scholarly journals A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study

10.2196/19786 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e19786 ◽  
Author(s):  
Ying Liu ◽  
Zhixiao Wang ◽  
Jingjing Ren ◽  
Yu Tian ◽  
Min Zhou ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Risk Stratification ◽  
Support System ◽  
Cross Validation ◽  
Mobile Terminal ◽  
Dynamic Risk ◽  
Risk Stratification Model ◽  
Fold Cross Validation

Background The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. Objective The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. Methods Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. Results DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients’ demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro–area under the curve were all above 0.71 in each scenario. Conclusions DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.

scholarly journals A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Zhixiao Wang ◽  
Jingjing Ren ◽  
Yu Tian ◽  
Min Zhou ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Risk Stratification ◽  
Support System ◽  
Cross Validation ◽  
Mobile Terminal ◽  
Dynamic Risk ◽  
Risk Stratification Model ◽  
Fold Cross Validation

BACKGROUND The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. OBJECTIVE The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. METHODS Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. RESULTS DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients’ demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro–area under the curve were all above 0.71 in each scenario. CONCLUSIONS DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.

scholarly journals A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study

2021 ◽  
Vol 11 (6) ◽  
pp. 2503
Author(s):  
Marco Alì ◽  
Natascha Claudia D’Amico ◽  
Matteo Interlenghi ◽  
Marina Maniglio ◽  
Deborah Fazzini ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Preliminary Study ◽  
Personalized Risk ◽  
Fold Cross Validation ◽  
Malignant Diagnosis

Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%.

Using Cross-Validation to Evaluate CERES-Maize Yield Simulations within a Decision Support System for Precision Agriculture

2007 ◽  
Vol 50 (4) ◽  
pp. 1467-1479 ◽  
Author(s):  
K. R. Thorp ◽  
W. D. Batchelor ◽  
J. O. Paz ◽  
A. L. Kaleita ◽  
K. C. DeJonge
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Precision Agriculture ◽  
Cross Validation ◽  
Maize Yield

Advanced Issues of Health Informatics and Clinical Decision Support System in Global Health Care

2018 ◽  
pp. 153-170 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Global Health ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Health Informatics ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Global Health Care

This chapter indicates the advanced issues of health informatics; the advanced issues of Clinical Decision Support System (CDSS); CDSS and Computerized Physician Order Entry (CPOE); the false positive alerts in CDSS; and CDSS and biomedical engineering. Health informatics and CDSS are the advanced health care technologies with the support of many technological fields. Health informatics and CDSS apply various computerized devices to provide enhanced health-related outcomes in terms of problem solving, analytical thinking, and decision making. Health informatics and CDSS help clinicians and health care providers to make complex information useful in supporting clinical decisions, thus delivering the best standard of care for each patient. The chapter argues that utilizing health informatics and CDSS has the potential to increase health outcomes and reach strategic goals in global health care.

A Decision Support System for Diabetes Care: Ethical Aspects

1999 ◽  
Vol 38 (04/05) ◽  
pp. 313-316 ◽  
Author(s):  
N. Shahsavar ◽  
H. Gill ◽  
G. Collste
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Diabetes Care ◽  
Good Health ◽  
Ethical Aspects ◽  
Ethical Perspective ◽  
Design And Implementation ◽  
Good Health Care

AbstractIn this paper the design and implementation of a decision support system for diabetes care is examined from an ethical perspective. It is noted that the system creates potential for enhancing the realization of the principle of autonomy through improved information to patients and participation by patients. However, there is also potential for using the system in a way that is contrary to good health care. It may provide patients with information they are unable to interpret and handle, and it may be used by healthcare authorities for controlling their personnel in ways contrary to good quality working conditions. In order for a decision support system to function as a well-integrated element in ethically based health care, different ethical aspects have to be taken into account during the design of the system. The ethical aspects also constitute one perspective of a systematic re-evaluation and re-design process.

scholarly journals Decision Support System to Risk Stratification in the Acute Coronary Syndrome Using Fuzzy Logic

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hui Xiao ◽  
Shah Nazir ◽  
Hanmin Li ◽  
Habib Ullah Khan ◽  
Chengwei Li
Keyword(s):  
Blood Flow ◽  
Acute Coronary Syndrome ◽  
Decision Support ◽  
High Risk ◽  
Decision Support System ◽  
Risk Stratification ◽  
Support System ◽  
Probabilistic Approach ◽  
Coronary Syndrome ◽  
Grace Score

Acute coronary syndrome (ACS) is a set of symptoms and signs which define a range of conditions related with the unexpected reduced blood flow to the heart. In ACS, the heart muscles cannot function properly due to the decrease of blood flow. Myocardial infarction (MI) is a condition which comes under the umbrella of acute coronary syndrome. The aim of risk stratification (RS) in ACS is to recognize patients at high risk of ischemic events. Yet, no investigative study is available to identify the patients at high risk. Therefore, to facilitate this process, it would be ideal to have a reliable and trustworthy method by the help of which the doctors can make early and easy decisions for the patient and for detecting the related disease. This research used the features of GRACE Score to RS in the ACS and presented decision support system (DSS). The concept of probabilistic approach has been used as a tool to model the identified features for decision-making (DM). This technique can be further used for DM purposes to RS in the ACS in healthcare. Furthermore, the result of the proposed method has proved closer and more reliable DM of patient and then eventually can be used for advice of medicine and rest accordingly by the doctors.

scholarly journals Framework for Patient Service Queue System for Decision Support System on Smart Health Care

2018 ◽  
Vol 7 (2.13) ◽  
pp. 337 ◽  
Author(s):  
Akim Manaor Hara Pardede ◽  
Herman Mawengkang ◽  
Muhammad Zarlis ◽  
T Tulus ◽  
Yani Maulita ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Health Service ◽  
Decision Support System ◽  
Support System ◽  
System Model ◽  
Patient Handling ◽  
Smart Healthcare ◽  
Smart Health ◽  
Patient Service

Improving the service of patient care in hospitals is important for all, prioritizing patient safety in the event of a sudden or catastrophic attack, in which case the priority is to provide services to the patient. In such situations a decision system is needed, in order for the system to be right and not wrong to do a decision because the handling of this issue is closely related to the patient's life. The patient handling technology supports highly smart healthcare technology, which of course is part of the Smart city. The purpose of this research is to get Smart Health Care model with Decision Support System model approach in public health service, where Decision Support System model for Smart Health Care can solve health service problem in order to make maximum service for patient 

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