Discussing Anomalous Situations using Decision Trees: A Head Injury Case Study

2001 ◽  
Vol 40 (05) ◽  
pp. 373-379 ◽  
Author(s):  
A. McQuatt ◽  
P. J. D. Andrews ◽  
V. Corruble ◽  
P. A. Jones ◽  
D. Sleeman
Keyword(s):  
Head Injury ◽  
Decision Trees ◽  
Demographic Data ◽  
Patient Data ◽  
Physiological Data ◽  
Royal Infirmary ◽  
Domain Experts ◽  
Seriously Ill Patients ◽  
Seriously Ill

Summary Objectives: Predicting the outcome of seriously ill patients is a challenging problem for clinicians. Methods: One alternative to clinical trials is to analyse existing patient data in an attempt to predict the several outcomes, and to suggest therapies. In this paper we use decision tree techniques to predict the outcome of head injury patients. The work is based on patient data from the Edinburgh Royal Infirmary which contains both background (demographic) data and temporal (physiological) data. Results: The focus of this paper is the discussion of the anomalous cases in the decision trees with the domain experts (the clinicians). Conclusions: These analyses led to the detection of several situations where both the data analysis and patient data collection should be enhanced, which in turn should lead to improved patient care.

Principles of care for seriously ill patients in palliative office

2020 ◽  
pp. 15-18
Author(s):  
Nina Tishchenko
Keyword(s):  
Palliative Care ◽  
The State ◽  
State Budget ◽  
Oncological Patients ◽  
Seriously Ill Patients ◽  
Seriously Ill

The article reflects the importance and importance of the work of nurses of the Department of Palliative Care for Oncological Patients of the State Budget Health Establishment «Samara Regional Clinical Oncological Clinic». Important stages and features of care when dealing with seriously ill patients.

Home Health Agencies: Empirical Evidence on the Patient-Driven Groupings Model’s Expected Effects on Agency Reimbursements

2021 ◽  
pp. 108482232199038
Author(s):  
Elizabeth Plummer ◽  
William F. Wempe
Keyword(s):  
Ordinary Least Squares ◽  
Process Quality ◽  
Home Health ◽  
Least Squares Regression ◽  
Residual Variation ◽  
Health Agencies ◽  
Dual Eligible ◽  
Home Health Agencies ◽  
Seriously Ill Patients ◽  
Seriously Ill

Beginning January 1, 2020, Medicare’s Patient-Driven Groupings Model (PDGM) eliminated therapy as a direct determinant of Home Health Agencies’ (HHAs’) reimbursements. Instead, PDGM advances Medicare’s shift toward value-based payment models by directly linking HHAs’ reimbursements to patients’ medical conditions. We use 3 publicly-available datasets and ordered logistic regression to examine the associations between HHAs’ pre-PDGM provision of therapy and their other agency, patient, and quality characteristics. Our study therefore provides evidence on PDGM’s likely effects on HHA reimbursements assuming current patient populations and service levels do not change. We find that PDGM will likely increase payments to rural and facility-based HHAs, as well as HHAs serving greater proportions of non-white, dual-eligible, and seriously ill patients. Payments will also increase for HHAs scoring higher on quality surveys, but decrease for HHAs with higher outcome and process quality scores. We also use ordinary least squares regression to examine residual variation in HHAs’ expected reimbursement changes under PDGM, after accounting for any expected changes related to their pre-PDGM levels of therapy provision. We find that larger and rural HHAs will likely experience residual payment increases under PDGM, as will HHAs with greater numbers of seriously ill, younger, and non-white patients. HHAs with higher process quality, but lower outcome quality, will similarly benefit from PDGM. Understanding how PDGM affects HHAs is crucial as policymakers seek ways to increase equitable access to safe and affordable non-facility-provided healthcare that provides appropriate levels of therapy, nursing, and other care.

The Surprise Question and Serious Illness Conversations: A pilot study

2021 ◽  
pp. 096973302098339
Author(s):  
Kathy Le ◽  
Jenny Lee ◽  
Sameer Desai ◽  
Anita Ho ◽  
Holly van Heukelom
Keyword(s):  
Pilot Study ◽  
Healthcare Professionals ◽  
Post Intervention ◽  
Acute Medicine ◽  
Serious Illness ◽  
Patient Goals ◽  
Ethical Approval ◽  
Seriously Ill Patients ◽  
Surprise Question ◽  
Seriously Ill

Background: Serious Illness Conversations aim to discuss patient goals. However, on acute medicine units, seriously ill patients may undergo distressing interventions until death. Objectives: To investigate the feasibility of using the Surprise Question, “Would you be surprised if this patient died within the next year?” to identify patients who would benefit from early Serious Illness Conversations and study any changes in the interdisciplinary team’s beliefs, confidence, and engagement as a result of asking the Surprise Question. Design: A prospective cohort pilot study with two Plan-Do-Study-Act cycles. Participants/context: Fifty-eight healthcare professionals working on Acute Medicine Units participated in pre- and post-intervention questionnaires. The intervention involved asking participants the Surprise Question for each patient. Patient charts were reviewed for Serious Illness Conversation documentation. Ethical considerations: Ethical approval was granted by the institutions involved. Findings: Equivocal overall changes in the beliefs, confidence, and engagement of healthcare professionals were observed. Six out of 23 patients were indicated as needing a Serious Illness Conversation; chart review provided some evidence that these patients had more Serious Illness Conversation documentation compared with the 17 patients not flagged for a Serious Illness Conversation. Issues were identified in equating the Surprise Question to a Serious Illness Conversation. Discussion: Appropriate support for seriously ill patients is both a nursing professional and ethical duty. Flagging patients for conversations may act as a filtering process, allowing healthcare professionals to focus on conversations with patients who need them most. There are ethical and practical issues as to what constitutes a “serious illness” and if answering “no” to the Surprise Question always equates to a conversation. Conclusion: The barriers of time constraints and lack of training call for institutional change in order to prioritise the moral obligation of Serious Illness Conversations.

Choices of seriously ill patients about cardiopulmonary resuscitation: Correlates and outcomes

Resuscitation ◽  
1996 ◽  
Vol 33 (1) ◽  
pp. 87 ◽  
Author(s):  
RS Phillips ◽  
NS Wenger ◽  
J Teno ◽  
RK Oye ◽  
S Youngner ◽  
...  
Keyword(s):  
Cardiopulmonary Resuscitation ◽  
Seriously Ill Patients ◽  
Seriously Ill

Combining decision trees classifiers: a case study of automatic sleep stage scoring

2012 ◽  
Vol 2 (1) ◽  
pp. 60 ◽  
Author(s):  
Natheer Khasawneh ◽  
Stefan Conrad ◽  
Luay Fraiwan ◽  
Eyad Taqieddin ◽  
Basheer Khasawneh
Keyword(s):  
Decision Trees ◽  
Sleep Stage ◽  
Sleep Stage Scoring

Severe head injury in children: a case study

2000 ◽  
Vol 8 (1) ◽  
pp. 16-19 ◽  
Author(s):  
Alison Williams
Keyword(s):  
Head Injury ◽  
Severe Head Injury

Driver Cognitive Load Classification Based on Physiological Data—Case Study 7

2021 ◽  
pp. 409-429
Author(s):  
Dengbo He ◽  
Martina Risteska ◽  
Birsen Donmez ◽  
Kaiyang Chen
Keyword(s):  
Cognitive Load ◽  
Physiological Data

HFTViz: Visualization for the exploration of high frequency trading data

2021 ◽  
pp. 147387162110649
Author(s):  
Javad Yaali ◽  
Vincent Grégoire ◽  
Thomas Hurtut
Keyword(s):  
Portfolio Management ◽  
High Frequency ◽  
High Speed ◽  
High Frequency Trading ◽  
Domain Experts ◽  
Risk Return ◽  
Visualization Tools ◽  
Liquidity Measures ◽  
Trading Volumes

High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.

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