Purpose Identify an algorithm using clinical and ultrasound (US) parameters with high diagnostic performance for acute cholecystitis. Methods Consecutive emergency department (ED) patients from 4/1/2019 to 12/31/2019 ...
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Purpose Identify an algorithm using clinical and ultrasound (US) parameters with high diagnostic performance for acute cholecystitis. Methods Consecutive emergency department (ED) patients from 4/1/2019 to 12/31/2019 were retrospectively reviewed to record non-US parameters and make US observations. Outcomes were categorized as either: (1) acute cholecystitis;or (2) negative acute cholecystitis. Pivot tables identified parameter combinations either not found with acute cholecystitis or with predictive value for acute cholecystitis to establish the algorithm. US Division radiologists finalized an US report prior to ED disposition without use of the algorithm. Radiologist impression and algorithm prediction for acute cholecystitis were categorized as either (1) acute cholecystitis;(2) negative acute cholecystitis;or (3) inconclusive. Results Three hundred and sixty-six studies on 357 patients (mean age, 51 yrs +/- 20 yrs;215 women) met the inclusion criteria. 10.9% (40/366) of US studies had acute cholecystitis, 12.6% (46/366) had pathologically identified chronic cholecystitis without acute cholecystitis, and 76.5% (280/366) were negative acute cholecystitis. algorithm compared to radiologist diagnostic performance was as follows: (1) sensitivity: 90.0% vs. 55.0%, p < 0.001;(2) augmented sensitivity (defined as when inconclusive categorization is considered consistent with acute cholecystitis): 100% vs. 85.0%, p < 0.001;(3) specificity: 93.6% vs. 94.8%, p = 0.50;(4) diagnostic rate (opposite of inconclusive rate): 96.4% vs. 93.2%, p = 0.04;(5) adverse outcome rate: 0.0% vs. 1.6%, p undefined. Conclusion For acute cholecystitis, an algorithm using non-binary ultrasound and clinical assessments had higher sensitivity, higher diagnostic rate, and fewer adverse outcomes, than subspecialty radiologist impressions.
In order to overcome the shortcomings of intrusion data mining method, which is easy to fall into local extremum and leads to poor mining effect, a distributed network intrusion data mining method based on fuzzy kerne...
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In order to overcome the shortcomings of intrusion data mining method, which is easy to fall into local extremum and leads to poor mining effect, a distributed network intrusion data mining method based on fuzzy kernel clustering algorithm is studied. In this method, a new feature vector is established by using the Gauss kernel function which accords with Mercer condition through the fuzzy kernel clustering algorithm, so that the data pattern space can be effectively mapped to the high-dimensional feature space. When the new data does not meet the clustering condition, a new cluster set and sub box are established, until the end of all data clustering, the distributed network intrusion data mining is realised. The experimental results show that the accuracy of mining is higher than 95%, the false alarm rate and the false alarm rate are lower than 2%, and the total mining time is only 446 ms.
Acute detection and high-resolution imaging of microRNAs (miRNAs) in living cancer cells have attracted great attention in clinical diagnosis and therapy. However, current methods suffer from low detection sensitivity...
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Acute detection and high-resolution imaging of microRNAs (miRNAs) in living cancer cells have attracted great attention in clinical diagnosis and therapy. However, current methods suffer from low detection sensitivity or heavy dependence on expensive and sophisticated spectrometers. Herein, a novel algorithm-assisted system of detecting and imaging miRNAs in living cancer cells was developed via the disassembly of plasmonic core-satellite probes coupled with strand displacement amplification (SDA). The target miRNAs in the system could trigger the disassembly of plasmonic core-satellite probes, leading to the color change in the scattering light of the probes, which could be captured by dark-field microscopy (DFM). The concentration of the target miRNAs was obtained by analyzing the dark-field image based on the proposed algorithm with a detection limit of 2 pM for miRNA-21. Thus, the performance in terms of simplicity and sensitivity of the system compared with one of the conventional spectrophotometers was well presented, which could inspire more clinical applications of inexpensive, intelligent, and rapid screening of cancer cells. The application software based on the proposed algorithm running on the Android platform was also developed, demonstrating the potential of remote diagnosis.
This manuscript reports the operation of a new algorithm for the fiducial points extraction from an electrocardiogram signal. The algorithm analyzes the Delta-sigma modulated slope of the ECG using a set of counters a...
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ISBN:
(数字)9781665497671
ISBN:
(纸本)9781665497671
This manuscript reports the operation of a new algorithm for the fiducial points extraction from an electrocardiogram signal. The algorithm analyzes the Delta-sigma modulated slope of the ECG using a set of counters and thresholds to locate characteristic patterns of a specific wave. The algorithm successfully processes recordings from the QT database achieving a 99.57% detection accuracy for the R peak, 99.51% for the P wave, and 97.85% for the T wave.
Aim To develop a treatment and management algorithm for vulvar abscess. Methods We evaluated the clinical findings and treatment modalities of patients hospitalized with vulvar abscess via a comprehensive literature r...
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Aim To develop a treatment and management algorithm for vulvar abscess. Methods We evaluated the clinical findings and treatment modalities of patients hospitalized with vulvar abscess via a comprehensive literature review and a clinical study. Patients with a diagnosis of vulvar abscess (n = 28) between 2015 and 2019 in the gynecology clinic of our hospital were included in the clinical study. Each patient's age, obstetric history, body mass index, presence of concomitant diseases, abscess culture and size, mean length of hospital stay, treatment modalities, and recurrence rate were recorded. Results The mean age and body mass index of the patients were 47.7 +/- 11.5 years and 30.3 +/- 2.7 kg/m(2), respectively. Diabetes mellitus was the most common concomitant disease (60.7%, n = 17). The abscesses of 22 (78.5%) patients drained spontaneously. The abscess cavities of the remaining six (21.4%) patients were treated via incisional drainage. Gentamicin + clindamycin or levofloxacin + metronidazole were used as the primary antibiotic treatment. Hemovac drains were placed in four (14.2%) patients with abscess sites greater than 5 cm. By applying our treatment methods, 26 (92.8%) of our patients were discharged with full recovery, and two patients (7.2%) were referred due to uncontrolled diabetes mellitus. The recurrence rate of vulvar abscess was 0%. Conclusions This is the first study in the literature to present a successful algorithm for the treatment and management of vulvar abscess. Our treatment methods shed light on the treatment and management of vulvar abscess.
Background This study aimed to clarify the features of superficial non-ampullary duodenal epithelial tumors (SNADETs) on magnifying endoscopy with narrow-band imaging (M-NBI) and magnifying endoscopy with acetic acid ...
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Background This study aimed to clarify the features of superficial non-ampullary duodenal epithelial tumors (SNADETs) on magnifying endoscopy with narrow-band imaging (M-NBI) and magnifying endoscopy with acetic acid and narrow-band imaging (M-AANBI), and evaluate the efficacy of M-NBI/M-AANBI to distinguish high-grade adenomas or adenocarcinomas (HGA/AC) from low-grade adenomas (LGA). Methods Clinicopathological data on 62 SNADETs in 58 patients who underwent preoperative M-NBI/M-AANBI and endoscopic resection were retrospectively reviewed. The pathological results were classified into two categories, LGA and HGA/AC. We evaluated microvascular patterns (MVPs) and microsurface patterns (MSPs) observed by M-NBI and MSPs observed by M-AANBI for characterizing LGA and HGA/AC. The kappa value was calculated to assess the interobserver and intraobserver agreements of evaluation of M-AANBI images. Results Pathologically, 38 lesions (61.3%) were LGA and 24 lesions (38.7%) were HGA/AC. HGA/AC tended to have irregular MVP and/or MSP on M-NBI. M-NBI diagnostic performance to distinguish HGA/AC from LGA showed 62.5% sensitivity, 68.4% specificity, and 66.1% accuracy. SNADETs had irregular MSP on M-AANBI. Three irregularity grades (iG) of MSP were observed by M-AANBI as follows: iG1, mild;iG2, moderate;iG3, significant. HGA/AC lesions had a significantly higher rate of iG3 than LGA lesions (p < 0.001). The iG2 was associated with HGA/AC in elevated lesions and LGA in depressed lesions. The diagnostic performance of M-AANBI was as follows: 95.8% sensitivity, 97.4% specificity, and 96.8% accuracy. The diagnostic accuracy of M-AANBI was significantly higher than that of M-NBI (p < 0.001). The kappa value for interobserver agreement on the diagnosis and irregularity grading of M-AANBI images was 0.742 and 0.719, respectively. These data indicate substantial interobserver agreement. Based on the above-mentioned results, we developed a M-AANBI diagnostic algorithm for SNADETs. Conclu
This paper provides an attempt to utilize machine learning algorithm,explicitly random-forest algorithm,to optimize the performance of dye sensitized solar cells(DSSCs)in terms of conversion *** optimization is implem...
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This paper provides an attempt to utilize machine learning algorithm,explicitly random-forest algorithm,to optimize the performance of dye sensitized solar cells(DSSCs)in terms of conversion *** optimization is implemented with respect to both the mesoporous TiO_(2) active layer thickness and ***,the porosity impact is reflected to the model as a variation in the effective refractive index and dye *** set has been established using our data in the literature as well as numerical data extracted from our numerical *** random-forest model is used for model regression,prediction,and optimization,reaching 99.87%*** agreement with experimental data was observed,with 4.17%conversion efficiency.
Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study and anal...
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Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study and analyzes the problems of "big data volume," "cold start," and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.
Aiming at low stability and high harmonic distortion of the traditional phase-generated carrier(PGC)demodulation algorithm,we propose a modified demodulation algorithm for distributed strain sensing in phase-sensitive...
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Aiming at low stability and high harmonic distortion of the traditional phase-generated carrier(PGC)demodulation algorithm,we propose a modified demodulation algorithm for distributed strain sensing in phase-sensitive optical frequency domain reflectometry(OFDR).The proposed algorithm combines the arctangent function and differential-self-multiplying(ARC-DSM),which eliminates the effect of modulation depth and realizes the high precision strain variation *** results show that distortion and harmonic distortion are effectively suppressed by the ARC-DSM *** minimum variation of the measurable static strain is about 0.3μεand the spatial resolution can reach 0.2 m within 270 m range.
algorithms are the essence of computational thinking, which refers to a set of problem-solving processes that help children become logical thinkers in this increasingly digital society. It is important for teachers of...
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algorithms are the essence of computational thinking, which refers to a set of problem-solving processes that help children become logical thinkers in this increasingly digital society. It is important for teachers of young children to carefully plan and implement algorithm design tasks that involve repeated step-by-step procedures to build strong foundational computational thinking skills. In this article, the authors present algorithm tasks, including following a recipe, creating a treasure map, modeling how to perform a task, and sharing a routine, which can be easily integrated in the daily activities in early childhood classrooms. Fostering young children's aptitude for algorithm-specific thinking-and-doing processes creates a foundation for logical thinking.
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