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Article ; Online: Less is more: information needs, information wants, and what makes causal models useful.

Kleinberg, Samantha / Marsh, Jessecae K

Cognitive research: principles and implications

2023 Volume 8, Issue 1, Page(s) 57

Abstract: Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them ... ...

Abstract	Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them successfully. We investigate the trade-off between simplicity and complexity in decision making, testing diagrams tailored to target choices (Experiments 1 and 2), and with relevant causal paths highlighted (Experiment 3), finding that simplicity or directing attention to simple causal paths leads to better decisions. We test the boundaries of this effect (Experiment 4), finding that including a small amount of information beyond that related to the target answer has a detrimental effect. Finally, we examine whether people know what information they need (Experiment 5). We find that simple, targeted, information still leads to the best decisions, while participants who believe they do not need information or seek out the most complex information performed worse.
Language	English
Publishing date	2023-08-30
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ISSN	2365-7464
ISSN (online)	2365-7464
DOI	10.1186/s41235-023-00509-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data.

Hameed, Hadia / Kleinberg, Samantha

Proceedings of machine learning research

2022 Volume 126, Page(s) 871–894

Abstract: Managing a chronic disease like Type 1 diabetes (T1D) is both challenging and time consuming, but new technologies that allow continuous measurement of glucose and delivery of insulin have led to significant improvements. The development of an artificial ...

Abstract	Managing a chronic disease like Type 1 diabetes (T1D) is both challenging and time consuming, but new technologies that allow continuous measurement of glucose and delivery of insulin have led to significant improvements. The development of an artificial pancreas (AP), which algorithmically determines insulin dosing and delivers insulin in a fully automated way, may transform T1D care but it is not yet widely available. Patient-led alternatives, like the Open Artificial Pancreas (OpenAPS), are being used by hundreds of individuals and have also led to a dramatic increase in the availability of patient generated health data (PGHD). All APs require an accurate forecast of blood glucose (BG). While there have been efforts to develop better forecasts and apply new ML techniques like deep learning to this problem, methods are often tested on small controlled datasets that do not indicate how they may perform in reality - and the most advanced methods have not always outperformed the simplest. We introduce a rigorous comparison of BG forecasting using both a small controlled research dataset and large heterogeneous PGHD. Our comparison advances the state of the art in BG forecasting by providing insight into how methods may fare when moving beyond small controlled studies to real-world use.
Language	English
Publishing date	2022-02-02
Publishing country	United States
Document type	Journal Article
ISSN	2640-3498
ISSN (online)	2640-3498
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Causal Discovery with Stage Variables for Health Time Series

Srikishan, Bharat / Kleinberg, Samantha

2023

Abstract: Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare. Discovering causal relationships in time-series health data is even more challenging when relationships change over ... ...

Abstract	Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare. Discovering causal relationships in time-series health data is even more challenging when relationships change over the course of a disease, such as medications that are most effective early on or for individuals with severe disease. Stage variables such as weeks of pregnancy, disease stages, or biomarkers like HbA1c, can influence what causal relationships are true for a patient. However, causal inference within each stage is often not possible due to limited amounts of data, and combining all data risks incorrect or missed inferences. To address this, we propose Causal Discovery with Stage Variables (CDSV), which uses stage variables to reweight data from multiple time-series while accounting for different causal relationships in each stage. In simulated data, CDSV discovers more causes with fewer false discoveries compared to baselines, in eICU it has a lower FDR than baselines, and in MIMIC-III it discovers more clinically relevant causes of high blood pressure.
Keywords	Computer Science - Artificial Intelligence ; Statistics - Methodology ; 62D20
Publishing date	2023-05-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.

Gomez, Louis A / Toye, Adedolapo Aishat / Hum, R Stanley / Kleinberg, Samantha

Journal of diabetes science and technology

2023 , Page(s) 19322968231181138

Abstract: Background: Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features ... ...

Abstract	Background: Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance. Methods: To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model). Results: Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets. Conclusions: We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.
Language	English
Publishing date	2023-06-23
Publishing country	United States
Document type	Journal Article
ISSN	1932-2968
ISSN (online)	1932-2968
DOI	10.1177/19322968231181138
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Hierarchical Information Criterion for Variable Abstraction.

Mirtchouk, Mark / Srikishan, Bharat / Kleinberg, Samantha

Proceedings of machine learning research

2022 Volume 149, Page(s) 440–460

Abstract: Large biomedical datasets can contain thousands of variables, creating challenges for machine learning tasks such as causal inference and prediction. Feature selection and ranking methods have been developed to reduce the number of variables and ... ...

Abstract	Large biomedical datasets can contain thousands of variables, creating challenges for machine learning tasks such as causal inference and prediction. Feature selection and ranking methods have been developed to reduce the number of variables and determine which are most important. However in many cases, such as in classification from diagnosis codes, ontologies, and controlled vocabularies, we must choose not only which variables to include but also at what level of granularity. ICD-9 codes, for example, are arranged in a hierarchy, and a user must decide at what level codes should be analyzed. Thus it is currently up to a researcher to decide whether to use any diagnosis of diabetes or whether to distinguish between specific forms, such as Type 2 diabetes with renal complications versus without mention of complications. Currently, there is no existing method that can automatically make this determination and methods for feature selection do not exploit this hierarchical information, which is found in other areas including nutrition (hierarchies of foods), and bioinformatics (hierarchical relationship of genes). To address this, we propose a novel Hierarchical Information Criterion (HIC) that builds on mutual information and allows fully automated abstraction of variables. Using HIC allows us to rank hierarchical features and select the ones with the highest score. We show that this significantly improves performance by an average AUROC of 0.053 over traditional feature selection methods and hand crafted features on two mortality prediction tasks using MIMIC-III ICU data. Our method also improves on the state of the art (Fu et al., 2019) with an AUROC increase from 0.819 to 0.887.
Language	English
Publishing date	2022-02-02
Publishing country	United States
Document type	Journal Article
ISSN	2640-3498
ISSN (online)	2640-3498
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Health Information Sourcing and Health Knowledge Quality: Repeated Cross-sectional Survey.

Korshakova, Elena / Marsh, Jessecae K / Kleinberg, Samantha

JMIR formative research

2022 Volume 6, Issue 9, Page(s) e39274

Abstract: Background: People's health-related knowledge influences health outcomes, as this knowledge may influence whether individuals follow advice from their doctors or public health agencies. Yet, little attention has been paid to where people obtain health ... ...

Abstract	Background: People's health-related knowledge influences health outcomes, as this knowledge may influence whether individuals follow advice from their doctors or public health agencies. Yet, little attention has been paid to where people obtain health information and how these information sources relate to the quality of knowledge. Objective: We aim to discover what information sources people use to learn about health conditions, how these sources relate to the quality of their health knowledge, and how both the number of information sources and health knowledge change over time. Methods: We surveyed 200 different individuals at 12 time points from March through September 2020. At each time point, we elicited participants' knowledge about causes, risk factors, and preventative interventions for 8 viral (Ebola, common cold, COVID-19, Zika) and nonviral (food allergies, amyotrophic lateral sclerosis [ALS], strep throat, stroke) illnesses. Participants were further asked how they learned about each illness and to rate how much they trust various sources of health information. Results: We found that participants used different information sources to obtain health information about common illnesses (food allergies, strep throat, stroke) compared to emerging illnesses (Ebola, common cold, COVID-19, Zika). Participants relied mainly on news media, government agencies, and social media for information about emerging illnesses, while learning about common illnesses from family, friends, and medical professionals. Participants relied on social media for information about COVID-19, with their knowledge accuracy of COVID-19 declining over the course of the pandemic. The number of information sources participants used was positively correlated with health knowledge quality, though there was no relationship with the specific source types consulted. Conclusions: Building on prior work on health information seeking and factors affecting health knowledge, we now find that people systematically consult different types of information sources by illness type and that the number of information sources people use affects the quality of individuals' health knowledge. Interventions to disseminate health information may need to be targeted to where individuals are likely to seek out information, and these information sources differ systematically by illness type.
Language	English
Publishing date	2022-09-28
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/39274
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series.

Zheng, Min / Kleinberg, Samantha

Proceedings of machine learning research

2020 Volume 106, Page(s) 474–489

Abstract: Increasingly large observational datasets from healthcare and social media may allow new types of causal inference. However, these data are often missing key variables, increasing the chance of finding spurious causal relationships due to confounding. ... ...

Abstract	Increasingly large observational datasets from healthcare and social media may allow new types of causal inference. However, these data are often missing key variables, increasing the chance of finding spurious causal relationships due to confounding. While methods exist for causal inference with latent variables in static cases, temporal relationships are more challenging, as varying time lags make latent causes more difficult to uncover and approaches often have significantly higher computational complexity. To address this, we make the key observation that while a variable may be latent in one dataset, it may be observed in another, or we may have domain knowledge about its effects. We propose a computationally efficient method that overcomes latent variables by using prior knowledge to reconstruct data for unobserved variables, while remaining robust to cases when the knowledge is wrong or does not apply. On simulated data, our approach outperforms the state of the art with a lower false discovery rate for causal inference. On real-world data from individuals with Type 1 diabetes, we show that our approach can discover causal relationships involving unmeasured meals and exercise.
Language	English
Publishing date	2020-02-17
Publishing country	United States
Document type	Journal Article
ISSN	2640-3498
ISSN (online)	2640-3498
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Causal Explanation Under Indeterminism: A Sampling Approach.

Merck, Christopher A / Kleinberg, Samantha

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence

2019 Volume 2016, Page(s) 1037–1043

Abstract: One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual's heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational ... ...

Abstract	One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual's heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health. However, most methods for explanation of specific events have provided theoretical approaches with limited applicability. In contrast we make two main contributions: an algorithm for explanation that calculates the strength of token causes, and an evaluation based on simulated data that enables objective comparison against prior methods and ground truth. We show that the approach finds the correct relationships in classic test cases (causal chains, common cause, and backup causation) and in a realistic scenario (explaining hyperglycemic episodes in a simulation of type 1 diabetes).
Language	English
Publishing date	2019-04-07
Publishing country	United States
Document type	Journal Article
ISSN	2159-5399
ISSN	2159-5399
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Replicability, Reproducibility, and Agent-based Simulation of Interventions.

Hum, R Stanley / Kleinberg, Samantha

AMIA ... Annual Symposium proceedings. AMIA Symposium

2018 Volume 2017, Page(s) 959–968

Abstract: Secondary use of medical data and use of observational data for causal inference has been growing. Yet these data bring many challenges such as confounding due to unobserved variables and variation in medical processes across settings. Further, while ... ...

Abstract	Secondary use of medical data and use of observational data for causal inference has been growing. Yet these data bring many challenges such as confounding due to unobserved variables and variation in medical processes across settings. Further, while methods exist to handle some of these problems, researchers lack ground truth to evaluate these methods. When a finding is not replicated across multiple sites, it is unknown whether this is a failure of an algorithm, a genuine difference between populations, or an artifact of structural differences between the sites. We show how agent-based simulation of medical interventions can be used to explore how bias, error, and variation across settings affect inference. Our approach enables users to model not only interventions and outcomes, but also the complex interaction between patients with different risks of mortality and providers with different observed and latent treatment effects. Ultimately we propose that such simulations can be used to better evaluate the behavior of new methods with known ground truth and better calculate sample size for EHR-based studies.
MeSH term(s)	Algorithms ; Bias ; Computer Simulation ; Electronic Health Records ; Humans ; Observational Studies as Topic/standards ; Reproducibility of Results
Language	English
Publishing date	2018-04-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients.

Yavuz, Tahsin T / Claassen, Jan / Kleinberg, Samantha

AMIA ... Annual Symposium proceedings. AMIA Symposium

2020 Volume 2019, Page(s) 942–951

Abstract: Consciousness is a highly significant indicator of an ICU patient's condition but there is still no method to automatically measure it. Instead, time consuming and subjective assessments are used. However, many brain and physiologic variables are ... ...

Abstract	Consciousness is a highly significant indicator of an ICU patient's condition but there is still no method to automatically measure it. Instead, time consuming and subjective assessments are used. However, many brain and physiologic variables are measured continuously in neurological ICU, and could be used as indicators for consciousness. Since many biological variables are highly correlated to maintain homeostasis, we examine whether changes in time lags between correlated variables may relate to changes in consciousness. We introduce new methods to identify changes in the time lag of correlations, which better handle noisy multimodal physiological data and fluctuating lags. On neurological ICU data from subarachnoid hemorrhage patients, we find that correlations among variables related to brain physiology or respiration have significantly longer lags inpatients with decreased levels of consciousness than in patients with higher levels of consciousness. This suggests that physiological data could potentially be used to automatically assess consciousness.
MeSH term(s)	Consciousness/physiology ; Humans ; Intensive Care Units ; Models, Biological ; Patient Acuity ; Research Design ; Stroke/complications ; Stroke/physiopathology ; Subarachnoid Hemorrhage/physiopathology ; Unconsciousness/diagnosis ; Unconsciousness/etiology ; Unconsciousness/physiopathology
Language	English
Publishing date	2020-03-04
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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