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Selection of Clinical Text Features for Classifying Suicide Attempts.

Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR phenotyping. Most STB phenotyping studies have used structured EHR data, but some are beginning to incorporate unstructured clinical text. In this study...

Engaging proactive control: Influences of diverse language experiences using insights from machine learning.

We used insights from machine learning to address an important but contentious question: Is bilingual language experience associated with executive control abilities? Specifically, we assess proactive executive control for over 400 young adult bilinguals via reaction time (RT) on an AX continuous performance task (AX-CPT). We measured bilingual experience as a continuous, multidimensional spectrum...

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (, body mass index (BMI), body fat distributi...

Part 1: Artificial intelligence technology in surgery.

Artificial intelligence (AI) is one of the disruptive technologies of the fourth Industrial Revolution that is changing our work practices. This techn...

ZiMM: A deep learning model for long term and blurry relapses with non-clinical claims data.

This paper considers the problems of modeling and predicting a long-term and "blurry" relapse that occurs after a medical act, such as a surgery. We d...

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical c...

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassif...

A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes.

OBJECTIVE: This study aims to develop and evaluate effective methods that can normalize diagnosis and procedure terms written by physicians to standar...

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback.

BACKGROUND: Due to reimbursement tied in part to patients' perception of their care, hospitals continue to stress obtaining patient feedback and under...

Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity.

BACKGROUND AND OBJECTIVE: This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim...

Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.

Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New informatics approaches including natural language p...

Robot-assisted gait training for balance and lower extremity function in patients with infratentorial stroke: a single-blinded randomized controlled trial.

BACKGROUND: Balance impairments are common in patients with infratentorial stroke. Although robot-assisted gait training (RAGT) exerts positive effect...

Interpretable deep learning to map diagnostic texts to ICD-10 codes.

BACKGROUND: Automatic extraction of morbid disease or conditions contained in Death Certificates is a critical process, useful for billing, epidemiolo...

Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China.

INTRODUCTION: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for prima...

Neural transfer learning for assigning diagnosis codes to EMRs.

OBJECTIVE: Electronic medical records (EMRs) are manually annotated by healthcare professionals and specialized medical coders with a standardized set...

Facilitating accurate health provider directories using natural language processing.

BACKGROUND: Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, qua...

[An online dynamic knowledge base in multiple languages on general medicine and primary care].

INTRODUCTION: The International Classification of Primary Care, Second version (ICPC-2) aligned with the 10th Revision of the International Classifica...

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) syst...

Optimization of the Cohesion Index in the SeDeM Diagram Expert System and application of SeDeM Diagram: An improved methodology to determine the Cohesion Index.

In this study, we suggest optimizing the methodology to determine the Cohesion Index (Icd) in order to avoid mistaken characterizations due to powder ...

Benchmarking deep learning models on large healthcare datasets.

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recogni...