Wound assessment is essential for evaluating wound healing. One cornerstone of wound care practice is the use of clinical guidelines that mandate regular documentation, including wound size and wound tissue composition, to determine the rate of wound healing. The traditional method requires wound care professionals to manually measure the wound area and tissue composition, which is time-consuming, costly, and difficult to reproduce. In this work, we propose an approach for automatic wound assessment that incorporates automatic color and measurement calibration and artificial intelligence algorithms. Our approach enables the comparison of images taken at different times, even if they were taken under different lighting conditions, distances, lenses, and camera sensors. We designed a calibration chart and developed automatic algorithms for color and measurement calibration. The wound area and wound composition on the images were annotated by three physicians with more than ten years of experience. Deep learning models were then developed to mimic what the physicians did on the images. We examined two network variants, U-Net with EfficientNet and U-Net with MobileNetV2, on wound images with a size of 1024 × 1024 pixels. Our best-performing algorithm achieved a mean intersection over union (IoU) of 0.6964, 0.3957, 0.6421, and 0.1552 for segmenting a wound area, epithelialization area, granulation tissue, and necrotic tissue, respectively. Our approach was able to accurately segment the wound area and granulation tissue but was inconsistent with respect to the epithelialization area and necrotic tissue. The calibration chart, which helps calibrate colors and scales, improved the performance of the algorithm. The approach could provide a thorough assessment of the wound, which could help clinicians tailor treatment to the patient’s condition.

Over time, large amounts of clinical data have accumulated in electronic health records (EHRs), making it difficult for healthcare professionals to navigate and make patient-centered decisions. This underscores the need for healthcare recommendation systems that help medical professionals make faster and more accurate decisions. This study addresses drug recommendation systems that generate an appropriate list of drugs that match patients’ diagnoses. Currently, recommendations are manually prepared by physicians, but this is difficult for patients with multiple comorbidities. We explored approaches to drug recommendations based on elderly patients with diabetes, hypertension, and cardiovascular disease who visited primary-care clinics and often had multiple conditions. We examined both collaborative filtering approaches and traditional machine-learning classifiers. The hybrid model between the two yielded a recall at 5 of 76.61%, a precision at 5 of 46.20%, a macro-averaged area under the curve of 74.52%, and an average physician agreement of 47.50%. Although collaborative filtering is widely used in recommendation systems, our results showed that it consistently underperformed traditional classification. Collaborative filtering was sensitive to class imbalances and favored the more popular classes. This study highlighted challenges that need to be addressed when developing recommendation systems in EHRs.

Medical coding plays an essential role in medical billing, health resource planning, clinical research and quality assessment. Automated coding systems offer promising solutions to streamline the coding process, improve accuracy and reduce the burden on medical coders. To date, there has been limited research focusing on inpatient diagnosis coding using an extensive comprehensive dataset and encompassing the full ICD-10 code sets. In this study, we investigate the use of language models for coding inpatient diagnoses and examine their performance using an institutional dataset comprising 230,645 inpatient admissions and 8677 diagnosis codes spanning over a six-year period. A total of three language models, including two general-purpose models and a domain-specific model, were evaluated and compared. The results show competitive performance among the models, with the domain-specific model achieving the highest micro-averaged F1 score of 0.7821 and the highest mean average precision of 0.8097. Model performance varied by disease and condition, with diagnosis codes with larger sample sizes producing better results. The rarity of certain diseases and conditions posed challenges to accurate coding. The results also indicated the potential difficulties of the model with long clinical documents. Our models demonstrated the ability to capture relevant associations between diagnoses. This study advances the understanding of language models for inpatient diagnosis coding and provides insights into the extent to which the models can be used.

Background/Objectives: Accurate age estimation is a critical task in forensic and legal contexts, where reliable and objective assessment is essential for age-dependent decision-making. This study aims to explore the application of convolutional neural network-based regression models for age estimation from panoramic radiographs, incorporating conformal prediction to generate prediction intervals representing uncertainty. The estimated ages were also evaluated for their application in legal age classification at thresholds of 10, 15, 18, and 20 years. Methods: A total of 1734 panoramic radiographs of individuals aged 8 to 23 years were used in this study. We employed six models, including ResNet-50, EfficientNet-B0, DenseNet-121, MobileNetV3-Small, VGG-16, and ResNeXt-50, to perform regression and provide point estimates of chronological age. Conformal prediction using a split conformal framework with absolute residual scores was implemented to construct statistically valid prediction intervals with finite-sample coverage guarantees. Results: The results revealed that ResNeXt-50 achieved the lowest mean absolute error for point estimates (0.92 years) and produced among the narrowest conformal margins, ranging from 1.71 to 3.72 years across non-coverage probabilities from 0.20 to 0.01. In legal age classification, the ResNeXt-50 model achieved its highest classification accuracy using point estimates, whereas the application of the conformal lower bound improved specificity and positive predictive value. This shift facilitates conservative decision-making, aligning with the forensic minimum-age principle. Conclusions: The proposed framework offers a practical solution for automated age estimation by quantifying uncertainty through prediction intervals, allowing for more informed assessments aligned with forensic protocols for legal age determination.

Diagnosis coding is a large-scale multi-label task in which each clinical encounter may require one or more coding labels from a large label space. Conventional top-k and threshold-based classifiers provide practical coding suggestions but do not directly characterize uncertainty over alternative coding sets. This study proposes sparse Mahalanobis conformal scoring for set prediction in diagnosis coding under extreme multi-label classification (XMC), intended for coding-assist workflows that require compact and reviewable coding suggestions. A sparse XMC model first generates candidate coding labels for each encounter. Candidate label sets are then constructed from the sparse proposal space and scored using a diagonal Mahalanobis nonconformity function calibrated on held-out data. Empirical conformal p-values are assigned to candidate sets, and downstream decision rules are used to obtain a final coding output from the retained region. The framework was evaluated using outpatient EHR data from a tertiary-care hospital, comprising approximately 8.0 million visits from 2018 to 2025 and up to 12,829 diagnosis labels. The primary SMaCS output achieved Micro-F1 close to the strongest threshold-based comparator and the highest exact match ratio among flexible-size decision rules. Compared with the other nonconformity scores, the Mahalanobis score produced a smaller retained region with fewer distinct labels, while preserving the same point-prediction performance. Additional analyses examined conformal region validity, robustness to label-frequency thresholds, code-depth performance, label-frequency subgroups, sample cardinality, department-level variation, and confidence–credibility stratification. Our results suggest that sparse Mahalanobis conformal scoring provides a useful framework for uncertainty-informed outpatient coding set prediction, while also highlighting the importance of candidate-space adequacy in extreme multi-label diagnosis coding.