scholarly journals Novel observational study protocol to develop a prediction model that identifies patients with Graves’ ophthalmopathy insensitive to intravenous glucocorticoids pulse therapy

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053173
Author(s):  
Yi Wang ◽  
Hui Wang ◽  
Lunhao Li ◽  
Yinwei Li ◽  
Jing Sun ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Observational Study ◽  
Prediction Model ◽  
Prediction Models ◽  
Morphological Changes ◽  
Treatment Options ◽  
Treatment Guideline ◽  
Pulse Therapy ◽  
Graves Ophthalmopathy ◽  
Intravenous Glucocorticoids

IntroductionIntravenous glucocorticoids pulse therapy is the first-line treatment for moderate-to-severe and active Graves’ ophthalmopathy, with a large proportion of patients having poor efficacy and exposed to the risk of glucocorticoids adverse effects. We introduce a novel protocol to develop a prediction model designed to identify patients with Graves’ ophthalmopathy who are not likely to benefit from intravenous glucocorticoids pulse therapy before administration, so that these patients can advance the time to receive appropriate treatment. Existing prediction models for prognosis of Graves’ ophthalmopathy have usually focused on traditional clinical indicators without adequate consideration of orbital soft tissue changes. Our protocol for model development will address this limitation by using artificial intelligence models to quantify facial morphological changes.Methods and analysisThis study is a single-centre, prospective and observational study. A sample size of 278 patients with moderate-to-severe and active Graves’ ophthalmopathy will be prospectively recruited at ophthalmology clinic of Shanghai Ninth People’s Hospital to collect clinical and artificial intelligence model’s baseline data as potential variables to develop the prediction model. They will receive 12-week intravenous glucocorticoids pulse therapy according to the 2021 European Group on Graves’ Orbitopathy treatment guideline. After standard medication course and following 12-week observation, patients will be evaluated for the effectiveness of treatment in our ophthalmology clinic and divided into glucocorticoids-sensitive and glucocorticoids-insensitive groups. The model will be developed by means of multivariate logistic regression to select the best variables for the prediction of glucocorticoids treatment efficacy before administration. The result of the study will provide evidence for the use of a prediction model to personalise treatment options for patients with moderate-to-severe and active Graves’ ophthalmopathy.Ethics and disseminationThe study received approval from the Ethics Committee of Shanghai Ninth People’s Hospital (ethical approval number: SH9H-2020-T211-1. Findings will be disseminated via peer-reviewed publications and conference presentations.Trial registration numberChiCTR2000036584 (Pre-results).

scholarly journals Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-3
Author(s):  
Makoto Hashizume
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Mathematical Analysis ◽  
Data Science ◽  
Prediction Models ◽  
Human Anatomy ◽  
Future Perspective ◽  
Computational Anatomy ◽  
Temporal Prediction ◽  
Based Medicine

Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of “dynamic living human anatomy.” It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extending from an embryo to post mortem body; (3) the functional axis on physiology or metabolism which is reflected in a variety of medical image modalities; and (4) the pathological axis, extending from a healthy physical condition to a diseased condition. It aims to integrate multiple prediction models such as multiscale prediction model, temporal prediction model, anatomy function prediction model, and anatomy-pathology prediction model. Artificial intelligence has been introduced to accelerate the calculation of statistic mathematical analysis. The future perspective is expected to promote the development of human resources as well as a new MCA-based scientific interdisciplinary field composed of mathematical statistics, information sciences, computing data science, robotics, and biomedical engineering and clinical applications. The MCA-based medicine might be one of the solutions to overcome the difficulties in the current medicine.

scholarly journals Combined Benefit of Prediction and Treatment: A Criterion for Evaluating Clinical Prediction Models

2014 ◽  
Vol 13s2 ◽  
pp. CIN.S13780
Author(s):  
Dean Billheimer ◽  
Eugene W. Gerner ◽  
Christine E. McLaren ◽  
Bonnie LaFleur
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Treatment Options ◽  
Cost Benefit ◽  
Treatment Decision ◽  
Net Benefit ◽  
Future Health ◽  
Statistical Quality ◽  
Poor Outcome

Clinical treatment decisions rely on prognostic evaluation of a patient's future health outcomes. Thus, predictive models under different treatment options are key factors for making good decisions. While many criteria exist for judging the statistical quality of a prediction model, few are available to measure its clinical utility. As a consequence, we may find that the addition of a clinical covariate or biomarker improves the statistical quality of the model, but has little effect on its clinical usefulness. We focus on the setting where a treatment decision may reduce a patient's risk of a poor outcome, but also comes at a cost; this may be monetary, inconvenience, or the potential side effects. This setting is exemplified by cancer chemoprevention, or the use of statins to reduce the risk of cardiovascular disease. We propose a novel approach to assessing a prediction model using a formal decision analytic framework. We combine the predictive model's ability to discriminate good from poor outcome with the net benefit afforded by treatment. In this framework, reduced risk is balanced against the cost of treatment. The relative cost–benefit of treatment provides a useful index to assist patient decisions. This index also identifies the relevant clinical risk regions where predictive improvement is needed. Our approach is illustrated using data from a colorectal adenoma chemoprevention trial.

Applying Support Vector Machines in Rebound Hammer Test

2013 ◽  
Vol 853 ◽  
pp. 600-604 ◽  
Author(s):  
Yu Ren Wang ◽  
Wen Ten Kuo ◽  
Shian Shien Lu ◽  
Yi Fan Shih ◽  
Shih Shian Wei
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Support Vector Machines ◽  
Prediction Model ◽  
Prediction Models ◽  
Percentage Error ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Rebound Hammer ◽  
Vector Machines

There are several nondestructive testing techniques available to test the compressive strength of the concrete and the Rebound Hammer Test is among one of the fast and economical methods. Nevertheless, it is found that the prediction results from Rebound Hammer Test are not satisfying (over 20% mean absolute percentage error). In view of this, this research intends to develop a concrete compressive strength prediction model for the SilverSchmidt test hammer, using data collected from 838 lab tests. The Q-values yield from the concrete test hammer SilverSchmidt is set as the input variable and the concrete compressive strength is set as the output variable for the prediction model. For the non-linear relationships, artificial intelligence technique, Support Vector Machines (SVMs), are adopted to develop the prediction models. The results show that the mean absolute percentage errors for SVMs prediction model, 6.76%, improves a lot when comparing to SilverSchmidt predictions. It is recommended that the artificial intelligence prediction models can be applied in the SilverSchmidt tests to improve the prediction accuracy.

scholarly journals Biochemical Variables are Predictive for Patient Survival after Surgery for Skeletal Metastasis. A Prediction Model Development and External Validation Study

2018 ◽  
Vol 12 (1) ◽  
pp. 469-481
Author(s):  
Michala Skovlund Sørensen ◽  
Elizabeth C. Silvius ◽  
Saniya Khullar ◽  
Klaus Hindsø ◽  
Jonathan A. Forsberg ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Treatment Options ◽  
External Validation ◽  
Model Development ◽  
Receiver Operator Characteristic Curve ◽  
Primary Cancer ◽  
Karnofsky Score ◽  
Biochemical Variables

Background: Predicting survival for patients with metastatic bone disease in the extremities (MBDex) is important for ensuring the implant will outlive the patient. Hitherto, prediction models for these patients have been constructed using subjective assessments, mostly lacking biochemical variables. Objectives: To develop a prediction model for survival after surgery due to MBDex using biochemical variables and externally validate the model. Methods: We created Bayesian Belief Network models to estimate likelihood of survival 1, 3, 6, and 12 months after surgery using 140 patients. We validated the models using the data of 130 other patients and calculated the area under the Receiver Operator Characteristic curve (ROC). Variables included: hemoglobin, neutrophil-count, C-reactive protein, alkaline phosphatase, primary cancer, Karnofsky-score, ASA-score, visceral metastases, bone metastases, days from diagnose of primary cancer to index surgery for MBDex, ischemic heart disease, diabetes, fracture/impending-fracture and age. Results: Survival probabilities were influenced by all biochemical variables. Validation showed ROC for the 1, 3, 6, and 12-months model: 68% (C.I.: 55%-80%), 69% (C.I.: 60%-78%), 81% (C.I.: 74%-87%) and 84% (C.I.: 77%-90%). Conclusion: Biochemical markers can be incorporated into a prediction model for survival in patients having surgery for MBDex allowing surgeons to offer more objective and individualized treatment options.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals Prediction of disease severity in young children presenting with acute febrile illness in resource-limited settings: a protocol for a prospective observational study

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e045826
Author(s):  
Arjun Chandna ◽  
Endashaw M Aderie ◽  
Riris Ahmad ◽  
Eggi Arguni ◽  
Elizabeth A Ashley ◽  
...  
Keyword(s):  
Observational Study ◽  
Prediction Model ◽  
Prospective Observational Study ◽  
Model Building ◽  
Venous Blood ◽  
Febrile Illness ◽  
Nasopharyngeal Swab ◽  
Acute Febrile Illness ◽  
Clinical Prediction ◽  
Open Access Journals

IntroductionIn rural and difficult-to-access settings, early and accurate recognition of febrile children at risk of progressing to serious illness could contribute to improved patient outcomes and better resource allocation. This study aims to develop a prognostic clinical prediction tool to assist community healthcare providers identify febrile children who might benefit from referral or admission for facility-based medical care.Methods and analysisThis prospective observational study will recruit at least 4900 paediatric inpatients and outpatients under the age of 5 years presenting with an acute febrile illness to seven hospitals in six countries across Asia. A venous blood sample and nasopharyngeal swab is collected from each participant and detailed clinical data recorded at presentation, and each day for the first 48 hours of admission for inpatients. Multianalyte assays are performed at reference laboratories to measure a panel of host biomarkers, as well as targeted aetiological investigations for common bacterial and viral pathogens. Clinical outcome is ascertained on day 2 and day 28.Presenting syndromes, clinical outcomes and aetiology of acute febrile illness will be described and compared across sites. Following the latest guidance in prediction model building, a prognostic clinical prediction model, combining simple clinical features and measurements of host biomarkers, will be derived and geographically externally validated. The performance of the model will be evaluated in specific presenting clinical syndromes and fever aetiologies.Ethics and disseminationThe study has received approval from all relevant international, national and institutional ethics committees. Written informed consent is provided by the caretaker of all participants. Results will be shared with local and national stakeholders, and disseminated via peer-reviewed open-access journals and scientific meetings.Trial registration numberNCT04285021.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

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