Evidence map›Paper›PMID 26646415›Full record

ReviewInternational journal of computer assisted radiology and surgery2016

Toward cognitive pipelines of medical assistance algorithms.

Patrick Philipp, Maria Maleshkova, Darko Katic, Christian Weber, Michael Götz, Achim Rettinger, Stefanie Speidel, Benedikt Kämpgen, Marco Nolden, Anna-Laura Wekerle and 4 more

Abstract readReview
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In one paragraph

Review in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Toward a standard ontology of surgical process models.International journal of computer assisted radiology and surgery · 2018
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Patrick PhilippInstitute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany. patrick.philipp@kit.edu.ORCID http://orcid.org/0000-0002-8657-3875
Maria MaleshkovaInstitute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Darko KaticInstitute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Christian WeberDivision of Medical and Biological Informatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Michael GötzDivision of Medical and Biological Informatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Achim RettingerInstitute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Stefanie SpeidelInstitute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Benedikt KämpgenInstitute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Marco NoldenDivision of Medical and Biological Informatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Anna-Laura WekerleKlinik für Allgemein-, Viszeral- und Transplantationschirurgie, University of Heidelberg, Heidelberg, Germany.
Rüdiger DillmannInstitute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Hannes KenngottKlinik für Allgemein-, Viszeral- und Transplantationschirurgie, University of Heidelberg, Heidelberg, Germany.
Beat MüllerKlinik für Allgemein-, Viszeral- und Transplantationschirurgie, University of Heidelberg, Heidelberg, Germany.
Rudi StuderInstitute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAssistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain.

methodsWe propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings.

resultsOur results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time.

conclusionThe proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.

Indexed as

AlgorithmsComputersCognitionHumansCognitive architectureComputer aided medicinePhase recognitionSemantic WebTumor progression mapping

Identifiers

PMID26646415

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.