AIMC Topic: Algorithms

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Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical studies model the average treatment effect (ATE), but apply this population-level effect to future individuals. Due to recent developments of machine learning algorithms with useful statistical guarantees, we argue instead for modeling the i...

Scaling Out and Evaluation of OBSecAn, an Automated Section Annotator for Semi-Structured Clinical Documents, on a Large VA Clinical Corpus.

AMIA ... Annual Symposium proceedings. AMIA Symposium
"Identifying and labeling" (annotating) sections improves the effectiveness of extracting information stored in the free text of clinical documents. OBSecAn, an automated ontology-based section annotator, was developed to identify and label sections ...

Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical concept recognition (CCR) is a fundamental task in clinical natural language processing (NLP) field. Almost all current machine learning-based CCR systems can only recognize clinical concepts of consecutive words (called consecutive clinical...

Automatic Classification of Structured Product Labels for Pregnancy Risk Drug Categories, a Machine Learning Approach.

AMIA ... Annual Symposium proceedings. AMIA Symposium
With regular expressions and manual review, 18,342 FDA-approved drug product labels were processed to determine if the five standard pregnancy drug risk categories were mentioned in the label. After excluding 81 drugs with multiple-risk categories, 8...

An Ensemble Method for Spelling Correction in Consumer Health Questions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Orthographic and grammatical errors are a common feature of informal texts written by lay people. Health-related questions asked by consumers are a case in point. Automatic interpretation of consumer health questions is hampered by such errors. In th...

Causal Phenotype Discovery via Deep Networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the wo...

POETenceph - Automatic identification of clinical notes indicating encephalopathy using a realist ontology.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Identifying inpatients with encephalopathy is important. The disorder is prevalent, often missed, and puts patients at risk. We describe POETenceph, natural language processing pipeline, which ranks clinical notes on the extent to which they indicate...

A hybrid manifold learning algorithm for the diagnosis and prognostication of Alzheimer's disease.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. Such data are difficult to compare, visualize, and analyze due to the heterogeneous nature of medical tests...

Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record.

Digestive diseases and sciences
BACKGROUND AND AIMS: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computa...

SIM-ELM: Connecting the ELM model with similarity-function learning.

Neural networks : the official journal of the International Neural Network Society
This paper moves from the affinities between two well-known learning schemes that apply randomization in the training process, namely, Extreme Learning Machines (ELMs) and the learning framework using similarity functions. These paradigms share a com...