Mohamed Seliaman ◽  
Abdulaziz Alhumam
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Large Scale ◽  
Cost Effective ◽  
World Health ◽  
Evaluation Metrics ◽  
Diabetic Patients ◽  
The World ◽  
The Government ◽  
The Cost

Diabetes Mellitus (DM) is one of the most common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of its medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient with only a handful of features can allow cost-effective, rapid, and widely-available screening of diabetes, thereby lessening the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic patients and compare the role of HbA1c and FPG as input features. By using five different machine learning classifiers, and using feature elimination through feature permutation and hierarchical clustering, we established good performance for accuracy, precision, recall, and F1-score of the models on the dataset implying that our data or features are not bound to specific models. In addition, the consistent performance across all the evaluation metrics indicate that there was no trade-off or penalty among the evaluation metrics. Further analysis was performed on the data to identify the risk factors and their indirect impact on diabetes classification. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing analysis of the disease using selected features, important factors specific to the Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learned from this research.

scholarly journals Predicting Adverse Drug Events in Chinese Pediatric Inpatients With the Associated Risk Factors: A Machine Learning Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Ze Yu ◽  
Huanhuan Ji ◽  
Jianwen Xiao ◽  
Ping Wei ◽  
Lin Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Adverse Drug Events ◽  
Length Of Hospital Stay ◽  
Gradient Boosting ◽  
Multiple Risk Factors ◽  
Extreme Gradient Boosting ◽  
Novel Method ◽  
Associated Risk Factors ◽  
Medical University

The aim of this study was to apply machine learning methods to deeply explore the risk factors associated with adverse drug events (ADEs) and predict the occurrence of ADEs in Chinese pediatric inpatients. Data of 1,746 patients aged between 28 days and 18 years (mean age = 3.84 years) were included in the study from January 1, 2013, to December 31, 2015, in the Children’s Hospital of Chongqing Medical University. There were 247 cases of ADE occurrence, of which the most common drugs inducing ADEs were antibacterials. Seven algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, AdaBoost, LightGBM, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and TPOT, were used to select the important risk factors, and GBDT was chosen to establish the prediction model with the best predicting abilities (precision = 44%, recall = 25%, F1 = 31.88%). The GBDT model has better performance than Global Trigger Tools (GTTs) for ADE prediction (precision 44 vs. 13.3%). In addition, multiple risk factors were identified via GBDT, such as the number of trigger true (TT) (+), number of doses, BMI, number of drugs, number of admission, height, length of hospital stay, weight, age, and number of diagnoses. The influencing directions of the risk factors on ADEs were displayed through Shapley Additive exPlanations (SHAP). This study provides a novel method to accurately predict adverse drug events in Chinese pediatric inpatients with the associated risk factors, which may be applicable in clinical practice in the future.

scholarly journals Diagnosis of Acute Central Dizziness With Simple Clinical Information Using Machine Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Bum Joon Kim ◽  
Su-Kyeong Jang ◽  
Yong-Hwan Kim ◽  
Eun-Jae Lee ◽  
Jun Young Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Medical Center ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Clinical Information ◽  
Machine Learning Algorithms ◽  
Common Symptom ◽  
Patient Demographics ◽  
Emergency Medical

Background: Acute dizziness is a common symptom among patients visiting emergency medical centers. Extensive neurological examinations aimed at delineating the cause of dizziness often require experience and specialized training. We tried to diagnose central dizziness by machine learning using only basic clinical information.Methods: Patients were enrolled who had visited an emergency medical center with acute dizziness and underwent diffusion-weighted imaging. The enrolled patients were dichotomized as either having central (with a corresponding central lesion) or non-central dizziness. We obtained patient demographics, risk factors, vital signs, and presentation (non-whirling type dizziness or vertigo). Various machine learning algorithms were used to predict central dizziness. The area under the receiver operating characteristic curve (AUROC) was measured to evaluate diagnostic accuracy. The SHapley Additive exPlanations (SHAP) value was used to explain the importance of each factor.Results: Of the 4,481 visits, 414 (9.2%) were determined as central dizziness. Central dizziness patients were more often older and male and had more risk factors and higher systolic blood pressure. They also presented more frequently with non-whirling type dizziness (79 vs. 54.4%) than non-central dizziness. Catboost model showed the highest AUROC (0.738) with a 94.4% sensitivity and 31.9% specificity in the test set (n = 1,317). The SHAP value was highest for previous stroke presence (mean; 0.74), followed by male (0.33), presentation as non-whirling type dizziness (0.30), and age (0.25).Conclusions: Machine learning is feasible for classifying central dizziness using demographics, risk factors, vital signs, and clinical dizziness presentation, which are obtainable at the triage.

scholarly journals Advanced Endothelial Dysfunction in Diabetic Patients with Multiple Risk Factors; Importance of Insulin Resistance

2007 ◽  
Vol 14 (6) ◽  
pp. 303-309 ◽  
Author(s):  
Kyoichiro Tsuchiya ◽  
Chisato Nakayama ◽  
Fumiko Iwashima ◽  
Haruna Sakai ◽  
Hajime Izumiyama ◽  
...  
Keyword(s):  
Risk Factors ◽  
Insulin Resistance ◽  
Endothelial Dysfunction ◽  
Diabetic Patients ◽  
Multiple Risk Factors ◽  
Multiple Risk

scholarly journals Mucormycosis complicating diabetes mellitus and COVID-19: a diagnostic and therapeutic threat

2021 ◽  
Vol 7 (4) ◽  
pp. 679
Author(s):  
Sophia Amalanathan ◽  
Kumaran Ramesh Colbert ◽  
Satish Kumar Chockalingam ◽  
Sankappa Pundalikappa Sinhasan ◽  
Chaitra Jadhav
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
High Risk ◽  
Orbital Cellulitis ◽  
Diabetic Patients ◽  
Complex Interplay ◽  
Multiple Risk Factors ◽  
Clinical Complications ◽  
Clinical Presentations ◽  
The World

<p class="abstract">COVID-19 infections is known to have a multitude of clinical presentations, and newer clinical complications continue to be reported from all over the world. It is now widely known that the diabetic patients with poor glycemic control are associated with a higher risk of developing severe COVID-19 infection. They are also at a high risk for developing secondary bacterial or fungal, co infections because of the complex interplay of multiple risk factors , necessitating an intensive medical care and monitoring in them. We are presenting a case of mucormycosis in a diabetic patient with moderate COVID pneumonia that presented to us as orbital cellulitis and the events that followed. There have been very few documented cases of mucormycosis in COVID infected diabetic patients so far during this pandemic and we also present a literature review of the same.</p>

scholarly journals Development and validation of a machine learning model to predict mortality risk in patients with COVID-19

2021 ◽  
Vol 28 (1) ◽  
pp. e100235
Author(s):  
Anna Stachel ◽  
Kwesi Daniel ◽  
Dan Ding ◽  
Fritz Francois ◽  
Michael Phillips ◽  
...  
Keyword(s):  
Machine Learning ◽  
New York ◽  
Hospital Admissions ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Mortality Prediction ◽  
Computer Assisted ◽  
Neutrophil Percentage ◽  
Patients At Risk ◽  
Development And Validation

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients was needed due to a sudden and massive increase in patients during the COVID-19 pandemic as healthcare providers incurred an exponential increase in workload,which created a strain on the staff and limited resources. Further, methods to better understand and characterise the predictors of morbidity and mortality was needed.MethodsWe developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3395 hospital admissions with COVID-19. Multiple methods were applied, and final model was selected based on performance. A variable importance algorithm was used for interpretability, and understanding of performance and predictors was applied to the best model. We built a model with an area under the receiver operating characteristic curve of 83–97 to identify predictors and patients with high risk of mortality due to COVID-19. Oximetry, respirations, blood urea nitrogen, lymphocyte per cent, calcium, troponin and neutrophil percentage were important features, and key ranges were identified that contributed to a 50% increase in patients’ mortality prediction score. With an increasing negative predictive value starting 0.90 after the second day of admission suggests we might be able to more confidently identify likely survivorsDiscussionThis study serves as a use case of a machine learning methods with visualisations to aide clinicians with a better understanding of the model and predictors of mortality.ConclusionAs we continue to understand COVID-19, computer assisted algorithms might be able to improve the care of patients.

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