scholarly journals DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups

Healthcare ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1632
Author(s):  
Md. Mohaimenul Islam ◽  
Guo-Hung Li ◽  
Tahmina Nasrin Poly ◽  
Yu-Chuan (Jack) Li
Keyword(s):  
Financial Incentives ◽  
Prediction Models ◽  
Diagnosis Related Groups ◽  
Hospital Performance ◽  
Poor Quality ◽  
Primary Diagnosis ◽  
Receiver Operating Curve ◽  
Ann Model ◽  
Clinical Coding ◽  
Model Training

Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients’ age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance.

scholarly journals Spatial Prediction of Future Flood Risk: An Approach to the Effects of Climate Change

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 25
Author(s):  
Mohammadtaghi Avand ◽  
Hamid Reza Moradi ◽  
Mehdi Ramazanzadeh Lasboyee
Keyword(s):  
Climate Change ◽  
Spatial Prediction ◽  
Flood Management ◽  
Management Program ◽  
Climate Change Scenarios ◽  
Ann Model ◽  
Probability Map ◽  
Flood Susceptibility ◽  
Flood Probability ◽  
Model Training

Preparation of a flood probability map serves as the first step in a flood management program. This research develops a probability flood map for floods resulting from climate change in the future. Two models of Flexible Discrimination Analysis (FDA) and Artificial Neural Network (ANN) were used. Two optimistic (RCP2.6) and pessimistic (RCP8.5) climate change scenarios were considered for mapping future rainfall. Moreover, to produce probability flood occurrence maps, 263 locations of past flood events were used as dependent variables. The number of 13 factors conditioning floods was taken as independent variables in modeling. Of the total 263 flood locations, 80% (210 locations) and 20% (53 locations) were considered model training and validation. The Receiver Operating Characteristic (ROC) curve and other statistical criteria were used to validate the models. Based on assessments of the validated models, FDA, with a ROC-AUC = 0.918, standard error (SE = 0.038), and an accuracy of 0.86% compared to the ANN model with a ROC-AUC = 0.897, has the highest accuracy in preparing the flood probability map in the study area. The modeling results also showed that the factors of distance from the River, altitude, slope, and rainfall have the greatest impact on floods in the study area. Both models’ future flood susceptibility maps showed that the highest area is related to the very low class. The lowest area is related to the high class.

scholarly journals Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 268-269
Author(s):  
Jaime Speiser ◽  
Kathryn Callahan ◽  
Jason Fanning ◽  
Thomas Gill ◽  
Anne Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Learning Methods ◽  
Life Study ◽  
Fall Injury ◽  
Machine Learning Methods

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals A Technology Roadmap for Innovative Approaches to Kidney Replacement Therapies

2019 ◽  
Vol 14 (10) ◽  
pp. 1539-1547 ◽  
Author(s):  
Joseph V. Bonventre ◽  
Frank P. Hurst ◽  
Melissa West ◽  
Iwen Wu ◽  
Prabir Roy-Chaudhury ◽  
...  
Keyword(s):  
Financial Incentives ◽  
The United States ◽  
Disruptive Innovation ◽  
Poor Quality ◽  
High Morbidity ◽  
Technology Roadmap ◽  
Number Of Patients ◽  
Interdisciplinary Approaches ◽  
Human Cost ◽  
Innovative Approaches

The number of patients dialyzed for ESKD exceeds 500,000 in the United States and more than 2.6 million people worldwide, with the expectation that the worldwide number will double by 2030. The human cost of health and societal financial cost of ESKD is substantial. Dialytic therapy is associated with an unacceptably high morbidity and mortality rate and poor quality of life. Although innovation in many areas of science has been transformative, there has been little innovation in dialysis or alternatives for kidney replacement therapy (KRT) since its introduction approximately 70 years ago. Advances in kidney biology, stem cells and kidney cell differentiation protocols, biomaterials, sensors, nano/microtechnology, sorbents and engineering, and interdisciplinary approaches and collaborations can lead to disruptive innovation. The Kidney Health Initiative, a public–private partnership between the American Society of Nephrology and the US Food and Drug Administration, has convened a multidisciplinary group to create a technology roadmap for innovative approaches to KRT to address patients’ needs. The Roadmap is a living document. It identifies the design criteria that must be considered to replace the myriad functions of the kidney, as well as scientific, technical, regulatory, and payor milestones required to commercialize and provide patient access to KRT alternatives. Various embodiments of potential solutions are discussed, but the Roadmap is agnostic to any particular solution set. System enablers are identified, including vascular access, biomaterial development, biologic and immunologic modulation, function, and safety monitoring. Important Roadmap supporting activities include regulatory alignment and innovative financial incentives and payment pathways. The Roadmap provides estimated timelines for replacement of specific kidney functions so that approaches can be conceptualized in ways that are actionable and attract talented innovators from multiple disciplines. The Roadmap has been used to guide the selection of KidneyX prizes for innovation in KRT.

Artificial intelligence based prediction models: sales forecasting application in automotive aftermarket

2021 ◽  
pp. 1-13
Author(s):  
Mert Girayhan Türkbayrağí ◽  
Elif Dogu ◽  
Y. Esra Albayrak
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Demand Characteristic ◽  
Product Portfolio ◽  
Ann Model ◽  
Nonlinear Relationships ◽  
Relationship Of ◽  
Automotive Aftermarket

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance.

scholarly journals Generalized Prediction of Shock in Intensive Care Units using Deep Learning

2021 ◽  
Author(s):  
Aditya Nagori ◽  
Anushtha Kalia ◽  
Arjun Sharma ◽  
Pradeep Singh ◽  
Harsh Bandhey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Models ◽  
Age Groups ◽  
Receiver Operating Curve ◽  
Learning Approach ◽  
New Delhi ◽  
Model Generalization ◽  
Pediatric Icu ◽  
Geographical Context

Shock is a major killer in the ICU and machine learning based early predictions can potentially save lives. Generalization across age and geographical context is an unaddressed challenge. In this retrospective observational study, we built real-time shock prediction models generalized across age groups and continents. More than 1.5 million patient-hours of novel data from a pediatric ICU in New Delhi and 5 million patient-hours from the adult ICU MIMIC database were used to build models. We achieved model generalization through a novel fractal deep-learning approach and predicted shock up to 12 hours in advance. Our deep learning models showed a receiver operating curve (AUROC) drop from 78% (95%CI, 73-83) on MIMIC data to 66% (95%CI, 54-78) on New Delhi data, outperforming standard machine learning by nearly a 10% gap. Therefore, better representations and deep learning can partly address the generalizability-gap of ICU prediction models trained across geographies. Our data and algorithms are publicly available as a pre-configured docker environment at https://github.com/SAFE-ICU/ShoQPred.

Perverse financial incentives reward poor quality care, says report

BMJ ◽  
2009 ◽  
Vol 339 (sep01 2) ◽  
pp. b3549-b3549
Author(s):  
M. Pownall
Keyword(s):  
Financial Incentives ◽  
Quality Care ◽  
Poor Quality ◽  
Poor Quality Care

The impact of surgical site infections on hospital contribution margin—a European prospective observational cohort study

2019 ◽  
Vol 40 (12) ◽  
pp. 1374-1379
Author(s):  
Marco von Strauss ◽  
Walter R. Marti ◽  
Edin Mujagic ◽  
Michael Coslovsky ◽  
Katharina Diernberger ◽  
...  
Keyword(s):  
Financial Incentives ◽  
Diagnosis Related Groups ◽  
Controlled Trial ◽  
Private Insurance ◽  
Surgical Site Infections ◽  
Positive Contribution ◽  
Contribution Margin ◽  
Multicenter Randomized Controlled Trial ◽  
Preoperative Antibiotic Prophylaxis ◽  
The Impact

AbstractBackground:Surgical site infections (SSIs) are common surgical complications that lead to increased costs. Depending on payer type, however, they do not necessarily translate into deficits for every hospital.Objective:We investigated how surgical site infections (SSIs) influence the contribution margin in 2 reimbursement systems based on diagnosis-related groups (DRGs).Methods:This preplanned observational health cost analysis was nested within a Swiss multicenter randomized controlled trial on the timing of preoperative antibiotic prophylaxis in general surgery between February 2013 and August 2015. A simulation of cost and income in the National Health Service (NHS) England reimbursement system was conducted.Results:Of 5,175 patients initially enrolled, 4,556 had complete cost and income data as well as SSI status available for analysis. SSI occurred in 228 of 4,556 of patients (5%). Patients with SSIs were older, more often male, had higher BMIs, compulsory insurance, longer operations, and more frequent ICU admissions. SSIs led to higher hospital cost and income. The median contribution margin was negative in cases of SSI. In SSI cases, median contribution margin was Swiss francs (CHF) −2045 (IQR, −12,800 to 4,848) versus CHF 895 (IQR, −2,190 to 4,158) in non-SSI cases. Higher ASA class and private insurance were associated with higher contribution margins in SSI cases, and ICU admission led to greater deficits. Private insurance had a strong increasing effect on contribution margin at the 10th, 50th (median), and 90th percentiles of its distribution, leading to overall positive contribution margins for SSIs in Switzerland. The NHS England simulation with 3,893 patients revealed similar but less pronounced effects of SSI on contribution margin.Conclusions:Depending on payer type, reimbursement systems with DRGs offer only minor financial incentives to the prevention of SSI.

scholarly journals Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

2020 ◽  
Vol 17 (3) ◽  
pp. 927
Author(s):  
Mei-Chin Su ◽  
Yi-Jen Wang ◽  
Tzeng-Ji Chen ◽  
Shiao-Hui Chiu ◽  
Hsiao-Ting Chang ◽  
...  
Keyword(s):  
Risk Management ◽  
Cost Effectiveness ◽  
Home Care ◽  
Readmission Rate ◽  
Prediction Models ◽  
Medical Center ◽  
Management Tool ◽  
Receiver Operating Curve ◽  
Limited Information ◽  
Net Reclassification Improvement

The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488–0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582–0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068–0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician’s model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.

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