Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records

Norman Hiob; Stefan Lessmann

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Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records

Norman Hiob, Stefan Lessmann

Machine Learning · Submitted 24 Jul 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The literature review confirms natural language processing to be a promising approach for electronic phenotyping.

how it is stated · asserted 0.64 read the record →

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arXiv ID1707.07425 (version 1)

Pith IDTJMJBTMO, Pith's permanent record of this paper and its review

CitationNorman Hiob, Stefan Lessmann. "Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records." arXiv:1707.07425 (2017). https://pith.science/paper/TJMJBTMO

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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,729 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.21 · human participants 0.64 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.11 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeling approaches. The literature review confirms natural language processing to be a promising approach for electronic phenotyping. However, accessibility and lack of natural language process standards for medical texts remain a challenge. Future research should develop such standards and further investigate which machine learning approaches are best suited to which type of medical data.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records.	contribution 1.00	0.78	0.07	asserted 1.00
s1	It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets.	contribution 0.96	0.56	0.04	asserted 1.00
s2	Special attention is given to preprocessing of in-put data and the different modeling approaches.	method 0.50	0.21	0.03	asserted 0.74
s3	The literature review confirms natural language processing to be a promising approach for electronic phenotyping.	result 0.83	0.28	0.48	asserted 0.64
s4	However, accessibility and lack of natural language process standards for medical texts remain a challenge.	limitation 0.37	0.20	0.09	asserted 1.00
s5	Future research should develop such standards and further investigate which machine learning approaches are best suited to which type of medical data.	future work 1.00	0.06	0.13	hedged possibility 0.48

What kind of paper the classifier reports this is: review 1.00 · evidence from literature 1.00.

What was examined, and what was not

sources read

arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeling approaches. The literature review confirms natural language processing to be a promising approach for electronic phenotyping. However, accessibility and lack of natural language process standards for medical texts remain a challenge. Future research should develop such standards and further investigate which machine learning approaches are best suited to which type of medical data.

Human review

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How the literature uses it

1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper

Many studies have been published on cohort retrieval [ref] ; some research work focuses on translating clinical trial eligibility criteria into machine-executable queries [ref] while other studies aim to identify a representative cohort from a broader population to minimize bias and improve fairness while optimizing patient data utilization [ref] .

ACR: A Benchmark for Automatic Cohort Retrieval · The need for scalable solutions exploiting the recent advances in AI · 0.91

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