scholarly journals Utility of a Clinical Decision Support System in Weight Loss Prediction After Head and Neck Cancer Radiotherapy

2019 ◽  
pp. 1-11
Author(s):  
Zhi Cheng ◽  
Minoru Nakatsugawa ◽  
Xian Chong Zhou ◽  
Chen Hu ◽  
Stephen Greco ◽  
...  
Keyword(s):  
Weight Loss ◽  
Head And Neck Cancer ◽  
Decision Support ◽  
Prediction Model ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Loss Probability ◽  
Significant Weight Loss ◽  
Data Set ◽  
Loss Prediction

PURPOSE To evaluate the utility of a clinical decision support system (CDSS) using a weight loss prediction model. METHODS A prediction model for significant weight loss (loss of greater than or equal to 7.5% of body mass at 3-month post radiotherapy) was created with clinical, dosimetric, and radiomics predictors from 63 patients in an independent training data set (accuracy, 0.78; area under the curve [AUC], 0.81) using least absolute shrinkage and selection operator logistic regression. Four physicians with varying experience levels were then recruited to evaluate 100 patients in an independent validation data set of head and neck cancer twice (ie, a pre-post design): first without and then with the aid of a CDSS derived from the prediction model. At both evaluations, physicians were asked to predict the development (yes/no) and probability of significant weight loss for each patient on the basis of patient characteristics, including pretreatment dysphagia and weight loss and information from the treatment plan. At the second evaluation, physicians were also provided with the prediction model’s results for weight loss probability. Physicians’ predictions were compared with actual weight loss, and accuracy and AUC were investigated between the two evaluations. RESULTS The mean accuracy of the physicians’ ability to identify patients who will experience significant weight loss (yes/no) increased from 0.58 (range, 0.47 to 0.63) to 0.63 (range, 0.58 to 0.72) with the CDSS ( P = .06). The AUC of weight loss probability predicted by physicians significantly increased from 0.56 (range, 0.46 to 0.64) to 0.69 (range, 0.63 to 0.73) with the aid of the CDSS ( P < .05). Specifically, more improvement was observed among less-experienced physicians ( P < .01). CONCLUSION Our preliminary results demonstrate that physicians’ decisions may be improved by a weight loss CDSS model, especially among less-experienced physicians. Additional study with a larger cohort of patients and more participating physicians is thus warranted for understanding the usefulness of CDSSs.

Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy

2021 ◽  
pp. 944-952
Author(s):  
Peijin Han ◽  
Sang Ho Lee ◽  
Kazumasa Noro ◽  
John W. Haller ◽  
Minoru Nakatsugawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Weight Loss ◽  
Decision Support ◽  
Early Identification ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Prediction Performance ◽  
Significant Weight Loss ◽  
Before And After

PURPOSE Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION Machine learning–based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.

scholarly journals MACHINE LEARNING BASED CLINICAL DECISION SUPPORT SYSTEM TO PREDICT FETAL HYPOXIA IN WOMEN DURING ANTENATAL CHECK-UP.

2021 ◽  
pp. 82-90
Author(s):  
Sajal Baxi
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Stress Test ◽  
Clinical Decision ◽  
Data Set ◽  
Remote Areas

BACKGROUND:Most under-five deaths occur within the first month after birth and intrapartum complications are a major contributor to the cause of death. These defects can be easily identified during the ante-natal check-up by use of a non-stress test. Due to the lack of availability of resources and medical experts in remote areas clinical decision support systems powered by machine learning models can provide information to the healthcare provider to make timely and better-informed decisions based on which course of treatment can be planned. AIM:The study aims to develop an accurate and sensitive clinical decision support system model that can identify pathological fetuses based on the fetal heart rate recordings taken during the non-stress test. METHOD: Foetal Heart rate recordings along with 10 other variables were collected from 1800 pregnant women in their third trimester. The data was put through a feature selection algorithm to identify important variables in the set. The data set was randomly divided into 2 independent random samples in the ratio of 70% for training and 30% for testing. After testing various machine learning algorithms based on specificity, sensitivity to accurately classify the fetus into normal, suspected, or pathological Random Forest algorithm was chosen. RESULT:The fetal status determined by Obstetrician 77.85% observations from the normal category, 19.88% from the suspected category, and 8.28% from the pathological category. The Boruta algorithm revealed that all 11 independent variables in the data set were important to predict the outcome in the test set. In the training set the model had an accuracy of 99.04% and in the testing set accuracy was 94.7% (p-value=< 2.2e-16) with the precision of 97.56% to detect the pathological category. CONCLUSION:With the ability of the model to accurately predict the pathological category the CDS can be used by healthcare providers in remote areas to identify high-risk pregnant women and take the decision on the medical care to be provided.

scholarly journals A Multivariable Prediction Model to Select Colorectal Surgical Patients for Co-Management

2020 ◽  
Vol 33 (13) ◽  
Author(s):  
Alexandra Bayão Horta ◽  
Carlos Geraldes ◽  
Cátia Salgado ◽  
Susana Vieira ◽  
Miguel Xavier ◽  
...  
Keyword(s):  
Decision Support ◽  
Prediction Model ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Surgical Patients ◽  
Outcome Variable ◽  
Data Set ◽  
Support Tool ◽  
Clinical Decision Support Tool

Introduction: Increased life expectancy leads to older and frailer surgical patients. Co-management between medical and surgical specialities has proven favourable in complex situations. Selection of patients for co-management is full of difficulties. The aim of this study was to develop a clinical decision support tool to select surgical patients for co-management.Material and Methods: Clinical data was collected from patient electronic health records with an ICD-9 code for colorectal surgery from January 2012 to December 2015 at a hospital in Lisbon. The outcome variable consists in co-management signalling. A dataset from 344 patients was used to develop the prediction model and a second data set from 168 patients was used for external validation.Results: Using logistic regression modelling the authors built a five variable (age, burden of comorbidities, ASA-PS status, surgical risk and recovery time) predictive referral model for co-management. This model has an area under the curve (AUC) of 0.86 (95% CI: 0.81 - 0.90), a predictive Brier score of 0.11, a sensitivity of 0.80, a specificity of 0.82 and an accuracy of 81.3%.Discussion: Early referral of high-risk patients may be valuable to guide the decision on the best level of post-operative clinical care. We developed a simple bedside decision tool with a good discriminatory and predictive performance in order to select patients for comanagement.Conclusion: A simple bed-side clinical decision support tool of patients for co-management is viable, leading to potential improvement in early recognition and management of postoperative complications and reducing the ‘failure to rescue’. Generalizability to other clinical settings requires adequate customization and validation.

1309-P: Utilizing a Clinical Decision Support System for Hypoglycemia Management

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision

A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh
Keyword(s):  
Decision Support ◽  
Cardiovascular Diseases ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
K Value ◽  
Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

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