Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods
Notice bibliographique
Résumé
The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».