ELIZA

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

257) Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data.

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PMID:32862761 DOI:10.1080/10503307.2020.1808729

2021 Psychotherapy research : journal of the Society for Psychotherapy Research

* A scoping review of machine learning in psychotherapy research.

- Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of current methods and considerations for clinical practice and directions for future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types of studies, those who developed and tested an ML model (k=44), and those who reported on the feasibility of a particular treatment tool that uses an ML algorithm (k=7). Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data. Overall, the current applications of ML in psychotherapy research demonstrated a range of possible benefits for indications of treatment process, adherence, therapist skills and treatment response prediction, as well as ways to accelerate research through automated behavioral or linguistic process coding. Given the novelty and potential of this research field, these proof-of-concept studies are encouraging, however, do not necessarily translate to improved clinical practice (yet).

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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 ---