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Journal articles on the topic 'Admission Prediction'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Honwadkar, Prof K. N. "COLLEGE PREDICTION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26421.

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The journey towards higher education is a pivotal chapter in a student's life, often marked by uncertainty and high stakes. In the context of engineering admissions in Maharashtra, where over 400 colleges are governed by the Directorate of Technical Education (DTE), the process can be labyrinthine. The Centralised Admission Process (CAP) involving multiple rounds further amplifies the complexity, leaving students perplexed about selecting the ideal college for their academic aspirations. The College Admission Prediction System is an innovative web application designed to address these challeng

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Shaik Naveed and K.Kumara Swamy. "Prediction of Graduation Admission." international journal of engineering technology and management sciences 9, no. 2 (2025): 628–37. https://doi.org/10.46647/ijetms.2025.v09i02.080.

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In recent years, the prediction of graduate admission has garnered significant attention due to theincreasing competitiveness and demand for higher education. This study explores the utilization ofmachine learning techniques to predict the likelihood of admission for prospective graduate students.By leveraging a dataset comprising various academic and non-academic factors, such as GREscores, undergraduate GPA, research experience, and letters of recommendation, we aim to developa predictive model that can assist both applicants and admissions committees. The primary objective of this research

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Boardman, Anthony P., Richard E. Hodgson, Martyn Lewis, and Keith Allen. "Social indicators and the prediction of psychiatric admission in different diagnostic groups." British Journal of Psychiatry 171, no. 5 (1997): 457–62. http://dx.doi.org/10.1192/bjp.171.5.457.

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BackgroundRecent findings indicate that the established association between social indicators of deprivation and psychiatric admission rates may not hold across all diagnoses.MethodAdmission rates in individuals aged 16–64 years for 71 electoral wards in North Staffordshire were calculated for six diagnostic groups using data from the Korner Episode System for 1987–1993. These were correlated with selected individual census variables, Townsend and Jarman indices. The ability of regression models to predict admission rates was tested.ResultsThe strongest correlations were found for total admiss

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Feigon, Sarah, and J. Ray Hays. "Prediction of Readmission of Psychiatric Inpatients." Psychological Reports 93, no. 3 (2003): 816–18. http://dx.doi.org/10.2466/pr0.2003.93.3.816.

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Using demographic and episode-based variables this study attempted to predict which patients would require frequent psychiatric hospitalizations. Records of 943 patients were randomly selected from 14,649 admissions and examined for a 5-yr. period following initial admission. Sex, ethnicity, and age at first admission were not significantly related to readmission. Among the demographic variables, marriage was inversely related to readmission. Involuntary commitment and a longer length of stay at the original admission were associated with a higher rate of readmission. Despite the results being

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Jawad, Baker Nawfal, Izzet Altintas, Jesper Eugen-Olsen, et al. "Prospective and External Validation of Machine Learning Models for Short- and Long-Term Mortality in Acutely Admitted Patients Using Blood Tests." Journal of Clinical Medicine 13, no. 21 (2024): 6437. http://dx.doi.org/10.3390/jcm13216437.

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Background: Predicting mortality in emergency departments (EDs) using machine learning models presents challenges, particularly in balancing simplicity with performance. This study aims to develop models that are both simple and effective for predicting short- and long-term mortality in ED patients. Our approach uses a minimal set of variables derived from one single blood sample obtained at admission. Methods: Data from three cohorts at two large Danish university hospitals were analyzed, including one retrospective and two prospective cohorts where prognostic models were applied to predict i

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Laios, Alexandros, Raissa Vanessa De Oliveira Silva, Daniel Lucas Dantas De Freitas, et al. "Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score." Journal of Clinical Medicine 11, no. 1 (2021): 87. http://dx.doi.org/10.3390/jcm11010087.

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Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive predi

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Barak Corren, Yuval, Joshua Merrill, Ronald Wilkinson, Courtney Cannon, Jonathan Bickel, and Ben Y. Reis. "Predicting surgical department occupancy and patient length of stay in a paediatric hospital setting using machine learning: a pilot study." BMJ Health Care Inform 29, no. 1 (2022): e100498. http://dx.doi.org/10.1136/bmjhci-2021-100498.

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ObjectiveEarly and accurate prediction of hospital surgical-unit occupancy is critical for improving scheduling, staffing and resource planning. Previous studies on occupancy prediction have focused primarily on adult healthcare settings, we sought to develop occupancy prediction models specifically tailored to the needs and characteristics of paediatric surgical settings.Materials and methodsWe conducted a single-centre retrospective cohort study at a surgical unit in a tertiary-care paediatric hospital in Boston, Massachusetts, USA. We developed a hierarchical modelling framework for predict

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Gao, Yan, Sunku Srivatsava, Hong Choon Oh, Siang Hiong Goh, and Hoon Chin Steven Lim. "Development and Validation of a Machine Learning Model That Predicts Short Inpatient Stays Among Urgent Admissions." Emergency Care and Medicine 2, no. 1 (2025): 11. https://doi.org/10.3390/ecm2010011.

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Background/Objectives: This study aimed to explore the feasibility of predicting short stays among urgent admissions to an acute hospital in Singapore. With an increase in the average length of stay (LOS) in hospitals in recent years, accurately predicting short stays could enable hospitals to better manage inpatient demand and reduce emergency department (ED) overcrowding. Methods: This was a retrospective study of urgent admissions to Changi General Hospital, Singapore, from 1 January 2016 to 30 June 2022. To identify potential short stayers, a total of 25 features comprising demographic cha

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Raftopoulos, George, Gregory Davrazos, and Sotiris Kotsiantis. "Fair and Transparent Student Admission Prediction Using Machine Learning Models." Algorithms 17, no. 12 (2024): 572. https://doi.org/10.3390/a17120572.

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Student admission prediction is a crucial aspect of academic planning, offering insights into enrollment trends, resource allocation, and institutional growth. However, traditional methods often lack the ability to address fairness and transparency, leading to potential biases and inequities in the decision-making process. This paper explores the development and evaluation of machine learning models designed to predict student admissions while prioritizing fairness and interpretability. We employ a diverse set of algorithms, including Logistic Regression, Decision Trees, and ensemble methods,

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Khan, Nikhat. "Prediction of Student Admission using Fuzzy based Education Data Mining." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 660–67. http://dx.doi.org/10.21275/sr221014151159.

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Dent, Andrew W., Tracey J. Weiland, Lisa Vallender, and Nicola E. Oettel. "Can medical admission and length of stay be accurately predicted by emergency staff, patients or relatives?" Australian Health Review 31, no. 4 (2007): 633. http://dx.doi.org/10.1071/ah070633.

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Objectives: To determine the accuracy of predictions of the need for hospital admission and, if admitted, length of stay (LOS) made early in an emergency attendance by emergency department (ED) doctors, nurses, patients and relatives, and the characteristics of ED presentations predictive of admission and short stays (~3 days). Methods: Prospective collection of predictions by medical and nursing staff, patients and relatives of ED departure status and LOS (1 day, 2-3 days, 4- 7 days or longer) of a convenience sample of adults presenting with medical symptoms. Predictions were made before ful

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Russ, Tom C., Mario A. Parra, Alison E. Lim, Emma Law, Peter J. Connelly, and John M. Starr. "Prediction of general hospital admission in people with dementia: Cohort study." British Journal of Psychiatry 206, no. 2 (2015): 153–59. http://dx.doi.org/10.1192/bjp.bp.113.137166.

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BackgroundPeople with dementia are extremely vulnerable in hospital and unscheduled admissions should be avoided if possible.AimsTo identify any predictors of general hospital admission in people with dementia in a well-characterised national prospective cohort study.MethodA cohort of 730 persons with dementia was drawn from the Scottish Dementia Research Interest Register (47.8% female; mean age 76.3 years, s.d. = 8.2, range 50–94), with a mean follow-up period of 1.2 years.ResultsIn the age- and gender-adjusted multivariable model (n = 681; 251 admitted), Neuropsychiatric Inventory score (ha

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Barak-Corren, Yuval, Isha Agarwal, Kenneth A. Michelson, et al. "Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis." Journal of the American Medical Informatics Association 28, no. 8 (2021): 1736–45. http://dx.doi.org/10.1093/jamia/ocab076.

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Abstract Objective To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician–computer models. Materials and Methods A single-center prospective observational study in a tertiary pediatric hospital in Boston, Massachusetts, United States. Nine emergency department (ED) attending physicians participated in the study. Physicians predicted the likelihood of admission for patients in the ED whose hospitalization disposition had not yet been decided. In parallel, a random-forest computer model was developed to predi

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Zhou, Haijiang, Tianfei Lan, and Shubin Guo. "Prognostic Prediction Value of qSOFA, SOFA, and Admission Lactate in Septic Patients with Community-Acquired Pneumonia in Emergency Department." Emergency Medicine International 2020 (April 6, 2020): 1–11. http://dx.doi.org/10.1155/2020/7979353.

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Background. Community-acquired pneumonia (CAP) is a leading cause of sepsis and common presentation to emergency department (ED) with a high mortality rate. The prognostic prediction value of sequential organ failure assessment (SOFA) and quick SOFA (qSOFA) scores in CAP in ED has not been validated in detail. The aim of this research is to investigate the prognostic prediction value of SOFA, qSOFA, and admission lactate compared with that of other commonly used severity scores (CURB65, CRB65, and PSI) in septic patients with CAP in ED. Methods. Adult septic patients with CAP admitted between

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Rodriguez-Nava, Guillermo, Daniela Patricia Trelles-Garcia, Maria Adriana Yanez-Bello, et al. "61. Using Machine Learning for Prediction of Poor Clinical Outcomes in Adult Patients Hospitalized with COVID-19." Open Forum Infectious Diseases 7, Supplement_1 (2020): S162—S163. http://dx.doi.org/10.1093/ofid/ofaa439.371.

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Abstract Background As the ongoing COVID-19 pandemic develops, there is a need for prediction rules to guide clinical decisions. Previous reports have identified risk factors using statistical inference model. The primary goal of these models is to characterize the relationship between variables and outcomes, not to make predictions. In contrast, the primary purpose of machine learning is obtaining a model that can make repeatable predictions. The objective of this study is to develop decision rules tailored to our patient population to predict ICU admissions and death in patients with COVID-1

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Son, Nguyen Thi Kim, Nguyen Van Bien, Nguyen Huu Quynh, and Chu Cam Tho. "Machine Learning Based Admission Data Processing for Early Forecasting Students' Learning Outcomes." International Journal of Data Warehousing and Mining 18, no. 1 (2022): 1–15. http://dx.doi.org/10.4018/ijdwm.313585.

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In this paper, the authors explore the factors to improve the accuracy of predicting student learning outcomes. The method can remove redundant and irrelevant factors to get a “clean” data set without having to solve the NP-Hard problem. The method can improve the graduation outcome prediction accuracy through logistic regression machine learning method for “clean” data set. They empirically evaluate the training and university admission data of Hanoi Metropolitan University from 2016 to 2020. From data processing results and the support from the machine learning techniques application program

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N, Joshi Padma, P. Chandana, G. .Kavya, J. Sreekar, and Y. Sowmith Reddy. "University Admission Prediction using Machine Learning." YMER Digital 21, no. 05 (2022): 558–63. http://dx.doi.org/10.37896/ymer21.05/64.

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In the current tutoring world there are various amounts of students who need to pursue higher preparation following Engineering or any Graduate confirmation course. High level training in the sense, a couple bunches need to do MTech through GATE or through any Instructive Institute Entrance Examination and a couple bunches need to do MBA through CAT or through any individual Educational Institute Entrance Examination such as GRE, TOEFL and a couple of gatherings need to do Masters in abroad schools. Student affirmation issue is indispensable in Educational Organizations. We are addressing AI m

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N, Joshi Padma, P. Chandana, G. .Kavya, J. Sreekar, and Y. Sowmith Reddy. "University Admission Prediction using Machine Learning." YMER Digital 21, no. 05 (2022): 552–57. http://dx.doi.org/10.37896/ymer21.05/63.

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The organizing of your computer documents is vital to your success in your professional and personal life. The average worker now loses over two hours looking for misplaced documents and emails on their computer. You can avoid this huge loss of time with organization through the use of folders and subfolders. Here’s when our Junk File Organizer comes into play. Due to the large number of files, it is a daunting task to sit and organize each file. To make that task easy the below Python script comes handy and all the files are organized in a well-manner way within seconds. It is easier to locat

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Stover-Baker, Blythe, Barbara Stahlman, and Marc Pollack. "Triage Nurse Prediction of Hospital Admission." Journal of Emergency Nursing 38, no. 3 (2012): 306–10. http://dx.doi.org/10.1016/j.jen.2011.10.003.

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Bhrugubanda, Meenakshi, Varsha Udutha, and Sri Charan Yella. "Post Graduate Admission Prediction Using ANN." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 219–22. http://dx.doi.org/10.22214/ijraset.2023.53463.

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Abstract: Nowadays, we see many students showing interest in higher studies away from their home countries. Generally, students often lack sufficient knowledge about the requirements, procedures, specific details of universities in countries like the USA, UK, Canada, etc. As a result, they often turn to education consultancy firms for assistance in securing admission to universities that best align with their profiles. However, this process typically requires significant financial investment in consultancy fees. The objective of this project is to create a system using Artificial Neural Networ

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Narnaware, Monu. "College Admission Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 520–23. http://dx.doi.org/10.22214/ijraset.2023.56539.

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Students struggle mightily to get accepted into the college of their choice. The present engineering admission process is little challenging when it comes to picking the right college based on test results and areas of interest. The candidates' ability to complete the application form accurately depends on their selection, which may differ depending on their academic performance and entrance exam results. Numerous colleges provide a variety of engineering courses. Students find it difficult to arrange and list the appropriate institutions of their choice for courses based on their performance

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Lin, Suhan, Wandong Hong, Zarrin Basharat, Qipin Wang, Jingye Pan, and Mengtao Zhou. "Blood Urea Nitrogen as a Predictor of Severe Acute Pancreatitis Based on the Revised Atlanta Criteria: Timing of Measurement and Cutoff Points." Canadian Journal of Gastroenterology and Hepatology 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9592831.

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Background and Aims. This study evaluated the prognostic accuracy of BUN for severe acute pancreatitis (SAP) and in-hospital mortality (IHM) in terms of the best timing for BUN measurement and the optimal BUN cutoff points.Methods. BUN determinants at the time of admission and 24 hrs after hospital admission were recorded and analyzed statistically. The ability of BUN in predicting the SAP and the occurrence of IHM were assessed using the area under the receiver-operating characteristic (ROC) curve.Results. For SAP, AUC of BUN at admission and 24 hrs after hospital admission was 0.75 and 0.80,

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Copeland-Fields, L., T. Griffin, T. Jenkins, M. Buckley, and LC Wise. "Comparison of outcome predictions made by physicians, by nurses, and by using the Mortality Prediction Model." American Journal of Critical Care 10, no. 5 (2001): 313–19. http://dx.doi.org/10.4037/ajcc2001.10.5.313.

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BACKGROUND: Critical care nurses must collaborate with physicians, patients, and patients' families when making decisions about aggressiveness of care. However, few studies address nurses' ability to predict outcomes. OBJECTIVES: To compare predictions of survival outcomes made by nurses, by physicians, and by using the Mortality Prediction Model. METHODS: Predictions of survival and function and attitudes toward aggressiveness of care based on the predictions were recorded on questionnaires in the emergency department by emergency and intensive care unit physicians and by intensive care unit

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Conway, Richard, Declan Byrne, Seán Cournane, Deirdre O’Riordan, and Bernard Silke. "The Problems with Risk Prediction during an Emergency Medical Admission Using Laboratory Data – Evidence from Potassium." Acute Medicine Journal 18, no. 1 (2019): 16–22. http://dx.doi.org/10.52964/amja.0743.

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Background: The prediction of clinical outcomes using biochemical markers is an important tool. Methods: We calculated a risk score for all emergency admissions 2002-2017. We related potassium and mortality in a multivariable fractional polynomial model. We investigated the potassium distribution and relationship of potassium to mortality over time. Results: There were 106,586 admissions in 54,928 patients. Mortality was higher for those with an admission potassium above the median – 6.1% vs 4.6% (p<0.001), OR 1.07 (95%CI: 1.06, 1.09). There was a progressive increase in mortality from the

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David, V., C. McInerney, V. Cheong, et al. "508 Scoping the opportunity for pharmacogenomic testing for older people’s medicine optimisation in secondary care." International Journal of Pharmacy Practice 31, Supplement_1 (2023): i41—i42. http://dx.doi.org/10.1093/ijpp/riad021.049.

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Abstract Introduction Pharmacogenomics (PGx) is the use of DNA variation to predict response to medication. This can help prescribers select medications, optimise dosage, and prevent adverse drug reactions (ADRs). Patients aged 65 or over are most likely to benefit because they are more susceptible to ADRs and many use multiple medications daily (polypharmacy). Aim We aimed to identify the prevalence of medications with PGx associations at planned or unplanned admission to hospital. Our study tested if the use of medicines with PGx associations can improve the prediction of length of hospital

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Yuli, Asbar, Sapnabiby, Pratama Angga, Simarmata Janner, and Abror. "Implementation of Linear Regression to Predict New Student Admissions as a First Step to Determine Campus Marketing Strategy." International Journal of Multidisciplinary Research and Analysis 06, no. 01 (2023): 103–7. https://doi.org/10.5281/zenodo.7515794.

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New student Admission is a routine activity carried out by all educational institutions in Indonesia every year. One of the educational institutions in Indonesia is a private university. Predictions of the development of new student admissions so far have only been made based on speculation using data from previous years. Linear regression is one method that can be used to predict good results. Therefore, this study uses a linear regression method. The test results in this study indicate that the linear regression method has a low error value measured by the error rate using the Mean Absolute

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Kraaijvanger, Nicole, Douwe Rijpsma, Lian Roovers, et al. "Development and validation of an admission prediction tool for emergency departments in the Netherlands." Emergency Medicine Journal 35, no. 8 (2018): 464–70. http://dx.doi.org/10.1136/emermed-2017-206673.

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ObjectiveEarly prediction of admission has the potential to reduce length of stay in the ED. The aim of this study is to create a computerised tool to predict admission probability.MethodsThe prediction rule was derived from data on all patients who visited the ED of the Rijnstate Hospital over two random weeks. Performing a multivariate logistic regression analysis factors associated with hospitalisation were explored. Using these data, a model was developed to predict admission probability. Prospective validation was performed at Rijnstate Hospital and in two regional hospitals with differen

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Althewini, Abdulaziz. "Saudi Standardized Tests and English Competence: Association and Prediction for Freshmen Medical Students’ Performance in Chemistry." International Education Studies 13, no. 5 (2020): 144. http://dx.doi.org/10.5539/ies.v13n5p144.

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The research is created to investigate the prediction of admission criteria for medical student achievement in chemistry in Saudi Arabia. It examines if the General Aptitude Test (GAT), the Scholastic Achievement Admission Test (SAAT), and English competence, can to a certain extent predict and foretell students&rsquo; achievement in the chemistry. The study sample consists of 240 participants, providing their grades in the admission criteria and chemistry. Regression analyses are utilized to define the weight of individual admission criterion prediction for student achievement in chemistr

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Shin, Hyun A., Hyeonji Kang, and Mona Choi. "Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review." Healthcare Informatics Research 31, no. 1 (2025): 23–36. https://doi.org/10.4258/hir.2025.31.1.23.

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Objectives: Emergency department (ED) overcrowding significantly impacts healthcare efficiency, safety, and resource management. Predictive models that utilize triage information can streamline the admission process. This review evaluates existing hospital admission prediction models that have been developed or validated using triage data for adult ED patients. Methods: A systematic search of PubMed, Embase, CINAHL, Web of Science, and the Cochrane Library was conducted. Studies were selected if they developed or validated predictive models for hospital admission using triage data from adult E

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Seanglaw, Dussadee, and Thotsaporn Morasert. "Development of a Prediction Model for Acute Kidney Injury after Colistin Treatment for Multidrug-resistant Acinetobacter baumanii Ventilator-Associated Pneumonia: A Pilot Study." Journal of Health Science and Medical Research 41, no. 1 (2022): 1. http://dx.doi.org/10.31584/jhsmr.2022891.

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Objective: Multidrug-resistant Acinetobacter baumanii (MDR-AB) ventilator-associated pneumonia (VAP) is the major complication following hospital admission in Thailand, increasing morbidity and prolonging hospital stay duration. Treatment of MDR-AB VAP usually requires colistin which has highly nephrotoxic properties. Therefore, we aimed to develop a pilot prediction model for acute kidney injury (AKI) after colistin treatment.Material and Methods: We conducted a retrospective cohort study. All MDR-AB VAP patients who received colistin for at least 72 hours in Suratthani Hospital from January

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Kim, Eun-Yeob, and Sung-Woo Ham. "Informally Patients Prediction Model of Admission Patients." Journal of the Korea Academia-Industrial cooperation Society 10, no. 11 (2009): 3465–72. http://dx.doi.org/10.5762/kais.2009.10.11.3465.

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Huling, Jared D., Menggang Yu, Muxuan Liang, and Maureen Smith. "Risk prediction for heterogeneous populations with application to hospital admission prediction." Biometrics 74, no. 2 (2017): 557–65. http://dx.doi.org/10.1111/biom.12769.

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Stylianou, Neophytos, Jason Young, Carol J. Peden, and Christos Vasilakis. "Developing and validating a predictive model for future emergency hospital admissions." Health Informatics Journal 28, no. 2 (2022): 146045822211015. http://dx.doi.org/10.1177/14604582221101538.

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Although many emergency hospital admissions may be unavoidable, a proportion of these admissions represent a failure of the care system. The adverse consequences of avoidable emergency hospital admissions affect patients, carers, care systems and substantially increase care costs. The aim of this study was to develop and validate a risk prediction model to estimate the individual probability of emergency admission in the next 12 months within a regional population. We deterministically linked routinely collected data from secondary care with population level data, resulting in a comprehensive

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S, Latha, and al. et. "Explainable AI for Hospitalization Duration Predictions." International Journal of Computational Learning & Intelligence 4, no. 4 (2025): 766–80. https://doi.org/10.5281/zenodo.15260285.

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Effective bed management is essential for reducing hospital expenses, improving operational efficiency, and enhancing patient care. This study introduces a predictive framework for ICU length of stay (LOS) at the time of admission, utilizing electronic health records (EHR). Our research applies supervised machine learning classification models to estimate ICU patients’ LOS within hospital clinical information systems (CIS). Notably, this work represents the first known application of explainable artificial intelligence (xAI) to real-world hospital stay data for interpretable machine lear

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Zahid, Muhammad, Adeel Ahmad Khan, Fateen Ata, et al. "Medical Admission Prediction Score (MAPS); a simple tool to predict medical admissions in the emergency department." PLOS ONE 18, no. 11 (2023): e0293140. http://dx.doi.org/10.1371/journal.pone.0293140.

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Introduction Overcrowding in the emergency departments (ED) is linked to adverse clinical outcomes, a negative impact on patient safety, patient satisfaction, and physician efficiency. We aimed to design a medical admission prediction scoring system based on readily available clinical data during ED presentation. Methods In this retrospective cross-sectional study, data on ED presentations and medical admissions were extracted from the Emergency and Internal Medicine departments of a tertiary care facility in Qatar. Primary outcome was medical admission. Results Of 320299 ED presentations, 218

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Verdoux, H., C. Bergey, F. Assens, et al. "Prediction of duration of psychosis before first admission." European Psychiatry 13, no. 7 (1998): 346–52. http://dx.doi.org/10.1016/s0924-9338(99)80701-9.

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SummaryObjective:To assess the factors predicting the delay between onset of psychotic symptoms and first admission in a populationbased sample.Method:The duration of psychosis before admission was ascertained in a standardised way for 59 consecutively first-admitted patients presenting with psychotic symptoms.Results:The median of the duration of psychosis before admission was 3 months (interquartile range 0.5-14). A delay ≥ 3 months was independently predicted by family history of psychiatric hospitalisation (odds ratio [OR] = 12.1, 95% confidence interval [CI] 1.15-97.0, P = 0.02). low educ

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Mitra, Samrat, and H. Sangwan. "Artificial intelligence in critical care: prediction of sepsis in patients in intensive care from first initial laboratory parameters." International Journal of Research in Medical Sciences 8, no. 4 (2020): 1509. http://dx.doi.org/10.18203/2320-6012.ijrms20201351.

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Background: Sepsis is a leading cause of morbidity and mortality in the critical care setting. The analysis of hemostatic parameters at admission have been proven to be a predictive marker for development of sepsis in the ICU. The present study aims to develop a machine learning model which can predict the development of sepsis after 72 hours of ICU admission, from initial assessment of hemostatic parameters.Methods: A total of 170 ICU admissions over six months (May 2018 - Dec 2018) period were included in the study. Hemostatic parameters including platelet counts, prothrombin time and Sonocl

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Ayshath, Suraiya M.A*1 &. Janitha krishnan2. "MANAGEMENT OF A HOSPITAL EMERGENCY DEPARTMENT IN A HEAVY TRAFFIC SCENARIO." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Metanoia 19 (April 4, 2019): 1–5. https://doi.org/10.5281/zenodo.2629129.

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Emergency department crowding has been one of the major issues in healthcare systems. One solution to this is the prediction of admissions in the emergency department and allocating resources needed for the admitted patients. In this paper, predictive model is developed using neural networks for predicting the admissions. An expert selection algorithm is developed to assign the department/doctors for the admitted patients. After getting the references the availability of resources needed for the admission is done and the available resources are allocated. Neural networks are used to build the

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Parker, Clare Allison, Nan Liu, Stella Xinzi Wu, Yuzeng Shen, Sean Shao Wei Lam, and Marcus Eng Hock Ong. "Predicting hospital admission at the emergency department triage: A novel prediction model." American Journal of Emergency Medicine 37, no. 8 (2019): 1498–504. http://dx.doi.org/10.1016/j.ajem.2018.10.060.

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Eyre, Andy, Gideon Y. Stein, Jacob Chen, and Danny Alon. "Physician performance scores used to predict emergency department admission numbers and excessive admissions burden." BMJ Health & Care Informatics 31, no. 1 (2024): e101080. http://dx.doi.org/10.1136/bmjhci-2024-101080.

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BackgroundOvercrowding in hospitals is associated with a panoply of adverse events. Inappropriate decisions in the emergency department (ED) contribute to overcrowding. The performance of individual physicians as part of the admitting team is a critical factor in determining the overall rate of admissions. While previous attempts to model admission numbers have been based on a range of variables, none have included measures of individual staff performance. We construct reliable objective measures of staff performance and use these, among other factors, to predict the number of daily admissions

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Safaei, Nima, Babak Safaei, Seyedhouman Seyedekrami, et al. "E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database." PLOS ONE 17, no. 5 (2022): e0262895. http://dx.doi.org/10.1371/journal.pone.0262895.

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Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients’ survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality predi

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Handler, J. A. "Instant Disposition: Validation of an Admission Prediction Model." Academic Emergency Medicine 10, no. 5 (2003): 537. http://dx.doi.org/10.1197/aemj.10.5.537.

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Kellett, J., S. Rasool, and B. McLoughlin. "Prediction of mortality 1 year after hospital admission." QJM 105, no. 9 (2012): 847–53. http://dx.doi.org/10.1093/qjmed/hcs099.

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Sabour, Siamak. "Prediction of Acute Biliary Pancreatitis Outcome on Admission." Pancreas 48, no. 2 (2019): e11. http://dx.doi.org/10.1097/mpa.0000000000001218.

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Lu, Kaining, Zhigang Jin, and Jun Zou. "A new admission control approach based on prediction." Journal of Electronics (China) 19, no. 2 (2002): 209–14. http://dx.doi.org/10.1007/s11767-002-0038-7.

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Ramadarsan, Mekala. "Emergency hospital admission prediction using data mining techniques." International Journal of Circuit, Computing and Networking 1, no. 1 (2020): 27–31. http://dx.doi.org/10.33545/27075923.2020.v1.i1a.7.

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Mamaril, Julius Cesar O., and Melvin A. Ballera. "Multiple educational data mining approaches to discover patterns in university admissions for program prediction." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 1 (2022): 45. http://dx.doi.org/10.11591/ijict.v11i1.pp45-56.

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<span>This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the

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Julius, Cesar O. Mamaril, and A. Ballera Melvin. "Multiple educational data mining approaches to discover patterns in university admissions for program prediction." International Journal of Informatics and Communication Technology 11, no. 1 (2022): 45–56. https://doi.org/10.11591/ijict.v11i1.pp45-56.

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This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission d

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Moore, Hannah, Douglas Bettarelli, Vishal Bandaru, et al. "59 Length of Stay Prediction Enhanced by Admission AM-PAC 6-Clicks Score." Journal of Burn Care & Research 46, Supplement_1 (2025): S47—S48. https://doi.org/10.1093/jbcr/iraf019.059.

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Abstract Introduction Burn injuries are some of the most severe injuries that enter hospitals. Length of stay (LOS) is often difficult to predict in these patients, and the variability in individual responses to care makes it difficult to estimate burn recovery. Burn mortality prediction is well-established, but as survival rates rapidly increase, formulas have to be readjusted. Physical therapists gauge mobility with a tool known as AM-PAC 6-clicks on a scale of 6 to 24, with lower scores implying worse mobility. These tools offer an interprofessional perspective on predicting LOS outcomes. M

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Chen, Shiyu, Weiwei Hu, Yuhui Yang, et al. "Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database." Journal of Clinical Medicine 12, no. 3 (2023): 870. http://dx.doi.org/10.3390/jcm12030870.

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Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, predi

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