COVID-19-Hospitals-Treatment-Plan

OpenML dataset with id 43550

Author name not available (Why is that?)

Full work available at URL: https://api.openml.org/data/v1/download/22102375/COVID-19-Hospitals-Treatment-Plan.arff

Upload date: 23 March 2022

Dataset Characteristics

Number of features: 18 (numeric: 11, symbolic: 0 and in total binary: 0 )
Number of instances: 318,438
Number of instances with missing values: 4,645
Number of missing values: 4,645

Description

Context The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care.

While healthcare management has various use cases for using data science, patient length of stay is one critical parameter to observe and predict if one wants to improve the efficiency of the healthcare management in a hospital.

This parameter helps hospitals to identify patients of high LOS risk (patients who will stay longer) at the time of admission. Once identified, patients with high LOS risk can have their treatment plan optimized to miminize LOS and lower the chance of staff/visitor infection. Also, prior knowledge of LOS can aid in logistics such as room and bed allocation planning. The problem is to manage the functioning of Hospitals in a professional and optimal manner.

Content The task is to accurately predict the Length of Stay for each patient on case by case basis so that the Hospitals can use this information for optimal resource allocation and better functioning. The length of stay is divided into 11 different classes ranging from 0-10 days to more than 100 days. Data : host_train.csv File containing features related to patient, hospital and Length of stay on case basis For each record in the dataset the following is provided::

 case_id                           
 Hospital                          
 Hospital_type                
 Hospital_city                  
 Hospital_region              
 Available-Extra-Rooms-in-Hospital        Number of Extra rooms available in the Hospital
 Department                      Department overlooking the case  ['radiotherapy' 'anesthesia' 'gynecology' 'TB  Chest disease' 'surgery']
 Ward_Type                       ['R' 'S' 'Q' 'P' 'T' 'U']
 Ward_Facility                   ['F' 'E' 'D' 'B' 'A' 'C']
 Bed_Grade                        Condition of Bed in the Ward
 patientid                        
 CityCodePatient           City Code for the patient
 Type of Admission            Admission Type registered by the Hospital   ['Emergency' 'Trauma' 'Urgent']
 Illness_Severity               Severity of the illness recorded at the time of admission   ['Extreme' 'Moderate' 'Minor']
 Patient_Visitors            
 Age                             Age category   ['51-60' '71-80' '31-40' '41-50' '81-90' '61-70' '21-30' '11-20' '0-10' '91-100']
 Admission_Deposit            Deposit at the Admission Time
 Stay_Days                           Stay Days by the patient (target)  ['0-10' '41-50' '31-40' '11-20' '51-60' '21-30' '71-80'
 'More than 100 Days' '81-90' '61-70' '91-100']

Starter Kernels

EDA and Random Forest Benchmark

Inspiration

Predict the Length of Stay for each patient Interpret best model(s) and mine influence factors in LOS risk

More References

COVID-19 length of hospital stay A retrospective cohort study in a Fangcang shelter hospital

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