Query: patient falls machine learning

title: Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study
abstract: Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi view ensemble learning and model based missing data imputation method. As input, over two thousand inpatient fall patients' demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clinical data warehouse from two separate time periods. The predictive classifier developed based on multi view ensemble learning with missing values (MELMV) outperformed other three baseline models; achieved a cross validated AUC of 0.713 (95% CI, 0.701 0.725) an AUC of 0.808 (95% CI, 0.740 0.876) on the separate testing set. Our studies show the efficacy of integrative machine learning based classifier model in dealing with multi source patient data, which in this case delivers robust predictive performance on the severity of patient falls. The severe fall index provided by the MELMV classifier is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard of care procedure for all high risk fall patients.

title: Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study.
abstract: Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi view ensemble learning and model based missing data imputation method. As input, over two thousand inpatient fall patients' demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clinical data warehouse from two separate time periods. The predictive classifier developed based on multi view ensemble learning with missing values (MELMV) outperformed other three baseline models; achieved a cross validated AUC of 0.713 (95% CI, 0.701 0.725) an AUC of 0.808 (95% CI, 0.740 0.876) on the separate testing set. Our studies show the efficacy of integrative machine learning based classifier model in dealing with multi source patient data, which in this case delivers robust predictive performance on the severity of patient falls. The severe fall index provided by the MELMV classifier is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard of care procedure for all high risk fall patients.

title: Scoring Patient Fall Reports Using Quality Rubric and Machine Learning
abstract: Patient falls, a subcategory of patient safety events, cause further harm and anxiety to patients in healthcare systems. Patient fall reports are a valuable resource to identify safety issues that demand further attention. Still, the main challenge for patient fall reports is the lack of quality and detail in writing. A method of evaluating patient fall reports would help us better understand the root causes of falls and prevent their recurrence to improve patient safety. Employing the Agency for Healthcare and Quality rubric for assessing the quality of fall reports, we compared three different machine learning models and identified the most effective method for scoring fall reports using AHRQ's rubric. The results of this study are intended to be applicable in healthcare facilities to score reports during reporting for reporters to improve report quality. The ultimate goal is to increase learning from fall reports for better prevention of patient falls.

title: Machine Learning in Falls Prediction; A cognition based predictor of falls for the acute neurological in patient population
abstract: Falls are associated with high direct and indirect costs, and significant morbidity and mortality for patients. Pathological falls are usually a result of a compromised motor system, and/or cognition. Very little research has been conducted on predicting falls based on this premise.  To demonstrate that cognitive and motor tests can be used to create a robust predictive tool for falls.  Three tests of attention and executive function (Stroop, Trail Making, and Semantic Fluency) a measure of physical function (Walk 12) a series of questions (concerning recent falls, surgery and physical function) and demographic information were collected from a cohort of 323 patients at a tertiary neurological center. The principal outcome was a fall during the in patient stay (n 54) Data driven, predictive modelling was employed to identify the statistical modelling strategies which are most accurate in predicting falls, and which yield the most parsimonious models of clinical relevance.  The Trail test was identified as the best predictor of falls. Moreover, addition of any others variables, to the results of the Trail test did not improve the prediction (Wilcoxon signed rank p .001) The best statistical strategy for predicting falls was the random forest (Wilcoxon signed rank p .001) based solely on results of the Trail test. Tuning of the model results in the following optimized values: 68% 7.7) sensitivity, 90% 2.3) specificity, with a positive predictive value of 60% when the relevant data is available.  Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test. Predictive evaluation shows this strategy to be robust, suggesting predictive modelling and machine learning as the standard for future predictive tools.

title: Computer Vision and Abnormal Patient Gait Assessment a Comparison of Machine Learning Models
abstract: Abnormal gait, its associated falls and complications have high patient morbidity, mortality. Computer vision detects, predicts patient gait abnormalities, assesses fall risk and serves as clinical decision support tool for physicians. This paper performs a systematic review of how computer vision, machine learning models perform an abnormal patient's gait assessment. Computer vision is beneficial in gait analysis, it helps capture the patient posture. Several literature suggests the use of different machine learning algorithms such as SVM, ANN, K Star, Random Forest, KNN, among others to perform the classification on the features extracted to study patient gait abnormalities.

title: Model based and Model free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease
abstract: In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center) Using machine learning techniques, we construct predictive models to discriminate fallers and non fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty related measurements. The model based and model free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5 fold) cross validation as well as by external out of bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g. patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD) Our findings suggest that, compared to other approaches, model free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70 80%

title: Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large Scale Health System Intelligence
abstract: Health information is generally fragmented across silos. Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization. Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more degrees confederated machine learning. We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.

title: Machine Learning for Prediction of Life of Arteriovenous Fistula
abstract: Millions of patients worldwide suffer from Kidney failure and require dialysis. In most cases, dialysis is started after the kidney function of the patient falls below a threshold. In this scenario the patients kidney is essentially non functional. In order to conduct dialysis, native arteriovenous fistulas are constructed to increase blood flow in the superficial vein, and hence facilitate dialysis. Over time, as dialysis continues, the patient may suffer from hypertension and reduced vein function leading to the collapse of the fistula. The ultrasound doppler test for checking the state of the fistula are expensive and doing it again and again is not feasible. We study work related to Chronic Kidney Disease which provides similar data points to those required for the health of the fistula and propose a mechanism to predict the life of a fistula.

title: Machine Learning Will Change Medicine
abstract: Why do we need artificial intelligence (AI) algorithms, or machine learning, in medicine? Is there not enough "natural" intelligence among physicians? Well, that clearly depends on the individual medical education of every single physician. But to be honest, no doctor in the world can be perfectly up to date on recent medical advances. We need help from the world of technology. Besides the problem of data overflow, there are other reasons why we need AI in medicine. One main problem with making decisions not only in medicine but in general is the optimism bias, or the difference between a person's expectation and the outcome that follows. In addition, physicians have an optimism bias of their own: they usually think that the patient's problem falls into the specific discipline to which the physician belongs. Imagine a patient with pain in his left arm. A cardiologist will always suppose first that this patient has a coronary problem, a neurosurgeon will check for a cervical disk problem, an orthopedic surgeon will have a look at the shoulder, and an angiologist will exclude a vascular problem. Someone will eventually figure out what the disease is, but it can take time. And now envision a system in which the patient first fills out a questionnaire based on numerous data obtained from patients with left arm pain and their validated diagnoses. Such a system would clearly have no optimism bias. Imagine how helpful this tool could be in rural areas with few well educated physicians. But even in high end first world countries, would we still need physicians to diagnose popular diseases or would a well trained physician's assistant be enough? We need AI for rare diseases. Some of the so called rare diseases are probably not actually rare but just often overlooked. And those that really are rare are difficult to remember even for specialists. Think of the numerous syndromes. Intelligent systems never forget anything. Regardless of whether one is looking at images, laboratory values, or genetic data, well trained intelligent systems will be faster and better than most physicians. Will radiologists or nuclear medicine physicians be replaced by AI? Recently, Obermeyer and Emanuel stated that "machine learning will displace much of the work of radiologists" (1) It will change our daily life. It will remove a lot of the routine work. AI will make it pretty easy to diagnose simple things such as intracerebral bleeding, typical bone fractures, and liver cysts. Well trained systems will become excellent advisers on mammograms, lung problems, and early stroke CT interpretation. Scientists all over the world are training systems to detect pulmonary embolism, characterize microcalcifications of the breast, and analyze the texture of the fetal brain and prostate cancer (2 5) Even the very first results are extremely promising. Those who do not like that scenario usually come up with medicolegal issues. Who is responsible if the system fails for example, if the system misdiagnoses? Well, if it could be said that no radiologist anywhere in the world ever fails, then failure of the system would be considered a major problem. Such is not the case. But besides that, look at laboratory medicine. Automatization in laboratory medicine has improved quality dramatically; we are not dependent any more on human skill in counting leukocytes, as we were 30 years ago. And because the question of who is responsible if the machine fails is clearly defined, there are strict quality control mechanisms in laboratory medicine. The same would become true for medical imaging: the system would see reference images all day, and the responsible physician would immediately realize when the system is out of order. An important question will be who is in the driver's seat for the final decision. It is like the autopilot in an aircraft. If you ask who is flying the aircraft, the answer will be the pilot, the reason being that the human pilot is finally responsible for the action. Initially, there will be at least two major problems in bringing AI to the medical imaging field. First, some diseases will not have a 1:1 translation into a specific image pattern. Information technology professionals may reply that radiologists probably do not see the specific pattern for a given disease right now, and this may be true for certain diseases. But from a pathophysiologic point of view, it is most likely that imaging will not always be specific. In these situations, the imaging specialist could be the integrator of imaging findings and other information about the patient. The system would provide a list of best matches from the imaging point of view, and the specialist would have to discuss the different possibilities with the referring physician. The second major problem will be to define normal. The spine of a 20 y old patient looks different from that of a 60 y old, and among 60 y old patients there is a huge variety of degeneration and often no correlation to back pain. Maybe it is not really helpful for a patient to learn that the degree of his spinal degeneration is beyond the 95th percentile for his age. Even if he never had back pain before, now he might get some! Regardless, these problems will not prohibit AI from coming into the medical imaging arena. Imaging is pattern recognition, and computers can recognize patterns. What many people may not realize is that the deep learning algorithms are there in part, available as freeware. A couple of months ago, during a job interview with a young mathematician who works in a very active AI lab, I asked why AI is entering medicine so slowly. Her answer was simple: Received Jan. 21, 2017; revision accepted Jan. 24, 2017. For correspondence or reprints contact: Institute of Diagnostic and Interventional Radiology and Neuroradiology, Essen University Hospital, University of Essen, Hufelandstrasse 55, D 45147 Essen, Germany. E mail: m.forsting@uni essen.de Published online Feb. 2, 2017. COPYRIGHT(c) 2017 by the Society of Nuclear Medicine and Molecular Imaging.

title: Using Machine Learning to Make Predictions in Patients Who Fall.
abstract: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model based on immediately available admission data. Secondary objectives were to predict who would be discharged home and determine which variables had the largest effect on prediction.  All patients who were admitted for fall between 2012 and 2017 at our level 1 trauma center were reviewed. Fourteen variables describing patient demographics, injury characteristics, and physiology were collected at the time of admission and were used for prediction modeling. Algorithms assessed included LR, decision tree classifier (DTC) and random forest classifier (RFC) Area under the receiver operating characteristic curve (AUC) values were calculated for each algorithm for mortality and discharge to home.  patients met inclusion criteria. The mean age was 61 20.5 y, Injury Severity Score 8 7, length of stay 5.8 7.6 d, intensive care unit length of stay 1.8+ 5.2 d, and ventilator days 0.7 4.2 d. The mortality rate was 3% and three times greater for elderly (aged 65 y and older) patients (5.0% versus 1.6% P 0.001) The AUC for predicting mortality for LR, DTC, and RFC was 0.78, 0.64, and 0.86, respectively. The AUC for predicting discharge to home for LR, DTC, and RFC was 0.72, 0.61, and 0.74, respectively. The top five variables that contribute to the prediction of mortality in descending order of importance are the Glasgow Coma Score (GCS) motor, GCS verbal, respiratory rate, GCS eye, and temperature.  RFC can accurately predict mortality and discharge home after a fall. This predictive model can be implemented at the time of patient arrival and may help identify candidates for targeted intervention as well as improve prognostication and resource utilization.