Machine learning model for predicting immediate postoperative desaturation using spirometry signal data.

Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories-signal property, inter-/intra-position correlation, peak value/interval variability, and demographics-were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19.

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.

Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population.

BACKGROUND: Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors. METHODS: Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference. RESULTS: In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition. CONCLUSION: We developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning.

Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults.

Older adults are more likely to require emergency department (ED) visits than others, which might be attributed to their medication use. Being able to predict the likelihood of an ED visit using prescription information and readily available data would be useful for primary care. This study aimed to predict the likelihood of ED visits using extensive medication variables generated according to explicit clinical criteria for elderly people and high-risk medication categories by applying machine learning (ML) methods. Patients aged ≥ 65 years were included, and ED visits were predicted with 146 variables, including demographic and comprehensive medication-related factors, using nationwide claims data. Among the eight ML models, the final model was developed using LightGBM, which showed the best performance. The final model incorporated 93 predictors, including six sociodemographic, 28 comorbidity, and 59 medication-related variables. The final model had an area under the receiver operating characteristic curve of 0.689 in the validation cohort. Approximately half of the top 20 strong predictors were medication-related variables. Here, an ED visit risk prediction model for older people was developed and validated using administrative data that can be easily applied in clinical settings to screen patients who are likely to visit an ED.

Menthol excites dural afferent neurons by inhibiting leak K+ conductance in rats.

Menthol-a natural organic compound-is widely used for relieving various pain conditions including migraine. However, a high dose of menthol reportedly decreases pain thresholds and enhances pain responses. Accordingly, in the present study, we addressed the effect of menthol on the excitability of acutely isolated dural afferent neurons, which were identified with a fluorescent dye, using the whole-cell patch-clamp technique. Under a voltage-clamped condition, menthol altered the holding current levels in a concentration-dependent manner. The menthol-induced current (IMenthol) remained unaffected by the addition of selective transient receptor potential melastatin 8 antagonists. Moreover, the reversal potential of IMenthol was similar to the equilibrium potential of K+. IMenthol was accompanied by an increase in input resistance, thereby suggesting that menthol decreases the leak K+ conductance. Under a current-clamped condition, menthol caused depolarization of the membrane potential and decreased the threshold for the generation of action potential. While the IMenthol was substantially inhibited by 10 µM XE-991, a selective KV7 blocker, the M-current mediated by KV7 was not detected in the nociceptive neurons tested in the present study. Moreover, IMenthol decreased under acidic extracellular pH conditions or in the presence of 3 µM A-1899, a selective K2P3.1 and K2P9.1 blocker. The present results suggest that menthol inhibits leak K+ channels, possibly acid-sensitive two-pore domain K+ channels, thereby increasing the excitability of nociceptive sensory neurons. The resultant increase in neuron excitability may partially be responsible for the pronociceptive effect mediated by high menthol doses.

Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability.

BACKGROUND AND OBJECTIVE: Neuromuscular disorders are diseases that damage our ability to control body movements. Needle electromyography (nEMG) is often used to diagnose neuromuscular disorders, which is an electrophysiological test measuring electric signals generated from a muscle using an invasive needle. Characteristics of nEMG signals are manually analyzed by an electromyographer to diagnose the types of neuromuscular disorders, and this process is highly dependent on the subjective experience of the electromyographer. Contemporary computer-aided methods utilized deep learning image classification models to classify nEMG signals which are not optimized for classifying signals. Additionally, model explainability was not addressed which is crucial in medical applications. This study aims to improve prediction accuracy, inference time, and explain model predictions in nEMG neuromuscular disorder classification. METHODS: This study introduces the nEMGNet, a one-dimensional convolutional neural network with residual connections designed to extract features from raw signals with higher accuracy and faster speed compared to image classification models from previous works. Next, the divide-and-vote (DiVote) algorithm was designed to integrate each subject's heterogeneous nEMG signal data structures and to utilize muscle subtype information for higher accuracy. Finally, feature visualization was used to identify the causality of nEMGNet diagnosis predictions, to ensure that nEMGNet made predictions on valid features, not artifacts. RESULTS: The proposed method was tested using 376 nEMG signals measured from 57 subjects between June 2015 to July 2020 in Seoul National University Hospital. The results from the three-class classification task demonstrated that nEMGNet's prediction accuracy of nEMG signal segments was 62.35%, and the subject diagnosis prediction accuracy of nEMGNet and the DiVote algorithm was 83.69 %, over 5-fold cross-validation. nEMGNet outperformed all models from previous works on nEMG diagnosis classification, and heuristic analysis of feature visualization results indicate that nEMGNet learned relevant nEMG signal characteristics. CONCLUSIONS: This study introduced nEMGNet and DiVote algorithm which demonstrated fast and accurate performance in predicting neuromuscular disorders based on nEMG signals. The proposed method may be applied in medicine to support real-time electrophysiologic diagnosis.

Using the Axial Oblique View of Computed Tomography (CT) in Evaluating Femoral Anteversion: A Comparative Cadaveric Study.

Twenty-five cadaveric adult femora's anteversion angles were measured to develop a highly efficient and reproducible femoral anteversion measurement method using computed tomography (CT). Digital photography captured the proximal femur's two reference lines, head-to-neck (H-N) and head-to-greater trochanter (H-G). Six reference lines (A/B in transverse section; C, axial oblique section; D/E, conventional 3D reconstruction; and M, volumetric 3D reconstruction) from CT scans were used. The posterior condylar line was used as a distal femoral reference. As measured with the H-N and H-G lines, the anteversion means were 10.43° and 19.50°, respectively. Gross anteversion measured with the H-G line had less interobserver bias (ICC; H-N = 0.956, H-G = 0.982). The 2D transverse and volumetric 3D CT sections' B/M lines were consistent with the H-N line (p: B = 0.925, M = 0.122) and the 2D axial oblique section's C line was consistent with the H-G line (p < 0.1). The D/E lines differed significantly from the actual gross images (p < 0.05). Among several CT scan femoral anteversion measurement methods, the novel anteversion angle measurement method using CT scans' axial oblique section was approximated with actual gross femoral anteversion angle from the femoral head to the greater trochanter.

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker.

Background: Depression and suicide are critical social problems worldwide, but tools to objectively diagnose them are lacking. Therefore, this study aimed to diagnose depression through machine learning and determine whether it is possible to identify groups at high risk of suicide through words spoken by the participants in a semi-structured interview. Methods: A total of 83 healthy and 83 depressed patients were recruited. All participants were recorded during the Mini-International Neuropsychiatric Interview. Through the suicide risk assessment from the interview items, participants with depression were classified into high-suicide-risk (31 participants) and low-suicide-risk (52 participants) groups. The recording was transcribed into text after only the words uttered by the participant were extracted. In addition, all participants were evaluated for depression, anxiety, suicidal ideation, and impulsivity. The chi-square test and student's T-test were used to compare clinical variables, and the Naive Bayes classifier was used for the machine learning text model. Results: A total of 21,376 words were extracted from all participants and the model for diagnosing patients with depression based on this text confirmed an area under the curve (AUC) of 0.905, a sensitivity of 0.699, and a specificity of 0.964. In the model that distinguished the two groups using statistically significant demographic variables, the AUC was only 0.761. The DeLong test result (p-value 0.001) confirmed that the text-based classification was superior to the demographic model. When predicting the high-suicide-risk group, the demographics-based AUC was 0.499, while the text-based one was 0.632. However, the AUC of the ensemble model incorporating demographic variables was 0.800. Conclusion: The possibility of diagnosing depression using interview text was confirmed; regarding suicide risk, the diagnosis accuracy increased when demographic variables were incorporated. Therefore, participants' words during an interview show significant potential as an objective and diagnostic marker through machine learning.

Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals.

INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm. METHOD: We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients. RESULTS: Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%). CONCLUSION: The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.

Real-Time Evaluation of Cerebral Autoregulation Based on Near-Infrared Spectroscopy to Predict Clinical Outcome after Bypass Surgery in Moyamoya Disease.

Impaired cerebral autoregulation (CA) can cause negative outcomes in neurological conditions. Real-time CA monitoring can predict and thereby help prevent postoperative complications for neurosurgery patients, especially those suffering from moyamoya disease (MMD). We applied the concept of moving average to the correlation between mean arterial blood pressure (MBP) and cerebral oxygen saturation (SCO2) to monitor CA in real time, revealing optimal window size for the moving average. The experiment was conducted with 68 surgical vital-sign records containing MBP and SCO2. To evaluate CA, the cerebral oximetry index (COx) and coherence obtained from transfer function analysis (TFA) were calculated and compared between patients with postoperative infarction and those who without. For real-time monitoring, the moving average was applied to COx and coherence to determine the differences between groups, and the optimal moving-average window size was identified. The average COx and coherence within the very-low-frequency (VLF) range (0.02-0.07 Hz) during the entire surgery were significantly different between the groups (COx: AUROC = 0.78, p = 0.003; coherence: AUROC = 0.69, p = 0.029). For the case of real-time monitoring, COx showed a reasonable performance (AUROC > 0.74) with moving-average window sizes larger than 30 minutes. Coherence showed an AUROC > 0.7 for time windows of up to 60 minutes; however, for windows larger than this threshold, the performance became unstable. With an appropriate window size, COx showed stable performance as a predictor of postoperative infarction in MMD patients.

Assessment of central cartilaginous tumor of the appendicular bone: inter-observer and intermodality agreement and comparison of diagnostic performance of CT and MRI.

BACKGROUND: Diagnostic performance, inter-observer agreement, and intermodality agreement between computed tomography (CT) and magnetic resonance imaging (MRI) in the depiction of the major distinguishing imaging features of central cartilaginous tumors have not been investigated. PURPOSE: To determine the inter-observer and intermodality agreement of CT and MRI in the evaluation of central cartilaginous tumors of the appendicular bones, and to compare their diagnostic performance. MATERIAL AND METHODS: Two independent radiologists retrospectively reviewed preoperative CT and MRI. Inter-observer and intermodality agreement between CT and MRI in the assessment of distinguishing imaging features, including lesion size, deep endosteal scalloping, cortical expansion, cortical disruption, pathologic fracture, soft tissue extension, and peritumoral edema, were evaluated. The agreement with histopathology and the accuracy of the radiologic diagnoses made with CT and MRI were also analyzed. RESULTS: A total of 72 patients were included. CT and MRI showed high inter-observer and intermodality agreements with regard to size, deep endosteal scalloping, cortical expansion, cortical disruption, and soft tissue extension (ICC = 0.96-0.99, k = 0.60-0.90). However, for the evaluation of pathologic fracture, MRI showed only moderate inter-observer agreement (k = 0.47). Peritumoral edema showed only fair intermodality agreement (k = 0.28-0.33) and moderate inter-observer agreement (k = 0.46) on CT. Both CT and MRI showed excellent diagnostic performance, with high agreement with the histopathology (k = 0.89 and 0.87, respectively) and high accuracy (91.7% for both CT and MRI). CONCLUSION: CT and MRI showed high inter-observer and intermodality agreement in the assessment of several distinguishing imaging features of central cartilaginous tumors of the appendicular bones and demonstrated comparable diagnostic performance.

Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury.

OBJECTIVE: Monitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination. METHODS: The first 24 hours of monitoring ICP and ABP in a total of 309 patients with TBI was retrospectively analyzed. An artifact elimination model for ICP and ABP was constructed via a stacked convolutional autoencoder (SCAE) and convolutional neural network (CNN) with 10-fold cross-validation tests. The prevalence and prognostic capacity of ICP- and ABP-related clinical events were compared before and after artifact elimination. RESULTS: The proposed SCAE-CNN model exhibited reliable accuracy in eliminating ABP and ICP artifacts (net prediction rates of 97% and 94%, respectively). The prevalence of ICP- and ABP-related clinical events (i.e., systemic hypotension, intracranial hypertension, cerebral hypoperfusion, and poor cerebrovascular reactivity) all decreased significantly after artifact removal. CONCLUSIONS: The SCAE-CNN model can be reliably used to eliminate artifacts, which significantly improves the reliability and efficacy of ICP- and ABP-derived clinical parameters for prognostic determinations after TBI.

Rhus verniciflua Stokes extract induces inhibition of cell growth and apoptosis in human chronic myelogenous leukemia K562 cells.

Rhus verniciflua Stokes has been widely used as a traditional medicinal plant with a variety of pharmacological activities. We investigated the mechanisms involved in mediating the effects of Rhus verniciflua Strokes (R. verniciflua) extract in human chronic myelogenous leukemia K562 cells, including caspase-dependent apoptotic pathways related to cell-cycle arrest, as well as the inhibition of nuclear factor NF-κB activation and upregulation of the mitogen-activated protein kinase (MAPK) signaling pathway. R. verniciflua extract suppressed the abnormal cellular proliferation of K562 cells in a dose- and time­dependent manner and increased the quantitative proportions of cells involved in the early and late process of apoptosis. Furthermore, R. verniciflua extract significantly mediated the mRNA levels of pro-apoptotic and anti-apoptotic regulators, such as Bcl-2, Bax, Mcl-1 and survivin in apoptotic cells. Particularly, the treatment of K562 cells with R. verniciflua extract augmented the caspase­3 activity and increased the expression of caspase­3 protein, while co-treatment with R. verniciflua extract and the permeant pan­caspase inhibitor Z-VAD-FMK and caspase­3 inhibitor Z-DEVD-FMK inversely enhanced the proliferation of K562 cells. The extract of R. verniciflua inhibited the activation of NF-κB and the phosphorylation of ERK. Collectively, these results indicated that the extract of R. verniciflua inhibited the proliferation of human chronic myelogenous leukemia K562 cells by activating the apoptotic process via caspase­3 overexpression and the regulation of the NF-κB and MAPK signaling.

Hemodynamic Instability and Cardiovascular Events After Traumatic Brain Injury Predict Outcome After Artifact Removal With Deep Belief Network Analysis.

BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine-learning model for the elimination of artifactual events shows promising results for improving signal quality. However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied. MATERIALS AND METHODS: The arterial blood pressure of 99 subjects with traumatic brain injury was continuously measured for 5 consecutive days, beginning on the day of admission. The machine-learning deep belief network was constructed to automatically identify and remove false incidences of hypotension, hypertension, bradycardia, tachycardia, and alterations in cerebral perfusion pressure (CPP). RESULTS: The prevalences of hypotension and tachycardia were significantly reduced by 47.5% and 13.1%, respectively, after suppressing false incidents (P=0.01). Hypotension was particularly effective at predicting outcome favorability and mortality after artifact elimination (P=0.015 and 0.027, respectively). In addition, increased CPP was also statistically significant in predicting outcomes (P=0.02). CONCLUSIONS: The prevalence of false incidents due to signal artifacts can be significantly reduced using machine-learning. Some clinical events, such as hypotension and alterations in CPP, gain particularly high predictive capacity for patient outcomes after artifacts are eliminated from physiological signals.

Accuracy of Diffusion Tensor Imaging for Diagnosing Cervical Spondylotic Myelopathy in Patients Showing Spinal Cord Compression.

OBJECTIVE: To assess the performance of diffusion tensor imaging (DTI) for the diagnosis of cervical spondylotic myelopathy (CSM) in patients with deformed spinal cord but otherwise unremarkable conventional magnetic resonance imaging (MRI) findings. MATERIALS AND METHODS: A total of 33 patients who underwent MRI of the cervical spine including DTI using two-dimensional single-shot interleaved multi-section inner volume diffusion-weighted echo-planar imaging and whose spinal cords were deformed but showed no signal changes on conventional MRI were the subjects of this study. Mean diffusivity (MD), longitudinal diffusivity (LD), radial diffusivity (RD), and fractional anisotropy (FA) were measured at the most stenotic level. The calculated performance of MD, FA, MD∩FA (considered positive when both the MD and FA results were positive), LD∩FA (considered positive when both the LD and FA results were positive), and RD∩FA (considered positive when both the RD and FA results were positive) in diagnosing CSM were compared with each other based on the estimated cut-off values of MD, LD, RD, and FA from receiver operating characteristic curve analysis with the clinical diagnosis of CSM from medical records as the reference standard. RESULTS: The MD, LD, and RD cut-off values were 1.079 × 10(-3), 1.719 × 10(-3), and 0.749 × 10(-3) mm(2)/sec, respectively, and that of FA was 0.475. Sensitivity, specificity, positive predictive value and negative predictive value were: 100 (4/4), 44.8 (13/29), 20 (4/20), and 100 (13/13) for MD; 100 (4/4), 27.6 (8/29), 16 (4/25), and 100 (8/8) for FA; 100 (4/4), 58.6 (17/29), 25 (4/16), and 100 (17/17) for MD∩FA; 100 (4/4), 68.9 (20/29), 30.8 (4/13), and 100 (20/20) for LD∩FA; and 75 (3/4), 68.9 (20/29), 25 (3/12), and 95.2 (20/21) for RD∩FA in percentage value. Diagnostic performance comparisons revealed significant differences only in specificity between FA and MD∩FA (p = 0.003), FA and LD∩FA (p < 0.001), FA and RD∩FA (p < 0.001), MD and LD∩FA (p = 0.024) and MD and RD∩FA (p = 0.024). CONCLUSION: Fractional anisotropy combined with MD, RD, or LD is expected to be more useful than FA and MD for diagnosing CSM in patients who show deformed spinal cords without signal changes on MRI.