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Internal Medicine Internal Medicine

Which risk predictors are more likely to indicate severe AKI in hospitalized patients?

In International journal of medical informatics ; h5-index 49.0

OBJECTIVES : Acute kidney injury (AKI) is a sudden episode of kidney failure or damage and the risk of AKI is determined by the complex interactions of patient factors. In this study, we aimed to find out which risk factors in hospitalized patients are more likely to indicate severe AKI.

METHODS : We constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital between November 2007 and December 2016. AKI predictors included demographic information, admission and discharge dates, medications, laboratory values, past medical diagnoses and admission diagnosis. We developed a machine learning-based knowledge mining model and a screening framework to analyze which risk predictors are more likely to imply severe AKI in hospitalized populations.

RESULTS : Among the final analysis cohort of 76,957 hospital admissions, AKI occurred in 7,259 (9.43 %) with 6,396 (8.31 %) at stage 1, 678 (0.88 %) at stage 2, and 185 (0.24 %) at stage 3. We compared the non-AKI (without AKI) vs any AKI (stages 1-3), and mild AKI (stage 1) vs severe AKI (stages 2 and 3), where the best cross-validated area under the receiver operator characteristic curve (AUC) were 0.81 (95 % CI, 0.79-0.82) and 0.66 (95 % CI, 0.62-0.71), respectively. Using the developed knowledge mining model and screening framework, we identified 33 risk predictors indicating that severe AKI may occur.

CONCLUSIONS : This study screened out 33 risk predictors that are more likely to indicate severe AKI in hospitalized patients, which would help strengthen the early care and prevention of patients.

Wu Lijuan, Hu Yong, Yuan Borong, Zhang Xiangzhou, Chen Weiqi, Liu Kang, Liu Mei

2020-Sep-11

Acute kidney injury (AKI), Electronic medical records, Knowledge mining model, Risk predictors, Severe AKI

Surgery Surgery

Sensor-based indicators of performance changes between sessions during robotic surgery training.

In Applied ergonomics

Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees' cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (rrm=-.25,p<.001) and gaze entropy (rrm=-.37,p<.001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.

Wu Chuhao, Cha Jackie, Sulek Jay, Sundaram Chandru P, Wachs Juan, Proctor Robert W, Yu Denny

2020-Sep-19

Electroencephalogram, Eye tracking, Performance, Robotic surgery, Simulated training

General General

Analysis of the transferability and robustness of GANs evolved for Pareto set approximations.

In Neural networks : the official journal of the International Neural Network Society

The generative adversarial network (GAN) is a good example of a strong-performing, neural network-based generative model, even though it does have some drawbacks of its own. Mode collapsing and the difficulty in finding the optimal network structure are two of the most concerning issues. In this paper, we address these two issues at the same time by proposing a neuro-evolutionary approach with an agile evaluation method for the fast evolution of robust deep architectures that avoid mode collapsing. The computation of Pareto set approximations with GANs is chosen as a suitable benchmark to evaluate the quality of our approach. Furthermore, we demonstrate the consistency, scalability, and generalization capabilities of the proposed method, which shows its potential applications to many areas. We finally readdress the issue of designing this kind of models by analyzing the characteristics of the best performing GAN specifications, and conclude with a set of general guidelines. This results in a reduction of the many-dimensional problem of structural manual design or automated search.

Garciarena Unai, Mendiburu Alexander, Santana Roberto

2020-Sep-16

Generative adversarial networks, Knowledge transferability, Multi-objective optimization, Neuro-evolution, Pareto front approximation

Surgery Surgery

Clinical diagnostic phenotypes in hospitalizations due to self-inflicted firearm injury.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : . Hospitalized self-inflicted firearm injuries have not been extensively studied, particularly regarding clinical diagnoses at the index admission. The objective of this study was to discover the diagnostic phenotypes (DPs) or clusters of hospitalized self-inflicted firearm injuries.

METHODS : . Using Nationwide Inpatient Sample data in the US from 1993 to 2014, we used International Classification of Diseases, Ninth Revision codes to identify self-inflicted firearm injuries among those ≥18 years of age. The 25 most frequent diagnostic codes were used to compute a dissimilarity matrix and the optimal number of clusters. We used hierarchical clustering to identify the main DPs.

RESULTS : . The overall cohort included 14072 hospitalizations, with self-inflicted firearm injuries occurring mainly in those between 16 to 45 years of age, black, with co-occurring tobacco and alcohol use, and mental illness. Out of the three identified DPs, DP1 was the largest (n=10,110), and included most common diagnoses similar to overall cohort, including major depressive disorders (27.7%), hypertension (16.8%), acute post hemorrhagic anemia (16.7%), tobacco (15.7%) and alcohol use (12.6%). DP2 (n=3,725) was not characterized by any of the top 25 ICD-9 diagnoses codes, and included children and peripartum women. DP3, the smallest phenotype (n=237), had high prevalence of depression similar to DP1, and defined by fewer fatal injuries of chest and abdomen.

LIMITATIONS : . Claims data.

CONCLUSIONS : . There were three distinct diagnostic phenotypes in hospitalizations due to self-inflicted firearm injuries. Further research is needed to determine how DPs can be used to tailor clinical care and prevention efforts.

Janeway Megan G, Zhao Xiang, Rosenthaler Max, Zuo Yi, Balasubramaniyan Kumar, Poulson Michael, Neufeld Miriam, Siracuse Jeffrey J, Takahashi Courtney E, Allee Lisa, Dechert Tracey, Burke Peter A, Li Feng, Kalesan Bindu

2020-Sep-15

Firearm, Hospitalization, Machine learning, Suicide

Ophthalmology Ophthalmology

Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Retinal imaging has two major modalities, traditional fundus photography (TFP) and ultra-widefield fundus photography (UWFP). This study demonstrates the feasibility of a state-of-the-art deep learning-based domain transfer from UWFP to TFP.

METHODS : A cycle-consistent generative adversarial network (CycleGAN) was used to automatically translate the UWFP to the TFP domain. The model was based on an unpaired dataset including anonymized 451 UWFP and 745 TFP images. To apply CycleGAN to an independent dataset, we randomly divided the data into training (90%) and test (10%) datasets. After automated image registration and masking dark frames, the generator and discriminator networks were trained. Additional twelve publicly available paired TFP and UWFP images were used to calculate the intensity histograms and structural similarity (SSIM) indices.

RESULTS : We observed that all UWFP images were successfully translated into TFP-style images by CycleGAN, and the main structural information of the retina and optic nerve was retained. The model did not generate fake features in the output images. Average histograms demonstrated that the intensity distribution of the generated output images provided a good match to the ground truth images, with an average SSIM level of 0.802.

CONCLUSIONS : Our approach enables automated synthesis of TFP images directly from UWFP without a manual pre-conditioning process. The generated TFP images might be useful for clinicians in investigating posterior pole and for researchers in integrating TFP and UWFP databases. This is also likely to save scan time and will be more cost-effective for patients by avoiding additional examinations for an accurate diagnosis.

Yoo Tae Keun, Ryu Ik Hee, Kim Jin Kuk, Lee In Sik, Kim Jung Sub, Kim Hong Kyu, Choi Joon Yul

2020-Sep-16

CycleGAN, Deep learning, Fundus photography, Generative adversarial networks, Ultra-widefield

General General

Dementia Medical Screening using Mobile Applications: A Systematic Review with A New Mapping Model.

In Journal of biomedical informatics ; h5-index 55.0

Early detection is the key to successfully tackling dementia, a neurocognitive condition common among the elderly. Therefore, screening using technological platforms such as mobile applications (apps) may provide an important opportunity to speed up the diagnosis process and improve accessibility. Due to the lack of research into dementia diagnosis and screening tools based on mobile apps, this systematic review aims to identify the available mobile-based dementia and mild cognitive impairment (MCI) apps using specific inclusion and exclusion criteria. More importantly, we critically analyse these tools in terms of their comprehensiveness, validity, performance, and the use of artificial intelligence (AI) techniques. The research findings suggest diagnosticians in a clinical setting use dementia screening apps such as ALZ and CognitiveExams since they cover most of the domains for the diagnosis of neurocognitive disorders. Further, apps such as Cognity and ACE-Mobile have great potential as they use machine learning (ML) and AI techniques, thus improving the accuracy of the outcome and the efficiency of the screening process. Lastly, there was overlapping among the dementia screening apps in terms of activities and questions they contain therefore mapping these apps to the designated cognitive domains is a challenging task, which has been done in this research.

Thabtah Fadi, Peebles David, Retzler Jenny, Hathurusingha Chanchala

2020-Sep-19

MCI, cognitive mapping, dementia, machine learning, mobile apps, neurodegenerative areas, screening methods, systematic review