The interviews were audio-recorded, transcribed and coded using NVivo, with the researchers then identifying emergent themes. The participants supported diagnostic screening during pregnancy for Chlamydia trachomatis and Neisseria gonorrhoeae . One expressed the view that “if there are new technologies, let’s move forward.” Their responses indicate that the proposed screening was viewed as a potentially acceptable way to identify infections that might otherwise be missed, but acceptance was conditional on the health system having the resources needed to deliver it reliably.

One provider said the system “sometimes it collapses—the supplies run off,” while another referred to times when even treatment was unavailable. These accounts suggest that adding diagnostic screening would occur within a service already managing shortages of essential supplies, rather than in an otherwise fully resourced system. Participants said they were already overwhelmed by their workloads, raising questions about whether existing personnel could absorb additional screening responsibilities.

Others described equipment that stopped working after it had been installed, indicating that maintenance was not consistently available. The providers also identified staff training, health education for patients and faster tests as factors that could facilitate implementation. Overall, the findings show strong professional support for antenatal diagnostic STI screening in Botswana, alongside clear warnings that electricity, physical space, stock levels, staffing and equipment maintenance would shape whether the approach could be delivered.

Because the research was limited to interviews with 15 providers in one city, it does not establish the feasibility of screening across Botswana or demonstrate effects on treatment uptake, maternal health or infant health. It instead offers a qualitative assessment of potential implementers’ views ahead of roll-out, identifying the health-system conditions that those providers considered essential.

Subject of Research: Healthcare providers’ perspectives on implementing antenatal diagnostic STI screening in Botswana

Article Title: “Provided power is there, it will be a good initiative”: healthcare provider perspectives on diagnostic screening for curable STIs during pregnancy in Botswana

Article References: Mussa, A., Russell, J. C., Hansman, E., Babalola, C. M., Bame, B., Ndlovu, N., Tamuthiba, L., Klausner, J. D., Morroni, C., & Wynn, A. (2026). “Provided power is there, it will be a good initiative”: healthcare provider perspectives on diagnostic screening for curable STIs during pregnancy in Botswana. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15608-6

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15608-6

Keywords: Botswana, antenatal care, chlamydia, gonorrhoea, diagnostic screening, sexually transmitted infections, healthcare providers, health systems, qualitative research, Provided, power, there

The research team, led by Pooya Eini of the Rajaie Cardiovascular Institute in Tehran alongside collaborators from Hamadan University of Medical Sciences in Iran and Michigan State University in the United States, worked with a large pediatric dataset known as ZZU-pECG. It contains 14,190 twelve-lead and nine-lead ECG recordings from 11,643 hospitalized children aged zero to fourteen years, each linked to diagnoses coded under the International Classification of Diseases, tenth revision. What makes the study distinctive is its choice of input data. In routine clinical practice, ECGs are rarely stored or exchanged as raw waveform files. Instead, they travel through hospital systems as structured reports: a list of expert diagnostic statements, patient demographics, and technical metadata. The researchers deliberately built their models on exactly this kind of report-level data, comprising 85 structured features that included the child’s age and sex, the number and identity of the expert ECG diagnostic statements attached to the recording, and per-lead signal-quality indices.

Crucially, the team excluded two variables that could have inflated the results artificially: recording duration and lead configuration. These are workflow characteristics rather than clinical signals, and including them would have allowed the models to learn shortcuts about how care is delivered rather than what the heart is doing. This kind of shortcut avoidance is a persistent worry in clinical machine learning, where algorithms have been known to achieve impressive apparent accuracy by picking up on hospital-specific artifacts rather than genuine physiology. By stripping out these workflow variables, the investigators forced their models to rely on the medical content of the report itself.

Seven different machine-learning classifiers were developed and compared, spanning the standard toolkit of modern predictive modeling: elastic-net logistic regression, random forest, histogram gradient boosting, LightGBM, support vector machine, k-nearest neighbors, and a multilayer perceptron, a small artificial neural network. Each model was trained to predict whether a child’s record carried an ICD-10-coded diagnosis from a set of 19 pediatric cardiovascular diseases. To guard against optimistic estimates, the models were tuned using three-fold cross-validation in which all recordings from the same patient stayed within the same fold, and final performance was measured on a patient-disjoint hold-out test set of 2,814 recordings. This patient-grouped design matters because multiple recordings from the same child would otherwise leak information between training and test data, making the model look better than it truly is.

The results, while promising, come with careful caveats. The best-performing model, histogram gradient boosting, achieved an area under the receiver-operating-characteristic curve of 0.821, with a 95 percent confidence interval of 0.805 to 0.837. In practical terms, that means the model could distinguish between children with and without a coded cardiovascular diagnosis reasonably well, though far from perfectly. The model was also well calibrated, meaning its predicted probabilities tracked actual outcomes closely, with a calibration slope of 1.02 and a Brier score of 0.141. Decision-curve analysis showed positive net benefit across the range of decision thresholds examined, suggesting the model could in principle add value at various levels of clinical aggressiveness.

Perhaps the most clinically meaningful numbers emerged at a high-sensitivity operating point, where the model identified 89.7 percent of recordings associated with a coded cardiovascular diagnosis, with a negative predictive value of 93.5%. In a screening context, a negative predictive value above 93 percent means that when the model says a child is unlikely to have cardiovascular disease, that reassurance is correct the vast majority of the time. For a condition that is often paucisymptomatic and easily missed at first contact, that kind of rule-out capability is exactly what a triage tool would need, flagging the small fraction of reports that deserve closer human scrutiny before they slip through the system unnoticed.

The researchers then probed where the predictive signal actually came from, and the answer is scientifically revealing. When the models were restricted to demographics and signal-quality features alone, discrimination fell to 0.748. Using only the expert ECG diagnostic statements, performance reached 0.776. Both restricted models performed significantly worse than the full model, with DeLong tests yielding P values below 0.001. Even after removing the most disease-proximal statements, the ECG statements most directly tied to a specific diagnosis, performance was largely preserved at 0.813, a statistically significant drop but a modest one. This suggests that the information content of the report is distributed across many features rather than concentrated in a few obvious diagnostic labels, and that subtler patterns in how ECG findings co-occur carry real predictive weight.

Not every subgroup fared equally well, however, and the authors are candid about these limitations. Performance dropped materially in infants, where the area under the curve fell to 0.635, and in nine-lead recordings, where it reached only 0.746. Infant ECGs are notoriously different from those of older children, with rapid heart rates and developmental patterns that make interpretation harder, and the reduced lead configuration of a nine-lead recording simply captures less cardiac information. These performance gaps, combined with the fact that the study relied entirely on internal validation within a single database, mean the models are not ready for clinical deployment. External validation on data from other hospitals, other populations, and other ECG systems would be essential before any real-world use.

There is also a deeper conceptual point that the authors emphasize: the predictors in this study are themselves expert interpretations of the ECG tracing, not the raw electrical signal. The models therefore describe the information content of the ECG report rather than of the ECG waveform itself. In one sense this is a limitation, because the models cannot exceed the diagnostic insight of the experts who wrote the original statements. In another sense it is a strength, because structured reports are what actually flow through hospital information systems today, meaning a tool built on report data could be integrated into existing workflows without requiring access to raw waveform archives that many institutions cannot easily share.

The study arrives at a moment when machine learning is being proposed for nearly every corner of medicine, and its disciplined approach offers a template for how such claims should be tested. Rather than promising a revolutionary diagnostic engine, the authors deliver a measured finding: structured pediatric ECG reports contain a reproducible, report-level signal for predicting coded cardiovascular diagnoses, strong enough to justify further research but not strong enough, yet, to change clinical practice. The work was funded by no external grants, the authors declare no competing interests, and the article is published open access under a Creative Commons license. As hospitals continue to accumulate structured ECG data on millions of children, studies like this one map out both the promise and the boundaries of what artificial intelligence can honestly extract from the paperwork of cardiology, and they remind us that the path from a promising area under a curve to a trustworthy bedside tool runs through external validation, subgroup analysis, and a healthy respect for what the data can and cannot say.

Subject of Research: Machine-learning prediction of pediatric cardiovascular diagnoses from structured electrocardiogram report data

Article Title: Machine-learning prediction of cardiovascular diagnoses from pediatric ECG reports in 11 643 children

Article References: Eini, P., serpoush, H., Rezayee, M., & Tremblay, J. (2026). Machine-learning prediction of cardiovascular diagnoses from pediatric ECG reports in 11 643 children. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07732-3

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07732-3

Keywords: machine learning, pediatric ECG, cardiovascular disease, clinical prediction model, electrocardiography, predictive medicine, children's health, gradient boosting, ICD-10 coding, diagnostic screening, Machine-learning, prediction

A new study published in BMC Infectious Diseases tackles this problem head-on, presenting a methodological benchmarking framework designed to make machine learning classifiers for HCV detection both more accurate and, crucially, more stable. Led by Muhammad Uzair Khan of the University of Engineering and Technology in Mardan, Pakistan, together with colleagues at institutions including Princess Nourah bint Abdulrahman University in Riyadh, the work does not claim a novel algorithm. Instead, its contribution lies in rigor: the authors systematically evaluated how standard classifiers behave when the messy realities of clinical data, class imbalance, outliers, and redundant features, are properly addressed before a model is ever trained. The result is a reproducible baseline methodology that future computer-aided screening studies can build upon.

The starting point was a public benchmark dataset of routine biochemical attributes, the kind of measurements any clinical laboratory can produce: liver enzymes and related blood parameters that shift characteristically when the liver is under viral attack. Using such data offers an attractive diagnostic pathway, because it avoids the cost and infrastructure demands of polymerase chain reaction confirmation at the first screening step. But it also carries well-known statistical hazards. In real-world cohorts, infected patients typically form a minority, meaning a naive classifier can achieve deceptively high accuracy simply by predicting that everyone is healthy. Outliers, whether from laboratory error or genuine physiological extremes, can further skew model training, and correlated or irrelevant features can obscure the signal that actually matters.

To confront these hazards, the researchers built their pipeline in three deliberate stages. First, they addressed class imbalance using data balancing techniques, a step reflected in their keyword list by the widely used SMOTE approach, which synthesizes new examples of the minority class rather than simply duplicating existing ones. Second, they applied a decision tree-based recursive feature elimination procedure, an embedded feature selection method that iteratively trains a tree ensemble, ranks predictors by importance, and discards the least informative ones. This retains only the necessary predictors, reducing noise and the risk of overfitting while keeping the final model interpretable enough for clinicians to understand which laboratory values drive a prediction. Third, they tuned the hyperparameters of each candidate model before any testing, ensuring that no classifier was handicapped by default settings.

With this framework in place, the team trained and evaluated a broad roster of machine learning classifiers under a single, consistent experimental protocol: k-nearest neighbors, support vector machine, logistic regression, random forest, naive Bayes, gradient boosting, extreme gradient boosting, and LightGBM. This breadth matters. Many published diagnostic studies showcase a single favored algorithm, leaving readers unable to judge whether its reported performance reflects genuine superiority or merely favorable tuning. By holding preprocessing, feature selection, and evaluation constant across all eight models, the benchmarking design isolates the true differences between learning algorithms and reveals which families of methods are inherently suited to this kind of tabular clinical data.

The headline result came from the random forest classifier. On an independent test cohort, data the model had never seen during training, it achieved 98.38 percent accuracy, a perfect 100 percent precision, and an F1-score of 93.02 percent. In diagnostic terms, perfect precision means that every patient the model flagged as HCV-positive genuinely was infected, an especially valuable property in a screening context where false alarms trigger unnecessary confirmatory testing and patient anxiety. The F1-score, which harmonizes precision and recall into a single figure, indicates that the model maintained this discipline without sacrificing its ability to catch the majority of true infections. Ensemble methods such as random forest are well suited to this task because they aggregate hundreds of decision trees trained on random subsets of the data, averaging away individual errors and resisting the influence of outliers.

Accuracy on a single test split, however, can be a statistical mirage, and the authors were careful not to rest on it. They therefore subjected every model to a stratified 5-fold cross-validation protocol, in which the dataset is partitioned into five folds, each fold serving once as the validation set while the remaining four train the model. Stratification preserves the class balance within every fold, so each validation round mirrors the original disease prevalence. Under this more demanding test, LightGBM, a gradient boosting framework that builds trees leaf-wise for efficiency, delivered the top mean accuracy of 95.77 plus or minus 1.45 percent, with an area under the receiver operating characteristic curve of 0.97 plus or minus 0.02. The small standard deviations are as significant as the means: they demonstrate that performance did not fluctuate wildly depending on which patients happened to land in each fold, a hallmark of genuine generalizability.

Notably, the simpler linear and margin-based models also held their own. Support vector machine and logistic regression classifiers showed stable and competitive results across the cross-validation protocol, suggesting that once the data are properly balanced and the feature space pruned, the HCV signal in routine biochemical measurements is strong enough that even classical methods capture it reliably. This is an encouraging finding for resource-constrained health systems, because logistic regression in particular is transparent, fast, and easy to deploy within existing hospital information systems. The comparison also underscores a recurring lesson in clinical machine learning: thoughtful data engineering often matters more than algorithmic novelty, and the most sophisticated model is not always the most dependable one.

When the authors set their results against previous studies in the literature, the improvements were concrete rather than cosmetic. The proposed models showed enhanced diagnostic sensitivity and stronger overall robustness, particularly in how they handled data imbalance and outliers, the two factors that most often cause published diagnostic models to collapse when moved from a curated dataset to real clinical conditions. Sensitivity, the proportion of true infections correctly identified, is the metric that matters most in screening, since a missed case is a patient who may progress to advanced liver disease. The benchmarking framework’s insistence on balancing and validation directly targets this vulnerability, and the cross-verified AUC approaching 0.97 indicates an excellent separation between infected and uninfected profiles across the full range of decision thresholds.

The study, published open access on 7 October 2026 and supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project, used fully anonymized secondary data from the public UCI Machine Learning Repository, so no individual patient consent was required. Its conclusions are measured but consequential: integrating structured data balancing, principled feature elimination, and careful hyperparameter tuning significantly stabilizes classifier performance on routine laboratory data. For clinicians, the work points toward a future in which a standard blood panel, interpreted by a validated algorithm, could flag probable HCV infection at the first point of contact, directing patients promptly to confirmatory testing and curative antiviral therapy. For researchers, it establishes a reliable baseline methodology, a reminder that in the race toward computer-aided diagnosis, the discipline of the pipeline is what turns a promising accuracy figure into a tool a clinic can actually trust.

Subject of Research: Machine learning benchmarking for hepatitis C virus diagnosis from routine biochemical data

Article Title: Enhancing the performance of machine learning models for the reliable diagnosis of hepatitis C virus

Article References: Khan, M. U., Muhammad, F., Khan, J., Awan, D., Khan, S., Idrees, F., & Al-Rasheed, A. (2026). Enhancing the performance of machine learning models for the reliable diagnosis of hepatitis C virus. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14179-5

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14179-5

Keywords: hepatitis C, machine learning, random forest, LightGBM, SMOTE, class imbalance, feature selection, cross-validation, diagnostic screening, clinical data, BMC Infectious Diseases, artificial intelligence