{ "data_id": "43550", "name": "COVID-19-Hospitals-Treatment-Plan", "exact_name": "COVID-19-Hospitals-Treatment-Plan", "version": 1, "version_label": "v1.0", "description": "Context\nThe 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.\n 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. \nThis 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.\nThe problem is to manage the functioning of Hospitals in a professional and optimal manner. \n \n \n \n\nContent\nThe 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.\nData : host_train.csv File containing features related to patient, hospital and Length of stay on case basis\nFor each record in the dataset the following is provided::\n\n \n case_id \n Hospital \n Hospital_type \n Hospital_city \n Hospital_region \n Available-Extra-Rooms-in-Hospital Number of Extra rooms available in the Hospital\n Department Department overlooking the case ['radiotherapy' 'anesthesia' 'gynecology' 'TB Chest disease' 'surgery']\n Ward_Type ['R' 'S' 'Q' 'P' 'T' 'U']\n Ward_Facility ['F' 'E' 'D' 'B' 'A' 'C']\n Bed_Grade Condition of Bed in the Ward\n patientid \n CityCodePatient City Code for the patient\n Type of Admission Admission Type registered by the Hospital ['Emergency' 'Trauma' 'Urgent']\n Illness_Severity Severity of the illness recorded at the time of admission ['Extreme' 'Moderate' 'Minor']\n Patient_Visitors \n Age Age category ['51-60' '71-80' '31-40' '41-50' '81-90' '61-70' '21-30' '11-20' '0-10' '91-100']\n Admission_Deposit Deposit at the Admission Time\n Stay_Days Stay Days by the patient (target) ['0-10' '41-50' '31-40' '11-20' '51-60' '21-30' '71-80'\n 'More than 100 Days' '81-90' '61-70' '91-100']\n \n\nStarter Kernels\n\nEDA and Random Forest Benchmark\n\nInspiration\n\nPredict the Length of Stay for each patient\nInterpret best model(s) and mine influence factors in LOS risk \n\nMore References\n\nCOVID-19 length of hospital stay\nA retrospective cohort study in a Fangcang shelter hospital", "format": "arff", "uploader": "Onur Yildirim", "uploader_id": 30126, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-23 13:49:02", "update_comment": null, "last_update": "2022-03-23 13:49:02", "licence": "CC0: Public Domain", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102375\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "COVID-19-Hospitals-Treatment-Plan", "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 " ], "weight": 5 }, "qualities": { "NumberOfInstances": 318438, "NumberOfFeatures": 18, "NumberOfClasses": null, "NumberOfMissingValues": 4645, "NumberOfInstancesWithMissingValues": 4645, "NumberOfNumericFeatures": 11, "NumberOfSymbolicFeatures": 0, "Dimensionality": 5.6525917133005484e-5, "PercentageOfNumericFeatures": 61.111111111111114, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 1.4586826949045026, "AutoCorrelation": null, "PercentageOfMissingValues": 0.08103792749469459 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" }, { "uploader": "38960", "tag": "Machine Learning" } ], "features": [ { "name": "case_id", "index": "0", "type": "numeric", "distinct": "318438", "missing": "0", "min": "1", "max": "318438", "mean": "159220", "stdev": "91925" }, { "name": "Hospital", "index": "1", "type": "numeric", "distinct": "32", "missing": "0", "min": "1", "max": "32", "mean": "18", "stdev": "9" }, { "name": "Hospital_type", "index": "2", "type": "numeric", "distinct": "7", "missing": "0", "min": "0", "max": "6", "mean": "1", "stdev": "2" }, { "name": "Hospital_city", "index": "3", "type": "numeric", "distinct": "11", "missing": "0", "min": "1", "max": "13", "mean": "5", "stdev": "3" }, { "name": "Hospital_region", "index": "4", "type": "numeric", "distinct": "3", "missing": "0", "min": "0", "max": "2", "mean": "1", "stdev": "1" }, { "name": "Available_Extra_Rooms_in_Hospital", "index": "5", "type": "numeric", "distinct": "18", "missing": "0", "min": "0", "max": "24", "mean": "3", "stdev": "1" }, { "name": "Department", "index": "6", "type": "string", "distinct": "5", "missing": "0" }, { "name": "Ward_Type", "index": "7", "type": "string", "distinct": "6", "missing": "0" }, { "name": "Ward_Facility", "index": "8", "type": "string", "distinct": "6", "missing": "0" }, { "name": "Bed_Grade", "index": "9", "type": "numeric", "distinct": "4", "missing": "113", "min": "1", "max": "4", "mean": "3", "stdev": "1" }, { "name": "patientid", "index": "10", "type": "numeric", "distinct": "92017", "missing": "0", "min": "1", "max": "131624", "mean": "65748", "stdev": "37980" }, { "name": "City_Code_Patient", "index": "11", "type": "numeric", "distinct": "37", "missing": "4532", "min": "1", "max": "38", "mean": "7", "stdev": "5" }, { "name": "Type_of_Admission", "index": "12", "type": "string", "distinct": "3", "missing": "0" }, { "name": "Illness_Severity", "index": "13", "type": "string", "distinct": "3", "missing": "0" }, { "name": "Patient_Visitors", "index": "14", "type": "numeric", "distinct": "28", "missing": "0", "min": "0", "max": "32", "mean": "3", "stdev": "2" }, { "name": "Age", "index": "15", "type": "string", "distinct": "10", "missing": "0" }, { "name": "Admission_Deposit", "index": "16", "type": "numeric", "distinct": "7300", "missing": "0", "min": "1800", "max": "11008", "mean": "4881", "stdev": "1087" }, { "name": "Stay_Days", "index": "17", "type": "string", "distinct": "11", "missing": "0" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }