Man vs. Machine: Comparison of a Machine Learning Algorithm to Clinician Intuition for Predicting Intensive Care Unit Readmission

2019 ◽  
Author(s):  
J.C. Rojas ◽  
P.G. Lyons ◽  
M. Kilaru ◽  
K.A. Carey ◽  
L.R. Venable ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Intensive Care Unit Readmission

scholarly journals Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study

2021 ◽  
Author(s):  
Ward H. van der Ven ◽  
Lotte E. Terwindt ◽  
Nurseda Risvanoglu ◽  
Evy L. K. Ie ◽  
Marije Wijnberge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Time To Event ◽  
Arterial Waveform ◽  
Icu Patients ◽  
Mechanically Ventilated

AbstractThe Hypotension Prediction Index (HPI) is a commercially available machine-learning algorithm that provides warnings for impending hypotension, based on real-time arterial waveform analysis. The HPI was developed with arterial waveform data of surgical and intensive care unit (ICU) patients, but has never been externally validated in the latter group. In this study, we evaluated diagnostic ability of the HPI with invasively collected arterial blood pressure data in 41 patients with COVID-19 admitted to the ICU for mechanical ventilation. Predictive ability was evaluated at HPI thresholds from 0 to 100, at incremental intervals of 5. After exceeding the studied threshold, the next 20 min were screened for positive (mean arterial pressure (MAP) < 65 mmHg for at least 1 min) or negative (absence of MAP < 65 mmHg for at least 1 min) events. Subsequently, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and time to event were determined for every threshold. Almost all patients (93%) experienced at least one hypotensive event. Median number of events was 21 [7–54] and time spent in hypotension was 114 min [20–303]. The optimal threshold was 90, with a sensitivity of 0.91 (95% confidence interval 0.81–0.98), specificity of 0.87 (0.81–0.92), PPV of 0.69 (0.61–0.77), NPV of 0.99 (0.97–1.00), and median time to event of 3.93 min (3.72–4.15). Discrimination ability of the HPI was excellent, with an area under the curve of 0.95 (0.93–0.97). This validation study shows that the HPI correctly predicts hypotension in mechanically ventilated COVID-19 patients in the ICU, and provides a basis for future studies to assess whether hypotension can be reduced in ICU patients using this algorithm.

scholarly journals A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study (Preprint)

2020 ◽  
Author(s):  
Angier Allen ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Hoyt Burdick ◽  
Gregory Braden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Racial Disparities ◽  
Equal Opportunity ◽  
Racial Bias ◽  
Learning Algorithm ◽  
Early Warning Score ◽  
Machine Learning Algorithm ◽  
Saps Ii

BACKGROUND Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. OBJECTIVE The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. METHODS Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care–III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). RESULTS The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, <i>P</i>=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, <i>P</i>=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, <i>P</i>=.006 and equal opportunity difference 0.074, <i>P</i><.001, respectively). CONCLUSIONS This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

scholarly journals Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

2017 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Carol Gu ◽  
Heidi Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Intensive Care ◽  
Length Of Stay ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithm ◽  
Implementation Phase ◽  
Before And After ◽  
Sepsis Detection

AbstractIntroductionSepsis is a major health crisis in US hospitals, and several clinical identification systems have been designed to help care providers with early diagnosis of sepsis. However, many of these systems demonstrate low specificity or sensitivity, which limits their clinical utility. We evaluate the effects of a machine learning algodiagnostic (MLA) sepsis prediction and detection system using a before-and-after clinical study performed at Cabell Huntington Hospital (CHH) in Huntington, West Virginia. Prior to this study, CHH utilized the St. John’s Sepsis Agent (SJSA) as a rules-based sepsis detection system.MethodsThe Predictive algoRithm for EValuation and Intervention in SEpsis (PREVISE) study was carried out between July 1, 2017 and August 30, 2017. All patients over the age of 18 who were admitted to the emergency department or intensive care units at CHH were monitored during the study. We assessed pre-implementation baseline metrics during the month of July, 2017, when the SJSA was active. During implementation in the month of August, 2017, SJSA and the MLA concurrently monitored patients for sepsis risk. At the conclusion of the study period, the primary outcome of sepsis-related in-hospital mortality and secondary outcome of sepsis-related hospital length of stay were compared between the two groups.ResultsSepsis-related in-hospital mortality decreased from 3.97% to 2.64%, a 33.5% relative decrease (P = 0.038), and sepsis-related length of stay decreased from 2.99 days in the pre-implementation phase to 2.48 days in the post-implementation phase, a 17.1% relative reduction (P < 0.001).ConclusionReductions in patient mortality and length-of-stay were observed with use of a machine learning algorithm for early sepsis detection in the emergency department and intensive care units at Cabell Huntington Hospital, and may present a method for improving patient outcomes.Trial RegistrationClinicalTrials.gov, NCT03235193, retrospectively registered on July 27th 2017.

scholarly journals A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

10.2196/22400 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e22400
Author(s):  
Angier Allen ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Hoyt Burdick ◽  
Gregory Braden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Racial Disparities ◽  
Equal Opportunity ◽  
Racial Bias ◽  
Learning Algorithm ◽  
Early Warning Score ◽  
Machine Learning Algorithm ◽  
Saps Ii

Background Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. Objective The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. Methods Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care–III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). Results The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). Conclusions This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

scholarly journals Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 273-280
Author(s):  
Christopher Nemeth ◽  
Adam Amos-Binks ◽  
Christie Burris ◽  
Natalie Keeney ◽  
Yuliya Pinevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Decision Support ◽  
Support System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tactical Combat Casualty Care ◽  
Combat Casualty ◽  
Design Requirements ◽  
Combat Casualty Care

ABSTRACT Introduction The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers’ ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation. Materials and Methods Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews. Machine learning algorithm methods included development of a model trained on publicly available Medical Information Mart for Intensive Care data, then on de-identified data from Mayo Clinic Intensive Care Unit. Results The project team interviewed 17 Army, Air Force, and Navy medical subject matter experts during design requirements review sessions. They had an average of 17 years of service in military medicine and an average of 4 deployments apiece and all had performed tactical combat casualty care on live patients during deployment. Comments provided requirements for shock identification and management in prehospital settings, including support for indication of shock probability and shock differentiation. The machine learning algorithm based on logistic regression performed best among other algorithms we tested and was able to predict shock onset 90 minutes before it occurred with better than 75% accuracy in the test dataset. Conclusions We expect the Trauma Triage, Treatment, and Training Decision Support system will augment a medic’s ability to make informed decisions based on salient patient data and to diagnose multiple types of shock through remotely trained, field deployed ML models.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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