ELIZA

return kwic search for patient out of >500 occurrences
1057411 occurrences (No.2 in the rank) during 5 years in the PubMed. [cache]

10) Including patient and health care professionals' input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community.

--- ABSTRACT ---

PMID:34226185 DOI:10.1136/annrheumdis-2021-220454

2021 Annals of the rheumatic diseases

* Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.

- Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support for therapy selection, and automated interpretation of clinical images. Nevertheless, such data-driven approaches could potentially model noise, or miss true clinical phenomena. One proposed solution to ensure clinically meaningful machine learning models is to involve primary stakeholders in their development and interpretation. Including patient and health care professionals' input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community. Here we describe outputs from workshops that involved healthcare professionals, and young people from the Your Rheum Young Person's Advisory Group, in the development of complex machine learning models. These were developed to better describe trajectory of early juvenile idiopathic arthritis disease, as part of the CLUSTER consortium. We further provide key instructions for reproducibility of this process.Involving people living with, and managing, a disease investigated using machine learning techniques, is feasible, impactful and empowering for all those involved.

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[frequency of next (right) word to patient]

(1)34 and	(20)4 communication	(39)3 prognosis	(58)2 interactions
(2)28 outcomes	(21)4 experiences	(40)3 satisfaction,	(59)2 monitoring,
(3)26 care	(22)4 knowledge	(41)3 stratification	(60)2 outcomes,
(4)13 safety	(23)4 outcome	(42)3 who	(61)2 outcomes;
(5)12 with	(24)3 access	(43)3 xenograft	(62)2 participation
(6)10 null	(25)3 care,	(44)2 cell	(63)2 perceptions
(7)9 populations	(26)3 characteristics	(45)2 choice	(64)2 perspectives
(8)7 data	(27)3 education	(46)2 classification	(65)2 plasma
(9)7 population	(28)3 group	(47)2 condition	(66)2 population,
(10)7 satisfaction	(29)3 groups	(48)2 costs	(67)2 portals
(11)7 was	(30)3 had	(49)2 demographics,	(68)2 preferences
(12)6 samples	(31)3 is	(50)2 engagement	(69)2 reported
(13)5 characteristics,	(32)3 language	(51)2 expectations	(70)2 samples,
(14)5 cohort	(33)3 medical	(52)2 experience	(71)2 suffering
(15)5 factors	(34)3 monitoring	(53)2 fibroblasts	(72)2 survival,
(16)5 in	(35)3 needs	(54)2 goals	(73)2 that
(17)5 management	(36)3 of	(55)2 groups,	(74)2 to
(18)5 navigation	(37)3 or	(56)2 health	(75)2 tumors
(19)5 survival	(38)3 perspective,	(57)2 information	(76)2 underwent

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--- WordNet output for patient --- =>がまん強い, 忍耐強い, 辛抱強い, 根気よく働く, 患者, 病人, 忠者 Overview of noun patient The noun patient has 2 senses (first 1 from tagged texts) 1. (73) patient -- (a person who requires medical care; "the number of emergency patients has grown rapidly") 2. affected role, patient role, patient -- (the semantic role of an entity that is not the agent but is directly involved in or affected by the happening denoted by the verb in the clause) Overview of adj patient The adj patient has 1 sense (first 1 from tagged texts) 1. (3) patient -- (enduring trying circumstances with even temper or characterized by such endurance; "a patient smile"; "was patient with the children"; "an exact and patient scientist"; "please be patient") --- WordNet end ---