scholarly journals Machine Learning for Clinical Decision-Making: Challenges and Opportunities

2019 ◽  
Author(s):  
Sergio Sanchez-Martinez ◽  
Oscar Camara ◽  
Gemma Piella ◽  
Maja Cikes ◽  
Miguel Angel Gonzalez Ballester ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Status ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Response To Treatment ◽  
Classical Pathway ◽  
Challenges And Opportunities ◽  
Four Levels

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

scholarly journals Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

2022 ◽  
Vol 8 ◽  
Author(s):  
Sergio Sanchez-Martinez ◽  
Oscar Camara ◽  
Gemma Piella ◽  
Maja Cikes ◽  
Miguel Ángel González-Ballester ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Status ◽  
Clinical Decision ◽  
Cardiovascular Imaging ◽  
Heterogeneous Data ◽  
Response To Treatment ◽  
Four Levels

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.

Machine learning in clinical decision making

Med ◽  
2021 ◽  
Author(s):  
Lorenz Adlung ◽  
Yotam Cohen ◽  
Uria Mor ◽  
Eran Elinav
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Machine learning to assist clinical decision-making during the COVID-19 pandemic

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Shubham Debnath ◽  
◽  
Douglas P. Barnaby ◽  
Kevin Coppa ◽  
Alexander Makhnevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

Interrogating metastatic prostate cancer treatment switch decisions.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 296-296
Author(s):  
David Lorente ◽  
Praful Ravi ◽  
Niven Mehra ◽  
Carmel Jo Pezaro ◽  
Aurelius Gabriel Omlin ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Bone Scan ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Response To Treatment ◽  
Clinical Progression ◽  
Online Questionnaire ◽  
Main Challenge ◽  
Access To Technology

296 Background: Increased availability of treatment options in CRPC requires improved biomarkers to optimize decision making for therapeutic sequencing. Despite evidence for the value of CTCs in assessing prognosis and response to treatment, their use in clinical practice is not widely implemented. Clinicians rely on PCWG2 criteria based on PSA, clinical and radiological criteria although these are only inconsistently used in clinical practice. We evaluated the trends for clinical decision-making by physicians treating CRPC. Methods: An online questionnaire was distributed to physicians treating PC from the UK, Switzerland and Australia. Questions on clinical practice, familiarity with progression criteria, use of CTCs and clinical-decision making were formulated. Results: 111 participants replied. Most (84.7%) were oncologists treating ≥ 50 patients per year (65.3%). Although only 39.6% usually used PCWG2 in clinical practice, 74.5% considered PSA, bone scans and CT to be useful for monitoring disease. 55.6% considered PSA to be an important biomarker. A minority were able to identify PSA (41.4%) and bone scan (39.4%) progression criteria by PCWG2. On average, more physicians discontinued cabazitaxel (28%) than docetaxel (10.4%) before cycle 4. Similar number of cycles were given to bone only disease compared to RECIST evaluable patients. Clinical progression was most important for switching treatment for most physicians (90.5%), followed by RECIST (71.6%), bone scan (47.7%), CTC (23.2%) and PSA (21.1%). The main challenge associated with the use of CTCs was the access to technology (84.7%). Most respondents (92%) would not stop therapy with rising PSA but falling CTC counts; most (88.8%) would not stop with declining PSA but rising CTCs. Although 50% acknowledged the prognostic value of CTCs, only 33% would use them to guide decision-making. Conclusions: A significant number of physicians discontinue treatment before 12 weeks. Most physicians rely on clinical progression for decision-making. Knowledge of PCWG2 response and progression criteria is generally suboptimal. Greater physician awareness, access to technology and further evidence and will be required for the implementation of CTCs as a routine biomarker in CRPC.

scholarly journals 579. Machine-Learning Based Models for Prediction of Recurrence-free Catheter Retention After ALT Treatment of CLABSI in a Pediatric Population

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S273-S274
Author(s):  
Lorne W Walker ◽  
Andrew J Nowalk ◽  
Shyam Visweswaran
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Blood Culture ◽  
Clinical Decision Making ◽  
Pediatric Population ◽  
Recurrent Infection ◽  
Clinical Decision ◽  
Definitive Treatment ◽  
Support Vector ◽  
The Future

Abstract Background Deciding whether to attempt salvage of an infected central venous catheter (CVC) can be challenging. While line removal is the definitive treatment for central-line associated bloodstream infection (CLABSI), salvage may be attempted with systemic antibiotics and antibiotic lock therapy (ALT). Weighing risk and benefit of CVC salvage is limited by uncertainty in the future viability of salvaged CVCs. If a CVC is likely to require subsequent removal (e.g., due to recurrent infection) salvage may not be beneficial, whereas discarding a viable CVC is also not desirable. Here we describe a machine learning approach to predicting outcomes in CVC salvage. Methods Episodes of pediatric CLABSI cleared with ALT were identified by retrospective record review between January 1, 2008 and December 31, 2018 and were defined by a single positive central blood culture of a known pathogen or two matching cultures of a possible contaminant. Clearance was defined as 48-hours of negative cultures and relapse was defined as a matching positive blood culture after clearance. Predictive models [logistic regression (LR), random forest (RF), support vector machine (SVM) and an ensemble combining the three] were used to predict recurrence-free CVC retention (RFCR) at various time points using a training and test set approach. Results Overall, 712 instances CLABSI cleared with ALT were identified. Demographic and microbiological data are summarized in Tables 1 and 2. Few (8%) instances recurred in the first 28 days. 58% recurred at any time within the study period. Rates of RFCR were 75%, 43%, 22% and 10% at 28, 91, 182 and 365 days. Machine learning (ML) models varied in their ability to predict RFCR (Table 3). RF models performed best overall, although no model performed well at 91 days. Conclusion ML models provide an opportunity to augment clinical decision making by learning patterns from data. In this case, estimating the likelihood of useful line retention in the future could help guide informed decisions on salvage vs. removal of infected CVCs. Limitations include the heterogeneity of clinical data and the use of an outcome capturing both clinical decision making (line removal) and infection recurrence. With further model development and prospective validation, practical machine learning models may prove useful to clinicians. Disclosures All authors: No reported disclosures.

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