A System of Clothing Boundary Recognition Using Machine Learning For Life Support Robots

Abstract Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

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Abstract 124: A Machine-Learning Based Shock Advice Algorithm for Reliable Rhythm Diagnosis During Out-Of-Hospital Cardiac Arrest

Circulation ◽

10.1161/circ.140.suppl_2.124 ◽

2019 ◽

Vol 140 (Suppl_2) ◽

Author(s):

Erik Alonso ◽

Unai Irusta ◽

Elisabete Aramendi ◽

Beatriz Chicote ◽

Carlos Corcuera ◽

...

Keyword(s):

Machine Learning ◽

Cardiac Arrest ◽

Performance Metrics ◽

Life Support ◽

Basque Country ◽

Training Set ◽

Hospital Cardiac Arrest ◽

Prompt Diagnosis ◽

External Defibrillator

Introduction: In out-of-hospital cardiac arrest (OHCA) a reliable rhythm diagnosis by the automated external defibrillator (AED) allows for the delivery of correct therapy, a prompt defibrillation for shockable rhythms and resuming cardiopulmonary resuscitation for non-shockable rhythms. The aim of this study was to develop a machine-learning (ML) based shock advice algorithm (SAA) for reliable rhythm diagnosis during OHCA. Materials and methods: The study analyzed data from 853 OHCA patients treated with AEDs by the basic life support personnel in the Basque Health service (Osakidetza, Basque Country, Spain) between 2013 and 2015. The datased used in the study contained 4212 5-s ECG segments, 489 shockable and 3723 non-shockable, annotated by consensus between six clinicians. The dataset was split patient-wise into training (60%) and test (40%) sets. Each ECG segment was preprocessed and 15 well-known waveform features were computed. The SAA was composed of two blocks. First, a low electrical activity (LEA) detector based on the power of the ECG for a prompt diagnosis of asystole. Second, a shock/no-shock ML algorithm based on a hidden Markov model that made the shock/no-shock classification of those segments not detected as asystole by the LEA detector. The training set was used to select the most discriminative features and develop/optimize the SAA. The test set was used to measure the performance of the method in terms of sensitivity (SE) and specificity (SP), and to compare it with the performance of a commercial AED. This procedure was repeated 50 times to estimate the distributions of the performance metrics. Results: The method showed a mean (SD) SE and SP of 97.7% (1.0) and 99.1% (0.4), respectively. While the commercial AED presented a SE and SP of 94.2% (1.3) and 99.8% (0.1). Conclusions: Both methods were compliant with the American Heart Association’s requirements (SE>90% and SP>95%). However, our ML approach outperformed the SAA of the commercial AED, increasing the SE in 3.5-points with a decrease in SP of 0.7-points. Therefore, a ML based SAA algorithm can accurately make shock/no-shock diagnoses during OHCA and might improve the performance of current algorithms.

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Assessment of coral restoration’s contribution to reef recovery using machine learning

10.1101/2020.12.10.418715 ◽

2020 ◽

Author(s):

Gaétan Morand ◽

Simon Dixon ◽

Thomas Le Berre

Keyword(s):

Machine Learning ◽

Life Support ◽

Fragment Size ◽

Survival Rates ◽

Large Sample Size ◽

Live Coral ◽

Anthropogenic Pressure ◽

Depth Limit ◽

Form Of Life ◽

Reef Recovery

AbstractCoral restoration emerged globally as a form of life support for coral reefs, awaiting urgent mitigation of anthropogenic pressure. Yet its efficiency is difficult to assess, as ambitious transplantation programs handle hundreds of thousands of fragments, with survival rates inherently time-intensive to monitor. Due to limited available data, the influence of most environmental and methodological factors is still unknown.We therefore propose a new method which leverages machine learning to track each colony’s individual health and growth on a large sample size. This is the first time artificial intelligence techniques were used to monitor coral at a colony scale, providing an unprecedented amount of data on coral health and growth. Here we show the influence of genus, depth and initial fragment size, alongside providing an outlook on coral restoration’s efficiency.We show that among 77,574 fragments, individual survival rate was 31% after 2 years (21% after 4 years), which is much lower than most reported results. In the absence of significant anthropogenic pressure, we showed that there was a depth limit below which Pocillopora fragments outperformed Acropora fragments, while the opposite was true past this threshold. During the mid-2019 heatwave, our research indicates that Pocillopora fragments were 37% more likely to survive than Acropora fragments.Overall, the total amount of live coral steadily increased over time, by more than 3,700 liters a year, as growth compensated for mortality. This supports the use of targeted coral restoration to accelerate reef recovery after mass bleaching events.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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