scholarly journals SPATIAL PREDICTION MODELS FOR LANDSLIDE ACTIVITY MAPPING USING VEGETATION ANOMALIES

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 441-449
Author(s):  
M. R. Mohd Salleh ◽  
Z. Ismail ◽  
S. A. Mohd Ariff ◽  
M. Z. Abd Rahman ◽  
M. F. Abdul Khanan ◽  
...  
Keyword(s):  
Prediction Models ◽  
Tree Height ◽  
Spatial Prediction ◽  
Receiver Operating Characteristics ◽  
Weight Of Evidence ◽  
Operating Characteristics ◽  
Landslide Prediction ◽  
Landslide Activity ◽  
Active Landslide ◽  
Colour Composite

Abstract. An area that located in Kundasang which in Ranau district in Sabah, Malaysia that lies along the bank of Kundasang valley was chosen for comparing the reliability of frequency ratio (FR) and weight of evidence (WoE) methods for landslide activity probability mapping by using related vegetation anomalies indicator. The locations of 47 and 189 of active and dormant landslides respectively were identified using 4 raster layers (topographic openness, hillshade, colour composite and high resolution orthophoto). Each landslide activites were randomly divided into two groups as training (70%) and testing (30%) datasets. Tree height irregularities, DVI, NDVI, SAVI, and OSAVI were considered as landslide bio-indicator. The landslide activity probability maps were prepared using the FR and WoE method. The generated maps were validated by calculating the success and prediction rates from area under receiver operating characteristics (ROC) curve. The results of WoE method were relatively reliable (AUC > 0.8) for dormant landslide while only about 40% of active landslide have been predicted accurately. Similar trend yielded for FR method where least accuracy for active landslide prediction.

scholarly journals Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jenna M. Reps ◽  
Patrick B. Ryan ◽  
Peter R. Rijnbeek ◽  
Martijn J. Schuemie
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Models ◽  
Control Design ◽  
Case Control ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Discriminative Performance ◽  
Cohort Design ◽  
Receiver Operating

Abstract Background The design used to create labelled data for training prediction models from observational healthcare databases (e.g., case-control and cohort) may impact the clinical usefulness. We aim to investigate hypothetical design issues and determine how the design impacts prediction model performance. Aim To empirically investigate differences between models developed using a case-control design and a cohort design. Methods Using a US claims database, we replicated two published prediction models (dementia and type 2 diabetes) which were developed using a case-control design, and trained models for the same prediction questions using cohort designs. We validated each model on data mimicking the point in time the models would be applied in clinical practice. We calculated the models’ discrimination and calibration-in-the-large performances. Results The dementia models obtained area under the receiver operating characteristics of 0.560 and 0.897 for the case-control and cohort designs respectively. The type 2 diabetes models obtained area under the receiver operating characteristics of 0.733 and 0.727 for the case-control and cohort designs respectively. The dementia and diabetes case-control models were both poorly calibrated, whereas the dementia cohort model achieved good calibration. We show that careful construction of a case-control design can lead to comparable discriminative performance as a cohort design, but case-control designs over-represent the outcome class leading to miscalibration. Conclusions Any case-control design can be converted to a cohort design. We recommend that researchers with observational data use the less subjective and generally better calibrated cohort design when extracting labelled data. However, if a carefully constructed case-control design is used, then the model must be prospectively validated using a cohort design for fair evaluation and be recalibrated.

scholarly journals Design Matters in Patient-Level Prediction: Evaluation of a Cohort vs. Case-Control Design When Developing Predictive Models in Observational Healthcare Datasets

2021 ◽  
Author(s):  
Jenna Reps ◽  
Patrick B. Ryan ◽  
Peter R. Rijnbeek ◽  
Martijn J. Schuemie
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Models ◽  
Control Design ◽  
Case Control ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Discriminative Performance ◽  
Cohort Design ◽  
Receiver Operating

Abstract Background: The study design used to develop prediction models in observational healthcare databases (e.g., case-control and cohort) may impact the clinical usefulness. We aim to quantify how the choice of design impacts prediction model performance. Aim: To empirically investigate differences between models developed using a case-control design and a cohort design.Methods: Using a US claims database, we replicated two published prediction models (dementia and type 2 diabetes) which were developed using a case-control design, and also train models for the same prediction questions using cohort designs. We validated each model on data mimicking the point in time the models would be applied in clinical practice. We calculate the models’ discrimination and calibration-in-the-large performances.Results: The dementia models obtained area under the receiver operating characteristics of 0.560 and 0.897 for the case-control and cohort designs respectively. The type 2 diabetes models obtained area under the receiver operating characteristics of 0.733 and 0.727 for the case-control and cohort designs respectively. The dementia and diabetes case-control models were both poorly calibrated, whereas the dementia cohort model achieved good calibration. We show that careful construction of a case-control design can lead to comparable discriminative performance as a cohort design, but case-control designs generally oversample the outcome leading to miscalibration. Conclusion: Any case-control design can be converted to a cohort design. We recommend that researchers with observational data use the less subjective and generally better calibrated cohort design. However, if a carefully constructed case-control design is used, then the model must be prospectively validated using a cohort design for fair evaluation and be recalibrated.

Kriteriumsbezogene Cut-Off-Werte für Tätigkeitsspielraum und Arbeitsintensität

Diagnostica ◽  
2019 ◽  
Vol 65 (3) ◽  
pp. 179-190 ◽  
Author(s):  
Vincent Mustapha ◽  
Renate Rau
Keyword(s):  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Receiver Operating

Zusammenfassung. Cut-Off-Werte ermöglichen eine ökonomische, binäre Beurteilung von Summenscores. Für Beanspruchungsfragebögen, die personenbezogene Merkmale erfragen, sind Cut-Off-Werte häufig vorhanden und in der klinischen Diagnostik unerlässlich. Für die Bewertung von Arbeitsmerkmalen sind Cut-Off-Werte ebenfalls wünschenswert. Bislang fehlen sie jedoch für die Beurteilung von Arbeitsmerkmalen wie Arbeitsintensität und Tätigkeitsspielraum. Zwischen 2006 und 2016 wurden daher in verschiedenen Branchen 801 objektive Arbeitsplatzanalysen durchgeführt, welche eine Unterteilung in gut und schlecht gestalteten Tätigkeitsspielraum sowie gut und schlecht gestaltete Arbeitsintensität nach DIN EN ISO 6385 (2016) ermöglichen. Anhand dieser Unterteilung wurden mit der Receiver-Operating-Characteristics-Analyse Cut-Off-Werte für den subjektiv-bedingungsbezogen Fragebogen zum Erleben von Arbeitsintensität und Tätigkeitsspielraum (FIT; Richter et al., 2000 ) ermittelt. Für den Tätigkeitsspielraum weisen Summenscores ≤ 22 und für die Arbeitsintensität Summenscores ≥ 15 auf eine schlechte Gestaltung des jeweiligen Arbeitsmerkmals hin. Anhand einer weiteren Stichprobe von 1 076 Arbeitenden konnte gezeigt werden, dass Arbeitende mit schlecht gestaltetem Tätigkeitspielraum vital erschöpfter sowie weniger engagiert sind und Arbeitende mit schlecht gestalteter Arbeitsintensität eine höhere Erholungsunfähigkeit sowie vitale Erschöpfung aufweisen.

Comparison of Rheumatological Diagnoses by a Bayesian Program and by Physicians

1991 ◽  
Vol 30 (03) ◽  
pp. 187-193 ◽  
Author(s):  
H. J. Moens ◽  
J. K. van der Korst
Keyword(s):  
Receiver Operating Characteristics ◽  
Bayesian Decision ◽  
Operating Characteristics ◽  
Likelihood Ratios ◽  
Improve Performance ◽  
Mathematical Basis ◽  
Human Expert ◽  
Test Population ◽  
Sensitivity Specificity ◽  
Diagnostic Outcome

AbstractA Bayesian decision support system was developed for the diagnosis of rheumatic disorders. Knowledge in this system is represented as evidential weights of findings. Simple weights were calculated as the logarithm of likelihood ratios on the basis of 1,000 consecutive patients from a rheumatological clinic. The effect of various methods to improve performance of the system by modification of the weights was studied. Three methods had a mathematical basis; a fourth consisted of weights adapted by a human expert, which allowed inclusion of diagnostic rules such as defined in widely accepted criteria sets. The system’s performance was measured in a test population of 570 different cases from the same clinic and compared with predictions of diagnostic outcome made by rheumatologists. The weights from a human expert gave optimal results (sensitivity 65% and specificity 96%), that were close to the physicians’ predictions (sensitivity 64% and specificity 98%). The methods to measure the performance of the various models used in this study emphasize sensitivity, specificity and the use of receiver operating characteristics.

The Role of Revascularisation in Patients Undergoing Non-cardiac Surgery

2010 ◽  
Vol 5 (1) ◽  
pp. 104
Author(s):  
Daniel S Menees ◽  
Eric R Bates ◽  
◽  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Models ◽  
Cardiovascular Complications ◽  
Weight Of Evidence ◽  
Beta Blockade ◽  
Cardiac Morbidity ◽  
Increased Risk ◽  
Reduction Strategies ◽  
Artery Disease ◽  
Risk Reduction Strategies

Coronary artery disease (CAD) affects millions of US citizens. As the population ages, an increasing number of people with CAD are undergoing non-cardiac surgery and face significant peri-operative cardiac morbidity and mortality. Risk-prediction models can be used to help identify those patients at increased risk of peri-operative cardiovascular complications. Risk-reduction strategies utilising pharmacotherapy with beta blockade and statins have shown the most promise. Importantly, the benefit of prophylactic coronary revascularisation has not been demonstrated. The weight of evidence suggests reserving either percutaneous or surgical revascularisation in the pre-operative setting for those patients who would otherwise meet independent revascularisation criteria.

scholarly journals Arterial Digital Pulse Photoplethysmography in Patients with Suspected Thoracic Outlet Syndrome: A Study of the “Ca+Pra” Maneuver

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1128
Author(s):  
Jeanne Hersant ◽  
Pierre Ramondou ◽  
Francine Thouveny ◽  
Mickael Daligault ◽  
Mathieu Feuilloy ◽  
...  
Keyword(s):  
Thoracic Outlet Syndrome ◽  
Pulse Amplitude ◽  
Receiver Operating Characteristics ◽  
Upper Limbs ◽  
Operating Characteristics ◽  
Digital Pulse ◽  
Arterial Inflow ◽  
Receiver Operating

The level of pulse amplitude (PA) change in arterial digital pulse plethysmography (A-PPG) that should be used to diagnose thoracic outlet syndrome (TOS) is debated. We hypothesized that a modification of the Roos test (by moving the arms forward, mimicking a prayer position (“Pra”)) releasing an eventual compression that occurs in the surrender/candlestick position (“Ca”) would facilitate interpretation of A-PPG results. In 52 subjects, we determined the optimal PA change from rest to predict compression at imaging (ultrasonography +/− angiography) with receiver operating characteristics (ROC). “Pra”-PA was set as 100%, and PA was expressed in normalized amplitude (NA) units. Imaging found arterial compression in 23 upper limbs. The area under ROC was 0.765 ± 0.065 (p < 0.0001), resulting in a 91.4% sensitivity and a 60.9% specificity for an increase of fewer than 3 NA from rest during “Ca”, while results were 17.4% and 98.8%, respectively, for the 75% PA decrease previously proposed in the literature. A-PPG during a “Ca+Pra” test provides demonstrable proof of inflow impairment and increases the sensitivity of A-PPG for the detection of arterial compression as determined by imaging. The absence of an increase in PA during the “Ca” phase of the “Ca+Pra” maneuver should be considered indicative of arterial inflow impairment.

Performance of the Surprise Question Compared to Prediction Models in Hemodialysis Patients: A Prospective Study

2017 ◽  
Vol 46 (5) ◽  
pp. 390-396 ◽  
Author(s):  
Rakesh Malhotra ◽  
Xia Tao ◽  
Yuedong Wang ◽  
Yuqi Chen ◽  
Rebecca H. Apruzzese ◽  
...  
Keyword(s):  
Linear Models ◽  
Prediction Models ◽  
Clinical Laboratory ◽  
Mortality Prediction ◽  
Operating Characteristics ◽  
Dialysis Treatment ◽  
Mortality Probability ◽  
A Prospective Study ◽  
Surprise Question

Background: The surprise question (SQ) (“Would you be surprised if this patient were still alive in 6 or 12 months?”) is used as a mortality prognostication tool in hemodialysis (HD) patients. We compared the performance of the SQ with that of prediction models (PMs) for 6- and 12-month mortality prediction. Methods: Demographic, clinical, laboratory, and dialysis treatment indicators were used to model 6- and 12-month mortality probability in a HD patients training cohort (n = 6,633) using generalized linear models (GLMs). A total of 10 nephrologists from 5 HD clinics responded to the SQ in 215 patients followed prospectively for 12 months. The performance of PM was evaluated in the validation (n = 6,634) and SQ cohorts (n = 215) using the areas under receiver operating characteristics curves. We compared sensitivities and specificities of PM and SQ. Results: The PM and SQ cohorts comprised 13,267 (mean age 61 years, 55% men, 54% whites) and 215 (mean age 62 years, 59% men, 50% whites) patients, respectively. During the 12-month follow-up, 1,313 patients died in the prediction model cohort and 22 in the SQ cohort. For 6-month mortality prediction, the GLM had areas under the curve of 0.77 in the validation cohort and 0.77 in the SQ cohort. As for 12-month mortality, areas under the curve were 0.77 and 0.80 in the validation and SQ cohorts, respectively. The 6- and 12-month PMs had sensitivities of 0.62 (95% CI 0.35–0.88) and 0.75 (95% CI 0.56–0.94), respectively. The 6- and 12-month SQ sensitivities were 0.23 (95% CI 0.002–0.46) and 0.35 (95% CI 0.14–0.56), respectively. Conclusion: PMs exhibit superior sensitivity compared to the SQ for mortality prognostication in HD patients.

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