RETRACTED ARTICLE: Drug recommendation with minimal side effects based on direct and temporal symptoms

Palanivinayagam, Ashokkumar; Sasikumar, Don

doi:10.1007/s00521-018-3794-5

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…This is similar to our problem setting. Palanivinayagam and Sasikumar (2020) proposed a drug recommendation system aiming at minimizing potential side effects. Balvert et al (2019) conducted personalized drug prescription based on information from cancer cell lines by selecting the prediction model with the best performance for each drug.…”

Section: Research Backgroundmentioning

confidence: 99%

Bayesian multitask learning for medicine recommendation based on online patient reviews

2023

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Motivation We propose a drug recommendation model that integrates information from both structured data (patient demographic information) and unstructured texts (patient reviews). It is based on multitask learning to predict review ratings of several satisfaction-related measures for a given medicine, where related tasks can learn from each other for prediction. The learned models can then be applied to new patients for drug recommendation. This is fundamentally different from most recommender systems in e-commerce, which do not work well for new customers (referred to as the cold-start problem). To extract information from review texts, we employ both topic modeling and sentiment analysis. We further incorporate variable selection into the model via Bayesian LASSO, which aims to filter out irrelevant features. To our best knowledge, this is the first Bayesian multitask learning method for ordinal responses. We are also the first to apply multitask learning to medicine recommendation. The sample code and data are made available at GitHub: Results We evaluate the proposed method on two sets of drug reviews involving 17 depression/high blood pressure related drugs. Overall, our method performs better than existing benchmark methods in terms of accuracy and AUC. It is effective even with a small sample size and only a few available features, and more robust to possible non-informative covariates. Due to our model explainability, insights generated from our model may work as a useful reference for doctors. In practice, however, a final decision should be carefully made by combining the information from the proposed recommender with doctors’ domain knowledge and past experience. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Research Backgroundmentioning

confidence: 99%

Bayesian multitask learning for medicine recommendation based on online patient reviews

2023

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“…The purpose of constructing star topology is because connecting and managing the people population over cities becomes easy [36,37]. Let the available support staff in a subgraph (entire district) be Ns and the total population in a city i is Nci.…”

Section: Edge Dummy Node Citymentioning

confidence: 99%

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning

Palanivinayagam

Panneerselvam

Kumar

et al. 2022

Computational and Mathematical Methods in Medicine

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The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that totally 4, 11,773 cases are positive, 2, 28,307 recovered, and the country reported 12,948 deaths as of 21 June 2020. Vaccination is the only way to prevent the spreading of COVID-19 disease. Due to various reasons, there is vaccine hesitancy across many people. Hence, the Indian government has the solution to avoid the spread of the disease by instructing their citizens to maintain social distancing, wearing masks, avoiding crowds, and cleaning your hands. Moreover, lots of poverty cases are reported due to social distancing, and hence, both the center government and the respective state governments decide to issue relief funds to all its citizens. The government is unable to maintain social distancing during the relief schemes as the population is huge and available support staffs are less. In this paper, the proposed algorithm makes use of graph theory to schedule the timing of the relief funds so that with the available support staff, the government would able to implement its relief scheme while maintaining social distancing. Furthermore, we have used LSTM deep learning model to predict the spread rate and analyze the daily positive COVID cases.

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An Optimized Machine Learning and Big Data Approach to Crime Detection

Palanivinayagam

Shankar

Bhattacharya

et al. 2021

Wireless Communications and Mobile Computing

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Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.

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RETRACTED ARTICLE: Drug recommendation with minimal side effects based on direct and temporal symptoms

Cited by 6 publications

References 19 publications

Bayesian multitask learning for medicine recommendation based on online patient reviews

Bayesian multitask learning for medicine recommendation based on online patient reviews

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning

An Optimized Machine Learning and Big Data Approach to Crime Detection

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