Modern Lifestyle and their Impact on Health: Development of the Human Disease Prediction Model

Prediction models in obstetrics: understanding the treatment paradox and potential solutions to the threat it poses.

Toward a Modern Era in Clinical Prediction: The TRIPOD Statement for Reporting Prediction Models

<p>Soil spectroscopy in the mid-infrared (MIR) allows the fast and cost-effective derivation of multiple physical and chemical soil properties, e.g., soil organic carbon (SOC) and soil texture, from a single reflectance spectrum. The recent development of extensive soil spectral libraries and field-portable handheld FTIR spectrometers have opened up new opportunities for the widespread application of soil reflectance spectroscopy in the geo- and environmental sciences. Compared to laboratory measurements on pre-treated soil material, field recordings of MIR spectra are impacted by in situ environmental conditions that modify and degrade the measured reflectance signal, most prominently variations in soil moisture and particle size across samples. These conditions prevent leveraging available MIR soil spectral libraries to build predictive models of soil properties directly.</p><p>We evaluated the capacity of the External Parameter Orthogonalization (EPO) algorithm to compensate for moisture and particle size-induced effects on MIR reflectance spectra recorded in the field to generate laboratory-equivalent spectra from the in-situ data, which would allow calibrations of predictive soil property models from soil spectral library data to be transferred to field-recorded spectra. An archive of 230 soils collected across five soil regions in Germany covering a broad range of parent materials, soil texture classes and organic carbon contents was used to evaluate the approach. For each soil sample, MIR reflectance spectra had been acquired both in the field, i.e., measured in situ on the soil surface, and in the laboratory on pre-treated (sieved and ground) soil material. Field spectra were corrected for environmental effects by EPO and used to predict SOC and soil texture with predictive models developed on the laboratory spectra.</p><p>Analysis of the EPO-transformed spectra showed that the algorithm could compensate for some of the significant environmental effects present in the field data, e.g., non-linear baseline shifts and large-scale water absorption features, effectively reducing variation across the soil samples that is not linked to the physical and chemical soil properties of interest. EPO-transformation of the spectra further allowed a robust transfer of calibrations developed on laboratory spectra of pre-treated soils to the field spectra. Predictive accuracies for SOC and soil texture were lower than for pure laboratory applications but generally in line with models developed with an extensive regional calibration sample directly on the field MIR spectra.</p><p>The correction of field MIR spectra with the EPO algorithm thus represents a promising approach to integrating existing soil spectral libraries into the development of predictive soil property models for in-situ MIR reflectance spectra as it would allow the development of predictive models without requiring a large number of additional regional calibration samples for field application of MIR soil spectroscopy.</p>

Clinical prediction models are widely used to predict adverse outcomes in patients, and are often employed to guide clinical decision-making. Clinical data typically consist of patients who received different treatments. Many prediction modeling studies fail to account for differences in patient treatment appropriately, which results in the development of prediction models that show poor accuracy and generalizability. In this paper, we list the most common methods used to handle patient treatments and discuss certain caveats associated with each method. We believe that proper handling of differences in patient treatment is crucial for the development of accurate and generalizable models. As different treatment strategies are employed for different diseases, the best approach to properly handle differences in patient treatment is specific to each individual situation. We use the Ma-Spore acute lymphoblastic leukemia data set as a case study to demonstrate the complexities associated with differences in patient treatment, and offer suggestions on incorporating treatment information during evaluation of prediction models. In clinical data, patients are typically treated on a case by case basis, with unique cases occurring more frequently than expected. Hence, there are many subtleties to consider during the analysis and evaluation of clinical prediction models.

Discovery of highly porous and permeable sandstones at great depths and temperatures has clearly demonstrated that porosity reduction with depth is not monotonic. Recognition that porosity can be created in sandstones at depth has spurred tremendous interest in developing predictive models of porosity evolution and distribution in sedimentary basins. Instead of predicting economic basement, emphasis has shifted to prediction of porosity windows in the subsurface. Historically, diagenesis has been considered a function of sandstone composition and temperature. However, it has become increasingly clear that this view is too simplistic. Factors such as pore fluid composition, flow rate, organic maturation, and time may significantly alter the course of diagenesis. Development of predictive models that provide for these parameters will, when coupled with structural-stratigraphic and hydrocarbon generation models, permit the relative timing of porosity evolution, hydrocarbon generation, and trap formation to be determined. In order to simulate the diagenetic evolution of basins and predict porosity distribution, the processes that lead to creation and destruction of porosity must be understood. Many of the important porosity-producing processes have been identified through petrologic studies: dissolution of carbonates, feldspars, and rock fragments. Formation of deep porosity in a variety of basins is commonly associated with precipitation of kaolinite and iron-rich carbonate, suggesting that, although the paths of diagenesis may be diverse, common trends exist. Development of predictive diagenetic models will require continued accumulation of petrologic data and case studies more fully using presently available technology (e.g., electron and ion microprobe, stable isotope geochemistry, age-dating techn ques). This will better document the time, temperature, and chemical environment of formation of diagenetic materials. Areas of research requiring attention are the following. (1) Fluid flow and heat transfer in sedimentary basins. What are the volumes of fluid, rates of flow and flow paths? How do these change as a basin evolves? (2) Geochemistry of subsurface fluids. Reliable analyses are required to identify compositional trends of subsurface fluids. What controls the pH of subsurface fluids? These questions will require further research on shale diagenesis, fluid diagenesis, fluid expulsion, and clay membrane filtration. (3) Diagenesis of organic matter in sediments. Recent studies have shown that by-products of petroleum generation (e.g., CO2, H2S, organic acids) may be an important factor in sandstone diagenesis. (4) Computer models simulating the chemical consequences of r ck-fluid interaction are restricted by the lack of reliable thermodynamic data for many common diagenetic minerals (e.g., clays, zeolites). We need additional information concerning rates of dissolution and precipitation of common minerals under various conditions. (5) Physical compaction of sandstones. (6) Relationship between depositional environment and subsequent diagenetic events. Because porosity prediction requires an understanding of many related disciplines, an integrated approach is required. By combining the talents and expertise of petrologists, organic and inorganic geochemists, fluid mechanicists, and structural geologists, not only will we be able to develop powerful models for porosity prediction, but we will also be better able to place porosity development in its proper context as one aspect of basin evolution and hydrocarbon accumulation. End_of_Article - Last_Page 463------------

Accurate measurement of air flow rate is essential in automatic building control using the variable air volume (VAV) system. In order to solve the problems of the existing air flow measurement method and improve the accuracy of air flow control, this study developed a data-based multiple regression air flow prediction model. The independent variables used in the development of the predictive model were selected as the factors used for control and monitoring when operating with variable air flow rate in the existing air conditioning system. Data collection and correlation between independent variables and air flow rate of the terminal unit were analyzed. Using the IBM SPSS statistics version 25, an air flow rate prediction model was developed using multiple regression analysis. Reliability of model was evaluated by comparing the measured airflow. The relative error of −9.3% to 10.4% is shown when comparing the estimated air flow rate by the developed model with the measured air flow rate.

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual’s risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual’s absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.Graphical abstract

Development of surrogate predictive models for the nonlinear elasto-plastic response of medium density fibreboard-based sandwich structures

Design of a testing strategy using non-animal based test methods: Lessons learnt from the ACuteTox project

Background/Objectives: The purpose of this study is to perform predictive modeling for disease onset by automating the analysis of diagnostic test results using machine learning and big data analysis for small- and medium-sized hospitals. Methods/Statistical analysis: Methods/Statistical analysis: Excel and CatBoost algorithms were used for preprocessing and analysis of the collected data. 21,140 pieces of valid data consisting of 27 attributes were obtained, and they were classified into a training set and a test set necessary for the development and use of machine learning-based predictive models. The training set was used as input data to develop the predictive models, and the test set was used as input data to evaluate the performance of the developed models. A decision tree analysis algorithm was applied, and performance analysis was performed by using accuracy, precision, and the receiver operating characteristic (ROC) area as indicators. Findings: First, the predictive models developed for three diseases, i.e., diabetes, hypertension, and hyperlipidemia, were found to have a prediction accuracy exceeding 80 to 90% and a large AUC. Second, the collected data was useful in predictive modeling research, based on a cluster analysis of the data of patients who tested positive for metabolic syndromes (diabetes, hypertension, hyperlipidemia) and the data of patients who tested negative. Third, from the above study results, diabetes, hyperlipidemia, and hypertension were found to be adult diseases, with a high level of association with blood circulation, and it was determined that it would be possible to predict cases in which a single person had multiple diseases at the same time. Fourth, it was deemed that it would be possible to obtain useful results by changing the data structure into a form of test result data for time series analysis and data with a display of disease diagnosis time point. Improvements/Applications: Through the development of predictive models for diseases using machine learning technology, predictive modeling is expected to evolve to enable prediction of diverse diseases, thereby improving the clinical environment and enhancing the reliability and competitiveness of medical care by preventing potential diagnostic errors.

Goal: To present a framework for data sharing, curation, harmonization and federated data analytics to solve open issues in healthcare, such as, the development of robust disease prediction models. Methods: Data curation is applied to remove data inconsistencies. Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. Results: The applicability of the framework is demonstrated in a case study of primary Sjögren's Syndrome, yielding harmonized data with increased quality and more than 85% agreement, along with lymphoma prediction models with more than 80% sensitivity and specificity. Conclusions: The framework provides data quality, harmonization and analytics workflows that can enhance the statistical power of heterogeneous clinical data and enables the development of robust models for disease prediction.

“Development and internal validation of a clinical prediction model for the diagnosis of immune thrombocytopenia”: comment from Beyan and Beyan

6598 Background: Despite the effectiveness of anthracycline (ACH) therapy in the adjuvant and MBC settings, neutropenic complications (NC) remain a common and often unpredictable problem. Consequences may include dose reductions or delays in chemotherapy, or hospitalization for fever or infection. This study describes the development of a cycle-based risk prediction model for NC during chemotherapy with traditional doxorubicin (DOX) or a pegylated liposomal formulation (PLD) for MBC. Methods: Data analyzed was from a randomized clinical trial of MBC patients (n=509), who received chemotherapy with DOX (60 mg/m2 every 3 wks) or PLD (50 mg/m2 every 4 wks) [O'Brien, 2004]. NC were defined as an absolute neutrophil count (ANC) = 1.5 x106 cells/L, febrile neutropenia or neutropenia with infection. Patient, treatment and hematological factors potentially associated with NC were evaluated. Factors with a p-value of ≤ 0.25 within a cycle were included in a generalized estimating equations (GEE) regression model. Using backward elimination, we derived a risk scoring algorithm (range 0–63) from the final reduced model. Results: Risk factors retained in the model included poor performance status, ANC = 2.0 × 106 cells/L at some point in the previous cycle, the first cycle of chemotherapy, DOX vs. PLD and older age. A precycle risk score from = 25 to < 40 for a given patient was identified as being the optimal threshold for sensitivity (58.0%) and specificity (78.7%). Patients with a score at or beyond this threshold would be considered at high risk for developing NC in later cycles. Risk scores below, within, or above this threshold predict a 0.3%–2%, 3%–8% and a 9%–45% probability risk of NC, respectively. Conclusion: This risk prediction tool demonstrated acceptable internal validity and can be readily applied by the clinician prior to a given cycle of chemotherapy. The application of this prediction tool may allow for identification and targeted intervention (such as growth factor support or the use of PLD) for those most likely to experience NC during anthracycline-based chemotherapy for MBC. No significant financial relationships to disclose.

Tricuspid Valve Surgical Interventions: Toward a Sound Risk Prediction!

L0-constrained regression using mixed integer linear programming