Development Of Predictive Models Research Articles

1868P Development and validation of a clinical prediction model for paclitaxel hypersensitivity reaction: Pac-HSR score

1868P Development and validation of a clinical prediction model for paclitaxel hypersensitivity reaction: Pac-HSR score

Development and validation of machine learning-based prediction model for severe pneumonia: A multicenter cohort study

Severe pneumonia (SP) is a prevalent respiratory ailment characterized by high mortality and poor prognosis. Current scoring systems for pneumonia are not only time-consuming but also exhibit limitations in early SP prediction. To address this gap, this study aimed to develop a machine-learning model using inflammatory markers from peripheral blood for early prediction of SP. A total of 204 pneumonia patients from seven medical centers were studied, with 143 (68 SP cases) in the training cohort and 61 (32 SP cases) in the test cohort. Clinical characteristics and laboratory test results were collected at diagnosis. Various models including Logistic Regression, Random Forest, Naïve Bayes, XGBoost, Support Vector Machine, and Decision Tree were built and evaluated. Seven predictors—age, sex, WBC count, T-lymphocyte count, NLR, CRP, TNF-α, IL-4/IFN-γ ratio, IL-6/IL-10 ratio—were selected through LASSO regression and clinical insight. The XGBoost model, exhibiting best performance, achieved an AUC of 0.901 (95 % CI: 0.827 to 0.985) in the test cohort, with an accuracy of 0.803, sensitivity of 0.844, specificity of 0.759, and F1_score of 0.818. Indeed, SHAP analysis emphasized the significance of elevated WBC counts, older age, and elevated CRP as the top predictors. The use of inflammatory biomarkers in this concise predictive model shows significant potential for the rapid assessment of SP risk, thereby facilitating timely preventive interventions.

Risk assessment tool for anemia of chronic disease in systemic lupus erythematosus: a prediction model.

This study aims to develop a predictive model for estimating the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE) and to elucidate the relationship between various factors and ACD METHODS: Individuals diagnosed with SLE for at least one year were enrolled and categorized into two groups: those with ACD and those without anemia symptoms. Patients were randomly assigned to training and test sets at an 8:2 ratio. The least absolute shrinkage and selection operator (LASSO) method was used to select predictors, followed by logistic regression for modeling. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) for both training and test sets. The study included a total of 216 patients, with 172 in the training set and 44 in the test set. LASSO identified 6 variables for constructing the predictive model, resulting in an area under the curve (AUC) of 0.833 (95% CI, 0.773-0.892) in the training set and 0.861 (95% CI, 0.750-0.972) in the test set. Calibration curves indicated consistency between expected and observed probabilities. DCA indicated that the model yielded a net benefit with threshold probabilities ranging from 20% to 90% in the training set and from 10% to 80% in the test set. This study presents a predictive model for assessing the risk of ACD in SLE patients. The model effectively captures the underlying mechanism of ACD in SLE and empowers clinicians to make well-informed treatment adjustments. Key Points • Development of a New Predictive Model: This study introduces a new predictive model to evaluate the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE). The model utilizes routine laboratory parameters to identify high-risk individuals, addressing a significant gap in current clinical practice. • Reflection of Potential Mechanisms for ACD Development: By incorporating the factors needed to construct the predictive model, this study also sheds light on the potential mechanisms of ACD development in SLE patients.

1469P Development of an efficacy prediction model for concurrent chemoradiotherapy in esophageal squamous cell carcinoma using deep learning and multimodal data integration

1469P Development of an efficacy prediction model for concurrent chemoradiotherapy in esophageal squamous cell carcinoma using deep learning and multimodal data integration

Development and Validation of a Clinical Prediction Model for Diagnosing Mycoplasma Infections in Gynecological Patients.

In adult females, mycoplasma infection is common and challenging to diagnose. This study aimed to use retrospective laboratory data to construct a nomogram for predicting the mycoplasma infection of individuals with probable urogenital tract mycoplasma infection. A total of 2,859 patients with suspected urogenital tract mycoplasma infection were retrospectively enrolled in this study. Demographics and routine examinations of leucorrhea were used to develop a nomogram for predicting mycoplasma infection. The least absolute shrinkage and selection operator (LASSO) method was applied to filter variables and select predictors, and multivariable logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The performance and clinical utility of the nomogram were generated by using Harrell's concordance index, calibration curve, and decision curve analysis (DCA). By using the LASSO regression method, seven variables (age, white blood cell, epithetical cell, cleanliness, candidiasis vaginalis, sialidases, and leukocyte esterase) were chosen, and a nomogram was constructed using these variables. The prediction nomogram (0.676, 95% CI: 0.611 - 0.744) demonstrated a satisfactory performance. The prediction model's AUC was 0.679 (95% CI: 0.660 - 0.691). Furthermore, the DCA showed a good clinical net benefit based on the mycoplasma infection nomogram. A nomogram was created in this study, which included seven demographic and clinical characteristics of female patients. The nomogram could be of great value for the diagnosis of mycoplasma infection.

The multinomial mixed-effect regression model for predicting PCOC phases in hospice patients

PurposeThe Palliative Care Outcomes Collaboration (PCOC) aims to enhance patient outcomes systematically. However, identifying crucial items and accurately determining PCOC phases remain challenging. This study aims to identify essential PCOC data items and construct a prediction model to accurately classify PCOC phases in terminal patients.MethodsA retrospective cohort study assessed PCOC data items across four PCOC phases: stable, unstable, deteriorating, and terminal. From July 2020 to March 2023, terminal patients were enrolled. A multinomial mixed-effect regression model was used for the analysis of multivariate PCOC repeated measurement data.ResultsThe dataset comprised 1933 terminally ill patients from 4 different hospice service settings. A total of 13,219 phases of care were analyzed. There were significant differences in the symptom assessment scale, palliative care problem severity score, Australia-modified Karnofsky performance status, and resource utilization groups-activities of daily living among the four PCOC phases of care. Clinical needs, including pain and other symptoms, declined from unstable to terminal phases, while psychological/spiritual and functional status for bed mobility, eating, and transfers increased. A robust prediction model achieved areas under the curves (AUCs) of 0.94, 0.94, 0.920, and 0.96 for stable, unstable, deteriorating, and terminal phases, respectively.ConclusionsCritical PCOC items distinguishing between PCOC phases were identified, enabling the development of an accurate prediction model. This model enhances hospice care quality by facilitating timely interventions and adjustments based on patients' PCOC phases.

Current trend of sexual maturity, teeth eruption, and bone fusion in the present female population, relative to documented age and development of prediction model for age of females

ABSTRACT Background: Understanding sexual maturity and bone fusion is crucial in forensic investigations and legal contexts. This study investigates the contemporary trend of sexual maturity and bone fusion in females, comparing the observed age-related characteristics with documented ages. Such analyses contribute to the accuracy of age estimation methods used in medico-legal scenarios. Methods: A comprehensive study was conducted involving a diverse sample of females, and their sexual maturity and bone fusion were assessed relative to documented ages. The research utilized a cross-sectional design, collecting data through interviews, medical examinations, and radiographic imaging. Ethical approval was obtained, and participants provided informed consent. Variables included documented age, age of menarche, stages of sexual maturity, dentition, and bone fusion. Validation of model performance was conducted. Results: Among 70 cases studied, the mean age of menarche was 10.48 years (range: 7–12 years). Maternal reports indicated menarche onset between 12–17 years. Tanner’s Staging 5 for pubic hair and fully developed breasts occurred at 14 years. Permanent teeth increased with age, with 28 or more teeth present at age 14 or above. Fusion of the distal end of the Radius and Ulna with the parent bone was positive from 15 years onwards. The regression model predicted age accurately (97.99% fit). Conclusion: The results highlight the early onset of sexual maturation, consistent teeth eruption, and bone fusion patterns in females. The observed correlations offer valuable insights for medicolegal practitioners and forensic experts.

Innovative salt replacement for green Spanish-style olives using potassium, calcium, and magnesium chlorides during packaging

This work aimed to enhance green Spanish-style Manzanilla table olives by replacing salt with K, Ca, and Mg chlorides in innovative packaging, utilising Response Surface Methodology (RSM). Both the added replacers and naturally occurring minerals were considered. RSM allowed the development of predictive models for K, Ca, Mg, and Mn (initially present) in olive flesh and their contributions to Reference Daily Intakes (RDI) based on the added salts. The sodium content in the new products decreased from 1.4 g/100 g flesh to 0.68 g/100 g flesh, while K, Ca, and Mg concentrations could increase up to 0.50, 0.45 and 0.15 g/100 g flesh, respectively. Added salt contributions to RDI could reach 25, 60, and 44 % for K, Ca, and Mg. Minimal differences between analytical data-derived minerals and predicted values were minimal, suggesting reliable model performance for nutrition labelling. Results assist the industry in creating nutritionally enhanced table olive products.

Development of Predictive Model for the Novel Ultra-Accelerated Shelf-Life Test (UASLT) for Shelf-Life of Packaged Beverage

The novel Ultra-Accelerated Shelf-Life Test (UASLT), which uses oxygen pressure (239 kPa) and moderate temperature (40 ºC), has been shown to degrade vitamins rapidly in packaged foods due to increased oxygen diffusivity. This method can help the food industry to commercialize their products faster. The study aimed to develop models for predicting the shelf-life of foods using the UASLT method based on vitamin degradation accelerated with increased temperature and oxygen diffusion. The predictive models used rate constant, k (day-1), from the 1st order degradation of vitamins A, C, and D3 in a model food to predict the shelf-life. The k values ranged from 0.080-0.088 day-1 for vitamin C, 0.048-0.051 day-1 for vitamin D3, and 0.051-0.054 day-1 for vitamin A at 22.5 - 40 ºC. The models predicted the shelf-life of 321-353 days for vitamin C, 529-583 days for vitamin D3, and 404-551 days for vitamin A at 22.5 ºC. This shows the potential for applying the predictive models with the UASLT method for predicting shelf-life.

Machine learning based prognostics and statistical optimization of the performance of biogas-biodiesel blends powered engine

In this study, waste biomass-derived biogas was employed as the main fuel while the biodiesel-diesel blend was used as pilot fuel. This paper describes the development of a Decision Tree and Response Surface methodology-based statistical framework for prediction modeling and optimization. The compression ratio, fuel injection time, fuel injection pressure, and biogas flow rate were employed as controllable inputs, and brake thermal efficiency, peak combustion pressure, and exhaust emission were selected as responses. The experimental data for model development was gathered for the development of prediction models and optimization. The decision tree-based models were robust with almost negligible mean squared errors and R2 values of more than 0.9487 for all models. Response surface methodology-based optimized engine parameters were validated with the following results compression ratio was 17.9, fuel injection pressure was 225 bar, fuel injection timing was 26.3-degree crank angle after top dead center, and the biogas flow rate was 0.85 kg/h. Validation results were within 5 % of the model-optimized results. The prognostic models for all control factors were developed with decision tree-based machine learning with high predictive efficiency and low errors.

Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning

Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability.

Determinants and prediction of hypertension among Chinese middle-aged and elderly adults with diabetes: a machine learning approach

ObjectiveMultimorbidity, particularly diabetes combined with hypertension (DCH), is a significant public health concern. Currently, there is a gap in research utilizing machine learning (ML) algorithms to predict hypertension risk in Chinese middle-aged and elderly diabetic patients, and gender differences in DCH comorbidity patterns remain unclear. We aimed to use ML algorithms to predict DCH and identify its determinants among middle-aged and elderly diabetic patients in China. Study designCross-sectional study. MethodsData were collected on 2,775 adults with diabetes aged ≥45 years from the 2015 China Health and Retirement Longitudinal Study. We employed nine ML algorithms to develop prediction models for DCH. The performance of these models was evaluated using the area under the curve (AUC). Additionally, we conducted variable importance analysis to identify key determinants. ResultsOur results showed that the best prediction models for the overall population, men, and women were extreme gradient boosting (AUC=0.728), light gradient boosting machine (AUC=0.734), and random forest (AUC=0.737), respectively. Age, waist circumference, body mass index, creatinine level, triglycerides, taking Western medicine, high-density lipoprotein cholesterol, blood urea nitrogen, total cholesterol, low-density lipoprotein cholesterol, and sleep disorders were identified as common important predictors by all three populations. ConclusionsML algorithms showed accurate predictive capabilities for DCH. Overall, non-linear ML models outperformed traditional logistic regression for predicting DCH. DCH predictions exhibited variations in predictors and model accuracy by gender. These findings could help identify DCH early and inform the development of personalized intervention strategies.

An Automated Malnutrition Screening Tool Using Routinely Collected Data for Older Adults in Long-Term Care: Development and Internal Validation of AutoMal

ObjectiveTo develop and internally validate a malnutrition screening tool based on routinely collected data in the long-term care setting. DesignDiagnostic prediction model development and internal validation study. Setting and ParticipantsResidents (n = 539) from 10 long-term care facilities in Australia. MethodsCandidate variables identified through expert consultation were collected from routinely collected data in a convenience sample of long-term care facilities. Logistic regression using the Subjective Global Assessment as the reference standard was conducted on 500 samples derived using bootstrapping from the original sample. Candidate variables were selected if included in more than 95% of samples using backwards stepwise elimination. The final model was developed using logistic regression of selected variables. Internal validation was conducted using bootstrapping to calculate the optimism-adjusted performance. Overall discrimination was evaluated via receiver operator characteristic curve and calculation of the area under the curve. Youden's Index was used to identify the optimal threshold value for classifying malnutrition. Sensitivity and specificity were calculated. ResultsBody mass index and weight change % over 6 months were included in the automated malnutrition screening model (AutoMal), identified in 100% of bootstrapped samples. AutoMal demonstrated excellent discrimination of malnutrition, with area under the curve of 0.8378 (95% CI, 0.80–0.87). Youden's Index value was 0.37, resulting in sensitivity of 78% (95% CI, 71%–83%) and specificity of 77% (72%–81%). Optimism-corrected area under the curve was 0.8354. Conclusions and ImplicationsThe AutoMal demonstrates excellent ability to differentiate malnutrition status. It makes automated identification of malnutrition possible by using 2 variables commonly found in electronic health records.

Prediction models for postpartum stress urinary incontinence: A systematic review

BackgroundPostpartum stress urinary incontinence significantly impacts the quality of life and the physical and mental health of women. A reliable predictive model for postpartum stress urinary incontinence can serve as a preventive tool. Currently, there have been numerous studies developing predictive models to assess the risk of postpartum stress urinary incontinence, but the quality and clinical applicability of these models remain unknown. ObjectiveTo systematically review and evaluate existing models for predicting stressful postpartum risks. MethodsPubMed, EBSCO, The Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, WanFang Data, SinoMed, and VIP Data databases were systematically searched from the time of database construction to October 2023. Two researchers used Critical appraisal and data extraction for systematic reviews of prediction modeling studies: the CHARMS checklist for data extraction. Three researchers used The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist for bias and applicability assessment. ResultsEight papers including ten postpartum stress urinary incontinence prediction models were finalized. The most common predictors in the prediction models were UI during pregnancy, followed by mode of delivery, Maternal age, parity, and UI before pregnancy. Nine of the prediction models reported discrimination with an area under the ROC curve (AUC) or C-index between 0.680-0.850. All included studies were at high risk of bias, mainly due to mishandling of continuous predictors, unreported or mishandled missing data, and inadequate assessment of predictive model performance. ConclusionsPostpartum stress urinary incontinence risk prediction models are in the initial development stage, and existing prediction models have a high risk of bias and poor modeling methodological quality, which may hinder their clinical application. In the future, healthcare practitioners should follow the norms of predictive model development and reporting to develop risk prediction models with superior predictive performance, low risk of bias, and easy clinical application.

Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Status Gizi Stunting Pada Balita

Child stunting is a major public health concern in Indonesia. This study uses the Naïve Bayes classification algorithm to assess the nutritional condition of stunted children based on demographic and anthropometric characteristics. The information used comes from the Toddler Weighing Month (Bulan Penimbangan Balita - BPB) in Majalengka Regency. Data type conversion, separating data into training and testing sets, and data normalization are all examples of preprocessing steps. The model's evaluation results reveal an accuracy of 94.65%, with precision and recall for each category of stunted nutritional status. This study makes a substantial contribution to early diagnosis and mitigation of stunting in Indonesia, as well as providing the framework for future development of more powerful predictive models.

Development and Verification of a Fire Occurrence Prediction Model with Social-Architectural Factors

Development and Verification of a Fire Occurrence Prediction Model with Social-Architectural Factors

A Comprehensive Review on : Aquaponic Farming Water Quality Prediction

Aquaponic farming, which combines aquaculture and hydroponics, depends strongly on maintaining optimal water quality to guarantee the health and productivity of both fish and plants. This review paper explores the latest developments in IoT-based automated water monitoring systems, focusing on their role in predicting and managing water quality in aquaponic systems. Regardless of significant progress there are several research gaps. Recent studies highlight challenges such as inconsistent sensor selection, calibration issues, insufficient publicly available data, and inadequate data cleaning and preprocessing. Also, the issues of imbalanced datasets, limited long-term data, and underdeveloped IoT and AI integration prevent the development of accurate predictive models. The scalability and maintenance of systems, understanding microbial dynamics, and nutrient management are also critical areas needing further exploration. This review also identifies the need for deeper case studies and advanced feature extraction methods to enhance prediction accuracy. By addressing these gaps, including system scalability and nutrient management, future research can improve data availability and quality, enabling more robust predictions and contributing to more efficient and sustainable aquaponic systems.

Proactive cyber threat mitigation: Integrating data-driven insights with user-centric security protocols

In the rapidly evolving cybersecurity landscape, proactive threat mitigation has become essential for protecting sensitive data and systems from increasingly sophisticated attacks. The paper explores a holistic approach to enhancing cybersecurity by merging data-driven insights with user-centric security practices. This study highlights the limitations of traditional reactive security measures and advocates for a shift towards a proactive strategy that anticipates and addresses potential threats before they materialize. The research emphasizes the importance of leveraging data analytics to gain actionable insights into potential vulnerabilities and emerging threats. By analyzing large datasets from network traffic, user behavior, and historical incidents, organizations can identify patterns and anomalies that signal potential risks. These data-driven insights enable the development of more effective threat detection systems and predictive models that enhance an organization’s ability to respond swiftly to evolving cyber threats. In addition to data analytics, the study underscores the significance of integrating user-centric security protocols. Traditional security measures often overlook the human element, which can be a critical factor in both the efficacy of security measures and the overall risk profile. User-centric protocols focus on enhancing user awareness, implementing tailored security training, and promoting best practices in cybersecurity. This approach ensures that security measures are not only technologically sound but also aligned with user behavior and needs, reducing the risk of human error and improving overall security posture. The research also discusses practical implementation strategies for combining data-driven insights with user-centric protocols, including the use of advanced analytics tools, continuous user education, and adaptive security frameworks. Case studies demonstrate the benefits of this integrated approach, such as improved threat detection rates, reduced incident response times, and enhanced overall security resilience. In conclusion, this study presents a comprehensive framework for proactive cyber threat mitigation, emphasizing the synergy between data-driven analytics and user-centric security practices as a robust strategy for safeguarding against modern cyber threats. Keywords: Cyber Threat, Proactive, Data-Driven, User-Centric, Security Protocols

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model.

Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis. LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power. Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses. This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields

The aim of this investigation is the development of robust models for the performance prediction and automatic monitoring of large photovoltaic systems, based on historical and real-time electric and thermal data. This issue is increasingly important due to the worldwide diffusion of large photovoltaic systems and their need to identify and predict failures and malfunctions, in order to promptly assess the convenience of maintenance actions. The present model describes the response to irradiance and temperature conditions of both modules and inverters and also it is able to predict shading conditions able to affect the energy yield. The model has been validated against real electric measurements in 6 large PV plants located in southern Italy and it demonstrated to be able to predict the real time power production within a 4.1 % error. Even more importantly, the model and its comparison with subhourly measurements over several years has demonstrated its effectiveness in detecting downtime conditions caused by inverter or string problems. Simulations and measurements revealed that missed energy production due to electrical grid coupling downtime can exceed 50 % on certain days and that the shading conditions (up to 5 % of the daily energy production) can be easily detected and separated from component problems, thus avoiding false alarms. Finally, the analysis of aerial infrared images allowed to further test the model in failure detection capability, assess the relationship between thermal anomalies and underperformance conditions and in predicting the yearly deterioration rate at the PV plants.