Discovering Drug-Drug Interactions Using Association Rule Mining from Electronic Health Records

In this paper, we propose utilising Electronic Health Records (EHR) to discover previously unknown drug-drug interactions (DDI) that may result in high rates of hospital readmissions. We used association rule mining and categorised drug combinations as high or low risk based on the adverse events they caused. We demonstrate that the drug combinations in the high-risk group contain significantly more drug-drug interactions than those in the low-risk group. This approach is efficient for discovering potential drug interactions that lead to negative outcomes, thus should be given priority and evaluated in clinical trials. In fact, severe drug interactions can have life-threatening consequences and result in adverse clinical outcomes. Our findings were achieved using a new association rule metric, which better accounts for the adverse drug events caused by DDI

HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users

Machine Learning models for predicting 30-day readmission of elderly patients using custom target encoding approach

The readmission rate is an important indicator of the hospital quality of care. With the upsetting increase in readmission rates worldwide, especially in geriatric patients, predicting unplanned readmissions becomes a very im-portant task, that can help to improve the patient’s well-being and reduce healthcare costs. With the aim of reducing hospital readmission, more atten-tion is to be paid to home healthcare services, since home healthcare pa-tients on average have more compromised health conditions. Machine Learning and Artificial intelligence algorithms were used to develop predic-tive models using MIMIC-IV repository. Developed predictive models ac-count for various patient details, including demographical, administrative, disease-related and prescription-related data. Categorical features were en-coded with a novel customized target encoding approach to improve the model performance avoiding data leakage and overfitting. This new risk-score based target encoding approach demonstrated similar performance to existing target encoding and Bayesian encoding approaches, with reduced data leakage, when assessed using Gini-importance. Developed models demonstrated good discriminative performance, AUC 0.75, TPR 0.69 TNR 0.67 for the best model. These encouraging results, as well as an effective feature engineering approach, can be used in further studies to develop more reliable 30-day readmission predictive models

A Comparative Machine Learning Modelling Approach for Patients' Mortality Prediction in Hospital Intensive Care Unit

Mortality prediction in a hospital Intensive Care Unit (ICU) is a challenge that must be addressed with high precision. Machine Learning (ML) is a powerful tool in predictive modelling but subject to the problem of class im-balance. In this study, we tackle class imbalance with combining new features, data re-sampling, ensemble learning and an appropriate selection of evaluation metrics in a clinical setting. We built and evaluated 126 ML mod-els to predict mortality in 48546 ICU admissions extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) repository. In our study design, six mortality prediction datasets are extracted; five of which are legacy dataset sets while the remainder is our new constructed dataset. For our combined data models, when testing on isolated data, our selection of features enhanced the prediction performances beyond those for the traditional legacy sets used in research. The legacy datasets are the Simplified Acute Physiology Score (SAPS II), the Sequential Organ Failure Assessment score (SOFA), the Glasgow Coma Scale (GCS), Elixhauser Comorbidity Index (ECI) and Demographics & Disease Groups (DDG). Our approach has a considerable impact on the classification; it resulted in an improvement in the mortality status prediction. For evaluation, we implement a comparative multi-stage evaluation filter for binary classification to compare all models. The best models are identified. The Area Under Receiver Operator Characteristic curves of the tested models range from 0.57 to 0.94. These encouraging results can guide further development of models to allow for more reliable ICU mortality predictions