Prediction of measles cases in US counties: A machine learning approach.

Background: Although measles was declared eliminated from the United States in 2000, the frequency of measles outbreaks has increased in recent years. The ability to predict the locations of future cases could aid efforts to prevent and contain measles in the United States.

Methods: We estimated county-level measles risk using a machine learning model with 17 predictor variables, which was trained on 2014 and 2018 United States county-level measles case data and tested on data from 2019.

Real-World Data Quality Framework for Oncology Time to Treatment Discontinuation Use Case: Implementation and Evaluation Study.

Background: The importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD).

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study.

Background: Heart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs.

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches.

Background: Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events that are difficult to predict. Improved methods to predict outbreaks at the county level would facilitate the optimal allocation of public health resources.

Generic medical concept embedding and time decay for diverse patient outcome prediction tasks.

Many fields, including Natural Language Processing (NLP), have recently witnessed the benefit of pre-training with large generic datasets to improve the accuracy of prediction tasks. However, there exist key differences between the longitudinal healthcare data (, claims) and NLP tasks, which make the direct application of NLP pre-training methods to healthcare data inappropriate. In this article, we developed a pre-training scheme for longitudinal healthcare data that leverages the pairing of medical history and a future event.

Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data.

Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments.

Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction.

Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). We analyzed the data of adult HFrEF patients from the IBM® MarketScan® Commercial and Medicare Supplement databases between January 1, 2015 and December 31, 2017.

Serendipity-A Machine-Learning Application for Mining Serendipitous Drug Usage From Social Media.

Serendipitous drug usage refers to the unexpected relief of comorbid diseases or symptoms when taking medication for a different known indication. Historically, serendipity has contributed significantly to identifying many new drug indications. If patient-reported serendipitous drug usage in social media could be computationally identified, it could help generate and validate drug-repositioning hypotheses.

Evaluation of the informatician perspective: determining types of research papers preferred by clinicians.

Background: To deliver evidence-based medicine, clinicians often reference resources that are useful to their respective medical practices. Owing to their busy schedules, however, clinicians typically find it challenging to locate these relevant resources out of the rapidly growing number of journals and articles currently being published. The literature-recommender system may provide a possible solution to this issue if the individual needs of clinicians can be identified and applied.