Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care
<p dir="ltr">Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to t...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2022
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| الموضوعات: | |
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| _version_ | 1864513508144578560 |
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| author | Regina Padmanabhan (14231606) |
| author2 | Adel Elomri (8984063) Ruba Yasin Taha (8984060) Halima El Omri (14778790) Hesham Elsabah (8984069) Abdelfatteh El Omri (18278683) |
| author2_role | author author author author author |
| author_facet | Regina Padmanabhan (14231606) Adel Elomri (8984063) Ruba Yasin Taha (8984060) Halima El Omri (14778790) Hesham Elsabah (8984069) Abdelfatteh El Omri (18278683) |
| author_role | author |
| dc.creator.none.fl_str_mv | Regina Padmanabhan (14231606) Adel Elomri (8984063) Ruba Yasin Taha (8984060) Halima El Omri (14778790) Hesham Elsabah (8984069) Abdelfatteh El Omri (18278683) |
| dc.date.none.fl_str_mv | 2022-12-28T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/ijerph20010526 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Prediction_of_Multiple_Clinical_Complications_in_Cancer_Patients_to_Ensure_Hospital_Preparedness_and_Improved_Cancer_Care/26641594 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Health sciences Health services and systems cancer care public health machine learning febrile neutropenia multidrug-resistant organism hematological malignancies Qatar |
| dc.title.none.fl_str_mv | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to the risk of therapy-induced myelosuppression, multiple infections, and febrile neutropenia (FN). Myelosuppression due to treatment increases the risk of sepsis and mortality in hematological cancer patients with febrile neutropenia. A high prevalence of multidrug-resistant organisms is also noted in such patients, which implies that these patients are left with limited or no-treatment options amidst severe health complications. Hence, early screening of patients for such organisms in their bodies is vital to enable hospital preparedness, curtail the spread to other weak patients in hospitals, and limit community outbreaks. Even though predictive models for sepsis and mortality exist, no model has been suggested for the prediction of multidrug-resistant organisms in hematological cancer patients with febrile neutropenia. Hence, for predicting three critical clinical complications, such as sepsis, the presence of multidrug-resistant organisms, and mortality, from the data available from medical records, we used 1166 febrile neutropenia episodes reported in 513 patients. The XGboost algorithm is suggested from 10-fold cross-validation on 6 candidate models. Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. The proposed model depicts improved recall and AUC (area under the curve) for sepsis (recall = 98%, AUC = 0.85), multidrug-resistant organism (recall = 96%, AUC = 0.91), and mortality (recall = 86%, AUC = 0.88) prediction. Our results encourage the need to popularize artificial intelligence-based devices to support clinical decision-making.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Environmental Research and Public Health<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/ijerph20010526" target="_blank">https://dx.doi.org/10.3390/ijerph20010526</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_34167685ae9fd59906e6e95a235ec84b |
| identifier_str_mv | 10.3390/ijerph20010526 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26641594 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer CareRegina Padmanabhan (14231606)Adel Elomri (8984063)Ruba Yasin Taha (8984060)Halima El Omri (14778790)Hesham Elsabah (8984069)Abdelfatteh El Omri (18278683)Biomedical and clinical sciencesClinical sciencesOncology and carcinogenesisHealth sciencesHealth services and systemscancer carepublic healthmachine learningfebrile neutropeniamultidrug-resistant organismhematological malignanciesQatar<p dir="ltr">Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to the risk of therapy-induced myelosuppression, multiple infections, and febrile neutropenia (FN). Myelosuppression due to treatment increases the risk of sepsis and mortality in hematological cancer patients with febrile neutropenia. A high prevalence of multidrug-resistant organisms is also noted in such patients, which implies that these patients are left with limited or no-treatment options amidst severe health complications. Hence, early screening of patients for such organisms in their bodies is vital to enable hospital preparedness, curtail the spread to other weak patients in hospitals, and limit community outbreaks. Even though predictive models for sepsis and mortality exist, no model has been suggested for the prediction of multidrug-resistant organisms in hematological cancer patients with febrile neutropenia. Hence, for predicting three critical clinical complications, such as sepsis, the presence of multidrug-resistant organisms, and mortality, from the data available from medical records, we used 1166 febrile neutropenia episodes reported in 513 patients. The XGboost algorithm is suggested from 10-fold cross-validation on 6 candidate models. Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. The proposed model depicts improved recall and AUC (area under the curve) for sepsis (recall = 98%, AUC = 0.85), multidrug-resistant organism (recall = 96%, AUC = 0.91), and mortality (recall = 86%, AUC = 0.88) prediction. Our results encourage the need to popularize artificial intelligence-based devices to support clinical decision-making.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Environmental Research and Public Health<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/ijerph20010526" target="_blank">https://dx.doi.org/10.3390/ijerph20010526</a></p>2022-12-28T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/ijerph20010526https://figshare.com/articles/journal_contribution/Prediction_of_Multiple_Clinical_Complications_in_Cancer_Patients_to_Ensure_Hospital_Preparedness_and_Improved_Cancer_Care/26641594CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/266415942022-12-28T09:00:00Z |
| spellingShingle | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care Regina Padmanabhan (14231606) Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Health sciences Health services and systems cancer care public health machine learning febrile neutropenia multidrug-resistant organism hematological malignancies Qatar |
| status_str | publishedVersion |
| title | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care |
| title_full | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care |
| title_fullStr | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care |
| title_full_unstemmed | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care |
| title_short | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care |
| title_sort | Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care |
| topic | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Health sciences Health services and systems cancer care public health machine learning febrile neutropenia multidrug-resistant organism hematological malignancies Qatar |