AIMC Topic: Electronic Health Records
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Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
Artificial intelligence in medicine
Jun 25, 2016
OBJECTIVE: The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational method...
Dense Annotation of Free-Text Critical Care Discharge Summaries from an Indian Hospital and Associated Performance of a Clinical NLP Annotator.
Journal of medical systems
Jun 24, 2016
Electronic Health Record (EHR) use in India is generally poor, and structured clinical information is mostly lacking. This work is the first attempt aimed at evaluating unstructured text mining for extracting relevant clinical information from Indian...
A new algorithmic approach for the extraction of temporal associations from clinical narratives with an application to medical product safety surveillance reports.
Journal of biomedical informatics
Jun 17, 2016
The sheer volume of textual information that needs to be reviewed and analyzed in many clinical settings requires the automated retrieval of key clinical and temporal information. The existing natural language processing systems are often challenged ...
Energy landscapes for a machine-learning prediction of patient discharge.
Physical review. E
Jun 17, 2016
The energy landscapes framework is applied to a configuration space generated by training the parameters of a neural network. In this study the input data consists of time series for a collection of vital signs monitored for hospital patients, and th...
Characteristics of outpatient clinical summaries in the United States.
International journal of medical informatics
Jun 11, 2016
In the United States, federal regulations require that outpatient practices provide a clinical summary to ensure that patients understand what transpired during their appointment and what to do before the next visit. To determine whether clinical sum...
Generation of open biomedical datasets through ontology-driven transformation and integration processes.
Journal of biomedical semantics
Jun 3, 2016
BACKGROUND: Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources, which makes difficult the integrated exploitation of such data. The Semantic Web paradigm offers a natural technological space for d...
Big Data and machine learning in radiation oncology: State of the art and future prospects.
Cancer letters
May 27, 2016
Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be int...
Radiotherapy
Data Mining
Radiotherapy Planning, Computer-Assisted
Radiation Dosage
Machine Learning
Decision Support Techniques
Unsupervised Machine Learning
Databases, Factual
Data Interpretation, Statistical
Radiation Oncology
Humans
Neural Networks, Computer
Risk Assessment
Neoplasms
Support Vector Machine
Electronic Health Records
Risk Factors
Biomedical Research
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values.
PloS one
May 19, 2016
This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious ...
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
Scientific reports
May 17, 2016
Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs....
Learning statistical models of phenotypes using noisy labeled training data.
Journal of the American Medical Informatics Association : JAMIA
May 12, 2016
OBJECTIVE: Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled traini...
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