AIMC Topic: Registries

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Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks.

Journal of the American Medical Informatics Association : JAMIA Jan 1, 2020
OBJECTIVE: We implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured...
Humans Neural Networks, Computer Machine Learning Neoplasms Support Vector Machine Natural Language Processing Registries Information Storage and Retrieval
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Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark.

JAMA psychiatry Jan 1, 2020
IMPORTANCE: Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may help advance the understanding of suicide risk.
Male Single-Payer System Humans Middle Aged Suicide, Completed Models, Statistical Machine Learning Risk Assessment Denmark Mental Disorders Suicide Registries Female Sex Factors Adult Marital Status
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Machine-learning-based knowledge discovery in rheumatoid arthritis-related registry data to identify predictors of persistent pain.

Pain Jan 1, 2020
Early detection of patients with chronic diseases at risk of developing persistent pain is clinically desirable for timely initiation of multimodal therapies. Quality follow-up registries may provide the necessary clinical data; however, their design...
Algorithms Female Sweden Risk Factors Aged Humans Machine Learning Adolescent Arthritis, Rheumatoid Young Adult Registries Pain Adult Knowledge Discovery Male Middle Aged
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Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer.

JCO clinical cancer informatics Oct 1, 2019
PURPOSE: Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand can...
Middle Aged Time Factors Decision Support Systems, Clinical Follow-Up Studies Natural Language Processing Registries Neoplasm Recurrence, Local Medical Informatics California Databases, Factual Female Aged Adult Breast Neoplasms Humans Algorithms
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Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study.

Neurosurgery Oct 1, 2019
INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis ...
Female Aged Adult Brain Neoplasms Male Humans Machine Learning Algorithms Prospective Studies Registries Middle Aged Postoperative Complications Cohort Studies Area Under Curve
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Validation of a Cyclic Algorithm to Proxy Number of Lines of Systemic Cancer Therapy Using Administrative Data.

JCO clinical cancer informatics Aug 1, 2019
PURPOSE: Researchers are automating the process for identifying the number of lines of systemic cancer therapy received by patients. To date, algorithm development has involved manual modifications to predefined classification rules. In this study, w...
Registries Supervised Machine Learning Humans Algorithms Research Design Neoplasms Antineoplastic Combined Chemotherapy Protocols
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Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

Clinical orthopaedics and related research Jun 1, 2019
BACKGROUND: Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating presurgical decision support. Machine learni...
Middle Aged Predictive Value of Tests Disability Evaluation Registries Arthroplasty, Replacement, Knee Patient Reported Outcome Measures Arthroplasty, Replacement, Hip Machine Learning Minimal Clinically Important Difference Retrospective Studies Female Humans Aged Male
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Editor's Spotlight/Take 5: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

Clinical orthopaedics and related research Jun 1, 2019
Humans Arthroplasty, Replacement, Knee Patient Reported Outcome Measures Arthroplasty, Replacement, Hip Male Minimal Clinically Important Difference Machine Learning Middle Aged Retrospective Studies Predictive Value of Tests Female Disability Evaluation Aged Registries
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Validity of Natural Language Processing for Ascertainment of and Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer.

JCO clinical cancer informatics May 1, 2019
PURPOSE: SEER registries do not report results of epidermal growth factor receptor () and anaplastic lymphoma kinase () mutation tests. To facilitate population-based research in molecularly defined subgroups of non-small-cell lung cancer (NSCLC), we...
Reproducibility of Results Kentucky Aged ErbB Receptors Anaplastic Lymphoma Kinase Humans Adult Lung Neoplasms Mutation Machine Learning Registries Male Carcinoma, Non-Small-Cell Lung Algorithms Genetic Testing Middle Aged Population Surveillance Female SEER Program Natural Language Processing DNA Mutational Analysis
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Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Journal of the American Heart Association Mar 5, 2019
Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System...
Risk Factors Humans Middle Aged Registries Aged Percutaneous Coronary Intervention Machine Learning Hospital Mortality Male Coronary Artery Disease Databases, Factual Treatment Outcome Data Mining Stroke Volume Female Age Factors New York
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