scholarly journals Artificial Intelligence Approach for Variant Reporting

2018 ◽  
pp. 1-13 ◽  
Author(s):  
Michael G. Zomnir ◽  
Lev Lipkin ◽  
Maciej Pacula ◽  
Enrique Dominguez Meneses ◽  
Allison MacLeay ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Decision Support ◽  
Next Generation Sequencing ◽  
Decision Support Tool ◽  
Bioinformatics Pipeline ◽  
Support Tool ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach ◽  
Generation Sequencing

Purpose Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models’ Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

scholarly journals Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital

2020 ◽  
Author(s):  
R.J. Lee ◽  
C. Zhou ◽  
O. Wysocki ◽  
R. Shotton ◽  
A. Tivey ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Decision Support ◽  
Cancer Patients ◽  
Sensitivity And Specificity ◽  
Decision Support Tool ◽  
Superior Performance ◽  
Evaluation Tool ◽  
Support Tool ◽  
Entire Cohort ◽  
Severity Prediction

AbstractBackgroundCancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical.ObjectiveTo identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET)MethodData was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission (≥24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool.ResultsTraining and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died.Conclusions and RelevanceCORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.

scholarly journals Autonomous, Connected Ultrasonic Microsystem Dedicated to Monitoring the Pathophysiology of the Phonic and Respiratory Apparatus

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Respiratory Failure ◽  
Healthcare Professionals ◽  
Decision Support Tool ◽  
Support Tool ◽  
Remote Assessment ◽  
Multidimensional Information ◽  
Respiratory Apparatus

This work tackles a combination of two technological fields: "integrated ultrasonic biosensors" and "connected modules" coupled with “Artificial Intelligence” algorithms to provide healthcare professionals with additional indices offering multidimensional information and a “Decision Support” tool. This device comprises a connected telemedical platform (PC or Smartphone) dedicated to the objective and remote assessment of pathophysiological states resulting from dysphonia of laryngeal origin or respiratory failure of inflammatory origin.

scholarly journals Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

2020 ◽  
Vol 10 (2) ◽  
pp. 425 ◽  
Author(s):  
Wei Chen ◽  
Yang Li ◽  
Paraskevas Tsangaratos ◽  
Himan Shahabi ◽  
Ioanna Ilia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Shaanxi Province ◽  
Kernel Logistic Regression ◽  
Alternating Decision Tree ◽  
Auc Value ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundwater springs and fourteen explanatory factors (elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use), which were divided into training and validation datasets. Ningtiaota region in the northern territory of Shaanxi Province, China, was considered as a test site. Frequency Ratio method was applied to provide to each factor’s class a coefficient weight, whereas the linear support vector machine method was used as a feature selection method to determine the optimal set of factors. The Receiver Operating Characteristic curve and the area under the curve (AUC) were used to evaluate the performance of each model using the training dataset, with the RF model providing the highest AUC value (0.909) followed by the KLR (0.877) and ADTree (0.812) models. The same performance pattern was estimated based on the validation dataset, with the RF model providing the highest AUC value (0.811) followed by the KLR (0.797) and ADTree (0.773) models. This study highlights that the artificial intelligence approach could be considered as a valid and accurate approach for groundwater spring potential zoning.

U-TEST, a simple decision support tool for the diagnosis of sarcopenia in orthopaedic patients: the Screening for People Suffering Sarcopenia in Orthopedic cohort of Kobe study (SPSS-OK)

2021 ◽  
pp. 1-8
Author(s):  
Tsukasa Kamitani ◽  
Takafumi Wakita ◽  
Osamu Wada ◽  
Kiyonori Mizuno ◽  
Noriaki Kurita
Keyword(s):  
Logistic Regression ◽  
Decision Support ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Decision Support Tool ◽  
Structured Interview ◽  
Internal Validation ◽  
Support Tool ◽  
Expert Opinions ◽  
Orthopaedic Patients

Abstract We aimed to develop and validate a new simple decision support tool (U-TEST) for diagnosis of sarcopenia in orthopaedic patients. We created seventeen candidate original questions to detect sarcopenia in orthopaedic patients with sarcopenia through expert opinions and a semi-structured interview. To derive a decision support tool, a logistic regression model with backward elimination was applied to select variables from the seventeen questions, age and underweight (BMI < 18·5 kg/m2). Sarcopenia was defined by Asian Working Group for Sarcopenia 2019 criteria. After assigning a score to each selected variable, the sum of scores was calculated. We evaluated the diagnostic performance of the new tool using a logistic regression model. A bootstrap technique was used for internal validation. Among a total of 1334 orthopaedic patients, sixty-five (4·9 %) patients were diagnosed with sarcopenia. We succeeded in developing a ‘U-TEST’ with scores ranging from 0 to 11 consisting of values for BMI (Underweight), age (Elderly) and two original questions (‘I can’t stand up from a chair without supporting myself with my arms’ (Strength) and ‘I feel that my arms and legs are thinner than they were in the past’ (Thin)). The AUC was 0·77 (95 % CI 0·71, 0·83). With the optimal cut-off set at 3 or greater based on Youden’s index, the sensitivity and the specificity were 76·1 and 63·6 %, respectively. In orthopaedic patients, our U-TEST scoring with two questions and two simple clinical variables can help to screen for sarcopenia.

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