Patient care classification using machine learning techniques

Melhem, Shatha; Alaiad, Ahmad; Al-Ayyad, Muhammad

doi:10.1109/icics52457.2021.9464582

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Melhem et al [1] built four models depending on the patient's circumstances and lab test results: support vector machine model, decision tree model, random forest model and k-nearest neighbours model. The major aim of their study was to make use of ML algorithms to categorize the patient treatment as an in-patient or out-patient, in order to lessen the time and effort expended by the healthcare experts, which reflects the kind of services provided to the patient.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Table V shows the classification report of the model. The findings of Melhem et al [1] reveal that out of four models i.e. Support Vector Machine (SVM) model, Decision Tree model, Random Forest model and K-Nearest Neighbors (KNN) model, Random Forest model had the best accuracy (77%), precision (72%) and sensitivity (65%).…”

Section: B Attribute Informationmentioning

confidence: 99%

“…This time-consuming approach requires doctors to exert considerable effort in order to determine if the patient is required to be in the hospital and closely watched or not. Also, the patient's life may be in risk if the wrong decision is made [1]. Clinical machine learning research is largely restricted to proof-of-concept studies.…”

Section: Introductionmentioning

confidence: 99%

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A Patient Care Predictive Model using Logistic Regression

Patel¹,

Saini²

2021

IJACSA

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Medical treatments and operations in hospitals are divided into in-patient and out-patient procedures. It is critical for patients to know and understand the differentiation between these two forms of treatment since it will affect the time of a patient's stay in a hospital or a medical institution as well as the cost of a treatment. In today's era of information, a person's talents and expertise may be put to good use by automating activities wherever possible. A medical service will be termed inpatient care if a doctor issues an order and the patient is admitted to the hospital on that order whereas a patient seeking outpatient care do not need to spend the night in a hospital. Choosing between in-patient and out-patient care is usually a matter of how involved the doctor wants to be with the patient's treatment. With the aid of numerous data points regarding the patients, their illnesses, and lab tests, our main objective is to develop a system as part of the hospital automation system that predicts and estimates whether the patient should be given an inpatient care or an out-patient care. The main idea of the paper is to understand and develop a logistic regression model to predict whether a patient needs to be treated as an in-patient or an outpatient depending on the results of laboratory tests. Furthermore, this study also focuses on how logistic regression performs for this dataset. In addition, research on how logistic regression performs for this dataset was also not done. From the study, the results show that logistic regression gives an accuracy of 75%, F1-score of 73%, precision of 74% and recall of 74%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: B Attribute Informationmentioning

confidence: 99%

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A Patient Care Predictive Model using Logistic Regression

Patel¹,

Saini²

2021

IJACSA

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Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

Nguyen

Pham

et al. 2023

Int. j. inf. tecnol.

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Overcrowding in hospitals in Vietnam has caused many disadvantages in receiving and treating patients. Especially at the stage of receiving and diagnosing procedures taking patients to the treatment departments in the hospital takes up much time. This study proposes a text-based disease diagnosis using text processing techniques (such as Bag of Words, Term Frequency- Inverse Document Frequency, and Tokenizer) combined with classifiers (such as Random Forests (RF), Multi-Layer Perceptron (MLP), Embeddings and Bidirectional Long Short-term memory (LSTM)) on symptoms. As observed from the results, deep Bidirectional LSTM can reach 0.982 in AUC in the classification of 10 diseases on 230,457 samples of pre-diagnosis collected from Vietnam hospitals used in the training and testing phases. The proposed approach is expected to provide a way to automate patient flow in hospitals to improve healthcare in the future.

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Patient care classification using machine learning techniques

Cited by 2 publications

References 16 publications

A Patient Care Predictive Model using Logistic Regression

A Patient Care Predictive Model using Logistic Regression

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

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