We present a novel data augmentation method to address the challenge of data scarcity in modeling longitudinal patterns in Electronic Health Records (EHR) of patients using natural language processing (NLP) algorithms. The proposed method generates augmented data by rearranging the orders of medical records within a visit where the order of elements are not obvious, if any. Applying the proposed method to the clopidogrel treatment failure detection task enabled up to 5.3% absolute improvement in terms of ROC-AUC (from 0.908 without augmentation to 0.961 with augmentation) when it was used during the pre-training procedure. It was also shown that the augmentation helped to improve performance during fine-tuning procedures, especially when the amount of labeled training data is limited.

We propose machine learning algorithms to automatically detect and predict clopidogrel treatment failure using longitudinal structured electronic health records (EHR). By drawing analogies between natural language and structured EHR, we introduce various machine learning algorithms used in natural language processing (NLP) applications to build models for treatment failure detection and prediction. In this regard, we generated a cohort of patients with clopidogrel prescriptions from UK Biobank and annotated if the patients had treatment failure events within one year of the first clopidogrel prescription; out of 502,527 patients, 1,824 patients were identified as treatment failure cases, and 6,859 patients were considered as control cases. From the dataset, we gathered diagnoses, prescriptions, and procedure records together per patient and organized them into visits with the same date to build models. The models were built for two different tasks, i.e., detection and prediction, and the experimental results showed that time series models outperform bag-of-words approaches in both tasks. In particular, a Transformer-based model, namely BERT, could reach 0.928 AUC in detection tasks and 0.729 AUC in prediction tasks. BERT also showed competence over other time series models when there is not enough training data, because it leverages the pre-training procedure using large unlabeled data.

One paradox about antibiotics is that, broadly speaking, the more we use them, the less they continue to work. The Darwinian process of bacteria growing resistant to antibiotics means that, when the drugs don't work, we can no longer treat infections, leading to groups like the World Health Organization warning about our ability to control major public health threats. Because of its ubiquity, one topic that's particularly concerning is urinary tract infections (UTIs), which affect half of all women and add almost $4 billion a year in unnecessary health-care costs. Doctors often treat UTIs using antibiotics called fluoroquinolones that are inexpensive and generally effective. However, they have also been found to put women at risk of becoming infected with other difficult-to-treat bacteria, such as C. difficile and certain species of Staphylococcus, and also to increase their risk of tendon injuries and life-threatening conditions like aortic tears. As a result of this, medical associations have issued guidelines recommending fluoroquinolones as "second-line treatments" that should only be used on a patient when other antibiotics are ineffective or have adverse reactions.

Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) say they've developed a recommendation algorithm that predicts the probability a patient's urinary tract infection (UTI) can be treated by first- or second-line antibiotics. With this information, the model makes a recommendation for a specific treatment that selects a first-line agent as frequently as possible, without leading to an excess of treatment failures. UTIs, which affect half of all women, add almost $4 billion a year in health care costs. Doctors often treat UTIs using antibiotics called fluoroquinolones, but they've been found to put women at risk of contracting other infections. They're also associated with a higher risk of tendon injuries and life-threatening conditions like aortic tears, leading medical associations to issue guidelines recommending fluoroquinolones as "second-line treatments."