Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study.

Background: Women have been underrepresented in clinical trials for many years. Machine-learning models trained on clinical trial abstracts may capture and amplify biases in the data. Specifically, word embeddings are models that enable representing words as vectors and are the building block of most natural language processing systems.

Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram.

Early detection of left ventricular systolic dysfunction (LVSD) may prompt early care and improve outcomes for asymptomatic patients. Standard 12-lead ECG may be used to predict LVSD. We aimed to compare the performance of Machine Learning Algorithms (MLA) and physicians in predicting LVSD from a standard 12-lead ECG.

Gender-sensitive word embeddings for healthcare.

Objective: To analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its performance.

Materials And Methods: We analyze gender bias in clinical trials described by 16 772 PubMed abstracts (2008-2018). We present a method to augment word embeddings, the core building block of NLP-centric representations, by weighting abstracts by the number of women participants in the trial.

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients.

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients.

On biases of attention in scientific discovery.

Summary: How do nuances of scientists' attention influence what they discover? We pursue an understanding of the influences of patterns of attention on discovery with a case study about confirmations of protein-protein interactions over time. We find that modeling and accounting for attention can help us to recognize and interpret biases in large-scale and widely used databases of confirmed interactions and to better understand missing data and unknowns. Additionally, we present an analysis of how awareness of patterns of attention and use of debiasing techniques can foster earlier discoveries.

Machine learning algorithm for early detection of end-stage renal disease.

Background: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database.

Development of a machine learning algorithm for early detection of opioid use disorder.

Background: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD.

Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning.

Despite effective medications, rates of uncontrolled glucose levels in type 2 diabetes remain high. We aimed to test the utility of machine learning applied to big data in identifying the potential role of concomitant drugs not taken for diabetes which may contribute to lowering blood glucose. Success in controlling blood glucose was defined as achieving HgA1c levels < 6.

Clinical pharmacology of old age.

: With the majority of elderly persons consuming multiple drugs, inappropriate drug use is a major issue in geriatric medicine. : We reviewed PubMed, Embase, and Cochrane from inception to 1 May 2019 for potentially inappropriate use of medications, polypharmacy, and age-dependent changes in pharmacokinetics and pharmacodynamics. We selected to highlight new aspects that have emerged in recent years: appropriate monitoring of drug adherence and the introduction of Big Data analysis in advancing geriatric pharmacology.

Chronic Use of β-Blockers and the Risk of Parkinson's Disease.

Background: Most patients with Parkinson's disease exhibit intracellular accumulation of the α-synuclein protein encoded by the α-synuclein gene. It was recently shown that β-adrenoreceptor agonists downregulate this gene, decreasing the apparent risk of Parkinson's disease by up to 40%. In contrast, exposure to β-blocking drugs increases production of the α-synuclein protein.

Prototype-Based Compound Discovery Using Deep Generative Models.

Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large ( Polishchuk , P. G.

Machine learning of big data in gaining insight into successful treatment of hypertension.

Despite effective medications, rates of uncontrolled hypertension remain high. Treatment protocols are largely based on randomized trials and meta-analyses of these studies. The objective of this study was to test the utility of machine learning of big data in gaining insight into the treatment of hypertension.