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General General

Reporting of screening and diagnostic AI rarely acknowledges ethical, legal, and social implications: a mass media frame analysis.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Healthcare is a rapidly expanding area of application for Artificial Intelligence (AI). Although there is considerable excitement about its potential, there are also substantial concerns about the negative impacts of these technologies. Since screening and diagnostic AI tools now have the potential to fundamentally change the healthcare landscape, it is important to understand how these tools are being represented to the public via the media.

METHODS : Using a framing theory approach, we analysed how screening and diagnostic AI was represented in the media and the frequency with which media articles addressed the benefits and the ethical, legal, and social implications (ELSIs) of screening and diagnostic AI.

RESULTS : All the media articles coded (n = 136) fit into at least one of three frames: social progress (n = 131), economic development (n = 59), and alternative perspectives (n = 9). Most of the articles were positively framed, with 135 of the articles discussing benefits of screening and diagnostic AI, and only 9 articles discussing the ethical, legal, and social implications.

CONCLUSIONS : We found that media reporting of screening and diagnostic AI predominantly framed the technology as a source of social progress and economic development. Screening and diagnostic AI may be represented more positively in the mass media than AI in general. This represents an opportunity for health journalists to provide publics with deeper analysis of the ethical, legal, and social implications of screening and diagnostic AI, and to do so now before these technologies become firmly embedded in everyday healthcare delivery.

Frost Emma K, Carter Stacy M

2020-Dec-10

Artificial intelligence, Diagnosis, Ethics, Frame analysis, Media framing, Screening

General General

Five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study.

In Respiratory research ; h5-index 45.0

BACKGROUND : Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur in both patients with and without acute respiratory distress syndrome (ARDS). The purpose of this study is to use machine learning to identify clinical phenotypes for critically ill patients with MV in intensive care units (ICUs).

METHODS : A retrospective cohort study was conducted with 5013 patients who had undergone MV and treatment in the Department of Critical Care Medicine, Peking Union Medical College Hospital. Statistical and machine learning methods were used. All the data used in this study, including demographics, vital signs, circulation parameters and mechanical ventilator parameters, etc., were automatically extracted from the electronic health record (EHR) system. An external database, Medical Information Mart for Intensive Care III (MIMIC III), was used for validation.

RESULTS : Phenotypes were derived from a total of 4009 patients who underwent MV using a latent profile analysis of 22 variables. The associations between the phenotypes and disease severity and clinical outcomes were assessed. Another 1004 patients in the database were enrolled for validation. Of the five derived phenotypes, phenotype I was the most common subgroup (n = 2174; 54.2%) and was mostly composed of the postoperative population. Phenotype II (n = 480; 12.0%) led to the most severe conditions. Phenotype III (n = 241; 6.01%) was associated with high positive end-expiratory pressure (PEEP) and low mean airway pressure. Phenotype IV (n = 368; 9.18%) was associated with high driving pressure, and younger patients comprised a large proportion of the phenotype V group (n = 746; 18.6%). In addition, we found that the mortality rate of Phenotype IV was significantly higher than that of the other phenotypes. In this subgroup, the number of patients in the sequential organ failure assessment (SOFA) score segment (9,22] was 198, the number of deaths was 88, and the mortality rate was higher than 44%. However, the cumulative 28-day mortality of Phenotypes IV and II, which were 101 of 368 (27.4%) and 87 of 480 (18.1%) unique patients, respectively, was significantly higher than those of the other phenotypes. There were consistent phenotype distributions and differences in biomarker patterns by phenotype in the validation cohort, and external verification with MIMIC III further generated supportive results.

CONCLUSIONS : Five clinical phenotypes were correlated with different disease severities and clinical outcomes, which suggested that these phenotypes may help in understanding heterogeneity in MV treatment effects.

Su Longxiang, Zhang Zhongheng, Zheng Fanglan, Pan Pan, Hong Na, Liu Chun, He Jie, Zhu Weiguo, Long Yun, Liu Dawei

2020-Dec-10

Clinical phenotype, Critically ill patients, Machine learning, Mechanical ventilation

General General

In Fortschritte der Neurologie-Psychiatrie

'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'.

Popovic David, Schiltz Kolja, Falkai Peter, Koutsouleris Nikolaos

2020-Nov

General General

Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification.

In Journal of neural engineering ; h5-index 52.0

OBJECTIVE : Understanding the cognitive load of drivers is crucial for road safety. Brain sensing has the potential to provide an objective measure of driver cognitive load. We aim to develop an advanced machine learning framework for classifying driver cognitive load using functional near-infrared spectroscopy (fNIRS).

APPROACH : We conducted a study using fNIRS in a driving simulator with the n-back task used as a secondary task to impart structured cognitive load on drivers. To classify different driver cognitive load levels, we examined the application of convolutional autoencoder (CAE) and Echo State Network (ESN) autoencoder for extracting features from fNIRS.

MAIN RESULTS : By using CAE, the accuracies for classifying two and four levels of driver cognitive load with the 30s window were 73.25% and 47.21, respectively. The proposed ESN autoencoder achieved state-of-art classification results for group-level models without window selection, with accuracies of 80.61% and 52.45 for classifying two and four levels of driver cognitive load.

SIGNIFICANCE : This work builds a foundation for using fNIRS to measure driver cognitive load in real-world applications. Also, the results suggest that the proposed ESN autoencoder can effectively extract temporal information from fNIRS data and can be useful for other fNIRS data classification tasks.

Liu Ruixue, Reimer Bryan, Song Siyang, Mehler Bruce, Solovey Erin

2020-Dec-11

convolutional autoencoder, driver cognitive load, echo state network, fnirs, functional near-infrared spectroscopy

oncology Oncology

Influence of abiraterone and enzalutamide on body composition in patients with metastatic castration resistant prostate cancer.

In Cancer treatment and research communications

INTRODUCTION : Loss of skeletal muscle (SM) and gain of subcutaneous fat (SCF) are known side-effects of androgen-deprivation in treatment of prostate cancer. Scarce data is available concerning the effects of abiraterone/pred (ABI) on body composition and no published data regarding enzalutamide (ENZA). Our objective was to analyse the effects of ENZA on SM/SCF and to compare the results with ABI in patients with metastatic castration-resistant prostate-cancer (mCRPC).

PATIENTS AND METHODS : 54 patients starting ABI (n = 17) or ENZA (n = 37) at a single-centre between 2012 and 2018 were retrospectively identified. SM and SCF were assessed using CT-scans at baseline and after a median of 10.8 months on treatment. A deep learning image-segmentation software was used to quantify SM and SCF. In a subgroup of patients receiving ENZA within a trial, we investigated change of SM using serial timepoints.

RESULTS : Median time of treatment with ABI/ENZA was 14.6 months. A significant loss of SM compared to baseline was observed for ENZA (mean loss 5.2%, p<0.0001) and ABI (mean loss 3.0%, p = 0.02). SCF was not significantly altered. The effects of both drugs did not differ significantly. Loss of SM occurred early on during treatment with ENZA.

CONCLUSION : Treatment with ENZA seems to lead to a loss of SM which is comparable to that of ABI. Further evaluation in larger patient-cohorts is warranted. In routine care, counselling of patients about side effects of ABI/ENZA should include discussions about SM loss.

Fischer Stefanie, Clements Sebastian, McWilliam Alan, Green Andrew, Descamps Tine, Oing Christoph, Gillessen Silke

2020-Dec-01

Abiraterone, Enzalutamide, Muscle loss, Prostate cancer, Sarcopenia

oncology Oncology

Understanding the organ tropism of metastatic breast cancer through the combination of liquid biopsy tools.

In European journal of cancer (Oxford, England : 1990)

BACKGROUND : Liquid biopsy provides real-time data about prognosis and actionable mutations in metastatic breast cancer (MBC). The aim of this study was to explore the combination of circulating tumour DNA (ctDNA) analysis and circulating tumour cells (CTCs) enumeration in estimating target organs more susceptible to MBC involvement.

METHODS : This retrospective study analysed 88 MBC patients characterised for both CTCs and ctDNA at baseline. CTCs were isolated through the CellSearch kit, while ctDNA was analysed using the Guardant360 NGS-based assay. Sites of disease were collected on the basis of imaging. Associations were explored both through uni- and multivariate logistic regression and Fisher's exact test and the random forest machine learning algorithm.

RESULTS : After multivariate logistic regression, ESR1 mutation was the only significant factor associated with liver metastases (OR 8.10), while PIK3CA was associated with lung localisations (OR 3.74). CTC enumeration was independently associated with bone metastases (OR 10.41) and TP53 was associated with lymph node localisations (OR 2.98). The metastatic behaviour was further investigated through a random forest machine learning algorithm. Bone involvement was described by CTC enumeration and alterations in ESR1, GATA3, KIT, CDK4 and ERBB2, while subtype, CTC enumeration, inflammatory BC diagnosis, ESR1 and KIT aberrations described liver metastases. PIK3CA, MET, AR, CTC enumeration and TP53 were associated with lung organotropism. The model, moreover, showed that AR, CCNE1, ESR1, MYC and CTC enumeration were the main drivers in HR positive MBC metastatic pattern.

CONCLUSIONS : These results indicate that ctDNA and CTCs enumeration could provide useful insights regarding MBC organotropism, suggesting a possible role for future monitoring strategies that dynamically focus on high-risk organs defined by tumourbiology.

Gerratana Lorenzo, Davis Andrew A, Polano Maurizio, Zhang Qiang, Shah Ami N, Lin Chenyu, Basile Debora, Toffoli Giuseppe, Wehbe Firas, Puglisi Fabio, Behdad Amir, Platanias Leonidas C, Gradishar William J, Cristofanilli Massimo

2020-Dec-08

Circulating tumour DNA, Circulating tumour cell, Liquid biopsy, Metastatic breast cancer, Organotropism, Precision medicine