AIMC Topic: Risk Assessment

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LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer.

Cancer research and treatment Dec 29, 2020
PURPOSE: The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning-based approach has not been widely s...
Lymphatic Metastasis Neoplasm Staging Immunohistochemistry Risk Assessment ROC Curve Lymph Nodes Lymphocytes, Tumor-Infiltrating Humans Machine Learning Prospective Studies Prognosis Retrospective Studies Female Aged Colorectal Neoplasms Male
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Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients.

Nursing outlook Dec 29, 2020
BACKGROUND: Nurses often document patient symptoms in narrative notes.
Humans Nursing Care Electronic Health Records Documentation New England Male Symptom Assessment Home Care Services Middle Aged Risk Assessment Natural Language Processing Emergency Service, Hospital Female Hospitalization Aged Aged, 80 and over Adult
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Characterization of Antiphospholipid Syndrome Atherothrombotic Risk by Unsupervised Integrated Transcriptomic Analyses.

Arteriosclerosis, thrombosis, and vascular biology Dec 24, 2020
OBJECTIVE: Our aim was to characterize distinctive clinical antiphospholipid syndrome phenotypes and identify novel microRNA (miRNA)-mRNA-intracellular signaling regulatory networks in monocytes linked to cardiovascular disease. Approach and Results:...
Humans Cells, Cultured Female Monocytes Signal Transduction Cluster Analysis MicroRNAs Unsupervised Machine Learning Gene Expression Profiling Thrombosis Transcriptome Gene Expression Regulation Case-Control Studies Oligonucleotide Array Sequence Analysis Protein Interaction Maps Antiphospholipid Syndrome Gene Regulatory Networks Risk Factors Adult Male Middle Aged Risk Assessment
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Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.

Annals of emergency medicine Dec 24, 2020
STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-lea...
Female Aged Male Risk Assessment Emergency Service, Hospital Humans Machine Learning Retrospective Studies Electronic Health Records Heart Failure
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Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

Annals of surgery Dec 23, 2020
OBJECTIVE: We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (ES) patients.
Risk Factors Aged Aged, 80 and over Male Risk Assessment Postoperative Complications Hospital Mortality Humans Artificial Intelligence Databases, Factual Female
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Assessing Drug Development Risk Using Big Data and Machine Learning.

Cancer research Dec 22, 2020
Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard ...
Drug Development Drug-Related Side Effects and Adverse Reactions Male Risk Assessment Neoplasms Antineoplastic Agents Big Data Machine Learning Patient Safety Female Drug Delivery Systems Humans
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Predicting the Development of Surgery-Related Pressure Injury Using a Machine Learning Algorithm Model.

The journal of nursing research : JNR Dec 21, 2020
BACKGROUND: Surgery-related pressure injury (SRPI) is a serious problem in patients who undergo cardiovascular surgery. Identifying patients at a high risk of SRPI is important for clinicians to recognize and prevent it expeditiously. Machine learnin...
Cohort Studies Child, Preschool Child ROC Curve Cardiac Surgical Procedures Male Middle Aged Risk Assessment Postoperative Complications Adolescent Pressure Ulcer Humans Machine Learning Algorithms Prospective Studies Female Risk Factors Aged Adult
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A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.

PloS one Dec 17, 2020
BACKGROUND: A powerful risk model allows clinicians, at the bedside, to ensure the early identification of and decision-making for patients showing signs of developing physiological instability during treatment. The aim of this study was to enhance t...
Acid-Base Equilibrium Humans Adult Retrospective Studies Male Risk Assessment Intensive Care Units Deep Learning Female Electrolytes
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Five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study.

Respiratory research Dec 10, 2020
BACKGROUND: Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur in both patients with and without acute respiratory distress syndrome (ARDS). The purpose of this study is to use machine le...
Treatment Outcome Health Status Predictive Value of Tests Female Ventilator-Induced Lung Injury Reproducibility of Results Risk Factors Aged Adult Male Phenotype Middle Aged Humans Databases, Factual Risk Assessment Machine Learning Retrospective Studies Respiration, Artificial Decision Support Techniques Critical Illness Lung Intensive Care Units
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Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach.

Scientific reports Dec 4, 2020
Mortality risk prediction can greatly improve the utilization of resources in intensive care units (ICUs). Existing schemes in ICUs today require laborious manual input of many complex parameters. In this work, we present a scheme that uses variation...
Neural Networks, Computer Algorithms Intensive Care Units Female Aged Humans Risk Assessment Adolescent Hospital Mortality Patient Acuity Vital Signs Aged, 80 and over Young Adult Adult Male Middle Aged
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