Relevant bibliographies by topics / Mortality and Length of stay Prediction

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Academic literature on the topic 'Mortality and Length of stay Prediction'

Author: Grafiati
Published: 2 November 2024

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Journal articles on the topic "Mortality and Length of stay Prediction"

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Awad, Aya, Mohamed Bader–El–Den, and James McNicholas. "Patient length of stay and mortality prediction: A survey." Health Services Management Research 30, no. 2 (March 22, 2017): 105–20. http://dx.doi.org/10.1177/0951484817696212.

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Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.
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Alghatani, Khalid, Nariman Ammar, Abdelmounaam Rezgui, and Arash Shaban-Nejad. "Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation." JMIR Medical Informatics 9, no. 5 (May 5, 2021): e21347. http://dx.doi.org/10.2196/21347.

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Background Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.
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Burdick, Hoyt, Eduardo Pino, Denise Gabel-Comeau, Andrea McCoy, Carol Gu, Jonathan Roberts, Sidney Le, et al. "Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals." BMJ Health & Care Informatics 27, no. 1 (April 2020): e100109. http://dx.doi.org/10.1136/bmjhci-2019-100109.

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BackgroundSevere sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.ObjectiveThe purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.DesignProspective clinical outcomes evaluation.SettingEvaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.ParticipantsAnalyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay (‘sepsis-related’ patients).InterventionsMachine learning algorithm for severe sepsis prediction.Outcome measuresIn-hospital mortality, length of stay and 30-day readmission rates.ResultsHospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis.ConclusionsReductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings.Trial registration numberNCT03960203
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Cai, Xiongcai, Oscar Perez-Concha, Enrico Coiera, Fernando Martin-Sanchez, Richard Day, David Roffe, and Blanca Gallego. "Real-time prediction of mortality, readmission, and length of stay using electronic health record data." Journal of the American Medical Informatics Association 23, no. 3 (September 15, 2015): 553–61. http://dx.doi.org/10.1093/jamia/ocv110.

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Objective To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs). Materials and Methods A Bayesian Network model was built to estimate the probability of a hospitalized patient being “at home,” in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32 634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years. Results The model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model’s predictive ability was highest within 24 hours from prediction (AUROC = 0.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84. Discussion We developed the first non–disease-specific model that simultaneously predicts remaining days of hospitalization, death, and readmission as part of the same outcome. By providing a future daily probability for each outcome class, we enable the visualization of future patient trajectories. Among these, it is possible to identify trajectories indicating expected discharge, expected continuing hospitalization, expected death, and possible readmission. Conclusions Bayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.
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Wang, Chen-Yu, Chen Liu, Hsien-Hui Yang, Pei-Ying Tseng, and Jong-Yi Wang. "The Association between Medical Utilization and Chronic Obstructive Pulmonary Disease Severity: A Comparison of the 2007 and 2011 Guideline Staging Systems." Healthcare 10, no. 4 (April 13, 2022): 721. http://dx.doi.org/10.3390/healthcare10040721.

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(1) Background: This study aimed to investigate the associations between the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging systems, medical costs, and mortality among patients with chronic obstructive lung disease (COPD). Predictions of the effectiveness of the two versions of the staging systems were also compared. (2) Purpose: this study investigated the associations between the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging systems, medical costs, and mortality among patients with COPD. Predicting effectiveness between the two versions of the staging systems was also compared. (3) Procedure: This study used a secondary clinical database of a medical center in central Taiwan to examine records between 2011 and 2017. A total of 613 patients with COPD were identified. The independent variables comprised the COPD GOLD Guideline staging of the 2007 and 2011 versions, demographic characteristics, health status, and physician seniority. The dependent variables included total medical cost, average length of hospital stay, and mortality. The statistical methods included binomial logistic regression and the general linear model (GLM). (4) Discussion: The total medical cost during the observation period for patients with COPD averaged TWD 292,455.6. The average length of hospital stay was 9.7 days. The mortality rate was 9.6%, compared with that of patients in Grade 1 of the 2007 version; patients in Grade 4 of the 2007 version had significantly higher odds of death (OR = 4.07, p = 0.02). The accuracy of mortality prediction for both the 2007 and 2011 versions of the staging was equal, at 90.4%. The adjusted GLM analysis revealed that patients in Group D of the 2011 version had a significantly longer length of hospital stay than those in Group A of the 2011 version (p = 0.04). No difference between the 2007 and 2011 versions was found regarding the total medical cost. Complications were significantly associated with the total medical cost and average length of hospital stay. (5) Conclusions: The COPD staging 2011 version was associated with an average length of hospital stay, whereas the COPD staging 2007 version was related to mortality risk. Therefore, the 2011 version can estimate the length of hospital stay. However, in predicting prognosis and mortality, the 2007 version is recommended.
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Ghorbani, Mohammad, Haleh Ghaem, Abbas Rezaianzadeh, Zahra Shayan, Farid Zand, and Reza Nikandish. "A study on the efficacy of APACHE-IV for predicting mortality and length of stay in an intensive care unit in Iran." F1000Research 6 (November 20, 2017): 2032. http://dx.doi.org/10.12688/f1000research.12290.1.

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Background:Clinical assessment of disease severity is an important part of medical practice for prediction of mortality and morbidity in Intensive Care Unit (ICU). A disease severity scoring system can be used as guidance for clinicians for objective assessment of disease outcomes and estimation of the chance of recovery. This study aimed to evaluate the hypothesis that the mortality and length of stay in emergency ICUs predicted by APACHE-IV is different to the real rates of mortality and length of stay observed in our emergency ICU in Iran.Methods:This was a retrospective cohort study conducted on the data of 839 consecutive patients admitted to the emergency ICU of Nemazi Hospital, Shiraz, Iran, during 2012-2015. The relevant variables were used to calculate APACHE-IV. Length of stay and death or discharge, Glasgow coma score, and acute physiology score were also evaluated. Moreover, the accuracy of APACHE-IV for mortality was assessed using area under the Receiver Operator Characteristic (ROC) curve.Results:Of the studied patients, 157 died and 682 were discharged (non-survivors and survivors, respectively). The length of stay in the ICU was 10.98±14.60, 10.22 ± 14.21 and 14.30±15.80 days for all patients, survivors, and non-survivors, respectively. The results showed that APACHE-IV model underestimated length of stay in our emergency ICU (p<0.001). In addition, the overall observed mortality was 17.8%, while the predicted mortality by APACHE-IV model was 21%. Therefore, there was an overestimation of predicted mortality by APACHE-IV model, with an absolute difference of 3.2% (p=0.036).Conclusion:The findings showed that APACHE-IV was a poor predictor of length of stay and mortality rate in emergency ICU. Therefore, specific models based on big sample sizes of Iranian patients are required to improve accuracy of predictions.
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Choi, Jeff, Edward B. Vendrow, Michael Moor, and David A. Spain. "Development and Validation of a Model to Quantify Injury Severity in Real Time." JAMA Network Open 6, no. 10 (October 9, 2023): e2336196. http://dx.doi.org/10.1001/jamanetworkopen.2023.36196.

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ImportanceQuantifying injury severity is integral to trauma care benchmarking, decision-making, and research, yet the most prevalent metric to quantify injury severity—Injury Severity Score (ISS)— is impractical to use in real time.ObjectiveTo develop and validate a practical model that uses a limited number of injury patterns to quantify injury severity in real time through 3 intuitive outcomes.Design, Setting, and ParticipantsIn this cohort study for prediction model development and validation, training, development, and internal validation cohorts comprised 223 545, 74 514, and 74 514 admission encounters, respectively, of adults (age ≥18 years) with a primary diagnosis of traumatic injury hospitalized more than 2 days (2017-2018 National Inpatient Sample). The external validation cohort comprised 3855 adults admitted to a level I trauma center who met criteria for the 2 highest of the institution’s 3 trauma activation levels.Main Outcomes and MeasuresThree outcomes were hospital length of stay, probability of discharge disposition to a facility, and probability of inpatient mortality. The prediction performance metric for length of stay was mean absolute error. Prediction performance metrics for discharge disposition and inpatient mortality were average precision, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Calibration was evaluated using calibration plots. Shapley addictive explanations analysis and bee swarm plots facilitated model explainability analysis.ResultsThe Length of Stay, Disposition, Mortality (LDM) Injury Index (the model) comprised a multitask deep learning model trained, developed, and internally validated on a data set of 372 573 traumatic injury encounters (mean [SD] age = 68.7 [19.3] years, 56.6% female). The model used 176 potential injuries to output 3 interpretable outcomes: the predicted hospital length of stay, probability of discharge to a facility, and probability of inpatient mortality. For the external validation set, the ISS predicted length of stay with mean absolute error was 4.16 (95% CI, 4.13-4.20) days. Compared with the ISS, the model had comparable external validation set discrimination performance (facility discharge AUROC: 0.67 [95% CI, 0.67-0.68] vs 0.65 [95% CI, 0.65-0.66]; recall: 0.59 [95% CI, 0.58-0.61] vs 0.59 [95% CI, 0.58-0.60]; specificity: 0.66 [95% CI, 0.66-0.66] vs 0.62 [95%CI, 0.60-0.63]; mortality AUROC: 0.83 [95% CI, 0.81-0.84] vs 0.82 [95% CI, 0.82-0.82]; recall: 0.74 [95% CI, 0.72-0.77] vs 0.75 [95% CI, 0.75-0.76]; specificity: 0.81 [95% CI, 0.81-0.81] vs 0.76 [95% CI, 0.75-0.77]). The model had excellent calibration for predicting facility discharge disposition, but overestimated inpatient mortality. Explainability analysis found the inputs influencing model predictions matched intuition.Conclusions and RelevanceIn this cohort study using a limited number of injury patterns, the model quantified injury severity using 3 intuitive outcomes. Further study is required to evaluate the model at scale.
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Barsasella, Diana, Karamo Bah, Pratik Mishra, Mohy Uddin, Eshita Dhar, Dewi Lena Suryani, Dedi Setiadi, et al. "A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients." Medicina 58, no. 11 (October 31, 2022): 1568. http://dx.doi.org/10.3390/medicina58111568.

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Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan’s National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning.
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Sessler, Daniel I., Jeffrey C. Sigl, Paul J. Manberg, Scott D. Kelley, Armin Schubert, and Nassib G. Chamoun. "Broadly Applicable Risk Stratification System for Predicting Duration of Hospitalization and Mortality." Anesthesiology 113, no. 5 (November 1, 2010): 1026–37. http://dx.doi.org/10.1097/aln.0b013e3181f79a8d.

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Background Hospitals are increasingly required to publicly report outcomes, yet performance is best interpreted in the context of population and procedural risk. We sought to develop a risk-adjustment method using administrative claims data to assess both national-level and hospital-specific performance. Methods A total of 35,179,507 patient stay records from 2001-2006 Medicare Provider Analysis and Review (MEDPAR) files were randomly divided into development and validation sets. Risk stratification indices (RSIs) for length of stay and mortality endpoints were derived from aggregate risk associated with individual diagnostic and procedure codes. Performance of RSIs were tested prospectively on the validation database, as well as a single institution registry of 103,324 adult surgical patients, and compared with the Charlson comorbidity index, which was designed to predict 1-yr mortality. The primary outcome was the C statistic indicating the discriminatory power of alternative risk-adjustment methods for prediction of outcome measures. Results A single risk-stratification model predicted 30-day and 1-yr postdischarge mortality; separate risk-stratification models predicted length of stay and in-hospital mortality. The RSIs performed well on the national dataset (C statistics for median length of stay and 30-day mortality were 0.86 and 0.84). They performed significantly better than the Charlson comorbidity index on the Cleveland Clinic registry for all outcomes. The C statistics for the RSIs and Charlson comorbidity index were 0.89 versus 0.60 for median length of stay, 0.98 versus 0.65 for in-hospital mortality, 0.85 versus 0.76 for 30-day mortality, and 0.83 versus 0.77 for 1-yr mortality. Addition of demographic information only slightly improved performance of the RSI. Conclusion RSI is a broadly applicable and robust system for assessing hospital length of stay and mortality for groups of surgical patients based solely on administrative data.
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Widyastuti, Yunita, Akhmad Yun Jufan, Untung Widodo, Calcarina Fitriani Retno Wisudarti, Sudadi ., Rizki Ahmad Fauzi, and Firman Ardiansyah. "A tertiary care center-based study of a novel ‘ICU Mortality and Prolonged Stay Risk Scoring System’." Anaesthesia, Pain & Intensive Care 28, no. 1 (February 4, 2024): 100–107. http://dx.doi.org/10.35975/apic.v28i1.2382.

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Background & objective: Intensive care has been associated with high cost and resource-intensive medical care. Therefore, a risk prediction model is required to plan time allocation, human resources, and the required equipment. Various risk predictions for ICU mortality and ‘Prolonged Length of Stay’ (PLOS) scores are already available. Still, the established model, such as the APACHE IV score or SAPS II, sometimes became impractical since they required many laboratory parameters. A model based on co-morbidities and demographic factors may be more useful in limited resources setting. Hence, we developed a simple ICU mortality and PLOS risk prediction model based on co-morbidities and demographic data. Methodology: This retrospective cohort study was performed to develop a risk scoring for mortality and PLOS, using data from Dr. Sardjito Hospital Yogyakarta database between January 01-December 31, 2019. Logistic regression and bootstrap methods were used to create a risk score for estimating the risk. The discrimination performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). The Hosmer-Lemeshow test was employed to assess the model’s calibration. Results: A total of 415 patients were included in this study. The risk factors for mortality were perioperative support medication, kidney failure, neurologic disorder, respiratory failure, and intraoperative blood transfusion. The mortality score of 6 was associated with a 100% probability of mortality. Medical cases, GCS < 8, vasoactive/inotropic medication, sepsis, respiratory failure, and kidney failure were the risk factors for PLOS. PLOS score of 3 was associated with a 100% probability of PLOS. The discrimination for either mortality or PLOS was considered excellent with the AUC (± 95% CI) for mortality 0.896 (0.853-0.94), while for PLOS 0.878 (0.80-0.90). The calibration test found that both models had good calibration with P values of 0.53 and 0.55 for mortality and PLOS, respectively. Conclusion: The ‘Mortality and Prolonged Length of Stay Prediction Score’ based on co-morbidities and demographic data upon admission to ICU had good accuracy and can be applied as a potential new scoring system in healthcare institutions. Abbreviations: APACHE- Acute Physiologic Assessment and Chronic Health Evaluation; AUC; Area Under the Curve GCS- Glasgow Coma Scale; ICU- Intensive Care Unit; PLOS- ‘Prolonged Length of Stay’; PRC- Packed Red Cells; SAPS- Simplified Acute Physiology Score Keywords: Risk Scoring; Mortality; Prolonged Length of Stay; ICU Citation: Widyastuti Y, Jufan AY, Widodo U, Wisudarti CFR, Sudadi, Fauzi RA, Ardiansyah F. A tertiary care center-based study of a novel ‘ICU Mortality and Prolonged Stay Risk Scoring System’. Anaesth. pain intensive care 2024;28(1):100−107; DOI: 10.35975/apic.v28i1.2382 Received: October 02, 2023; Reviewed: November 27, 2023; Accepted: December 17, 2023
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Dissertations / Theses on the topic "Mortality and Length of stay Prediction"

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Shin, Jung-Ho. "New outcome-specific comorbidity scores excelled in predicting in-hospital mortality and healthcare charges in administrative databases." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263579.

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Cissoko, Mamadou Ben Hamidou. "Adaptive time-aware LSTM for predicting and interpreting ICU patient trajectories from irregular data." Electronic Thesis or Diss., Strasbourg, 2024. https://publication-theses.unistra.fr/restreint/theses_doctorat/2024/CISSOKO_MamadouBenHamidou_2024_ED269.pdf.

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En médecine prédictive personnalisée, modéliser avec précision la maladie et les processus de soins d'un patient est crucial en raison des dépendances temporelles à long terme inhérentes. Cependant, les dossiers de santé électroniques (DSE) se composent souvent de données épisodiques et irrégulières, issues des admissions hospitalières sporadiques, créant des schémas uniques pour chaque séjour hospitalier.Par conséquent, la construction d'un modèle prédictif personnalisé nécessite une considération attentive de ces facteurs pour capturer avec précision le parcours de santé du patient et aider à la prise de décision clinique.LSTM sont efficaces pour traiter les données séquentielles comme les DSE, mais ils présentent deux limitations majeures : l'incapacité à interpréter les résultats des prédictions et à prendre en compte des intervalles de temps irréguliers entre les événements consécutifs. Pour surmonter ces limitations, nous introduisons de nouveaux réseaux neuronaux à mémoire dynamique profonde appelés Multi-Way Adaptive et Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM etAMITA), conçus pour les données séquentielles collectées de manière irrégulière.L'objectif principal des deux modèles est de tirer parti des dossiers médicaux pour mémoriser les trajectoires de maladie et les processus de soins, estimer les états de maladie actuels et prédire les risques futurs, offrant ainsi un haut niveau de précision et de pouvoir prédictif
In personalized predictive medicine, accurately modeling a patient's illness and care processes is crucial due to the inherent long-term temporal dependencies. However, Electronic Health Records (EHRs) often consist of episodic and irregularly timed data, stemming from sporadic hospital admissions, which create unique patterns for each hospital stay. Consequently, constructing a personalized predictive model necessitates careful consideration of these factors to accurately capture the patient's health journey and assist in clinical decision-making. LSTM networks are effective for handling sequential data like EHRs, but they face two significant limitations: the inability to interpret prediction results and to take into account irregular time intervals between consecutive events. To address limitations, we introduce novel deep dynamic memory neural networks called Multi-Way Adaptive and Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM and AMITA) designed for irregularly collected sequential data. The primary objective of both models is to leverage medical records to memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power
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Lipovich, Carol Jean. "Analysis of Ventilator Associated Pneumonia Patients' Hospital and Intensive Care Charges, Length of Stay and Mortality." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366228755.

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Sundareshan, Padma. "Clostridium difficile Infection (CDI) Incidence Rate and CDI-Associated Length of Stay, Total Hospital Charges and Mortality." The University of Arizona, 2009. http://hdl.handle.net/10150/623982.

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Class of 2009 Abstract
OBJECTIVES: The purpose of the study was to determine the rate of Clostridium difficile infections (CDI) in hospitalized patients and the various factors that were associated with the risk of developing CDI by examining patient discharge data for hospitals in 37 states in the United States using Healthcare Cost and Utilization Project (HCUP). METHODS: Patient discharge information for all patients obtained using HCUP census for the years 2002-2005, either for primary or secondary (all-listed) occurrences of CDI using the ICD-9-CM code (008.45) specific for intestinal infections due to C. difficile, were included in the study. Regression analysis, either Generalized Linear Model log-link or power-link, or a logistic regression was employed to control for the multiple independent variables. RESULTS: The incidence rate for CDI was 9.4% for the years 2002-2005. Among the concomitant diagnoses and procedures, essential hypertension, volume depletion, congestive heart failure, urinary tract infection and venous catheterization were the top 5. The length of stay (LOS) for CDI was associated with being Black, Hispanic or Other race category, number of diagnoses and procedures, primary expected payer of Medicaid, private insurance and other (including worker’s compensation, CHAMPUS,CHAMPVA etc), and all groups classified based on median household income category for patient’s zip code. Predictors of CDI related to inpatient total hospital charges were being female, race (other than black), number of diagnoses and procedures, Death, LOS, patient location and with self-pay and no charge categories as primary expected payer. Predictors of higher CDI related inpatient hospital deaths were age, female sex, Hispanic race, number of diagnoses and procedures, LOS and having Medicaid, self-pay or other as primary expected payer. CONCLUSIONS: LOS, inpatient total hospital charges, and inpatient mortality were dependent on several patient and other characteristics.
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Spencer, Patricia L. "The influence of specialized cancer hospitals in Florida on mortality, length of stay, and charges of care." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002725.

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Fletcher, Emily A., and Robert S. Lawson. "Characteristics of Hospital Inpatient Charges, Length of Stay, and Inpatient Mortality in Patients with Ovarian Cancer from 2002-2005." The University of Arizona, 2009. http://hdl.handle.net/10150/623991.

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Class of 2009
OBJECTIVES: To determine and characterize the relative impact of patient demographics on hospital inpatient charges, length of stay, and inpatient mortality in patients with ovarian cancer from 2002-2005. METHODS: A retrospective database analysis of AHRQ’s Health Care Cost and Utilization Project (HCUP) Nationwide Inpatient Sample databases was conducted spanning from January 1, 2002, to December 31, 2005.Data were collected regarding age, race, payer status, median household income, location of hospital (urban/rural), comorbidities, procedures, total charges, length of stay, and inpatient mortality. Multivariate and gamma regression methods were utilized to examine incremental risks associated with length of stay, total charges, and inpatient mortality, after controlling for all other variables. RESULTS: Overall, data from 246,012 hospital admissions were obtained. The average length of stay of patients was 6.58 days (SD = 7.22), the average number of diagnoses was 7.18 (SD = 3.36), the average number of procedures performed was 2.71 (SD = 2.66). A total of 14,485 (5.9%) patients died during hospitalization. The average total charge was $29,698 (SD = $42,951). The IRR was 0.886 (95%CI, -0.105 to -0.04) for patients who were Hispanic, and 1.089 (95%CI, 0.017–0.153) for patients who were Black compared to patients who were white. When compared to patients who lived in large, metropolitan areas, the IRR was 0.88 (95%CI, -0.146 to - 0.109) for patients located in smaller, metropolitan areas, and the IRR was 0.74 (95%CI, -0.335 to -0.268) for patients located in non- urban areas. CONCLUSIONS: Patient demographics were found to have associations, both directly and indirectly, with length o
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Pattakos, Gregory. "Predicting Length of Stay and Non-Home Discharge: A Novel Approach to Reduce Wasted Resources after Cardiac Surgery." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1291145768.

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Leitch, David B. "Predictive patterns of institutional misconduct, pro-social behavior, and length of stay of incarcerated youth in a secure, long-term, juvenile rehabilitation facility." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1529614192152508.

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Straathof, Bas Theodoor. "A Deep Learning Approach to Predicting the Length of Stay of Newborns in the Neonatal Intensive Care Unit." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282873.

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Recent advancements in machine learning and the widespread adoption of electronic healthrecords have enabled breakthroughs for several predictive modelling tasks in health care. One such task that has seen considerable improvements brought by deep neural networks is length of stay (LOS) prediction, in which research has mainly focused on adult patients in the intensive care unit. This thesis uses multivariate time series extracted from the publicly available Medical Information Mart for Intensive Care III database to explore the potential of deep learning for classifying the remaining LOS of newborns in the neonatal intensive care unit (NICU) at each hour of the stay. To investigate this, this thesis describes experiments conducted with various deep learning models, including long short-term memory cells, gated recurrentunits, fully-convolutional networks and several composite networks. This work demonstrates that modelling the remaining LOS of newborns in the NICU as a multivariate time series classification problem naturally facilitates repeated predictions over time as the stay progresses and enables advanced deep learning models to outperform a multinomial logistic regression baseline trained on hand-crafted features. Moreover, it shows the importance of the newborn’s gestational age and binary masks indicating missing values as variables for predicting the remaining LOS.
Framstegen inom maskininlärning och det utbredda införandet av elektroniska hälsoregister har möjliggjort genombrott för flera prediktiva modelleringsuppgifter inom sjukvården. En sådan uppgift som har sett betydande förbättringar förknippade med djupa neurala nätverk är förutsägelsens av vistelsetid på sjukhus, men forskningen har främst inriktats på vuxna patienter i intensivvården. Den här avhandlingen använder multivariata tidsserier extraherade från den offentligt tillgängliga databasen Medical Information Mart for Intensive Care III för att undersöka potentialen för djup inlärning att klassificera återstående vistelsetid för nyfödda i den neonatala intensivvårdsavdelningen (neonatal-IVA) vid varje timme av vistelsen. Denna avhandling beskriver experiment genomförda med olika djupinlärningsmodeller, inklusive longshort-term memory, gated recurrent units, fully-convolutional networks och flera sammansatta nätverk. Detta arbete visar att modellering av återstående vistelsetid för nyfödda i neonatal-IVA som ett multivariat tidsserieklassificeringsproblem på ett naturligt sätt underlättar upprepade förutsägelser över tid och gör det möjligt för avancerade djupa inlärningsmodeller att överträffaen multinomial logistisk regressionsbaslinje tränad på handgjorda funktioner. Dessutom visar det vikten av den nyfödda graviditetsåldern och binära masker som indikerar saknade värden som variabler för att förutsäga den återstående vistelsetiden.
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Oliveira, Ana Rita Castelo Branco. "Pneumonias adquiridas durante o internamento hospitalar : impacte na saúde e implicação nos custos." Master's thesis, Universidade Nova de Lisboa. Escola Nacional de Saúde Pública, 2012. http://hdl.handle.net/10362/9702.

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RESUMO - Introdução: O presente estudo pretende analisar o impacte na saúde e a implicação nos custos da Pneumonia adquirida durante o internamento hospitalar. Está comprovado que as infeções hospitalares constituem um problema de Saúde pública dos hospitais em todo o mundo. Metodologia: A população em estudo abrange 97 033 episódios de internamento, ocorridos em 10 hospitais, no ano de 2010. O trabalho compreende três fases: i) caracterização da população em estudo; ii) identificação das variáveis que influenciam os resultados em saúde; iii) estimação dos custos do internamento com Pneumonia. Resultados: Os episódios de internamento com Pneumonia ocorreram maioritariamente no sexo masculino (58.1%). A faixa etária com mais episódios foi a dos 80 aos 89 anos. A taxa de Prevalência foi de 4.16% e a taxa de Mortalidade foi de 34.56%. Os doentes com Pneumonia tiveram uma demora média superior em 13 dias em relação aos doentes sem Pneumonia para o mesmo conjunto de GDH. Pertencer ao sexo masculino e os episódios de internamento ocorridos em hospitais não universitários levam a um aumento da probabilidade de morrer. Por sua vez apresentar uma maior duração de internamento e um número superior de comorbilidades levam a uma diminuição deste risco. Os custos em excesso dos episódios de internamento devido à aquisição de Pneumonia como doença secundária foram de aproximadamente 18 milhões de euros. Conclusão: O trabalho foi elaborado tendo em vista a quantificação do fenómeno em Portugal, tanto em termos da carga da doença, como das implicações financeiras. Os valores encontrados são preocupantes, pelo que se torna necessário tomar medidas e introduzir práticas na atividade hospitalar que minimizem as infeções hospitalares em geral e da Pneumonia em particular. Por sua vez é expectável, face ao descrito na literatura internacional, que a introdução destas práticas melhor os resultados em saúde e o desempenho financeiro dos hospitais.
ABSTRACT - Introduction: The main goal of this study is to analyze the health and the costs due to acquired Pneumonia during hospital stay. There is evidence that hospital infections are a public health problem in hospitals worldwide. Methods: The population analyzed is 97,033 hospital admissions, occurred in 10 hospitals in the year 2010. The work comprises three phases: i) characterization of the population, ii) identification of variables that influence health outcomes, iii) estimating the costs of acquired Pneumonia. Results: Admissions with acquired Pneumonia are more frequent on males (58.1%). The most relevant age group was from 80 to 89 years. The prevalence rate was 4.16% and the in-hospital mortality rate was 34.56%. The patients with acquired Pneumonia had an increase of the length of stay circa 13 days compared with patients without acquired Pneumonia for the same set of GDH. The males and admissions on non-teaching hospitals lead to an increased risk of hospital death. Moreover larger length of stay and higher number of comorbidities had decreased the risk of hospital death. The increase on admissions costs due to acquired Pneumonia were circa 18 million euros. Conclusions: The study presents some poor health outcomes, as well as costs increase due to acquired Pneumonia in Portuguese public hospitals. These results should be considered as a real problem in Portugal, and therefore it is necessary to be more evidenced based on hospital guidelines definition and in clinical management practice in order to increase hospital’s effectiveness and efficiency.
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Books on the topic "Mortality and Length of stay Prediction"

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Commission, Colorado Health Data, ed. Colorado hospital outcomes: Mortality, length of stay, and charges for cardiovascular and other diseases, 1992. Denver: Colorado Health Data Commission, Office of Public and Private Initiatives, Dept. of Health Care Policy & Financing, 1994.

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Pennsylvania Health Care Cost Containment Council., ed. Pennsylvania's guide to coronary artery bypass graft surgery, 2002: Information about hospitals and cardiothoracic surgeons. Harrisburg, PA: Pennsylvania Health Care Cost Containment Council, 2004.

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Kakai, Hayder. Can Early Tracheostomy Decrease Mortality Rate, Length of ICU Stay and Duration of Mechanical Ventilation When Compared with Late Tracheostomy ? Independently Published, 2018.

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Pennsylvania's guide to coronary artery bypass graft surgery, 2000: Information about hospitals and cardiothoracic surgeons. Harrisburg, PA: Pennsylvania Health Care Cost Containment Council, 2002.

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Alhazzani, Waleed, and Deborah J. Cook. Stress ulcer prophylaxis and treatment drugs in critical illness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0041.

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Many changes have occurred over the last three decades in the field of stress ulcer gastrointestinal bleeding and its prevention. The topic is controversial, fuelled by disparate data, studies at risk of bias, and the impression that the problem is not as serious as it once was. Indeed, compared with over four decades ago when mucosal ulceration of the stomach causing serious bleeding was first described, a relatively small proportion of critically-ill patients now develop clinically important bleeding. Acid suppression is commonly prescribed for stress ulcer prophylaxis (SUP), targeting subgroups of patients at high risk in the intensive care unit (ICU), rather than universal prevention. The randomized clinical trials to date suggest a significant reduction in CIB with use of histamine-2-receptor antagonists (H2RAs) compared with no SUP, with no impact on pneumonia, ICU mortality, or length of stay. However, these trials are of moderate quality. More recent RCTs suggest proton pump inhibitors compared with H2RAs may significantly reduce the risk of CIB without influencing the risk of pneumonia, ICU mortality, or length of stay. These trials are also of moderate quality. Today, the decision whether to use SUP, and which agent to use, is complex. Clinical considerations include local epidemiological data (for centres documenting these outcomes), and patient-specific risks of gastrointestinal bleeding and infection, indexed to case mix.
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Sharples, Edward. Acute kidney injury. Edited by Rutger Ploeg. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199659579.003.0127.

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Acute kidney injury (AKI) is a common, major cause of morbidity and mortality in hospitalized patients, and contributes significantly to length of stay and hence costs. Large epidemiological studies consistently demonstrate an incidence of AKI of 5–18% depending on the definition of AKI utilized. Even relatively small changes in renal function are associated with increased mortality, and this has led to strict definition and staging of AKI. Early recognition with good clinical assessment, diagnosis, and management are critical to prevent progression of AKI and reduce the potential complications, including long-term risk of end-stage renal failure. In this chapter, the pathophysiology, causes, and early management of AKI are discussed. Hypovolaemia and sepsis are the most common causes in hospitalized patients, across medical and surgical specialities. Other common causes are discussed, as well as diagnostic criteria.
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Wunsch, Hannah, and Andrew A. Kramer. The role and limitations of scoring systems. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0028.

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Scoring systems for critically-ill patients provide a measure of the severity of illness of patients admitted to intensive care units (ICUs). They are primarily based on patient characteristics, physiological derangement, and/or clinical assessments. Severity scores themselves allow for risk-adjusting outcomes, but they can also be used to provide a prediction of the overall risk of death, length of stay, or other outcome for critically ill patients. This allows for comparison of outcomes between different cohorts of patients or between observed and predicted ICU performance. There are a number of general ICU scoring systems that are in use. All scoring systems have limitations. Future scoring systems may include prediction of longer-term outcomes, and assimilation of granular data temporally and at the molecular level that could result in more personalized severity scores to help guide individual care decisions.
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Dubose, Arielle C., and SreyRam Kuy. A Comparison of Laparoscopically Assisted and Open Colectomy for Colon Cancer. Edited by SreyRam Kuy and Miguel A. Burch. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199384075.003.0010.

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The landmark COST trial compared outcomes between minimally invasive surgery and open surgical resection for patients with colon cancer. The study found that patients with operable right, left, or sigmoid colon cancer, either laparoscopic-assisted or open colectomy may be offered without compromising the risk of tumor recurrence or mortality. Patients who underwent colectomy via a laparoscopic-assisted approach, compared to open, had a shorter length of hospital stay and required parenteral narcotics and oral analgesics for less time, while having equal rates of intraoperative and postoperative complications. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case.
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Rello, Jordi, and Bárbara Borgatta. Pathophysiology of pneumonia. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0115.

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Airway colonization, ventilator-associated tracheobronchitis (VAT), and hospital-acquired (HAP) and ventilator-associated pneumonia (VAP) are three manifestations having the presence of micro-organisms in airways in common. Newer definitions have to consider worsening of oxygenation, in addition to purulent respiratory secretions, chest-X rays opacities, and biomarkers of inflammation. Bacteria are the main causes of HAP/VAP. During hospitalization there’s a shift of airway’s colonizing flora from core organisms to enteric and non-fermentative ones. Macro- and micro-aspiration is the most important source of pneumonia. Endotracheal tube secretion leakage is an important source, serving biofilm as a reservoir. Exogenous colonization is infrequent, but it may contribute to cross-infection with resistant species. Prevention of VAP can be achieved by implementing multidisciplinary care bundles focusing on oral/hand hygiene and control of sedation. Pneumonia develops when micro-organisms overwhelm host defences, resulting in a multifocal process. Risk and severity of pneumonia is determined by bacterial burden, organism virulence and host defences. Innate and adaptive immune responses are altered, decreasing clearing of pathogens. Some deficits of the complement pathway in intubated patients are associated with increased risk for VAP and higher mortality. Micro-arrays have demonstrated specific different immunological signatures for VAP and VAT. Early antibiotic therapy is associated with a decrease in early HAP/VAP incidence, but selects for MDR organisms. Attributable mortality is lower than 10%, but HAP/VAP prolongs length of stay, and dramatically increase costs and use of health care resources.
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Fawcett, William J. Anaesthesia for abdominal surgery. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0061.

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Care of patients undergoing major gastrointestinal surgery has been revolutionized in the last decade. The widespread adoption of laparoscopic surgery has bought benefits but also new challenges. Anaesthetic techniques, particularly refinements in analgesic regimens and fluid management, have also brought benefits to patients. However, many more elderly and frail patients are undergoing major surgery which is a challenge in both expertise and resources. Anaesthesia for patients undergoing gastrointestinal surgery has evolved into a package of perioperative care, with the anaesthetist increasingly viewed as the perioperative physician. Anaesthetists are now involved not only within the operating theatre, but with assessing risk for patients, optimizing them prior to surgery, and supervising postoperative care and in particular early recognition and treatment of complications. Liver surgery has become routine for patients particularly with secondary colorectal metastases. Previously, 5-year survival was very rare in these groups of patients, but now approximately half of patients are alive at 5 years. Colorectal surgery has also been transformed and the enhanced recovery programme has typified the way in which many years of dogma have been challenged, to be replaced by evidence-based pathways. Overall, for major elective surgery, results have improved and in general, morbidity, mortality, complications, and length of hospital stay for patients have reduced. For emergency patients, although there have been improvements too, there is still widespread concern about high mortality and marked variation in care between centres.
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Book chapters on the topic "Mortality and Length of stay Prediction"

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Pick, Fergus, Xianghua Xie, and Lin Yuanbo Wu. "Contrastive Multitask Transformer for Hospital Mortality and Length-of-Stay Prediction." In Lecture Notes in Computer Science, 134–45. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67278-1_11.

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Touati Hamad, Zineb, Mohamed Ridda Laouar, and Gadri Dhouha. "Machine Learning Algorithms for Hospital Length of Stay Prediction." In Lecture Notes in Networks and Systems, 149–63. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60591-8_13.

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Stoean, Ruxandra, Catalin Stoean, Adrian Sandita, Daniela Ciobanu, and Cristian Mesina. "Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer." In Advances in Computational Intelligence, 444–57. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19258-1_37.

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Alsinglawi, Belal, Fady Alnajjar, Omar Mubin, Mauricio Novoa, Ola Karajeh, and Omar Darwish. "Benchmarking Predictive Models in Electronic Health Records: Sepsis Length of Stay Prediction." In Advanced Information Networking and Applications, 258–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44041-1_24.

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Neto, Cristiana, Maria Brito, Hugo Peixoto, Vítor Lopes, António Abelha, and José Machado. "Prediction of Length of Stay for Stroke Patients Using Artificial Neural Networks." In Trends and Innovations in Information Systems and Technologies, 212–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45688-7_22.

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Silva, Cristiana, Daniela Oliveira, Hugo Peixoto, José Machado, and António Abelha. "Data Mining for Prediction of Length of Stay of Cardiovascular Accident Inpatients." In Communications in Computer and Information Science, 516–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02843-5_43.

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Mahmud, Farhanahani, Ahmad Zahran Md Khudzari, Cheong Ping Pau, Mohd Faizal Ramli, Norfazlina Jaffar, and Intan Fariza Gaaffar. "Pre-assessment of Machine Learning Approaches for Patient Length of Stay Prediction." In Springer Proceedings in Physics, 369–78. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8903-1_32.

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Osop, Hamzah, Basem Suleiman, Muhammad Johan Alibasa, Drew Wrigley, Alexandra Helsham, and Anne Asmaro. "Improving Patients’ Length of Stay Prediction Using Clinical and Demographics Features Enrichment." In Computational Science – ICCS 2023, 120–28. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36021-3_9.

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Coimbra, Ana, Henrique Vicente, António Abelha, M. Filipe Santos, José Machado, João Neves, and José Neves. "Prediction of Length of Hospital Stay in Preterm Infants a Case-Based Reasoning View." In Intelligent Decision Technologies 2016, 115–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39630-9_10.

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Naemi, Amin, Thomas Schmidt, Marjan Mansourvar, Ali Ebrahimi, and Uffe Kock Wiil. "Prediction of Length of Stay Using Vital Signs at the Admission Time in Emergency Departments." In Innovation in Medicine and Healthcare, 143–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3013-2_12.

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Conference papers on the topic "Mortality and Length of stay Prediction"

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Swara Iskandar, Muh Arga, Tessy Badriyah, and Iwan Syarif. "Prediction of Length of Stay in Hospital Using Hyperparameter Optimization in the Convolutional Neural Networks Method." In 2024 International Electronics Symposium (IES), 460–65. IEEE, 2024. http://dx.doi.org/10.1109/ies63037.2024.10665859.

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Bardak, Batuhan, and Mehmet Tan. "Prediction of Mortality and Length of Stay with Deep Learning." In 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. http://dx.doi.org/10.1109/siu53274.2021.9477707.

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Gu, C., H. Burdick, E. Pino, D. Gabel-Comeau, A. McCoy, S. Le, J. Roberts, et al. "Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a6009.

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Bardak, Batuhan, and Mehmet Tan. "Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions." In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2021. http://dx.doi.org/10.1109/cibcb49929.2021.9562819.

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Kim, Tae Hyun, Won Seok Jang, Sun Cheol Heo, MinDong Sung, and Yu Rang Park. "Personalized Progressive Federated Learning with Leveraging Client-Specific Vertical Features." In International Conference on Computer Science and Machine Learning (CSML 2023). Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130106.

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Federated learning (FL) has been used for model building across distributed clients. However, FL cannot leverage vertically partitioned features to increase the model complexity. In this study, we proposed a personalized progressive federated learning (PPFL) model, which is a multimodel PFL approach that allows the leveraging of vertically partitioned client-specific features. The performance of PPFL was evaluated using the Physionet Challenges 2012 dataset. We compared the performance of in-hospital mortality and length of stay prediction between our model and the FedAvg, FedProx, and local models. The PPFL showed an accuracy of 0.849 and AUROC of 0.790 in average in hospital mor-tality prediction, which are the highest scores compared to client-specific algorithm. For length-of-stay prediction, PPFL also showed an AUROC of 0.808 in average which was the highest among all comparators.
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Liu, Peng, Lei Lei, Junjie Yin, Wei Zhang, Wu Naijun, and Elia El-Darzi. "Healthcare Data Mining: Prediction Inpatient Length of Stay." In 2006 3rd International IEEE Conference Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/is.2006.348528.

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Lacerda, Anisio, and Gisele L. Pappa. "Deep Thompson Sampling for Length of Stay Prediction." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533667.

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Grampurohit, Sneha, and Sagar Sunkad. "Hospital Length of Stay Prediction using Regression Models." In 2020 IEEE International Conference for Innovation in Technology (INOCON). IEEE, 2020. http://dx.doi.org/10.1109/inocon50539.2020.9298294.

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Buskard, Stevenson, Frize, and Solven. "Estimation of ventilation, length of stay, and mortality using artificial neural networks." In Proceedings of Canadian Conference on Electrical and Computer Engineering CCECE-94. IEEE, 1994. http://dx.doi.org/10.1109/ccece.1994.405854.

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Shinozaki, R., A. Schwingshackl, N. Srivastava, T. Grogan, and R. Kelly. "Interfacility Pediatric Critical Care Transport Effects on Mortality and Length of Stay." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a1618.

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Reports on the topic "Mortality and Length of stay Prediction"

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Cherian, Jerald, Jodi Segal, Ritu Sharma, Allen Zhang, Eric Bass, and Michael Rosen. Patient Safety Practices Focused on Sepsis Prediction and Recognition. Agency for Healthcare Research and Quality (AHRQ), April 2024. http://dx.doi.org/10.23970/ahrqepc_mhs4sepsis.

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Objectives. Patient safety practices (PSPs) focused on sepsis prediction and recognition, encompass interventions designed to identify patients with sepsis early and improve timely adherence to guidelines. Our objectives were to review the evidence published after the previous Making Healthcare Safer (MHS) report to determine the effectiveness of sepsis prediction and recognition PSPs on patient safety related outcomes. Methods. We searched PubMed and the Cochrane library for systematic reviews and primary studies published from January 2018 through August 2023, supplemented by gray literature searches. We included reviews and primary studies of sepsis prediction and recognition PSPs reporting measures of clinical process (time to diagnosis or treatment, adherence to guidelines, Severe Sepsis and Septic Shock Early Management Bundle), patient outcomes (hospital or intensive care unit (ICU) length of stay, mortality), implementation (use, barriers, and facilitators), or costs. Findings. We focused on 7 systematic reviews and 8 primary studies that were eligible for full review, and briefly summarized 36 pre-post studies that lacked a separate comparison group. All the sepsis prediction and recognition PSPs were multi-component interventions. Across the systematic reviews and primary studies of neonates, the PSPs improved clinical process measures (low strength of evidence), but evidence was insufficient about length of stay or mortality outcomes. Across the systematic reviews and primary studies of adults, the PSPs did not demonstrate an effect on clinical process, length of stay, or mortality outcomes. In primary studies of adults, evidence was insufficient in the prehospital setting for mortality, length of stay, and clinical process measures. In the emergency department setting, strength of evidence was low for mortality and clinical process measures and insufficient for length of stay. In ward or hospitalwide settings, strength of evidence was low across all three outcome types. The secondary outcome of alerting system performance (e.g., positive predictive value) could not be meaningfully compared across studies due to diversity in populations and interventions. Conclusions. This review finds that recent primary studies and systematic reviews do not support that specific PSPs for sepsis prediction and recognition are effective at reducing mortality or length of stay or improve clinical processes in adults in pre-hospital, emergency department, or hospitalwide settings as compared to usual care. Sepsis prediction and recognition PSPs may improve clinical process outcomes in neonates in ICUs.
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Neodo, Anna, Fiona Augsburger, Jan Waskowski, Joerg C. Schefold, and Thibaud Spinetti. Monocytic HLA-DR expression and clinical outcomes in adult ICU patients with sepsis – a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0119.

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Review question / Objective: The scope of this review was defined using PICOTS framework where 1) population: adult critically ill patients with sepsis or septic shock; 2) index prognostic factor: cell surface protein expression of mHLA-DR in blood; 3) comparative factor: none; 4) outcomes to be predicted: mortality, secondary infections, length of stay, and organ dysfunction score (sequential organ failure assessment [SOFA], multiple organ dysfunction score [MODS], logistic organ dysfunction score [LODS]), composite outcomes where component endpoints consist of at least one of the outcomes stated above (e.g., “adverse outcome” defined as death or secondary infection), 5) timing (of the prediction horizon and the moment of prognosis): any; and 6) setting: ICU. Condition being studied: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to severe infections. It can further progress to septic shock, which includes hemodynamic failure and increased mortality rates. A recent worldwide epidemiological study estimated 48.9 million sepsis cases and 11 million of sepsis-related deaths (~20% of global deaths in 2017). Although its management has advanced considerably, sepsis remains deadly and challenging to treat. The 28/30-day mortality averages around 25% for sepsis and 38% for septic shock in high-income countries. Current models describe the underlying pathophysiologic mechanisms of sepsis as an interplay between concurrent dysfunctional pro- and anti-inflammatory immune response.
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Tipton, Kelley, Brian F. Leas, Nikhil K. Mull, Shazia M. Siddique, S. Ryan Greysen, Meghan B. Lane-Fall, and Amy Y. Tsou. Interventions To Decrease Hospital Length of Stay. Agency for Healthcare Research and Quality (AHRQ), September 2021. http://dx.doi.org/10.23970/ahrqepctb40.

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Background. Timely discharge of hospitalized patients can prevent patient harm, improve patient satisfaction and quality of life, and reduce costs. Numerous strategies have been tested to improve the efficiency and safety of patient recovery and discharge, but hospitals continue to face challenges. Purpose. This Technical Brief aimed to identify and synthesize current knowledge and emerging concepts regarding systematic strategies that hospitals and health systems can implement to reduce length of stay (LOS), with emphasis on medically complex or vulnerable patients at high risk for prolonged LOS due to clinical, social, or economic barriers to timely discharge. Methods. We conducted a structured search for published and unpublished studies and conducted interviews with Key Informants representing vulnerable patients, hospitals, health systems, and clinicians. The interviews provided guidance on our research protocol, search strategy, and analysis. Due to the large and diverse evidence base, we limited our evaluation to systematic reviews of interventions to decrease hospital LOS for patients at potentially higher risk for delayed discharge; primary research studies were not included, and searches were restricted to reviews published since 2010. We cataloged the characteristics of relevant interventions and assessed evidence of their effectiveness. Findings. Our searches yielded 4,364 potential studies. After screening, we included 19 systematic reviews reported in 20 articles. The reviews described eight strategies for reducing LOS: discharge planning; geriatric assessment or consultation; medication management; clinical pathways; inter- or multidisciplinary care; case management; hospitalist services; and telehealth. All reviews included adult patients, and two reviews also included children. Interventions were frequently designed for older (often frail) patients or patients with chronic illness. One review included pregnant women at high risk for premature delivery. No reviews focused on factors linking patient vulnerability with social determinants of health. The reviews reported few details about hospital setting, context, or resources associated with the interventions studied. Evidence for effectiveness of interventions was generally not robust and often inconsistent—for example, we identified six reviews of discharge planning; three found no effect on LOS, two found LOS decreased, and one reported an increase. Many reviews also reported patient readmission rates and mortality but with similarly inconsistent results. Conclusions. A broad range of strategies have been employed to reduce LOS, but rigorous systematic reviews have not consistently demonstrated effectiveness within medically complex, high-risk, and vulnerable populations. Health system leaders, researchers, and policymakers must collaborate to address these needs.
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Miller, Kaleigh. US Guided Management of Undifferentiated Dyspneic Patient in the ED. University of Tennessee Health Science Center, March 2020. http://dx.doi.org/10.21007/com.lsp.2020.0001.

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Intro: Undifferentiated dyspnea can be a complicated presentation muddled by patient comorbidities and similar symptomology shared among etiologies. Some studies have shown increased mortality and length of stay in the hospital when incorrectly initially diagnosed in the ED. US has been shown more effective at differentiating these causes and improves diagnostic accuracy. This study will implement US exam upon initial exam of patient and chart time to diagnosis/treatment, length of stay in ED, length of stay in hospital admissions versus discharge rates, and 30 day mortality. ADHF and COPD/asthma patient differentiation will be the focus. Methods: Prospective cohort study of more than 18 years that present with the primary complaint of dyspnea with more than one complicating comorbid condition. Initial exam by physician will be accompanied by cardiothoracic US previously verified. Results: Study powered by previous year average of time to diagnosis of institution. Patient characteristics, distribution by diagnostic category, and characteristics found on US in correlation with diagnosis will be included for multivariate analysis. Conclusions: We expect to see a singificant difference in our time to diagnosis/treatment and mortality rate.
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Nam, Jae Hyun, Hee Jin Kwack, Woo Seob Ha, and Jee-Eun Chung. Resuscitation fluids for patients with risk factors of multiple organ failure: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2022. http://dx.doi.org/10.37766/inplasy2022.7.0091.

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Review question / Objective: P: patients with risk factors of multiple organ failure I: balanced crystalloids C: normal saline O: mortality, in-hospital mortality, renal failure, length of ICU stay, length of hospital stay. Condition being studied: In clinical field, aggressive fluid resuscitation therapy is administered to prevent the progression of multiple organ failures by maintaining tissue and organ perfusion. Normal saline is frequently used, but it has been some concerns. Although large-scale studies with balanced crystalloids have been conducted, they couldn’t reach significant conclusions due to the diversity of disease severity. Therefore, we aims to evaluate and identify the best fluid for patients at high risk of multiple organ failure by comparing the effects of normal saline and balanced crystalloids.
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Zeng, Siyao, Lei Ma, Lishan Yang, Xiaodong Hu, Xinxin Guo, Yi Li, Yao Zhang, et al. Advantages of damage control surgery over conventional surgery inmultiple trauma: a meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0006.

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Review question / Objective: This meta-analysis aims to explore whether damage control surgery has advantages over traditional surgery in the treatment of multiple trauma. Information sources: The Chinese Biomedical literature (CBM), Chinese National Knowledge Infrastructure (CNKI), Weipu (VIP), Duxiu, WanFang, Web of sciense, PubMed, Scopus, Ovid, EMbase, ProQuest, Cochrane, Chinese clinical trial Registry and Clinical Trials.gov databases. Main outcome(s): mortality rate, the success rate of rescue, In-hospital length of stay, ICU length of stay, the overall incidence rate of complications, incidence of disseminated intravascular coagulation (DIC), incidence of multiple organ dysfunction syndrome (MODS) , incidence of shock.
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Opazo, Yoselyn, Ruvistay Gutierrez-Arias, and Pamela Seron. Effectiveness of non-pharmacological interventions in the prevention of delirium in adult hospitalized. An overview of systematic review and meta-analyses. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2021. http://dx.doi.org/10.37766/inplasy2021.8.0023.

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Review question / Objective: The aim of this overview is to determine the effectiveness of non-pharmacological interventions in terms of incidence of delirium, in-hospital mortality, length of hospital stay, and other secondary outcomes, in hospitalized adults. Information sources: The databases to be consulted will be MEDLINE, Embase, Cochrane Library, Epistemonikos and CINAHL. In addition, the protocol registers of the SRs (PROSPERO and INPLASY) will be searched, and the list of references of the SRs included in this overview will be reviewed.
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Uhl, Stacey, Shazia Mehmood Siddique, Liam McKeever, Aaron Bloschichak, Kristen D’Anci, Brian Leas, Nikhil K. Mull, and Amy Y. Tsou. Malnutrition in Hospitalized Adults: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), October 2021. http://dx.doi.org/10.23970/ahrqepccer249.

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Objectives. To review the association between malnutrition and clinical outcomes among hospitalized patients, evaluate effectiveness of measurement tools for malnutrition on clinical outcomes, and assess effectiveness of hospital-initiated interventions for patients diagnosed with malnutrition. Data sources. We searched electronic databases (Embase®, MEDLINE®, PubMed®, and the Cochrane Library) from January 1, 2000, to June 3, 2021. We hand-searched reference lists of relevant studies and searched for unpublished studies in ClinicalTrials.gov. Review methods. Using predefined criteria and dual review, we selected (1) existing systematic reviews (SRs) to assess the association between malnutrition and clinical outcomes, (2) randomized and non-randomized studies to evaluate the effectiveness of malnutrition tools on clinical outcomes, and (3) randomized controlled trials (RCTs) to assess effectiveness of hospital-initiated treatments for malnutrition. Clinical outcomes of interest included mortality, length of stay, 30-day readmission, quality of life, functional status, activities of daily living, hospital acquired conditions, wound healing, and discharge disposition. When appropriate, we conducted meta-analysis to quantitatively summarize study findings; otherwise, data were narratively synthesized. When available, we used pooled estimates from existing SRs to determine the association between malnutrition and clinical outcomes, and assessed the strength of evidence. Results. Six existing SRs (including 43 unique studies) provided evidence on the association between malnutrition and clinical outcomes. Low to moderate strength of evidence (SOE) showed an association between malnutrition and increased hospital mortality and prolonged hospital length of stay. This association was observed across patients hospitalized for an acute medical event requiring intensive care unit care, heart failure, and cirrhosis. Literature searches found no studies that met inclusion criteria and assessed effectiveness of measurement tools. The primary reason studies did not meet inclusion criteria is because they lacked an appropriate control group. Moderate SOE from 11 RCTs found that hospital-initiated malnutrition interventions likely reduce mortality compared with usual care among hospitalized patients diagnosed with malnutrition. Low SOE indicated that hospital-initiated malnutrition interventions may also improve quality of life compared to usual care. Conclusions. Evidence shows an association between malnutrition and increased mortality and prolonged length of hospital stay among hospitalized patients identified as malnourished. However, the strength of this association varied depending on patient population and tool used to identify malnutrition. Evidence indicates malnutrition-focused hospital-initiated interventions likely reduce mortality and may improve quality of life compared to usual care among patients diagnosed with malnutrition. Research is needed to assess the clinical utility of measurement tools for malnutrition.
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Chang, Min Cheol, Yoo Jin Choo, and Sohyun Kim. Effect of Prehabilitation for Patients with Frailty Undergoing Colorectal Cancer Surgery: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0105.

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Review question / Objective: We performed a meta-analysis to assess the impact of prehabilitation before colorectal surgery on functional outcome and postoperative complications in patients with frailty. Condition being studied: Colorectal cancer is a common disease in the elderly, and over 65 years of age accounts for more than 50% of all patients with colorectal cancer. The patients with colorectal cancer surgery showed 8.7% major morbidity and mortality and 31.6% minor complications. The high complication rate of patients with colorectal surgery is related to the fact that there are many elderly patients. Frailty is common in elderly patients, and the frailty is associated with adverse perioperative outcomes. The frail patients with colorectal surgery showed worse postoperative morbidity, mortality and prolonged length of hospital stay. Although the frailty results from irresistible aging-associated decline in reserve and function across multiple physiologic systems, several attempts have been conducted to improve frailty in patients with colorectal cancer surgery and consequently improve the postoperative outcomes. Prehabilitation was one of these attempts for improving physical activity and postoperative outcomes on patients with frailty undergoing colorectal cancer surgery. So far, several studies conducted clinical trials for determining whether prehabilitation has positive effect on improving postoperative outcomes in patients with frailty undergoing colorectal surgery. However, the results of these previous studies are controversial.
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