Machine learning models combined with resampling methods can predict amiodarone-induced thyroid dysfunction and serve as a support tool for individualized risk prediction and clinical decision support.
BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.
No abstract text is available for this article
Cuproptosis is a recently identified controlled process of cell death that functions in tumor development and treatment...
Accumulating studies demonstrated that DNA methylation may be potential prognostic hallmarks of various cancers...
Neuropeptides play pivotal roles in different physiological processes and are related to different kinds of diseases...
Understanding of the association between nutritional risk and clinical outcomes in hospitalised patients with overweight is still at an early stage...
Based on register data, we developed a gradient boosting diagnostic model returning acceptable prediction of patients with moderate-to-severe psoriasis.
The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.
The aims of this study were to perform pre-surgery miRNA profiling of patients who develop Vasoplegic syndrome (VS) after coronary artery bypass grafting (CABG) and identify those miRNAs that could be used as VS prognostic tools and biomarkers...
The web-based calculator incorporating the GNRI, the TFI, surgical approach, and comorbidity could successfully predict total complications among elderly patients with gastric cancer with good accuracy in a convenient manner. Geriatr Gerontol Int 2023; ••: ••-••.
No abstract text is available for this article
Gastric cancer (GC) is a common primary stomach tumor of the central nervous system with a poor prognosis...
The proposed deep learning-based integrative model combining the two modalities improved the survival prediction in patients with NSCLC.
The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.
Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research...
The aim of this study was to develop a CT-based radiomics and clinical variable diagnostic model for the preoperative prediction of uric acid calculi...
Even after model adjustment, the calibration performances of the 4 models did not meet the recommended threshold for perfect calibration. This finding is suggestive of models over/underestimating the predicted risk of in-hospital mortality, potentially harmful clinically. Therefore, researchers may consider other alternatives, such as ensemble techniques to combine these models into a meta-model to improve out-of-sample predictive performance.
Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer...
Sexual minorities (SMs) who are current/former members of the Church of Jesus Christ of Latter-day Saints (LDSs) report high levels of depression and risk for suicide...
The ATS model had excellent prognostic discriminatory power to stratify patients relative to PRS.
Novel biomarkers and additional scientific insights with hemodynamic feedback strongly aid in the prognostication and risk prediction of chronic HF.
It has been proven that the significance of screening with chest X-ray as a predictor of mortality is minimal. However, TB screening at least 60% of the population (chest X-ray in adults and immunological tests in children) have provided relationship between the TB screening rate and TB mortality rate (TB mortality rate increases with an increase in the population coverage and, conversely, decreases with a decrease in the population coverage).
A logistic regression model was created to predict which patients may require spine surgery. Simple clinical variables appeared more predictive than variables created using NLP. However, additional research with more data samples is needed to validate this model and fully evaluate the usefulness of NLP for this task.
A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.
Combined HC and QUS data identified women at risk of sPTB with better AUC (0.68, 95% confidence interval [CI]: 0.57-0.78) compared with HC data alone (0.53, 95% CI: 0.40-0.66) and HC data + cervical length at 18-20 wk of gestation (average AUC = 0.51, 95% CI: 0.38-0.64). A likelihood ratio test for significance of QUS features in the classification model was highly statistically significant (p < 0.01).
STS risk score has low accuracy in predicting clinical outcomes after TEER. Adding LAP measurements can improve reclassification and identify those prone to adverse outcomes.
We developed and externally validated a patient-specific prediction model for hepatic toxicity based on the entire DVH and clinical factors that can integrate both photon and proton therapy cohorts. This model complements the new ASTRO clinical practice guidelines and could support value-driven integration of proton therapy into the management of HCC.
Our findings indicate that the risk-specific PRS might not be a clinically useful tool in cancer prognosis prediction and further studies focusing on the development of polygenic prognostic score are warranted.
We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.
The radiomic nomogram is an effective and invasive tool to predict pCR in LARC patients after nCRT, which outperforms radiologists.
The high throughput and liver-on-chip system exhibit enhanced in vivo-like function and demonstrate the potential utility of these platforms for hepatotoxicity risk assessment. Tenofovir-inarigivr associated hepatotoxicity was observed and correlates with the clinical manifestation of DILI observed in patients.
This study provides a novel and non-invasive prognostic EV mRNA signature for risk stratification and survival prediction of PDAC patients.
Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.
We established a comprehensive and robust survival prediction model based on the T-cell phenotype in the TIME and PBLs for GC prognosis.
The ROP risk scoring model can help to predict which infants with first-stage ROP might show progression to severe ROP and may identify infants who require referral to a pediatric ophthalmologist for treatment.
The developed PBPK model adequately predicted tumor and organ uptake for this GEP-NET population. Relevant organ uptake differences between [68Ga]Ga-DOTATATE and [68Ga]Ga-HA-DOTATATE were caused by different affinity profiles, while tumor uptake was mainly affected by tumor blood flow and blood volume. Furthermore, tumor sink predictions showed that for the majority of patients a tumor sink effect is not expected to be clinically relevant.
No abstract text is available for this article
Some imaging and clinical features correlated with post-SBRT pain response in patients with spinal metastases. The model based on these characteristics has a good predictive value and can provide valuable information for clinical decision-making.
The combined radiomics-clinical model was better able to predict ICH-associated PSE compared to the clinical model. This can help clinicians better predict an individual patient's risk of PSE following a first-ever ICH and facilitate earlier PSE diagnosis and treatment.
No abstract text is available for this article
Patterns of hepatitis B virus reactivation (HBV-R) in HBsAg (-)/HBcAb (+) patients with B-cell non-Hodgkin lymphoma (NHL) receiving rituximab based immunochemotherapy have not been well described...
The prognostic nomogram developed in the analytical data of SEER it provided high accuracy and reliability in predicting the survival outcomes of primary bladder SRCC patients and could be used to comprehensively assess the risk of SRCC. Moreover, they could enable clinicians to make more precise treatment decisions for primary bladder SRCC patients.
OBJECTIVES: Antibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs)...
A nomogram prediction model for comorbid behavioral problems in children with TD was established. The prediction model demonstrated a good discriminative ability and predictive performance for beneficial clinical decisions. This model further provides a comprehensive understanding of associated sociodemographic and clinical characteristics by visual graphs and allows clinicians to rapidly identify patients with a higher risk of behavioral problems and tailor necessary interventions to improve clinical outcomes.
We have demonstrated that the clinical decision support system is useful for supporting differential diagnoses and preventing diagnostic errors. We propose that differential diagnosis by physicians and learning-to-rank by machine has a high affinity. We found that information retrieval and clinical decision support systems have much in common (Target data, learning-to-rank, etc.). We propose that Clinical Decision Support Systems have the potential to support: (1) recall of rare diseases, (2) differential diagnoses for difficult-to-diagnoses cases, and (3) prevention of diagnostic errors. Our system can potentially evolve into an explainable clinical decision support system.
Flexible machine learning models like XGB can supplement traditional statistical methods like multinomial logistic regression in occupational health research by providing a benchmark for predictive performance and traditional statistical models' ability to capture important associations for a given set of predictors as well as potential violations of linearity.
As new drug targets, human microbes are proven to be closely related to human health...
Cardiovascular disease (CVD), the leading cause of death globally, is associated with complicated underlying risk factors...
This study established an effective prognostic proteomics signature with reliable predictive performance for survival, immune activity, and drug sensitivity. It might provide a novel perspective into the protein function in BC, and guide the individual treatment strategies for BC patients.
These findings support investigating serine as an important candidate biomarker for MS onset and disability progression.
The ONCOTHROMB score for VTE risk in outpatients with cancer, which takes into account both clinical and genetic variables, better identifies patients who might benefit from primary thromboprophylaxis than does the Khorana score.
All but 1 study used an abstraction of the audiogram as a modeled outcome; however, specific outcome measures varied. Consistently used predictors were age, baseline hearing, cumulative cisplatin dose, and radiation dose to the cochlea. Just 5 studies were judged to have an overall low risk of bias. Future studies should attempt to minimize bias by following statistical best practices including not selecting multivariate predictors based on univariate analysis, validation in independent cohorts, and clearly reporting the management of missing and censored data. Future modeling efforts should adopt a transdisciplinary approach to define a unified set of clinical, treatment, and/or genetic risk factors. Creating a flexible model that uses a common set of predictors to forecast the full post-treatment audiogram may accelerate work in this area. Such a model could be adapted for use in counseling, treatment planning, and follow-up by audiologists and oncologists and could be incorporated into ototoxicity genetic association studies as well as clinical trials investigating otoprotective agents.
A new risk stratification system for ICC patients has been developed, which can be a practical tool for patient management.
The presented nomogram could be useful for predicting placental accreta spectrum (PAS).
Tyrosine kinase inhibitor therapy revolutionized chronic myeloid leukemia treatment and showed how targeted therapy and molecular monitoring could be used to substantially improve survival outcomes...
The New Zealand cardiovascular disease risk prediction equations reasonably predicted the observed 5-year cardiovascular disease risk in survivors of cancer in the country, in whom risk prediction was considered clinically appropriate. Prediction could be improved by adding cancer-specific variables and considering competing risks. Our findings suggest that the equations are reasonable clinical tools for use in survivors of cancer in New Zealand.
In conclusion, our study shows that the performance of conventional diabetes risk scores in PLWH is promising, especially for Balkau and FINDRISC2 which showed good discriminatory power. These scores may help identify patients at low risk of T2D in whom careful assessment of modifiable T2D risk factors can be spared.
This study demonstrated the feasibility of developing practical NPC risk prediction models using EMR-wide ML and patient graph CDR analysis, without requiring EBV data. These models could enable broader implementation of NPC risk evaluation and screening recommendations for larger populations in urban community health centers and rural clinics.