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Predicting the pharmaceutical needs of hospitals using machine learning algorithms

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Abstract

People’s lives are always threatened by various diseases. The role of health and medical services, in particular medicine, is undeniable in protecting their lives. Timely preparation and providing medicine for patients is vital since medicine shortage can endanger their lives while excessive accumulation of medicine can put them at expiration risk and waste health budgets. To this end, in this paper, we aim at introducing a model for the prediction of commonly used medicine (type and amount) in hospitals. For that, in our applied research, we initially used patients’ data from the Afzalipur Hospital in Kerman collected for 3 years consisting of 283 features, which included over 9351 different medicine and 121,690 patients. Then, nine features were selected using experts’ feedback and were fed into the random forest and neural network algorithms. For the prediction task, medicine types and their amounts were predicted for each individual using different training sets. In addition, the right prediction time was also found which is when predictions have a promising accuracy while the executive team of a hospital has enough time to provide the right amounts of the most used medicine. The performance of algorithms was evaluated using a confusion matrix (precision, recall, F1, and accuracy metrics). Our results showed that the random forest had a promising performance in predicting the amounts of the most used medicine for a month using 2 years of data (accuracy 83.3%) while its accuracy in predicting medicine was 35.9%. Therefore, we conclude that the random forest algorithm has the potential to effectively and accurately predict medicine amounts by analyzing large amounts of data and detecting patterns that may not be easily discernible to humans.

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Authors and Affiliations

  1. Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, 7616913555, Iran

    Amir Hossein Nabizadeh & Mohammad Mehdi Ghaemi

  2. INESC-ID, Lisboa, Portugal

    Amir Hossein Nabizadeh & Daniel Goncalves

  3. Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Portugal

    Daniel Goncalves

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  1. Amir Hossein Nabizadeh
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  2. Mohammad Mehdi Ghaemi
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  3. Daniel Goncalves
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Contributions

The first author designed the prediction method, wrote the manuscript, and conducted the evaluation. The second author collected the dataset and helped in improving the initial idea. The last author revised the manuscript and assisted the first author in the evaluation task.

Corresponding author

Correspondence to Mohammad Mehdi Ghaemi.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Kerman University of Medical Sciences, Kerman, Iran (IR.KMU.REC.1401.076).

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Communicated by Ana Carolina Lorena.

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Appendix A

Appendix A

1.1 A.1 Accuracy of the generated models

In Table 11, higher accuracies are bolded. As shown in this table, RF has been superior to NN models in most cases. Sometimes the accuracy of the models is the same up to two decimal places, but considering more decimal places, their accuracy will be slightly different.

Table 13 Precision, recall and F1 of NN and RF before balancing
Full size table
Table 14 Precision, recall and F1 of NN and RF after balancing
Full size table

1.2 A.2 Required time to generate the models

In Table 12, we presented the required time to build each model. It provides a better understanding of generating the models, and also when the accuracy of models is similar, it can be used as a selection criteria. The required time to generate NN models is significantly less than the RF models.

1.3 A.3 Precision, recall and F1 of NN and RF models

The results presented in Tables 13 and 14 are compatible with the ones in Tables 4 and 5. In Tables 13 and 14, the recall of the NN model is sometimes equal to 100%. It shows that the probability of not predicting the prescribed medicine is very low.

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Nabizadeh, A.H., Ghaemi, M.M. & Goncalves, D. Predicting the pharmaceutical needs of hospitals using machine learning algorithms. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00530-z

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