AIMC Topic: Machine Learning

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Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.

JAMA network open Dec 1, 2020
IMPORTANCE: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.
Public Health Surveillance Emergency Service, Hospital Information Storage and Retrieval Cross-Sectional Studies Humans Machine Learning Public Health Forecasting Suicide United States
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Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.

JAMA network open Dec 1, 2020
IMPORTANCE: An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (...
Carcinoma, Non-Small-Cell Lung Predictive Value of Tests Neoplasm Staging Mutation Middle Aged Antineoplastic Agents Lung Neoplasms Antineoplastic Protocols Survival Analysis Humans Proportional Hazards Models ErbB Receptors Protein Kinase Inhibitors Machine Learning Prognosis Female Male
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Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model.

JAMA network open Dec 1, 2020
IMPORTANCE: Knowledge about use of health care services (health care utilization) and expenditures among unauthorized immigrant populations is uncertain because of limitations in ascertaining legal status in population data.
Data Collection Emigrants and Immigrants Patient Acceptance of Health Care Population Groups Minority Health Undocumented Immigrants Middle Aged Family Characteristics Los Angeles Health Expenditures Female Cross-Sectional Studies Male Humans Machine Learning Databases, Factual
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Assessment of Machine Learning to Estimate the Individual Treatment Effect of Corticosteroids in Septic Shock.

JAMA network open Dec 1, 2020
IMPORTANCE: The survival benefit of corticosteroids in septic shock remains uncertain.
Humans Machine Learning Randomized Controlled Trials as Topic Hydrocortisone Aged Prognosis Critical Care Organ Dysfunction Scores Male Intensive Care Units Risk Adjustment Fludrocortisone Glucocorticoids Clinical Decision-Making Shock, Septic Female
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Using machine learning tools to predict outcomes for emergency department intensive care unit patients.

Scientific reports Dec 1, 2020
The number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring s...
Area Under Curve Emergency Service, Hospital Models, Biological Male Middle Aged ROC Curve Treatment Outcome Intensive Care Units Female Aged Humans Machine Learning
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Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.

Scientific reports Dec 1, 2020
Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late intubation. This study evaluates whether machine learning can predict the need for intubation within...
ROC Curve Critical Care Calibration Intubation, Intratracheal Aged Male Middle Aged Cohort Studies Humans Machine Learning Algorithms Female Hospitalization
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Machine learning predicts live-birth occurrence before in-vitro fertilization treatment.

Scientific reports Dec 1, 2020
In-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to ...
Young Adult Adult Male Middle Aged Humans Machine Learning Algorithms Female Linear Models Live Birth Adolescent ROC Curve Fertilization in Vitro
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Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.

Nature communications Dec 1, 2020
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through...
Precision Medicine Antineoplastic Agents Drug Interactions Humans Machine Learning Bortezomib Crizotinib Cell Line, Tumor Lymphoma
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Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

Scandinavian journal of trauma, resuscitation and emergency medicine Dec 1, 2020
BACKGROUND: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency depar...
Female SARS-CoV-2 Aged Sensitivity and Specificity Machine Learning Software COVID-19 Retrospective Studies Humans Male Middle Aged Diagnosis, Computer-Assisted Reverse Transcriptase Polymerase Chain Reaction
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Biologically-informed neural networks guide mechanistic modeling from sparse experimental data.

PLoS computational biology Dec 1, 2020
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are train...
Neural Networks, Computer Machine Learning Computer Simulation Nonlinear Dynamics
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