Design of a Clinical Decision Support System for Predicting Erectile Dysfunction in Men Using NHIRD Dataset

Chen, Yung-Fu; Lin, Ching Heng; Hong, Chun-Fu; Lee, Dah-Jye; Sun, Changming; Lin, Hung Hsin

doi:10.1109/jbhi.2018.2877595

Cited by 20 publications

(11 citation statements)

References 75 publications

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“…The majority of the 90 included studies (68/90, 76%) investigated the use of AI in relation to a specific medical condition. Conditions studied were vascular diseases including hypertension, hypercholesteremia, peripheral arterial disease, and congestive heart failure (10/90, 11%) [ 40 - 49 ]; infectious diseases including influenza, herpes zoster, tuberculosis, urinary tract infections, and subcutaneous infections (8/90, 9%) [ 50 - 57 ]; type 2 diabetes (5/90, 6%) [ 58 - 62 ]; respiratory disorders including chronic obstructive pulmonary disease and asthma (6/90, 8%) [ 63 - 69 ]; orthopedic disorders including rheumatoid arthritis, gout, and lower back pain (5/90, 5%) [ 36 , 39 , 70 - 72 ]; neurological disorders including stroke, Parkinson disease, Alzheimer disease [ 73 - 75 ], and cognitive impairments (6/90, 5%) [ 76 , 77 ]; cancer including colorectal cancer, and head and neck cancer (4/90, 4%) [ 78 - 81 ]; psychological disorders including depression and schizophrenia (3/90, 3%) [ 82 - 84 ]; diabetic retinopathy (3/90, 3%) [ 85 - 87 ]; suicidal ideations (2/90, 2%) [ 88 , 89 ]; tropical diseases including malaria (2/90, 2%) [ 90 , 91 ]; renal disorders (2/90, 2%) [ 92 , 93 ]; autism spectrum disorder (2/90, 2%) [ 94 , 95 ]; venous disorders including deep vein thrombosis and venous ulcers (2/90, 2%) [ 96 , 97 ]; and other health conditions (8/90, 8%) [ 98 - 105 ].…”

Section: Resultsmentioning

confidence: 99%

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

et al. 2021

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Background Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. Objective We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. Methods We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. Results We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). Conclusions We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

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Section: Resultsmentioning

confidence: 99%

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

et al. 2021

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“…By contrast, ANNs are computational models inspired by biological neural networks; they are currently the commonest practiced models of artificial intelligence used for risk prediction and decision-making [42,43]. Furthermore, ANNs are suitable for NHIRD-based prediction of certain illnesses [44,45]. RF modeling is an ensemble learning method that performs a computationally extensive and robust data-mining and can accommodate large sets of proposed variables as inputs to identify factors associated with the outcomes of interest [46].…”

Section: Discussionmentioning

confidence: 99%

The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus

Hsieh

Sun

Lin

et al. 2019

Cancers

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Objective: Early reports indicate that individuals with type 2 diabetes mellitus (T2DM) may have a greater incidence of breast malignancy than patients without T2DM. The aim of this study was to investigate the effectiveness of three different models for predicting risk of breast cancer in patients with T2DM of different characteristics. Study design and methodology: From 2000 to 2012, data on 636,111 newly diagnosed female T2DM patients were available in the Taiwan’s National Health Insurance Research Database. By applying their data, a risk prediction model of breast cancer in patients with T2DM was created. We also collected data on potential predictors of breast cancer so that adjustments for their effect could be made in the analysis. Synthetic Minority Oversampling Technology (SMOTE) was utilized to increase data for small population samples. Each datum was randomly assigned based on a ratio of about 39:1 into the training and test sets. Logistic Regression (LR), Artificial Neural Network (ANN) and Random Forest (RF) models were determined using recall, accuracy, F1 score and area under the receiver operating characteristic curve (AUC). Results: The AUC of the LR (0.834), ANN (0.865), and RF (0.959) models were found. The largest AUC among the three models was seen in the RF model. Conclusions: Although the LR, ANN, and RF models all showed high accuracy predicting the risk of breast cancer in Taiwanese with T2DM, the RF model performed best.

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“…However, models have different abilities to predict survival. Some studies used machine methods for an early diagnosis of bipolar disorder, prostate-cancer-specific survival, erectile dysfunction, CKD, and medical cost [ 13 , 14 , 15 , 16 , 17 ]. This research used multiple-stage selection methods to uncover potential collinearity among variable subsets and evaluate the response variable’s predictive performance.…”

Section: Methodsmentioning

confidence: 99%

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Huang

Chen

et al. 2021

Healthcare

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Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.

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Design of a Clinical Decision Support System for Predicting Erectile Dysfunction in Men Using NHIRD Dataset

Cited by 20 publications

References 75 publications

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

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