Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction. This study explores the efficacy of four distinct variable selection techniques: LASSO, RSF-vs, SCAD, and CoxBoost, in the context of non-parametric biomedical survival prediction. Leveraging these methods, we conducted comprehensive variable selection processes. Subsequently, survival analysis models-specifically CoxPH, RSF, and DeepHit NN-were employed to construct predictive models based on the selected variables. Furthermore, we introduce a novel approach wherein only variables consistently selected by a majority of the aforementioned feature selection techniques are considered. This innovative strategy, referred to as the proposed method, aims to enhance the reliability and robustness of variable selection, subsequently improving the predictive performance of the survival analysis models. To evaluate the effectiveness of the proposed method, we compare the performance of the proposed approach with the existing LASSO, RSF-vs, SCAD, and CoxBoost techniques using various performance metrics including integrated brier score (IBS), concordance index (C-Index) and integrated absolute error (IAE) for numerous high-dimensional survival datasets. The real data applications reveal that the proposed method outperforms the competing methods in terms of survival prediction accuracy.

SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers.

Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics.

Overall mortality risk analysis for rectal cancer using deep learning-based fuzzy systems.

Colorectal cancer is a leading cause of cancer mortality worldwide, with an increasing incidence rate in developing countries. Integration of genetic information with cancer therapy guidance has shown promise in cancer treatment, indicating its potential as an essential tool in translation oncology. However, the high-throughput analysis and variability of genomic data poses a major challenge to conventional analytic approaches. In this study, we propose an advanced analytic approach, named "Fuzzy-based RNNCoxPH," incorporated fuzzy logic, recurrent neural networks (RNNs), and Cox proportional hazards regression (CoxPH) for detecting missense variants associated with high-risk of all-cause mortality in rectum adenocarcinoma. The test data set was downloaded from "Rectum adenocarcinoma, TCGA-READ" the Genomic Data Commons (GDC) portal. In this study, four model-based risk score models were derived using RNN, CoxPH, RNNCoxPHAddition, and RNNCoxPHMultiplication. The RNNCoxPHAddition and RNNCoxPHMultiplication models were obtained as the sum and product of the RNN risk degree matrix and the CoxPH risk degree matrix, respectively. Moreover, the fuzzy logic system was used to calculate the survival risk values of missense variants and classified their membership grade to improve the identification of high-risk gene variation locations associated with cancer mortality. The four models were integrated to develop an advanced risk estimation model. There were 20 028 variants associated with survival status, amongst 17 638 variants were associated with survival and 2390 variants associated with mortality. The proposed Fuzzy-based RNNCoxPH model obtained a balanced accuracy of 93.7%, which was significantly higher than that of the other four test methods. In particular, the CoxPH model is commonly used in medical researches and the XGBoost model is famous for its high accuracy in machine learning. The results suggest that the Fuzzy-based RNNCoxPH model exhibits a higher efficacy in identifying and classifying the missense variants related to mortality risk in rectum adenocarcinoma.

Pregnant women follow-up service, Shewa, Ethiopia.

Background and Aims: The goal of this study was to demonstrate the effects of factors related with time to developing pre-eclampsia (PE) among pregnant women follow-up service at Arerti Primary Hospital. Methods: A survival analysis was employed on a pregnant women's follow-up service from September 2018 to June 2019 at the Arerti Primary Hospital. A closed-form sample size formula for estimating the effect of the time-to-event data was used. Both the descriptive method and Cox proportional hazards model were applied to compute the research survival data. Results: Using the Kaplan-Meier estimation technique, the univariable analysis shows that the survival time median is 7 months and 3 weeks. The graph of Kaplan-Meier estimate of total survival functions indicates a decreasing pattern of survivorship function. We used the Kaplan-Meier estimates to investigate the effects of observed differences among different categories of the factors, we applied the Log-rank test. The final survival model outcomes weight, marital status, age, history of PE, and multiplicity were related to a substantial hazard of evolving PE. Conclusion: On the basis of our final survival model results, we recommended that all pregnant women having such risk factors should see a health care professional and control their medical condition before and during pregnancy. Advising women about proper body weight in each follow-up period is supported. Finally, health experts should advise pregnant women about potential risk factors related to PE.

PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery.

For high-dimensional expression data, most prognostic models perform feature selection based on individual genes, which usually lead to unstable prognosis, and the identified risk genes are inherently insufficient in revealing complex molecular mechanisms. Since most genes carry out cellular functions by forming protein complexes-basic representatives of functional modules, identifying risk protein complexes may greatly improve our understanding of disease biology. Coupled with the fact that protein complexes have been shown to have innate resistance to batch effects and are effective predictors of disease phenotypes, constructing prognostic models and selecting features with protein complexes as the basic unit should improve the robustness and biological interpretability of the model. Here, we propose a protein complex-based, group lasso-Cox model (PCLasso) to predict patient prognosis and identify risk protein complexes. Experiments on three cancer types have proved that PCLasso has better prognostic performance than prognostic models based on individual genes. The resulting risk protein complexes not only contain individual risk genes but also incorporate close partners that synergize with them, which may promote the revealing of molecular mechanisms related to cancer progression from a comprehensive perspective. Furthermore, a pan-cancer prognostic analysis was performed to identify risk protein complexes of 19 cancer types, which may provide novel potential targets for cancer research.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Environmental persistence of foot-and-mouth disease virus applied to endemic regions.

The consequences of foot-and-mouth disease impact regional economies and food security through animal mortality and morbidity, trade restrictions and burdens to veterinary infrastructure. Despite efforts to control the disease, some regions, mostly in warmer climates, persistently report disease outbreaks. Consequently, it is necessary to understand how environmental factors influence transmission, of this economically devastating disease. Extensive research covers basic aetiology and transmission potential of livestock and livestock products for foot-and-mouth disease virus (FMDV), with a subset evaluating environmental survival. However, this subset, completed in the early to mid-20th century in Northern Europe and the United States, is not easily generalized to today's endemic locations. This review uncovered 20 studies, to assess current knowledge and analyse the effects of environmental variables on FMDV survival, using a Cox proportional hazards (Coxph) model. However, the dataset is limited, for example pH was included in three studies and only five studies reported both relative humidity (RH) and temperature. After dropping pH from the analysis, our results suggest that temperature alone does not describe FMDV survival; instead, interactions between RH and temperature have broader impacts across various conditions. For instance, FMDV is expected to survive longer during the wet season (survival at day 50 is ~90% at 16°C and 86% RH) versus the dry season (survival at day 50 approaches 0% at 16°C and 37.5% RH) or comparatively in the UK versus the Southwestern United States. Additionally, survival on vegetation topped 70% on day 75 when conditions exceeded 20°C with high RH (86%), drastically higher than the survival on inanimate surfaces at the same temperature and RH (~0%). This is important in tropical regions, where high temperatures can persist throughout the year, but RH varies. Therefore, parameter estimates, for disease modelling and control in endemic areas, require environmental survival data from a wider range of conditions.

An evaluation of outcomes in patients with traumatic brain injury at a referral hospital in Tanzania: evidence from a survival analysis.

OBJECTIVE: The purpose of this study was to determine if patients with traumatic brain injury (TBI) in low- and middle-income countries who receive surgery have better outcomes than patients with TBI who do not receive surgery, and whether this differs with severity of injury. METHODS: The authors generated a series of Kaplan-Meier plots and performed multiple Cox proportional hazard models to assess the relationship between TBI surgery and TBI severity. The TBI severity was categorized using admission Glasgow Coma Scale scores: mild (14, 15), moderate (9-13), or severe (3-8). The authors investigated outcomes from admission to hospital day 14. The outcome considered was the Glasgow Outcome Scale-Extended, categorized as poor outcome (1-4) and good outcome (5-8). The authors used TBI registry data collected from 2013 to 2017 at a regional referral hospital in Tanzania. RESULTS: Of the final 2502 patients, 609 (24%) received surgery and 1893 (76%) did not receive surgery. There were significantly fewer road traffic injuries and more violent causes of injury in those receiving surgery. Those receiving surgery were also more likely to receive care in the ICU, to have a poor outcome, to have a moderate or severe TBI, and to stay in the hospital longer. The hazard ratio for patients with TBI who underwent operation versus those who did not was 0.17 (95% CI 0.06-0.49; p < 0.001) in patients with moderate TBI; 0.2 (95% CI 0.06-0.64; p = 0.01) for those with mild TBI, and 0.47 (95% CI 0.24-0.89; p = 0.02) for those with severe TBI. CONCLUSIONS: Those who received surgery for their TBI had a lower hazard for poor outcome than those who did not. Surgical intervention was associated with the greatest improvement in outcomes for moderate head injuries, followed by mild and severe injuries. The findings suggest a reprioritization of patients with moderate TBI-a drastic change to the traditional practice within low- and middle-income countries in which the most severely injured patients are prioritized for care.

Long term effect of air pollution on incident hospital admissions: Results from the Italian Longitudinal Study within LIFE MED HISS project.

BACKGROUND: The LIFE MED HISS project aims at setting up a surveillance system on the long term effects of air pollution on health, using data from National Health Interview Surveys and other currently available sources of information in most European countries. Few studies assessed the long term effect of air pollution on hospital admissions in European cohorts. OBJECTIVE: The objective of this paper is to estimate the long term effect of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) on first-ever (incident) cause-specific hospitalizations in Italy. METHODS: We used data from the Italian Longitudinal Study (ILS), a cohort study based on the 1999-2000 National Health Interview Survey (NHIS), followed up for hospitalization (2001-2008) at individual level. The survey contains information on crucial potential confounders: occupational/educational/marital status, body mass index (BMI), smoking habit and physical activity. Annual mean exposure to PM2.5 and NO2 was assigned starting from simulated gridded data at spatial resolution of 4 × 4 km2 firstly integrated with data from monitoring stations and then up-scaled at municipality level. Statistical analyses were conducted using Cox proportional hazard models with robust variance estimator. RESULTS: For each cause of hospitalization we estimated the hazard ratios (HRs) adjusted for confounders with 95% Confidence Interval (CI) related to a 10 µg/m3 increase in pollutants. For PM2.5 and NO2, respectively, we found positive associations for circulatory system diseases [1.05(1.03-1.06); 1.05(1.03-1.07)], myocardial infarction [1.15(1.12-1.18); 1.15(1.12-1.18)], lung cancer [1.18(1.10-1.26); 1.20(1.12-1.28)], kidney cancer [1.24(1.11-1.29); 1.20(1.07-1.33)], all cancers (but lung) [1.06(1.04-1.08); 1.06(1.04-1.08)] and Low Respiratory Tract Infections (LRTI) [1.07 (1.04-1.11); 1.05 (1.02-1.08)]. DISCUSSION: Our results add new evidence on the effects of air pollution on first-ever (incident) hospitalizations, both in urban and rural areas. We demonstrated the feasibility of a low-cost monitoring system based on available data.

A join point survival model for brain tumor patients.

To investigate the relationship between medical improvements and the survival experienced by the patient population, it would be useful to find out when and how much the cancer treatment breakthroughs and early diagnosis have significantly improved the prognosis of brain cancer patients. A join point model facilitates the identification of trends with significant change-points in survival; the main goal of such a model would be to find out when cancer survival starts exhibiting a pattern of improvement. The model will be applied to grouped relative survival data for major cancer sites from the 'Surveillance, epidemiology and end results' program of the National Cancer Institute.