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Bibliografie tematiche / Mortality and Length of stay Prediction

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Letteratura scientifica selezionata sul tema "Mortality and Length of stay Prediction"

Autore: Grafiati

Pubblicato: 2 novembre 2024

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Tesi sul tema "Mortality and Length of stay Prediction"

1

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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2

Cissoko, Mamadou Ben Hamidou. "Adaptive time-aware LSTM for predicting and interpreting ICU patient trajectories from irregular data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD012.

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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

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3

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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4

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<br>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

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5

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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6

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<br>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

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7

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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8

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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9

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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Abstract (sommario):

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 n

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10

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

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