Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a syn...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/bc64a0ada52a41ec9ee29b7542662d38 |
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| Sumario: | The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602. |
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