Heart rate time series characteristics for early detection of infections in critically ill patients

Fetal Heart Rate Extraction Based on Wavelet Transform to Prevent Fetal Distress In Utero

In order to improve the effective extraction of fetal heart rate and prevent fetal distress in utero, a study of fetal heart rate feature extraction based on wavelet transform to prevent fetal distress in utero was proposed. This paper adopts a fetal heart rate detection method based on the maximum value of the binary wavelet transform modulus. The method is simulated by the Doppler fetal heart signal obtained from the clinic. Compared with the original curve, the transformed curve can roughly see the change rule of the original signal and identify the peak point of the signal, but due to the large disturbance of the peak point, the influence on the computer processing is also great. The periodicity of the transformed signal is greatly enhanced, making it easier to deal with the computation. A total of 300 pregnant women with full-term fetal heart monitoring from January 2018 to January 2020 were selected as the research subjects and divided into the observation group and the control group. The observation group consisted of 100 patients with abnormal fetal heart monitoring, and the control group consisted of 200 patients with normal fetal heart monitoring. The uterine contractions and fetal heart rate were recorded, and the incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid and fecal contamination were observed. The incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid fecal stain in the observation group were significantly higher than those in the control group. Fetal heart monitoring can accurately judge the situation of the fetus in pregnant women and timely diagnose the abnormal fetal heart rate, which has a better effect on the prognosis of perinatal infants and can reduce their mortality. It can effectively solve the problems existing in the autocorrelation algorithm and extract the fetal heart rate more accurately. It is an effective improved scheme of fetal heart rate extraction. It is very helpful in preventing fetal distress in utero.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Analysis of factors associated with extended recovery time after colonoscopy

Background & aims

A common limiting factor in the throughput of gastrointestinal endoscopy units is the availability of space for patients to recover post-procedure. This study sought to identify predictors of abnormally long recovery time after colonoscopy performed with procedural sedation. In clinical research, this type of study would be performed using only one regression modeling approach. A goal of this study was to apply various “machine learning” techniques to see if better prediction could be achieved.

Methods

Procedural data for 31,442 colonoscopies performed on 29,905 adult patients at Massachusetts General Hospital from 2011 to 2015 were analyzed to identify potential predictors of long recovery times. These data included the identities of hospital personnel, and the initial statistical analysis focused on the impact of these personnel on recovery time via multivariate logistic regression. Secondary analyses included more information on patient vitals both to identify secondary predictors and to predict long recoveries using more complex techniques.

Results

In univariate analysis, the endoscopist, procedure room nurse, recovery room nurse, and surgical technician all showed a statistically significant relationship to long recovery times, with p-value below 0.0001 in all cases. In the multivariate logistic regression, the most significant predictor of a long recovery time was the identity of the recovery room nurse, with the endoscopist also showing a statistically significant relationship with a weaker effect. Complex techniques led to a negligible improvement over simple techniques in prediction of long recovery periods.

Conclusion

The hospital personnel involved in performing a colonoscopy show a strong association with the likelihood of a patient spending an abnormally long time recovering from the procedure, with the most pronounced effect for the nurse in the recovery room. The application of more advanced approaches to improve prediction in this clinical data set only yielded modest improvements.

Journal of Clinical Monitoring and Computing 2017 end of year summary: cardiovascular and hemodynamic monitoring

Hemodynamic monitoring provides the basis for the optimization of cardiovascular dynamics in intensive care medicine and anesthesiology. The Journal of Clinical Monitoring and Computing (JCMC) is an ideal platform to publish research related to hemodynamic monitoring technologies, cardiovascular (patho)physiology, and hemodynamic treatment strategies. In this review, we discuss selected papers published on cardiovascular and hemodynamic monitoring in the JCMC in 2017.