bluuebunny/crossref_metadata_2025_split huggingface dataset:
In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records
Authors: Bopche Rajeev , Gustad Lise Tuset , Afset Jan Egil , Ehrnström Birgitta , Damås Jan Kristian , Nytrø Øystein 
Published: September 2024

Abstract

Objective
This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).

Methods
Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional machine learning (ML) models with interpretations and statistical analysis of predictors of mortality, readmission, and PLOS.

Results
Our framework demonstrated exceptional predictive performance with a notable area under the receiver operating characteristic (AUROC) of 0.9625 and an area under the precision-recall curve (AUPRC) of 0.8575 for 30-day mortality at discharge and an AUROC of 0.9545 and AUPRC of 0.8419 at admission. For the readmission and PLOS risk, the highest AUROC achieved were 0.8198 and 0.9797, respectively. The tree-based models consistently outperformed the traditional ML models in all 4 prediction tasks. The key predictors were age, derived temporal features, routine laboratory tests, and diagnostic and procedural codes.

Conclusion
The study underscores the potential of leveraging medical history for enhanced hospital predictive analytics. We present an accurate and intuitive framework for early warning models that can be easily implemented in the current and developing digital health platforms to predict adverse outcomes accurately.
Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity
Authors: Bandyopadhyay Anindita , Albashayreh Alaa , Zeinali Nahid , Fan Weiguo , Gilbertson-White Stephanie 
Published: September 2024

AbstractObjectiveThis study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers.Materials and MethodsWe analyzed EHR data of 8156 adults diagnosed with cancer who underwent cancer treatment from 2017 to 2020. Structured and unstructured EHR data were sourced from the Enterprise Data Warehouse for Research at the University of Iowa Hospital and Clinics. Several predictive models, including logistic regression, random forest (RF), and XGBoost, were employed to forecast symptom development. The performances of the models were evaluated by F1-score and area under the curve (AUC) on the testing set. The SHapley Additive exPlanations framework was used to interpret these models and identify the predictive risk factors associated with fatigue as an exemplar.ResultsThe RF model exhibited superior performance with a macro average AUC of 0.755 and an F1-score of 0.729 in predicting a range of cancer-related symptoms. For instance, the RF model achieved an AUC of 0.954 and an F1-score of 0.914 for pain prediction. Key predictive factors identified included clinical history, cancer characteristics, treatment modalities, and patient demographics depending on the symptom. For example, the odds ratio (OR) for fatigue was significantly influenced by allergy (OR = 2.3, 95% CI: 1.8-2.9) and colitis (OR = 1.9, 95% CI: 1.5-2.4).DiscussionOur research emphasizes the critical integration of multimorbidity and patient characteristics in modeling cancer symptoms, revealing the considerable influence of chronic conditions beyond cancer itself.ConclusionWe highlight the potential of ML for predicting cancer symptoms, suggesting a pathway for integrating such models into clinical systems to enhance personalized care and symptom management.
Analysis toolkit for evaluation of drug titration practice in acute lymphoblastic leukemia maintenance
Authors: Mungle Tushar , Mahadevan Ananya , Mukhopadhyay Jayanta , Bhattacharya Sangeeta Das , Saha Vaskar , Krishnan Shekhar 
Published: September 2024

Abstract

Objective
During the 2-year maintenance treatment phase (MT) of acute lymphoblastic leukemia (ALL), personalized patient-specified titration of oral antimetabolite drug doses is required to ensure maximum tolerated systemic drug exposure. Drug titration is difficult to implement in practice and insufficient systemic drug exposure resulting from inadequate dose titration increases risk of ALL relapse.

Materials and Methods
We developed an open-source R-based analytical toolkit, including the allMT R package and an interactive web-based R Shiny VIATAMIN application, to evaluate antimetabolite drug titration during MT.

Results
Evaluation is initiated with basic visualization analysis of drug titration, in both individual patients and patient cohorts. Observations are supplemented with descriptive analyses of hematological toxicity frequency and prescriber compliance rates with protocol drug titration rules. In individual patients, visual and quantitative analyses indicate recurring potentially correctable suboptimal drug titration patterns. In patient cohorts, the toolkit enables evaluation of the impact of interventions to optimize MT drug titration.

Discussion
Incorporation of the toolkit in a forthcoming clinical decision support system for MT would enable real-time examination and course correction of drug titration practice.

Conclusion
Future versions will be enhanced to include other variables that influence drug titration practice, including clinical toxicity data and later, pharmacological markers of antimetabolite, adherence, and drug activity.
325 Longitudinal retrospective study of performance and biomarkers of energy balance and gastrointestinal functionality in unweaned calves subjected to transport or entire commercialization at arrival
Authors: Marti Sonia , Pisoni Lucia , Blanch Marta , Bassols Anna , Saco Yolanda , Pato Raquel , Pujols Joan , Devant Maria 
Published: September 2024

Abstract
Data from 347 unweaned male dairy beef calves enrolled in 3 studies from 2021 to 2023 were used to evaluate the impact of transport or entire commercialization (marketing and transport) on performance recovery [average daily gain (ADG) and concentrate intake (CI)] and biomarkers of energy balance [non-esterified fatty acids (NEFA), beta-hydroxybutyrate (BHB), and glucose] and gastrointestinal functionality [gut permeability (Cr-EDTA) and enterocyte mass (citrulline)] at farm arrival. Calves were distributed in 3 treatments according to the different managements subjected in the previous studies: calves from a sole dairy farm that were not fasted neither transported (CTR), calves from a sole dairy farm that were transported and fasted for 19 h (TRA), and multi-origin calves commercially marketed and transported for 9 h (COM). On arrival at the rearing farm (d 0), calves were allocated individually, and body weight (BW) was recorded. Calves were fed milk replacer twice daily, and they had ad libitum access to concentrate and water. Concentrate (CI) and milk replacer intake were recorded daily, and BW and the biomarkers of energy balance and gut dysfunction were obtained on d 0 (arrival), 1, 2, and 7. Data were analyzed with mixed models. An interaction (P &lt; 0.01) between treatment and time was observed for CI. On d 0 CTR and TRA had similar CI, while COM had the least CI. On d 1, all treatments decreased CI except CTR. While TRA recovered CI to CTR levels, COM had 50% less CI compared with the other calves on d 7. The COM calves had the least (P &lt; 0.01) ADG compared with CTR and TRA calves. On d 0, COM calves had greater (P &lt; 0.01) concentrations of NEFA and BHB compared with TRA and CTR, and TRA greater (P &lt; 0.01) than CTR calves. The COM calves did not recover NEFA and BHB basal concentrations by d 7 as observed in TRA calves. Concentration of Cr-EDTA was greater on d 0 for TRA calves compared with CTR and COM calves, however concentration of Cr-EDTA was greater during the first 7 d in COM calves compared with CTR and TRA calves. Finally, citrulline concentration was less in COM calves compared with CTR and TRA calves independently of the sampling day. The entire commercialization process, marketing and transportation, of unweaned dairy beef calves impairs intake recovery and growth, and these results are aligned with negative energy balance and comprised gastrointestinal functionality at farm arrival.
423 Comparative post-weaning performance characteristics of four breeds of rabbit in hot humid climate of Nigeria
Authors: Abanikannda Olatunji T F , Kareem-Ibrahim Khadeejah O , Olukoya Faidat O , Sanni Aisha , Okulaja Temiloluwa , Otun Islamiyyah O 
Published: September 2024

Abstract
Rabbits are one of the most popular livestock breeds raised for their meat, fur, and companionship. Rabbit meat is fast gaining prominence as a healthier meat compared with beef, mutton, pork and chevon, which are mostly consumed in Nigeria. This study investigated breed differences in mortality, survival rates and post-weaning growth rate of rabbits reared under inimical environmental conditions. The animals were fed commercially compounded feed with 18% crude protein and 2300Kcal metabolizable energy with intermittent feeding of Panicum sp and Tridax sp. The study area, located at latitude 6.5867° N and longitude 3.9700° E, had an average daily temperature of 35°C and relative humidity of 64%, which negatively impacted the adaptation, survival rates and growth performance of the rabbits. A total of 48 weaner rabbits 8 wk of age, comprising 12 each (9 does and 3 bucks) of Checkered, Chestnut, Dutch and Hyla breeds were reared for 10 wk post-weaning. Mortality was recorded across the breeds and the body weights (BW) of the rabbits were also recorded on a weekly basis for the entire 10-wk period. The study evaluated survival rates across breeds and sex and BW gain. Statistical analyses were conducted using the Minitab software for Chi Squared test of independence for counts and descriptive statistics and general linear model analysis of variance for bodyweight gain. The statistical model describing the analysis was Yijk = µ + αi + (α:β)ij + eijk where αi is the ith fixed effect of breed and βj is the jth fixed effect of sex. The model accounted for 67.03% variation in total weight gain. Further mean separation was done using the Tukey’s multiple comparison procedure. Mortality rates across breeds on the average was 56.25%, with Checkered, Chestnut, Dutch and Hyla respectively recording 66.67%, 41.67%, 50.00% and 66.67% mortality, respectively. Mortality rate across sexes was 63.89% for does and 33.33% for bucks; however, mortality rates were independent (P &gt; 0.05) of both breed and sex with a Chi-Squared value of 2.285 and 3.41, respectively, despite the numerical differences in mortality rates across breeds and sexes. Average total BW gain during the study period across the breeds and within breeds is as presented in Table 1. The nesting of sex within breed revealed that the only significant (P &gt; 0.05) difference was noticed between the Hyla buck and Chestnut doe, while other means were not statistically different (P &gt; 0.05). The Chestnut breed with the least BW gain adapted well in the environment with greatest survival rate, while the Hyla breed had the greatest BW gain and least survival rate throughout the study. This study indicated a negative relationship between survival rates and BW gain, that lighter breeds of rabbit tend to have greater survival rates than the heavier breeds.
144 Assessing economic implications of thermal stress of Bos indicus and Bos taurus species during finishing in northern Colorado
Authors: Ritten John , Schilling-Hazlett Ashley K , Stackhouse-Lawson Kimberly R , Place Sara E , Carvalho Pedro H V 
Published: September 2024

Abstract
The objective of this study was to determine the economic implications of thermal stress on climate adaptive capacity, resiliency, and impact of Bos indicus and Bos taurus finishing steers using common finishing systems. The experimental design was a completely randomized design, with a 2 by 2 factorial arrangement of finishing system (‘natural’ or ‘conventional’) and species of cattle (Bos indicus or Bos taurus). Steers [n = 200; body weight (BW) ~450 kg] were housed in 20 feedlot pens (10 steers/pen) for the first 84 d, where cattle were blocked by initial BW. For the remainder of the 180 d (96 d) finishing period, cattle were moved to four Climate Smart Research Pens (50 steers/pen, 1 pen/ treatment, and species combination). Within each Climate Smart Research Pen, five SmartFeed systems were used to evaluate individual animal feed intake, one GreenFeed emission measurement system was used to evaluate individual animal gas emissions flux of enteric CH4, O2, H2, and CO2, and were equipped for nutrient mass balance of carbon, nitrogen, and phosphorus. Animal BW were collected every 28 d for analysis of average daily gain (ADG) and feed efficiency. Thermal stress behaviors were evaluated by one trained personnel on a subset of 7 steers/treatment via the measurement of respiration rate, open mouth breathing, rumen temperature, number of daily water intake events, lying and standing behavior, and pen surface temperature for the last 96 d of the finishing period. At harvest, carcass characteristics, liver scores, and heart scores were collected. Economic analyses were conducted post-hoc to determine the profitability of each species based on economic implications, tradeoffs, carcass quality and quantity, and time to finish. Selection of cattle for climate adaptive features may involve production tradeoffs (e.g., depressed rate of gain, greater yield risk, lower technical efficiency) and adjustment costs (e.g., re-optimizing feed for a new breed), which affect the adoption of less common breeds into the US cattle supply chain. Adding to the complexity of this evaluation, consumers preferences may not adapt quickly, affecting the prospect of Bos indicus cuts of meat in the market. We also, therefore, explore the supply- and demand-side effects of climate change adaptation through breed selection in the beef industry.
82 Linear mixed models for dissecting social and direct genetic effects of automatic feeder visit sequences
Authors: Nunez Andrea N , Steibel Juan P , Chen Ching-Yi , Holl Justin W 
Published: September 2024

Abstract
In this study, we delved into the feeding behaviors of 548 swine, using 114,263 records from FIRE feeders to uncover the direct and social genetic effects of feeding duration. We aimed at dissecting direct and social effects and genetics and environmental components of feeding duration using linear mixed-effects models (LMMs). To accomplish this, we fit and compared several models. The full model included direct and social genetic effects and permanent environmental effects or the social effects or both sets of effects. All models included the effect of the social group. This model facilitates a comprehensive understanding of the determinants of feeding times as it related to social interaction sequences. A novel aspect of our study is the use of sequence of feeder visits as a proxy to social interaction at the feeder, so separate social genetic and social permanent environmental effects of an individual from the ‘Social Group’ effect. The analysis reveals that the full model offers the best fit to the data, as indicated by the smallest Akaike Information Criterion (AIC). However, comparisons with reduced models suggest a partial confounding between additive genetic and permanent environmental effects at both additive and social levels. Notably, the variance explained by the social group remains consistent across models, implying minimal confounding with this factor and underscoring the distinct impact of social dynamics. The observation that social direct genetic effects do not significantly overlap (e.g., M1 vs M2) is promising, suggesting the potential to address the confounding between additive genetic (A) and permanent environmental (PE) effects by incorporating a more detailed pedigree analysis. By presenting variance components and model fit indices (Table 1), our research illuminates the intricate interactions among genetic, environmental, and social factors affecting swine behavior. The use of mixed-effects modeling in this study advances our understanding of the temporal dynamics of animal social behavior, emphasizing the importance of detailed statistical analysis in behavioral research.
84 Low-cost machine learning algorithms to predict growth and carcass traits in pigs
Authors: Billah Masum , Bermann Matias , Chen Ching-Yi , Psota Eric , Holl Justin W , Misztal Ignacy , Lourenco Daniela 
Published: September 2024

Abstract
Genomic prediction involves analyzing pedigree, phenotype, and genomic data, facilitating the early prediction of the genetic merit of an individual. Phenotyping is an essential component of the genomic selection program. However, depending on the trait, traditional phenotyping can be time-consuming and labor-intensive. On the other hand, with the advancement in digital phenotyping, obtaining regular images of animals has become increasingly accessible. Coupled with computer vision and machine learning algorithms, phenotypes for traits of interest can be extracted from the images and used for different purposes, including genomic predictions. Previous attempts to predict genetic merit using machine learning were ineffective and computationally expensive for routine genetic evaluations. This research aimed to present a low-cost algorithm for predicting traits that are commonly used in pig breeding programs such as weight (WT), loin depth (LD), and backfat thickness (BF) from image data. The dataset included two-dimensional side views of 9,283 pigs captured between 2021 and 2023 and manually recorded phenotypes for WT, LD, and BF. We proposed a pipeline consisting of two major tasks to predict phenotypes: body segmentation and feature extraction using a convolutional neural network (CNN). Initially, we used the YOLOv7 model to leverage existing pre-trained layers to extract the individual region of interest from the source image. Then, low-variance images were removed to ensure a wide range of diversity within the individual. The images are then annotated with prerecorded traits and divided into training and test sets (80% and 20%, respectively), with no overlap. Our study benchmarked VGG16, InceptionV3, and ResNet50 model architectures by extracting features from the outputs of the final convolutional blocks or layers to reduce the impact of the fully connected layer on potential feature loss. The results show that the features extracted from the ResNet50 model had the lowest mean absolute error, with values of 5.22 for weight, 5.28 for loin depth, and 1.31 for backfat thickness. The performance could be improved further by fine-tuning the model parameters. As a future step for this study, the predicted phenotypes will be utilized for genomic evaluations using a multiple-trait model, where the subset of predicted animals will have prerecorded traits to account for the prediction error.
86 Awardee Talk: Improving climatic resilience in pigs through genomics: From trait definition and genetic modeling to biological validation
Authors: Brito Luiz F , Johnson Jay S 
Published: September 2024

Abstract
Breeding programs have substantially improved productive and reproductive efficiency in worldwide swine populations. However, increased performance is often accompanied by reduced climatic resilience due to greater metabolic heat production. Genomic selection is an effective tool for alleviating the negative effects of heat stress, but it requires traits that effectively capture the physiological, behavioral, and morphological mechanisms regulating the heat stress response. In this presentation we will first describe the genetic background of heat tolerance based on routinely recorded phenotypes and climatic variables obtained from public-weather-station databases. There are clear genotype-by-environment interactions across environmental conditions and the definition of the critical periods for quantifying the environmental gradient directly impacts the accuracy of genomic prediction of breeding values for heat tolerance. Secondly, we will describe the genetic background of various behavioral, physiological, and morphological indicators of heat stress in maternal line pigs, including automatically recorded vaginal temperature, skin temperature measured in different body locations and times of the day, respiration rate, panting score, body condition score, hair cortisol, hair density, and ear area. We will also present recently developed indicators of climatic resilience based on variability metrics of longitudinal measurements of automatically recorded body temperature in lactating sows. These traits are heritable and can be successfully included in genomic breeding programs. Furthermore, we will discuss the transgenerational effects of heat stress in maternal-line pigs from quantitative and molecular perspectives, including an epigenomic analyses of in utero heat stress that identified over 200 genomic regions that are differentially methylated in in utero heat stressed pigs and provide results from a comprehensive biological validation study based on in-depth phenotyping of genetically divergent animals. We will also discuss key challenges and opportunities for breeding for improved climatic resilience in pigs. In summary, we will: 1) provide a comprehensive description of the environmental-gradient variables and critical periods fitted in genomic evaluations of heat tolerance; 2) describe the genetic background of various indicators of heat tolerance and climatic resilience; 3) present the accuracies of genomic predictions for each indicator trait; 5) describe key results from biological validation studies; and, 6) make recommendations for the implementation of genomic selection for improved heat tolerance in pigs.