Causal inference and counterfactual prediction in machine learning for actionable healthcare

Prosperi, Mattia; Guo, Yi; Sperrin, Matthew; Koopman, James S.; Min, Jae; He, Xiaowen; Rich, Shannan N.; Wang, Mo; Buchan, Iain; Bian, Jiang

doi:10.1038/s42256-020-0197-y

Cited by 240 publications

(146 citation statements)

References 52 publications

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“…In recent years, causal analysis has become one of the most challenging fields in machine learning [8], [9], [10], [11], [12], [13], [14]. For examples, some researchers proposed causal analysis methods in medical diagnosis [15], [16] and social sciences [17], [18]. Causal analysis plays an important role in revealing the essential relationship of things and identifying causal relationship is important for effective management recommendations on climate, agriculture, epidemiology, financial regulation, and much else [6].…”

Section: Introductionmentioning

confidence: 99%

Causal Research on Soil Temperature and Moisture Content at Different Depths

Cao

et al. 2021

IEEE Access

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The soil system is complex and dynamic, making it difficult to understand using traditional statistical approaches. In this paper, we analyze the causal relationship of soil temperature and moisture content at different depths in summer and winter based on dynamic empirical modelling. Specifically, we describe the complexity of soil temperature and moisture content system through mathematical methods. Moreover, we demonstrate the direction and magnitude of causal relationship between soil moisture content and temperature at different depths by equation-free methods. Besides, we describe the difference of soil system properties in summer and winter through causal research. The experiments show that results obtained are consistent with the actual soil environment. The causality is described by dynamic empirical modelling rather than prior soil knowledge. The paper may provide a new idea for soil dynamics research.

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Section: Introductionmentioning

confidence: 99%

Causal Research on Soil Temperature and Moisture Content at Different Depths

Cao

et al. 2021

IEEE Access

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“…The Personalized scores along with Digital Phenotype Score related categorization can be used to generate actionable insights into a patient's healthcare protocol [21,23]. These insights in future can also be used by the clinician to design a perfect intervention strategies/policies (Figure 3) for better health outcome and improving the quality of life of the patient [24,25]. Let us take an example scenario, A child categorized as having Very Poorly Controlled asthma using the DPS, suffering with Personalized Digital Phenotype Score, Healthcare Management and Intervention Strategies… DOI: http://dx.doi.org/10.5772/intechopen.97430 a high symptom score and Highly Compliant for the controller medication.…”

Section: Causal Intervention and Actionable Insightsmentioning

confidence: 99%

Personalized Digital Phenotype Score, Healthcare Management and Intervention Strategies Using Knowledge Enabled Digital Health Framework for Pediatric Asthma

Jaimini¹,

Sheth²

2022

Recent Advances in Asthma Research and Treatments

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Asthma is a personalized, and multi-trigger respiratory condition which requires continuous monitoring and management of symptoms and medication adherence. We developed kHealth: Knowledge-enabled Digital Healthcare Framework to monitor and manage the asthma symptoms, medication adherence, lung function, daily activity, sleep quality, indoor, and outdoor environmental triggers of pediatric asthma patients. The kHealth framework collects up to 1852 data points per patient per day. It is practically impossible for the clinicians, parents, and the patient to analyze this vast amount of multimodal data collected from the kHealth framework. In this chapter, we describe the personalized scores, clinically relevant asthma categorization using digital phenotype score, actionable insights, and potential intervention strategies for better pediatric asthma management.

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“…Indirect measures such as developing dedicated guidelines for antimicrobials use in COVID-19 patients, increasing public awareness, being logistically able to preserve sufficient bed capacity, and avoiding excessive re-deployment of infection-control and antimicrobial stewardship experts during possible future peaks of this or other pandemics could also be crucial from the perspective of health-care systems to prevent an unintended spread of AMR [45]. Finally, automated collection of large data from electronic records to exploit possible advantages of machine learning techniques may also improve our ability to counteract both the clinical and societal impact of AMR, although both machine learning algorithms and automated data collection are not exempt from biases, with legal and ethical aspects also needing standardization [46,47].…”

Section: Expert Opinionmentioning

confidence: 99%