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  1. Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions
    Bhatt P, Sethi A, Tasgaonkar V, Shroff J, Pendharkar I, Desai A, et al.
    Brain Inform, 2023 Jul 31;10(1):18.
    PMID: 37524933 DOI: 10.1186/s40708-023-00196-6
    Human behaviour reflects cognitive abilities. Human cognition is fundamentally linked to the different experiences or characteristics of consciousness/emotions, such as joy, grief, anger, etc., which assists in effective communication with others. Detection and differentiation between thoughts, feelings, and behaviours are paramount in learning to control our emotions and respond more effectively in stressful circumstances. The ability to perceive, analyse, process, interpret, remember, and retrieve information while making judgments to respond correctly is referred to as Cognitive Behavior. After making a significant mark in emotion analysis, deception detection is one of the key areas to connect human behaviour, mainly in the forensic domain. Detection of lies, deception, malicious intent, abnormal behaviour, emotions, stress, etc., have significant roles in advanced stages of behavioral science. Artificial Intelligence and Machine learning (AI/ML) has helped a great deal in pattern recognition, data extraction and analysis, and interpretations. The goal of using AI and ML in behavioral sciences is to infer human behaviour, mainly for mental health or forensic investigations. The presented work provides an extensive review of the research on cognitive behaviour analysis. A parametric study is presented based on different physical characteristics, emotional behaviours, data collection sensing mechanisms, unimodal and multimodal datasets, modelling AI/ML methods, challenges, and future research directions.
  2. 'First week' is the crucial period for deciding living donor liver transplantation in patients with acute-on-chronic liver failure
    Choudhury A, Vijayaraghavan R, Maiwall R, Kumar M, Duan Z, Yu C, et al.
    Hepatol Int, 2021 Oct 04.
    PMID: 34608586 DOI: 10.1007/s12072-021-10206-6
    BACKGROUND AND AIMS: Acute-on-chronic liver failure (ACLF) is a rapidly progressive illness with high short-term mortality. Timely liver transplant (LT) may improve survival. We evaluated various indices for assessment of the severity of liver failure and their application for eligibility and timing of living donor LT (LDLT).

    METHODS: Altogether 1021 patients were analyzed for the severity and organ failure at admission to determine transplant eligibility and 28 day survival with or without transplant.

    RESULTS: The ACLF cohort [mean age 44 ± 12.2 years, males 81%) was of sick patients; 55% willing for LT at admission, though 63% of them were ineligible due to sepsis or organ failure. On day 4, recovery in sepsis and/or organ failure led to an improvement in transplant eligibility from 37% at baseline to 63.7%. Delay in LT up to 7 days led to a higher incidence of multiorgan failure (p p 

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