Deep reinforcement learning for multi-class imbalanced training: applications in healthcare.

With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the capability of handling multi-class imbalanced training. Using real-world clinical case studies, we demonstrate that our proposed framework outperforms current state-of-the-art imbalanced learning methods, achieving more fair and balanced classification, while also significantly improving the prediction of minority classes. Supplementary Information: The online version contains supplementary material available at 10.1007/s10994-023-06481-z.

A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.

BACKGROUND: Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system-which we introduce as full-stack federated learning-to train and evaluate machine learning models across four UK hospital groups without centralising patient data. METHODS: We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion. FINDINGS: Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560-0·589) at OUH and 0·622 (0·608-0·637) at PUH using the locally trained models to 0·872 (0·862-0·882) at OUH and 0·876 (0·865-0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893-0·942), with 89·7% sensitivity (83·6-93·6) and 76·6% specificity (73·9-79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01). INTERPRETATION: We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.

Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning.

As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability.

An adversarial training framework for mitigating algorithmic biases in clinical machine learning.

Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.

Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening.

As patient health information is highly regulated due to privacy concerns, most machine learning (ML)-based healthcare studies are unable to test on external patient cohorts, resulting in a gap between locally reported model performance and cross-site generalizability. Different approaches have been introduced for developing models across multiple clinical sites, however less attention has been given to adopting ready-made models in new settings. We introduce three methods to do this-(1) applying a ready-made model "as-is" (2); readjusting the decision threshold on the model's output using site-specific data and (3); finetuning the model using site-specific data via transfer learning. Using a case study of COVID-19 diagnosis across four NHS Hospital Trusts, we show that all methods achieve clinically-effective performances (NPV > 0.959), with transfer learning achieving the best results (mean AUROCs between 0.870 and 0.925). Our models demonstrate that site-specific customization improves predictive performance when compared to other ready-made approaches.

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening.

BACKGROUND: Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12-24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department. METHODS: We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC). FINDINGS: 72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858-0·881, 95% CI 0·838-0·912, for CURIAL-Lab and 0·836-0·854, 0·814-0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5-85·7, for CURIAL-Lab and 83·5%, 81·8-85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9-71·8) for CURIAL-Lab and 63·6% (63·1-64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7-62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6-88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4-91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32-64), 16 min (26·3%) sooner than with LFDs (61 min, 37-99; log-rank p<0·0001), and 6 h 52 min (90·2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0·0001). Classification performance was high, with sensitivity of 87·5% (95% CI 52·9-97·8), specificity of 85·4% (81·3-88·7), and negative predictive value of 99·7% (98·2-99·9). CURIAL-Rapide correctly excluded infection for 31 (58·5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR. INTERPRETATION: Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reasons. We aimed to develop and validate two early-detection models for COVID-19, screening for the disease among patients attending the emergency department and the subset being admitted to hospital, using routinely collected health-care data (laboratory tests, blood gas measurements, and vital signs). These data are typically available within the first hour of presentation to hospitals in high-income and middle-income countries, within the existing laboratory infrastructure. METHODS: We trained linear and non-linear machine learning classifiers to distinguish patients with COVID-19 from pre-pandemic controls, using electronic health record data for patients presenting to the emergency department and admitted across a group of four teaching hospitals in Oxfordshire, UK (Oxford University Hospitals). Data extracted included presentation blood tests, blood gas testing, vital signs, and results of PCR testing for respiratory viruses. Adult patients (>18 years) presenting to hospital before Dec 1, 2019 (before the first COVID-19 outbreak), were included in the COVID-19-negative cohort; those presenting to hospital between Dec 1, 2019, and April 19, 2020, with PCR-confirmed severe acute respiratory syndrome coronavirus 2 infection were included in the COVID-19-positive cohort. Patients who were subsequently admitted to hospital were included in their respective COVID-19-negative or COVID-19-positive admissions cohorts. Models were calibrated to sensitivities of 70%, 80%, and 90% during training, and performance was initially assessed on a held-out test set generated by an 80:20 split stratified by patients with COVID-19 and balanced equally with pre-pandemic controls. To simulate real-world performance at different stages of an epidemic, we generated test sets with varying prevalences of COVID-19 and assessed predictive values for our models. We prospectively validated our 80% sensitivity models for all patients presenting or admitted to the Oxford University Hospitals between April 20 and May 6, 2020, comparing model predictions with PCR test results. FINDINGS: We assessed 155 689 adult patients presenting to hospital between Dec 1, 2017, and April 19, 2020. 114 957 patients were included in the COVID-negative cohort and 437 in the COVID-positive cohort, for a full study population of 115 394 patients, with 72 310 admitted to hospital. With a sensitive configuration of 80%, our emergency department (ED) model achieved 77·4% sensitivity and 95·7% specificity (area under the receiver operating characteristic curve [AUROC] 0·939) for COVID-19 among all patients attending hospital, and the admissions model achieved 77·4% sensitivity and 94·8% specificity (AUROC 0·940) for the subset of patients admitted to hospital. Both models achieved high negative predictive values (NPV; >98·5%) across a range of prevalences (≤5%). We prospectively validated our models for all patients presenting and admitted to Oxford University Hospitals in a 2-week test period. The ED model (3326 patients) achieved 92·3% accuracy (NPV 97·6%, AUROC 0·881), and the admissions model (1715 patients) achieved 92·5% accuracy (97·7%, 0·871) in comparison with PCR results. Sensitivity analyses to account for uncertainty in negative PCR results improved apparent accuracy (ED model 95·1%, admissions model 94·1%) and NPV (ED model 99·0%, admissions model 98·5%). INTERPRETATION: Our models performed effectively as a screening test for COVID-19, excluding the illness with high-confidence by use of clinical data routinely available within 1 h of presentation to hospital. Our approach is rapidly scalable, fitting within the existing laboratory testing infrastructure and standard of care of hospitals in high-income and middle-income countries. FUNDING: Wellcome Trust, University of Oxford, Engineering and Physical Sciences Research Council, National Institute for Health Research Oxford Biomedical Research Centre.

Hormone use and risk for lung cancer: a pooled analysis from the International Lung Cancer Consortium (ILCCO).

BACKGROUND: The association between oral contraceptive (OC) use, hormone replacement therapy (HRT) and lung cancer risk in women is still debated. METHODS: We performed a pooled analysis of six case-control studies (1961 cases and 2609 controls) contributing to the International Lung Cancer Consortium. Potential associations were investigated with multivariable unconditional logistic regression and meta-analytic models. Multinomial logistic regressions were performed to investigate lung cancer risk across histologic types. RESULTS: A reduced lung cancer risk was found for OC (odds ratio (OR)=0.81; 95% confidence interval (CI): 0.68-0.97) and HRT ever users (OR=0.77; 95% CI: 0.66-0.90). Both oestrogen only and oestrogen+progestin HRT were associated with decreased risk (OR=0.76; 95% CI: 0.61-0.94, and OR=0.66; 95% CI: 0.49-0.88, respectively). No dose-response relationship was observed with years of OC/HRT use. The greatest risk reduction was seen for squamous cell carcinoma (OR=0.53; 95% CI: 0.37-0.76) in OC users and in both adenocarcinoma (OR=0.79; 95% CI: 0.66-0.95) and small cell carcinoma (OR=0.37; 95% CI: 0.19-0.71) in HRT users. No interaction with smoking status or BMI was observed. CONCLUSION: Our findings suggest that exogenous hormones can play a protective role in lung cancer aetiology. However, given inconsistencies with epidemiological evidence from cohort studies, further and larger investigations are needed for a more comprehensive view of lung cancer development in women.

Pleiotrophin gene therapy for peripheral ischemia: evaluation of full-length and truncated gene variants.

Pleiotrophin (PTN) is a growth factor with both pro-angiogenic and limited pro-tumorigenic activity. We evaluated the potential for PTN to be used for safe angiogenic gene therapy using the full length gene and a truncated gene variant lacking the domain implicated in tumorigenesis. Mouse myoblasts were transduced to express full length or truncated PTN (PTN or T-PTN), along with a LacZ reporter gene, and injected into mouse limb muscle and myocardium. In cultured myoblasts, PTN was expressed and secreted via the Golgi apparatus, but T-PTN was not properly secreted. Nonetheless, no evidence of uncontrolled growth was observed in cells expressing either form of PTN. PTN gene delivery to myocardium, and non-ischemic skeletal muscle, did not result in a detectable change in vascularity or function. In ischemic hindlimb at 14 days post-implantation, intramuscular injection with PTN-expressing myoblasts led to a significant increase in skin perfusion and muscle arteriole density. We conclude that (1) delivery of the full length PTN gene to muscle can be accomplished without tumorigenesis, (2) the truncated PTN gene may be difficult to use in a gene therapy context due to inefficient secretion, (3) PTN gene delivery leads to functional benefit in the mouse acute ischemic hindlimb model.

Expanding the diversity of mycobacteriophages: insights into genome architecture and evolution.

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists.

Nickel requires hypoxia-inducible factor-1 alpha, not redox signaling, to induce plasminogen activator inhibitor-1.

Human epidemiological and animal studies have associated inhalation of nickel dusts with an increased incidence of pulmonary fibrosis. At the cellular level, particulate nickel subsulfide inhibits fibrinolysis by transcriptionally inducing expression of plasminogen activator inhibitor (PAI)-1, an inhibitor of the urokinase-type plasminogen activator. Because nickel is known to mimic hypoxia, the present study examined whether nickel transcriptionally activates PAI-1 through the hypoxia-inducible factor (HIF)-1 alpha signaling pathway. The involvement of the NADPH oxidase complex, reactive oxygen species, and kinases in mediating nickel-induced HIF-1 alpha signaling was also investigated. Addition of nickel to BEAS-2B human airway epithelial cells increased HIF-1 alpha protein levels and elevated PAI-1 mRNA levels. Pretreatment of cells with the extracellular signal-regulated kinase inhibitor U-0126 partially blocked HIF-1 alpha protein and PAI-1 mRNA levels induced by nickel, whereas antioxidants and NADPH oxidase inhibitors had no effect. Pretreating cells with antisense, but not sense, oligonucleotides to HIF-1 alpha mRNA abolished nickel-stimulated increases in PAI-1 mRNA. These data indicate that signaling through extracellular signal-regulated kinase and HIF-1 alpha is required for nickel-induced transcriptional activation of PAI-1.

AP-1-dependent induction of plasminogen activator inhibitor-1 by nickel does not require reactive oxygen.

Inhalation of nickel dust has been associated with an increased incidence of pulmonary fibrosis. Nickel may promote fibrosis by transcriptionally activating plasminogen activator inhibitor (PAI)-1 and inhibiting fibrinolysis. The current studies examined whether nickel stimulated the PAI-1 promoter though an oxidant-sensitive activator protein (AP)-1 signaling pathway. Addition of nickel to BEAS-2B human airway epithelial cells stimulated intracellular oxidation, induced c-Jun and c-Fos mRNA levels, increased phospho- and total c-Jun protein levels, and elevated PAI-1 mRNA levels over a 24-h time course. Pretreatment of the cells with antioxidants did not affect increased c-Jun protein or PAI-1 mRNA levels. Expression of the dominant negative inhibitor of AP-1, TAM67, prevented nickel-stimulated AP-1 DNA binding, AP-1-luciferase reporter construct activity, and PAI-1 mRNA levels. Overexpression of c-Jun, however, failed to induce the AP-1 luciferase reporter construct or PAI-1 mRNA levels. These data indicated that nickel activated AP-1 through an oxidant-independent pathway and that basal AP-1 is necessary for nickel-induced expression of PAI-1.

Effect of arsenite on induction of CYP1A, CYP2B, and CYP3A in primary cultures of rat hepatocytes.

In earlier studies, sodium arsenite treatment was shown to decrease induction of enzymatic activities associated with hepatic CYPs in rats. Here we investigated the effect of sodium arsenite on induction of CYP2B, CYP1A, and CYP3A in primary cultures of rat hepatocytes. Arsenite decreased the induction of all three families of CYP, as measured enzymatically and immunochemically. These decreases in CYPs occurred at concentrations of arsenite (2.5-10 microM) at which no toxicity was observed; however, toxicity was observed at 25 microM arsenite. With 3-methylcholanthrene as inducer, 5 microM arsenite caused a 55% decrease in CYP1A1 immunoreactive protein and enzyme activity, but only a 25% decrease in CYP1A1 mRNA. With phenobarbital (PB) as the inducer, 2.5 microM arsenite decreased CYP2B enzyme activity and immunoreactive protein 50%, with only a 25% decrease in CYP2B1 mRNA. 5 microM Arsenite decreased CYP2B enzyme activity and immunoreactive protein 80%, but decreased CYP2B1 mRNA only 50%, while CYP3A protein was decreased greater than 75% with no decrease in CYP3A23 mRNA. With dexamethasone (DEX) as inducer, 5 microM sodium arsenite caused a 50% decrease in immunoreactive CYP3A and a 30% decrease in CYP3A23 mRNA. Although arsenite-mediated increases in heme oxygenase (HO) inversely correlated with decreases in CYP2B or CYP1A activity, inclusion of heme in cultures treated with inducers of CYP1A or CYP2B did not prevent the arsenite-mediated decreases in these CYPs. Even though added heme induced HO to similar levels with and without arsenite, decreases in CYPs were only observed in the presence of arsenite. These results suggest that, in rat hepatocytes, elevated levels of HO alone are not responsible for arsenite-mediated decreases in CYP.