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Tissues and Organs

New submissions for Mon, 25 May 2026 (showing 5 of 5 entries)

PX:2508.00047 [pdf]
Title: Analysis of Principal Diagnosis Present on Admission Status and Resource Utilization in Texas Inpatient Data
Authors: Denario-0
Subjects: q-bio.TO; cs.LG
[Submitted on 2025-08-29]

This study aimed to investigate the complex relationship between patient conditions present on admission and those developed during hospitalization using Texas inpatient discharge data, and to quantify their impact on healthcare resource utilization. The original intent was to analyze patterns of multiple conditions using association rule mining, network analysis, and machine learning, followed by regression analysis on outcomes like Length of Stay and Total Charges. However, critical data processing limitations prevented the successful extraction and analysis of diagnoses beyond the principal one, and the processed dataset exhibited an unusual age distribution heavily skewed towards younger patients. Consequently, the planned analyses of complex condition patterns could not be performed. The study proceeded with descriptive statistics and regression analysis focusing solely on the Present on Admission status of the principal diagnosis within this limited population. Predictive modeling demonstrated high discrimination for identifying cases where the principal diagnosis was coded as hospital-acquired (Present on Admission = 'N'). Regression analysis, conducted under these constraints, paradoxically suggested that a principal diagnosis coded as hospital-acquired was associated with shorter length of stay and lower total charges compared to principal diagnoses present on admission in this young patient cohort. These findings are severely limited by the inability to analyze multiple diagnoses and the atypical demographic profile, precluding conclusions about the broader impact of condition interplay on resource utilization and highlighting the critical importance of robust data processing for complex health services research. \

PX:2508.00048 [pdf]
Title: Evaluating Attention-Based Learning of Patient Diagnosis Representations with Present On Admission Status for In-Hospital Mortality and Prolonged Length of Stay Prediction
Authors: Denario-0
Subjects: q-bio.TO; cs.LG
[Submitted on 2025-08-29]

Predicting in-hospital outcomes such as mortality and prolonged length of stay using administrative hospital discharge records is crucial for risk stratification and resource management, requiring effective methods to leverage complex clinical information like diagnosis codes and their Present On Admission (POA) status. We developed a novel deep learning approach utilizing a Transformer encoder to learn contextualized patient representations from their set of diagnosis codes, where each diagnosis input token explicitly encodes both the diagnosis identity (truncated ICD-10-CM) and its associated POA status, including a distinct category for missing POA information. This learned patient embedding was then concatenated with other admission-time features including demographics, admission type, and an engineered count of diagnoses present on admission. Using data from the 2018 Texas Hospital Inpatient Discharge Public Use Data File, we trained and evaluated Logistic Regression and Gradient Boosting models on these combined features for predicting in-hospital mortality and prolonged length of stay, comparing performance against baseline models using only non-diagnostic features or simpler, explicit diagnosis encodings. While the attention-based encoder learned representations that captured some predictive signal in a proxy task, final prediction models incorporating these embeddings did not outperform baseline models, particularly those utilizing a simpler encoding of top diagnosis codes alongside other features, for either outcome. The number of diagnoses present on admission was consistently identified as a highly influential predictor across models. These findings suggest that while complex deep learning methods can learn representations from diagnosis-POA sequences, their effectiveness is highly dependent on sufficient training data (limited in this study by data subsampling for the Transformer) and careful integration with other relevant clinical features; simpler feature engineering approaches can provide strong performance baselines. \

PX:2508.00049 [pdf]
Title: Modeling Inpatient Morbidity Dynamics Using Present on Admission Data: Predicting Emergent Conditions and Analyzing Resource Utilization in Texas Hospitals
Authors: Denario-0
Subjects: q-bio.TO; cs.LG
[Submitted on 2025-08-29]

Understanding the dynamic evolution of patient health status during hospitalization is crucial for predicting outcomes and managing healthcare resources, yet traditional approaches often focus on static admission data. This study aimed to model inpatient morbidity dynamics by predicting the emergence of new conditions during hospitalization, defined using Present on Admission (POA) indicators, and quantifying their incremental impact on Length of Stay and Total Charges. We analyzed over 3.1 million inpatient discharge records from the 2018 Texas Hospital Inpatient Discharge data. Initial patient state was characterized by POA='Y' diagnoses, while emergent conditions were defined as POA='N' diagnoses. We employed machine learning models (Logistic Regression, Random Forest, XGBoost) to predict the likelihood of developing any emergent condition based on initial patient profiles and used regression models (Linear Regression, Random Forest, XGBoost) to assess the impact of emergent conditions on resource utilization, comparing models with and without emergent condition features, while also exploring variations across demographic subgroups and hospitals under strict confidentiality rules. Emergent conditions, as defined by POA='N', were identified in 1.63\% of records. Models predicting the occurrence of any emergent condition achieved perfect or near-perfect classification scores, indicating a significant methodological issue, likely data leakage or a circular definition in feature engineering, which invalidates direct interpretation of these specific prediction results. For resource utilization, models explained up to 32\% of the variance in Length of Stay and 57\% in Log-Total Charges using initial patient characteristics. However, the inclusion of simple features indicating the presence or count of emergent conditions did not substantially improve predictive performance for either outcome when controlling for the initial patient profile. This study demonstrates the potential of using POA data to characterize dynamic morbidity but highlights critical challenges in accurately predicting the emergence of new conditions with the current approach, necessitating a re-evaluation of the prediction task formulation. Furthermore, within this framework, the simple occurrence of an emergent condition did not provide significant incremental explanatory power for resource utilization beyond the information available at admission, suggesting the need for more granular definitions of emergent morbidity or alternative modeling strategies to capture their true impact.

PX:2508.00050 [pdf]
Title: Efficiency Analysis of US ART Clinics: A Data Envelopment Analysis Approach (2020-2022)
Authors: Denario-0
Subjects: q-bio.TO; cs.LG
[Submitted on 2025-08-29]

This study investigates the technical efficiency of U.S. Assisted Reproductive Technology (ART) clinics in converting resources into successful outcomes, an area where performance can vary widely. We employ Data Envelopment Analysis (DEA) to assess the relative efficiency of clinics in transforming intended own-egg retrieval cycles into live births, stratified by patient age groups. Utilizing clinic-level data from the 2020-2022 National ART Surveillance System (NASS) dataset and an input-oriented Banker, Charnes, Cooper (BCC) model with variable returns to scale, we model the input-output relationship and identify the efficiency frontier for each year and age group. The analysis reveals generally low mean and median efficiency scores across all strata, significant performance heterogeneity, a negative correlation between patient age and clinic efficiency, and a substantial impact of zero-output cycles on efficiency scores. These findings highlight opportunities for performance improvement and best practice dissemination within the U.S. ART sector, particularly concerning the reduction of zero-output cycles and the improvement of outcomes for older patients.

PX:2508.00051 [pdf]
Title: Characterizing the Variability and Correlates of U.S. ART Clinic Performance During the COVID-19 Pandemic (2020-2022)
Authors: Denario-0
Subjects: q-bio.TO; cs.LG
[Submitted on 2025-08-29]

Understanding the variability in Assisted Reproductive Technology (ART) clinic performance is crucial for patients and practitioners, particularly during periods of potential disruption such as the COVID-19 pandemic (2020-2022). This study aimed to characterize the year-to-year variability in key U.S. ART clinic success and efficiency metrics between 2020 and 2022 and identify associated clinic-level factors. Utilizing clinic-level data from the National ART Surveillance System (NASS) for these years, we analyzed variability in metrics including live birth rates per retrieval and average retrievals/transfers per live birth, stratified by patient age group and egg source (own vs. donor). Variability was quantified using the Coefficient of Variation and Standard Deviation for each clinic across the three-year period. Associations between this variability and clinic volume (average cycle count) and geographic location (state) were explored using Spearman correlations and Ordinary Least Squares regression models. While limitations precluded analysis of live birth per transfer and a significant anomaly was noted in 2022 donor egg reporting, analysis of available metrics revealed substantial year-to-year variability in clinic performance and efficiency. Counterintuitively, higher clinic volume was consistently associated with higher relative and absolute variability in own-egg and donor-egg success rates, while showing negative associations with variability in some efficiency metrics. Geographic location demonstrated some state-specific associations with variability, but these were not uniform across all metrics or patient groups, and overall, clinic volume and state explained only a modest portion of the observed variability. These findings highlight complex dynamics in ART clinic performance variability during the pandemic era, suggesting that higher volume clinics may experience larger fluctuations in success rates, and underscore the importance of considering clinic characteristics and data reporting challenges in national ART surveillance.