AIMC Topic: Humans

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A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In the electronic health record, the majority of clinically relevant information is stored within clinical notes. Most clinical notes follow a set organizational structure composed of canonicalized section headers that facilitate clinical review and ...

Accelerating Epidemiological Investigation Analysis by Using NLP and Knowledge Reasoning: A Case Study on COVID-19.

AMIA ... Annual Symposium proceedings. AMIA Symposium
COVID-19 is threatening the health of the entire human population. In order to control the spread of the disease, epidemiological investigations should be conducted, to trace the infection source of each confirmed patient and isolate their close cont...

AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Allergy mention normalization is challenging because of the wide range of possible allergens including medications, foods, plants, animals, and consumer products. This paper describes the process of mapping free-text allergy information from an elect...

Extracting and Learning Fine-Grained Labels from Chest Radiographs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful o...

Timely and Efficient AI Insights on EHR: System Design.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A patient's electronic health record (EHR) contains extensive documentation of the patient's medical history but is difficult for clinicians to review and find what they are looking for under the time constraints of the clinical setting. Although rec...

Analyzing Description, User Understanding and Expectations of AI in Mobile Health Applications.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Previous research has studied medical professionals' perception of artificial intelligence (AI). However, there has been a limited understanding of how healthcare consumers perceive and use AI-powered technologies such as mobile health apps. We colle...

Patient Cohort Retrieval using Transformer Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a g...

Natural Language Processing and Machine Learning to Enable Clinical Decision Support for Treatment of Pediatric Pneumonia.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Pneumonia is the most frequent cause of infectious disease-related deaths in children worldwide. Clinical decision support (CDS) applications can guide appropriate treatment, but the system must first recognize the appropriate diagnosis. To enable CD...

EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinic...

User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, w...