scholarly journals Development of an Artificial Intelligence–Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database (Preprint)

2020 ◽  
Author(s):  
Md Mohaimenul Islam ◽  
Hsuan-Chia Yang ◽  
Tahmina Nasrin Poly ◽  
Yu-Chuan Jack Li
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Health Insurance ◽  
Laboratory Test ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Recommendation System ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Insurance Database

BACKGROUND Laboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice. OBJECTIVE The objective of this study was to develop an artificial intelligence–based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs. METHODS A retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model. RESULTS We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity. CONCLUSIONS The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.

scholarly journals Development of an Artificial Intelligence–Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database

10.2196/24163 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e24163
Author(s):  
Md Mohaimenul Islam ◽  
Hsuan-Chia Yang ◽  
Tahmina Nasrin Poly ◽  
Yu-Chuan Jack Li
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Health Insurance ◽  
Laboratory Test ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Recommendation System ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Insurance Database

Background Laboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice. Objective The objective of this study was to develop an artificial intelligence–based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs. Methods A retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model. Results We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity. Conclusions The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.

scholarly journals Significant Physical and Exercise-Related Variables for Exercise-Centred Lifestyle: Big Data Analysis for Gynaecological Cancer Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Eun Joo Yang ◽  
Hyunseok Jee
Keyword(s):  
Ovarian Cancer ◽  
Health Insurance ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
National Health Insurance ◽  
National Health ◽  
Operating Characteristic ◽  
Uterine Cancer ◽  
Characteristic Curve ◽  
Operating Characteristic Curve

This study investigated the characteristics of gynaecological cancers and is aimed at identifying significant risk variables using the National Health Insurance Sharing Service database to develop practical interventions for affected patients. Data regarding patients with uterine and ovarian cancer from the National Health Insurance Sharing Service database were collected and analysed using Student’s t -test, logistic regression, and receiver operating characteristic curve analyses. Student’s t -test analyses revealed that age, body mass index, blood pressure, and waist variables differed significantly among patients with uterine cancer. Gamma-glutamyl transpeptidase levels were higher in patients with ovarian cancer than in patients with uterine cancer. Physical fitness function tests reflected the status of patients with cancer. Moreover, physical disability was associated with an increased incidence of ovarian cancer. Intensive exercise for 20 min more than 1 time per week must be avoided to prevent uterine cancer. Receiver operating characteristic curve analyses showed that the optimal cutoff value for one-leg standing time, a prognostic and preventive factor in ovarian cancer, was 9.50 s (sensitivity, 94.9%; specificity, 96.9%). Controlling significant variables for each gynaecological cancer type in an individualised and optimised manner is recommended, including by maintenance of an adjusted exercise-centred lifestyle.

scholarly journals Severity of Disease Estimation and Risk-Adjustment for Comparison of Outcomes in Mechanically Ventilated Patients Using Electronic Routine Care Data

2015 ◽  
Vol 36 (7) ◽  
pp. 807-815 ◽  
Author(s):  
Maaike S. M. van Mourik ◽  
Karel G. M. Moons ◽  
Michael V. Murphy ◽  
Marc J. M. Bonten ◽  
Michael Klompas ◽  
...  
Keyword(s):  
Disease Severity ◽  
Risk Adjustment ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Routine Care ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Operating Characteristic Curve ◽  
Ventilated Patients

BACKGROUNDValid comparison between hospitals for benchmarking or pay-for-performance incentives requires accurate correction for underlying disease severity (case-mix). However, existing models are either very simplistic or require extensive manual data collection.OBJECTIVETo develop a disease severity prediction model based solely on data routinely available in electronic health records for risk-adjustment in mechanically ventilated patients.DESIGNRetrospective cohort study.PARTICIPANTSMechanically ventilated patients from a single tertiary medical center (2006–2012).METHODSPredictors were extracted from electronic data repositories (demographic characteristics, laboratory tests, medications, microbiology results, procedure codes, and comorbidities) and assessed for feasibility and generalizability of data collection. Models for in-hospital mortality of increasing complexity were built using logistic regression. Estimated disease severity from these models was linked to rates of ventilator-associated events.RESULTSA total of 20,028 patients were initiated on mechanical ventilation, of whom 3,027 deceased in hospital. For models of incremental complexity, area under the receiver operating characteristic curve ranged from 0.83 to 0.88. A simple model including demographic characteristics, type of intensive care unit, time to intubation, blood culture sampling, 8 common laboratory tests, and surgical status achieved an area under the receiver operating characteristic curve of 0.87 (95% CI, 0.86–0.88) with adequate calibration. The estimated disease severity was associated with occurrence of ventilator-associated events.CONCLUSIONSAccurate estimation of disease severity in ventilated patients using electronic, routine care data was feasible using simple models. These estimates may be useful for risk-adjustment in ventilated patients. Additional research is necessary to validate and refine these models.Infect. Control Hosp. Epidemiol. 2015;36(7):807–815

scholarly journals The Role of Corneal Biomechanics for the Evaluation of Ectasia Patients

2020 ◽  
Vol 17 (6) ◽  
pp. 2113 ◽  
Author(s):  
Marcella Q. Salomão ◽  
Ana Luisa Hofling-Lima ◽  
Louise Pellegrino Gomes Esporcatte ◽  
Bernardo Lopes ◽  
Riccardo Vinciguerra ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Corneal Biomechanics ◽  
Healthy Group ◽  
Biomechanical Parameter ◽  
Operating Characteristic Curve ◽  
Corneal Tomography ◽  
Fellow Eyes

Purpose: To review the role of corneal biomechanics for the clinical evaluation of patients with ectatic corneal diseases. Methods: A total of 1295 eyes were included for analysis in this study. The normal healthy group (group N) included one eye randomly selected from 736 patients with healthy corneas, the keratoconus group (group KC) included one eye randomly selected from 321 patients with keratoconus. The 113 nonoperated ectatic eyes from 125 patients with very asymmetric ectasia (group VAE-E), whose fellow eyes presented relatively normal topography (group VAE-NT), were also included. The parameters from corneal tomography and biomechanics were obtained using the Pentacam HR and Corvis ST (Oculus Optikgeräte GmbH, Wetzlar, Germany). The accuracies of the tested variables for distinguishing all cases (KC, VAE-E, and VAE-NT), for detecting clinical ectasia (KC + VAE-E) and for identifying abnormalities among the VAE-NT, were investigated. A comparison was performed considering the areas under the receiver operating characteristic curve (AUC; DeLong’s method). Results: Considering all cases (KC, VAE-E, and VAE-NT), the AUC of the tomographic-biomechanical parameter (TBI) was 0.992, which was statistically higher than all individual parameters (DeLong’s; p < 0.05): PRFI- Pentacam Random Forest Index (0.982), BAD-D- Belin -Ambrosio D value (0.959), CBI -corneal biomechanical index (0.91), and IS Abs- Inferior-superior value (0.91). The AUC of the TBI for detecting clinical ectasia (KC + VAE-E) was 0.999, and this was again statistically higher than all parameters (DeLong’s; p < 0.05): PRFI (0.996), BAD-D (0.995), CBI (0.949), and IS Abs (0.977). Considering the VAE-NT group, the AUC of the TBI was 0.966, which was also statistically higher than all parameters (DeLong’s; p < 0.05): PRFI (0.934), BAD- D (0.834), CBI (0.774), and IS Abs (0.677). Conclusions: Corneal biomechanical data enhances the evaluation of patients with corneal ectasia and meaningfully adds to the multimodal diagnostic armamentarium. The integration of biomechanical data and corneal tomography with artificial intelligence data augments the sensitivity and specificity for screening and enhancing early diagnosis. Besides, corneal biomechanics may be relevant for determining the prognosis and staging the disease.

scholarly journals Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Demilade Adedinsewo ◽  
Rickey E. Carter ◽  
Zachi Attia ◽  
Patrick Johnson ◽  
Anthony H. Kashou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Left Ventricular Systolic Dysfunction ◽  
Characteristic Curve ◽  
Left Ventricular ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

scholarly journals An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

2021 ◽  
Vol 8 ◽  
Author(s):  
Tommaso Banzato ◽  
Marek Wodzinski ◽  
Federico Tauceri ◽  
Chiara Donà ◽  
Filippo Scavazza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pleural Effusion ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Computer Aided Detection ◽  
Radiographic Findings ◽  
Computer Aided ◽  
Operating Characteristic Curve

An artificial intelligence (AI)-based computer-aided detection (CAD) algorithm to detect some of the most common radiographic findings in the feline thorax was developed and tested. The database used for training comprised radiographs acquired at two different institutions. Only correctly exposed and positioned radiographs were included in the database used for training. The presence of several radiographic findings was recorded. Consequenly, the radiographic findings included for training were: no findings, bronchial pattern, pleural effusion, mass, alveolar pattern, pneumothorax, cardiomegaly. Multi-label convolutional neural networks (CNNs) were used to develop the CAD algorithm, and the performance of two different CNN architectures, ResNet 50 and Inception V3, was compared. Both architectures had an area under the receiver operating characteristic curve (AUC) above 0.9 for alveolar pattern, bronchial pattern and pleural effusion, an AUC above 0.8 for no findings and pneumothorax, and an AUC above 0.7 for cardiomegaly. The AUC for mass was low (above 0.5) for both architectures. No significant differences were evident in the diagnostic accuracy of either architecture.

Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip

2020 ◽  
Vol 102-B (11) ◽  
pp. 1574-1581
Author(s):  
Si-Cheng Zhang ◽  
Jun Sun ◽  
Chuan-Bin Liu ◽  
Ji-Hong Fang ◽  
Hong-Tao Xie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Developmental Dysplasia ◽  
Learning System ◽  
Pelvic Radiographs ◽  
Operating Characteristic Curve ◽  
Dysplasia Of The Hip

Aims The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. Methods In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. Results In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). Conclusion The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574–1581.

scholarly journals Evidence-Based Cutoff Threshold Values from Receiver Operating Characteristic Curve Analysis for Knee Osteoarthritis in the 50-Year-Old Korean Population: Analysis of Big Data from the National Health Insurance Sharing Service

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hyunseok Jee ◽  
Hae-Dong Lee ◽  
Sae Yong Lee
Keyword(s):  
Health Insurance ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
National Health Insurance ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Age Group ◽  
Operating Characteristic Curve ◽  
Cutoff Threshold

We aimed to investigate the characteristics of patients with osteoarthritis (OA), using the data of all Koreans registered in the National Health Insurance Sharing Service Database (NHISS DB), and to provide ideal alternative cutoff thresholds for alleviating OA symptoms. Patients with OA (codes M17 and M17.1–M17.9 in the Korean Standard Classification of Disease and Causes of Death) were analyzed using SAS software. Optimal cutoff thresholds were determined using receiver operating characteristic curve analysis. The 50-year age group was the most OA pathogenic group (among 40~70 years, n=2088). All exercise types affected the change of body mass index (p<0.05) and the sex difference in blood pressure (BP) (p<0.01). All types of exercise positively affected the loss of waist circumference and the balance test (standing time on one leg in seconds) (p<0.01). The cutoff threshold for the time in seconds from standing up from a chair to walking 3 m and returning to the same chair was 8.25 (80% sensitivity and 100% specificity). By using the exercise modalities, categorized multiple variables, and the cutoff threshold, an optimal alternative exercise program can be designed for alleviating OA symptoms in the 50-year age group.

scholarly journals Development and Evaluation of an AI System for COVID-19 Diagnosis

2020 ◽  
Author(s):  
Cheng Jin ◽  
Weixiang Chen ◽  
Yukun Cao ◽  
Zhanwei Xu ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Chest Ct ◽  
Large Dataset ◽  
Operating Characteristic Curve ◽  
Timely Treatment

AbstractEarly detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 970 CT volumes of 496 patients with confirmed COVID-19 and 260 negative cases from three hospitals in Wuhan, China, and 1,125 negative cases from two publicly available chest CT datasets. Trained using only 312 cases, our diagnosis system, which is based on deep convolutional neural network, is able to achieve an accuracy of 94.98%, an area under the receiver operating characteristic curve (AUC) of 97.91%, a sensitivity of 94.06%, and a specificity of 95.47% on an independent external verification dataset of 1,255 cases. In a reader study involving five radiologists, only one radiologist is slightly more accurate than the AI system. The AI system is two orders of magnitude faster than radiologists and the code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

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