The prediction of hospital length of stay using unstructured data
Abstract Objective This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gend...
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| Format: | Article |
| Language: | English |
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BMC
2021-12-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-021-01722-4 |
| _version_ | 1819009740928188416 |
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| author | Jan Chrusciel François Girardon Lucien Roquette David Laplanche Antoine Duclos Stéphane Sanchez |
| author_facet | Jan Chrusciel François Girardon Lucien Roquette David Laplanche Antoine Duclos Stéphane Sanchez |
| author_sort | Jan Chrusciel |
| collection | DOAJ |
| description | Abstract Objective This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender and major ICD diagnosis. Methods This study was an observational retrospective cohort study and analyzed patient stays admitted between 1 January to 24 September 2019. For each stay, a patient was admitted through the Emergency Department (ED) and stayed for more than two days in the subsequent service. LOS was predicted using two random forest models. The first included unstructured text extracted from electronic health records (EHRs). A word-embedding algorithm based on UMLS terminology with exact matching restricted to patient-centric affirmation sentences was used to assess the EHR data. The second model was primarily based on structured data in the form of diagnoses coded from the International Classification of Disease 10th Edition (ICD-10) and triage codes (CCMU/GEMSA classifications). Variables common to both models were: age, gender, zip/postal code, LOS in the ED, recent visit flag, assigned patient ward after the ED stay and short-term ED activity. Models were trained on 80% of data and performance was evaluated by accuracy on the remaining 20% test data. Results The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. The two models produced a similar prediction in 86.6% of cases. In a secondary analysis restricted to intensive care patients, the accuracy of both models was also similar (76.3% vs 75.0%). Conclusions LOS prediction using unstructured data had similar accuracy to using structured data and can be considered of use to accurately model LOS. |
| first_indexed | 2024-12-21T01:01:11Z |
| format | Article |
| id | doaj.art-842777e50b814280ab9771feeb090a79 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-21T01:01:11Z |
| publishDate | 2021-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-842777e50b814280ab9771feeb090a792022-12-21T19:21:10ZengBMCBMC Medical Informatics and Decision Making1472-69472021-12-012111910.1186/s12911-021-01722-4The prediction of hospital length of stay using unstructured dataJan Chrusciel0François Girardon1Lucien Roquette2David Laplanche3Antoine Duclos4Stéphane Sanchez5Pôle Territorial Santé Publique et Performance, Centre Hospitalier de TroyesResearch and Consulting, CODOC SASResearch and Consulting, CODOC SASPôle Territorial Santé Publique et Performance, Centre Hospitalier de TroyesResearch on Healthcare Performance Lab, INSERM U1290 RESHAPE, Université Claude Bernard Lyon 1Pôle Territorial Santé Publique et Performance, Centre Hospitalier de TroyesAbstract Objective This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender and major ICD diagnosis. Methods This study was an observational retrospective cohort study and analyzed patient stays admitted between 1 January to 24 September 2019. For each stay, a patient was admitted through the Emergency Department (ED) and stayed for more than two days in the subsequent service. LOS was predicted using two random forest models. The first included unstructured text extracted from electronic health records (EHRs). A word-embedding algorithm based on UMLS terminology with exact matching restricted to patient-centric affirmation sentences was used to assess the EHR data. The second model was primarily based on structured data in the form of diagnoses coded from the International Classification of Disease 10th Edition (ICD-10) and triage codes (CCMU/GEMSA classifications). Variables common to both models were: age, gender, zip/postal code, LOS in the ED, recent visit flag, assigned patient ward after the ED stay and short-term ED activity. Models were trained on 80% of data and performance was evaluated by accuracy on the remaining 20% test data. Results The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. The two models produced a similar prediction in 86.6% of cases. In a secondary analysis restricted to intensive care patients, the accuracy of both models was also similar (76.3% vs 75.0%). Conclusions LOS prediction using unstructured data had similar accuracy to using structured data and can be considered of use to accurately model LOS.https://doi.org/10.1186/s12911-021-01722-4Emergency departmentLength of stayData miningHealth services research |
| spellingShingle | Jan Chrusciel François Girardon Lucien Roquette David Laplanche Antoine Duclos Stéphane Sanchez The prediction of hospital length of stay using unstructured data BMC Medical Informatics and Decision Making Emergency department Length of stay Data mining Health services research |
| title | The prediction of hospital length of stay using unstructured data |
| title_full | The prediction of hospital length of stay using unstructured data |
| title_fullStr | The prediction of hospital length of stay using unstructured data |
| title_full_unstemmed | The prediction of hospital length of stay using unstructured data |
| title_short | The prediction of hospital length of stay using unstructured data |
| title_sort | prediction of hospital length of stay using unstructured data |
| topic | Emergency department Length of stay Data mining Health services research |
| url | https://doi.org/10.1186/s12911-021-01722-4 |
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